the application of nmr-based metabolomics in ......coelomic fluid: a complimentary biological medium...
TRANSCRIPT
THE APPLICATION OF NMR-BASED METABOLOMICS IN ASSESSING THE SUB-LETHAL TOXICITY OF
ORGANOHALOGENATED PESTICIDES TO EARTHWORMS
BY
Jimmy Yuk
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Graduate Department of Chemistry University of Toronto
© Copyright by Jimmy Yuk, (2012)
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The application of NMR-based metabolomics in assessing the sub-
lethal toxicity of organohalogenated pesticides to earthworms
Doctor of Philosophy Degree, 2012
Jimmy Yuk
Graduate Department of Chemistry
University of Toronto
ABSTRACT
The extensive agricultural usage of organohalogenated pesticides has raised many
concerns about their potential hazards especially in the soil environment. Environmental
metabolomics is an emerging field that investigates the changes in the metabolic profile of native
organisms in their environment due to the presence of an environmental stressor. Research
presented here explores the potential of Nuclear Magnetic Resonance (NMR)-based
metabolomics to examine the sub-lethal exposure of the earthworm, Eisenia fetida to sub-lethal
concentrations of organohalogenated pesticides. Various one-dimensional (1-D) and two-
dimensional (2-D) NMR techniques were compared in a contact filter paper test earthworm
metabolomic study using endosulfan, a prevalent pesticide in the environment. The results
determined that both the 1H Presaturation Utilizing Gradients and Echos (PURGE) and the 1H-
13C Heteronuclear Single Quantum Coherence (HSQC) NMR techniques were most effective in
discriminating and identifying significant metabolites in earthworms due to contaminant
exposure. These two NMR techniques were further explored in another metabolomic study
using various sub-lethal concentrations of endosulfan and an organofluorine pesticide, trifluralin
to E. fetida. Principal component analysis (PCA) tests showed increasing separation between the
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exposed and unexposed earthworms as the concentrations for both contaminants increased. A
neurotoxic mode of action (MOA) for endosulfan and a non-polar narcotic MOA for trifluralin
were delineated as many significant metabolites, arising from exposure, were identified. The
earthworm tissue extract is commonly used as the biological medium for metabolomic studies.
However, many overlapping resonances are apparent in an earthworm tissue extract NMR
spectrum due to the abundance of metabolites present. To mitigate this spectral overlap, the
earthworm’s coelomic fluid (CF) was tested as a complementary biological medium to the tissue
extract in an endosulfan exposure metabolomic study to identify additional metabolites of stress.
Compared to tests on the tissue extract, a plethora of different metabolites were identified in the
earthworm CF using 1-D PURGE and 2-D HSQC NMR techniques. In addition to the
neurotoxic MOA identified previously, an apoptotic MOA was also postulated due to endosulfan
exposure. This thesis also explored the application of 1-D and 2-D NMR techniques in a soil
metabolomic study to understand the exposure of E. fetida to sub-lethal concentrations of
endosulfan and its main degradation product, endosulfan sulfate. The earthworm’s CF and tissue
extract were both analyzed to maximize the significant metabolites identified due to contaminant
exposure. The PCA results identified similar toxicity for both organochlorine contaminants as
the same separation, between exposed to the unexposed earthworms, were detected at various
concentrations. Both neurotoxic and apopotic MOAs were observed as identical fluctuations of
significant metabolites were found. This research demonstrates the potential of NMR-based
metabolomics as a powerful environmental monitoring tool to understand sub-lethal
organohalogenated pesticide exposure in soil using earthworms as living probes.
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ACKNOWLEDGMENTS
First I would like to thank my supervisor, Dr. André Simpson for making this dissertation
possible. Thank you for all the guidance and help you have provided. You are the most creative
and innovative scientist I have ever had the pleasure to work with. Thank you for providing me
the motivation throughout my degree and also connections to my future career. Thank you to Dr.
Myrna Simpson for providing valuable discussions and advice throughout my projects and
excellent critiques that greatly improved my chapters 2, 3, 4, and 5. Thank you to Dr. Jennifer
Murphy for guiding me throughout my degree and watching over me to ensure my success
during these years. Thank you to Dr. Jon Abbatt for graciously taking the time to be my fourth
member in my PhD final examination committee. Thank you to Dr. Philip Britz-McKibbin for
becoming my external and taking the time to come from McMaster University.
Thank you to all the members, past and present, in both Simpson labs. You have been
an unlimited support system for me throughout my PhD degree. I enjoyed all the interesting
conversations about science and life and I wish you all success in your future endeavours.
Specifically, thank you to Brian Lankadurai, who started the same time as me, went through all
the classes and the oral comprehensive together. I also had the pleasure of travelling and
exploring other places during our conferences like Edmonton and Australia. I don’t think I
would have gotten this far or had the same motivation if you had not done this program. Thank
you to Hashim Farooq for being a great colleague and pillar of support in and outside the lab.
Thank you for teaching me your mystical ways of working out. Good luck to you both and I
can’t wait to celebrate with you guys when you are done!
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I would like to acknowledge funding sources for the projects contained within this thesis:
the Government of Ontario for an Early Researcher Award (AJS) and an Ontario Graduate
Scholarship (JY); the Natural Sciences and Engineering Research Council (NSERC) of Canada
Strategic Grant; a University of Toronto Fellowship, a University of Toronto Scarborough
Student Travel Grant, University of Toronto Arts and Science Travel Grant and a University of
Toronto Chem Club Graduate Scholarship (JY).
I would like to thank all my friends especially Simon La and Dorothy Yu for being a
great source of entertainment and giving me encouragement whenever I needed it. I would like
to also thank my Sensei, Suenori Tominaga for providing me with my favourite past-time outside
the lab, karate, and many valuable lessons about life in which I will never forget.
Thank you to my mother, Lily and brother, Tommy for their unconditional love and
support. Without them watching over me and teaching me many hard lessons, I will not be the
person I am today.
Finally, special thank you to Diana Tseng. Your love and encouragement is the main
source of my strength to finish this degree. I will never forget what you have inspired and taught
me and wish you the best in your life.
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TABLE OF CONTENTS ABSTRACT .................................................................................................................................... ii
ACKNOWLEDGMENTS ............................................................................................................. iv
TABLE OF CONTENTS ............................................................................................................... vi
LIST OF TABLES ......................................................................................................................... xi
LIST OF FIGURES ...................................................................................................................... xii
LIST OF ABBREVIATIONS ..................................................................................................... xvii
PREFACE ..................................................................................................................................... xx
CHAPTER ONE .................................................................................................................. 1
Introduction: Organohalogenated argochemicals, ecotoxicology, earthworms in
ecotoxicology and environmental NMR-based metabolomics
1.1 Organhalogenated agrochemicals ................................................................. 2
1.1.1 Soil contamination and assessing environmental risk .................................................... 6
1.2 Ecotoxicology ................................................................................................... 7
1.2.1 Regulatory approaches to ecotoxicological testing ........................................................ 8
1.2.2 Limitations in current approaches .................................................................................. 9
1.3 Metabolomics ................................................................................................12
1.3.1 Environmental metabolomics ....................................................................................... 13
1.4 Earthworms in Ecotoxicity tests ..................................................................16
1.5 Nuclear Magnetic Resonance-based metabolomics using earthworms ...21
1.5.1 One-dimensional NMR techniques .............................................................................. 24
1.5.2 Two-dimensional NMR techniques .............................................................................. 26
1.5.3 Statistical methods for metabolomic analysis .............................................................. 27
1.6 Study Objectives ...........................................................................................30
1.7 Thesis Summary ............................................................................................31
1.8 References ......................................................................................................35
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CHAPTER TWO .............................................................................................................. 49
Comparison of 1-D and 2-D NMR techniques for screening earthworm responses to sub-
lethal endosulfan exposure.
Jimmy Yuk, Jennifer McKelvie, Myrna Simpson, Manfred Spraul and André Simpson Environ. Chem. 2010, 7(6), 524-36.
2.1 Abstract ..........................................................................................................50
2.2 Introduction ...................................................................................................51
2.3 Experimental methods ..................................................................................55
2.3.1 Earthworm maintenance and contact tests .................................................................... 55
2.3.2 Earthworm tissue extraction ......................................................................................... 56
2.3.3 NMR spectroscopy ....................................................................................................... 57
2.3.4 Data analysis ................................................................................................................. 59
2.4 Results and Discussion..................................................................................61
2.4.1 1H NMR spectroscopic characterization....................................................................... 61
2.4.2 Partial least square discriminant analysis (PLS-DA) on 1-D NMR spectra ................. 63
2.4.3 2-D NMR spectroscopic characterization ..................................................................... 66
2.4.4 Partial least square discriminant analysis (PLS-DA) on 2-D NMR spectra ................. 68
2.4.5 Merits of 1-D and 2-D NMR analysis as biomarker screening tools ........................... 74
2.5 Conclusion .....................................................................................................79
2.6 Acknowledgment ...........................................................................................80
2.7 References ......................................................................................................80
CHAPTER THREE ........................................................................................................ 86
1-D and 2-D NMR metabolomics of earthworm responses to sub-lethal trifluralin and
endosulfan exposure
Jimmy Yuk, Myrna J. Simpson and André J. Simpson Environ. Chem. 2011 , 8(3), 281-94.
3.1 Abstract ..........................................................................................................87
3.2 Introduction ...................................................................................................88
3.3 Experimental methods ..................................................................................91
3.3.1 Earthworm contact test preparation and exposure ........................................................ 91
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3.3.2. Earthworm tissue extraction and preparation for NMR .............................................. 92
3.3.3. 1-D NMR Spectroscopy .............................................................................................. 93
3.3.4 2-D NMR Spectroscopy ............................................................................................... 93
3.3.5. Data and Statistical Analysis ....................................................................................... 94
3.4 Results and Discussions ................................................................................96
3.4.1 Earthworm weight change during exposure ................................................................. 96
3.4.2 Multivariate statistical analysis of trifluralin exposure ................................................ 97
3.4.3 Relative metabolite changes in trifluralin-exposed earthworms .................................. 99
3.4.4 Multivariate statistical analysis of endosulfan exposure ............................................ 105
3.4.5 Relative metabolite changes in endosulfan-exposed earthworms .............................. 107
3.4.6 Trajectory multivariate statistical analysis of trifluralin and endosulfan ................... 113
3.5 Conclusion ...................................................................................................115
3.6 Acknowledgment .........................................................................................116
3.7 References ....................................................................................................117
CHAPTER FOUR .......................................................................................................... 126
Coelomic fluid: A complimentary biological medium to assess sub-lethal endosulfan
exposure using 1H NMR-based earthworm metabolomics
Jimmy Yuk, Myrna J. Simpson, André J. Simpson Ecotoxicology. 2012, (In Press)
4.1 Abstract ........................................................................................................127
4.2 Introduction .................................................................................................128
4.3 Experimental methods ................................................................................131
4.3.1 Earthworm contact test preparation and exposure ...................................................... 131
4.3.2 Earthworm coelomic fluid extraction and preparation for NMR ............................... 132
4.3.3 Earthworm tissue extraction and preparation for NMR ............................................. 133
4.3.4 NMR Spectroscopy..................................................................................................... 134
4.3.5 Data and Statistical Analysis ...................................................................................... 135
4.4 Results and Discussions ..............................................................................137
4.4.1 Comparison of 1H and 1H-13C HSQC NMR spectra of earthworm extracts .............. 137
4.4.2 Multivariate statistical analysis of endosulfan exposure on earthworms ................... 140
4.4.3 Relative metabolite changes in endosulfan-exposed earthworms .............................. 142
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4.5. Conclusions .................................................................................................149
4.6 Acknowledgment .........................................................................................151
4.7 References ....................................................................................................152
CHAPTER FIVE ............................................................................................................ 160
1-D and 2-D NMR metabolomics of earthworm responses to sub-lethal endosulfan and
endosulfan sulfate exposure in soil
Jimmy Yuk, Myrna J. Simpson and André J. Simpson Environmental Pollution, 2012, (Submitted)
5.1 Abstract ........................................................................................................161
5.2 Introduction .................................................................................................162
5.3 Experimental Methods ...............................................................................164
5.3.1 Soil Spiking ................................................................................................................ 164
5.3.2 Earthworm exposure ................................................................................................... 165
5.3.3 Earthworm coelomic fluid and tissue extraction and preparation for NMR .............. 166
5.3.4 1-D and 2-D NMR Spectroscopy ............................................................................... 166
5.3.5 Data and Statistical Analysis ...................................................................................... 166
5.4 Results and Discussions ..............................................................................168
5.4.1 Multivariate analysis on earthworm CF and tissue extracts ....................................... 168
5.4.2 Metabolic response after endosulfan and endosulfan sulfate exposure ...................... 170
5.4.3 Comparison of endosulfan- and endosulfan sulfate- exposed earthworm CF and tissue extracts using multivariate analysis ..................................................................................... 176
5.5 Conclusion ...................................................................................................179
5.6.Acknowledgment .........................................................................................182
5.7 Reference .....................................................................................................183
CHAPTER SIX ................................................................................................................ 189
Conclusions and future research
6.1 Conclusions ..................................................................................................190
6.2 Future Research ..........................................................................................191
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6.2.1 Assessment of organohalogenated field soils using NMR-based metabolomics ....... 191
6.2.2 Application of NMR-based metabolomics to pesticide mixtures ............................... 193
6.2.3 Application of NMR-based metabolomics to genetic modified plants ...................... 195
6.2.4 Validation of biomarkers using a systems biology approach……………………….195
6.2.5 Application of various analytical platforms for metabolomics studies……………..197
6.3 References ....................................................................................................201
Appendix A:Supplementary Material for Chapter Three ...............................205
Appendix B:Supplementary Material for Chapter Four .................................212
Appendix C:Supplementary Material for Chapter Five ..................................214
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LIST OF TABLES
Table 1.1 Summary of the earthworm, Eisenia fetida, biomarker responses experimentally
exposed to environmental contaminants
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Table 2.1 Summary of metabolites identified with significant chemical shifts from partial least-squares discriminant analysis (PLS-DA) loadings and acquisition times for the 1-D and 2-D NMR techniques. Note the 2-D experiments were optimised for fast acquisition (see Experimental section)
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Table 5.1 Summary of metabolites identified after endosulfan or endosulfan sulfate exposure in E.fetida: A) coelomic fluid using 1-D NMR, B) tissue extract using 1-D NMR and C) tissue extract using 2-D NMR techniques.
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Table 6.1 Analytical techniques used in metabolomics 201
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LIST OF FIGURES
Figure 1.1 The number of organohalogenated agrochemicals from the period of 1940-2008
3
Figure 1.2 Structure of: A) Endosulfan and B) Endosulfan sulfate
4
Figure 1.3 Structure of Trifluralin
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Figure 1.4 The different types of omic technologies
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Figure 2.1 Average 1H NMR spectra (n=10) of control worm tissue extracts acquired using: (a) Presaturation Utilising Relaxation Gradients and Echos (PURGE); (b) Carr–Purcell–Meiboom–Gill (CPMG); and (c) J-Resolved (J-RES) projections. An asterisk represents the residual H2O/HOD.
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Figure 2.2 Partial least-squares discriminant analysis (PLS-DA) scores plot of data-reduced 1H NMR spectra for control Eisenia fetida (●) and E. fetida exposed to endosulfan (▲) using 1-D NMR techniques. (a) Presaturation Utilising Relaxation Gradients and Echos (PURGE); (b) Carr–Purcell–Meiboom–Gill (CPMG); and (c) J-Resolved (J-RES) projections. The P value for MANOVA (Wilks’ lambda) of control and exposed earthworms for the PLS components is reported. Scores for control and endosulfan-exposed earthworms are significantly different at the P <0.05 level.
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Figure 2.3 Partial least-squares discriminant analysis (PLS-DA) 2-D loadings plots of data-reduced 1HNMR spectra for control and endosulfan-exposed Eisenia fetida using three 1-D NMR techniques: (a) Presaturation Utilising Relaxation Gradients and Echos (PURGE); (b) Carr–Purcell–Meiboom–Gill (CPMG); and (c) J-Resolved (J-RES) projections. Each oval region represents areas of significance determined using a two-sample t-test with a confidence interval of 95% (P<0.05). The asterisk indicates chemical shifts unidentified by the 1-D NMR technique alone (these regions are later identified using 2-D NMR).
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Figure 2.4 2-D NMR spectra of control worm tissue extract using: (a) 1H–J-RES, (b) 1H–1H COSY, and (c) 1H–13C Single Quantum Coherence (HSQC) spectroscopy.
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Figure 2.5
Partial least-squares discriminant analysis (PLS-DA) scores plot of data-reduced 1H NMR spectra for control Eisenia fetida (●) and E. fetida exposed to endosulfan (▲) (n=10) using two 2-D NMR techniques: (a) 1H–J-RES and (b) 1H–1H COSY. The P value for MANOVA (Wilks’ lambda) of control and exposed earthworms for the PLS components is reported. Scores for control and endosulfan-exposed earthworms are significantly different at the P<0.05 level.
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Figure 2.6 Partial least-squares discriminant analysis (PLS-DA) scores plot of data-reduced 1HNMR spectra for control Eisenia fetida (●) and E. fetida exposed to endosulfan (▲) (n=10) using 1H–13C Single Quantum Coherence (HSQC) spectroscopy in the chemical shift range of: (a) 1H=6.0–0.25 ppm; 13C=110.0–10.0 ppm and (b) 1H=2.5–0.25 ppm; 13C=50.0–10.0 ppm. The P value for MANOVA (Wilks’ lambda) of control and exposed earthworms for the PLS components is reported. Scores for control and endosulfan-exposed earthworms are significantly different at the P<0.05 level.
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Figure 2.7 Partial least-squares discriminant analysis (PLS-DA) 2-D loadings plots of 2-D NMR spectra for control and endosulfan-exposed Eisenia fetida: (a) 1H–J-RES and (b) 1H–1H COSY. Each oval region represents areas of significance determined using a two-sample t-test with a confidence interval of 95% (P<0.05) (see Figure 5).
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Figure 2.8 Partial least-squares discriminant analysis (PLS-DA) 2-D loadings plots of 2-D NMR spectra for control and endosulfan-exposed Eisenia fetida using 1H–13C Single Quantum Coherence (HSQC) spectroscopy in the chemical shift range of: (a) 1H=6.0–0.25 ppm; 13C=110.0–10.0 ppm and (b) 1H=2.5–0.25 ppm; 13C=50.0–10.0 ppm. Each oval region represents areas of significance determined using a two-sample t-test with a confidence interval of 95% (P<0.05).
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Figure 3.1 Mean principal component analysis (PCA) score plots of PC1 v. PC2 of trifluralin-exposed Eisenia fetida aqueous tissue extracts using: (a) 1-D presaturation utilising relaxation gradients and echos (PURGE) and (b) 2-D 1H-13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance spectra. Each point represents the mean PCA score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the trifluralin exposure concentration for each point.
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Figure 3.2 t-test filtered 1H nuclear magnetic resonance (NMR) difference spectra of Eisenia
fetida tissue extracts are obtained by subtracting the mean buckets of each trifluralin-exposed earthworm concentration: (a) 0.1, (b) 0.5 and (c) 1.0 mg cm-2 with the mean buckets of the control earthworms. Signals that are significantly different from the control (P<0.05) are retained whereas others are excluded.
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Figure 3.3 t-test filtered 1H–13C heteronuclear single quantum coherence (HSQC) difference nuclear magnetic resonance (NMR) spectra of Eisenia fetida tissue extracts are obtained by subtracting the mean buckets of each trifluralin-exposed earthworm concentration: (a) 0.1, (b) 0.5 and (c) 1.0 mg cm-2 with the mean buckets of the control earthworms. Signals that are significantly different from the control (P<0.05) are retained whereas everything else is excluded. Only metabolites that are detected in the 2-D NMR spectra and not in the 1-D NMR spectra are shown.
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Figure 3.4 Percentage change (%) of all identified metabolites from the t-test-filtered 1-D and 2-D nuclear magnetic resonance (NMR) difference spectra of trifluralin-exposed Eisenia fetida tissue extracts. Percentage changes that are significantly different from the control (P<0.05) are labelled with an asterisk. Each percentage change is shown with their associated standard error.
103
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Figure 3.5 Mean principal component analysis (PCA) score plots of PC1 v. PC2 of endosulfan-exposed Eisenia fetida aqueous tissue extracts using: (a) 1-D presaturation utilising relaxation gradients and echos (PURGE) and (b) and 2-D 1H-13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance spectra. Each point represents the mean PCA score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the endosulfan exposure concentration for each point. The asterisk represents the mean concentrations that are significantly different from the control (P<0.05) using Dunnett’s multiple comparison test.
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Figure 3.6 t-test filtered 1H nuclear magnetic resonance difference spectra of Eisenia fetida tissue extracts are obtained by subtracting the mean buckets of each endosulfan-exposed earthworm: (a) 0.5, (b) 1.0 and (c) 2.0 mg cm-2 with the mean buckets of the control earthworms. Signals that are significantly different from the control (P<0.05) are retained whereas everything else is excluded.
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Figure 3.7 t-test filtered 1H–13C heteronuclear single quantum coherence (HSQC) difference nuclear magnetic resonance (NMR) spectra of Eisenia fetida tissue extracts are obtained by subtracting the mean buckets of each endosulfan exposed earthworm concentration: (a) 0.5, (b) 1.0 and (c) 2.0 mg cm-2 with the mean buckets of the control earthworms. Signals that are significantly different from the control (P<0.05) are retained whereas everything else is excluded. Only metabolites that are detected in the 2-D NMR spectra and not the 1-D NMR spectra are identified.
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Figure 3.8 Percentage change (%) of identified metabolites from the t-test-filtered 1-D nuclear magnetic resonance difference spectra of endosulfan exposed Eisenia fetida tissue extracts. Percentage changes that are significantly different from the control (P<0.05) are labelled with an asterisk. Each percentage change is shown with their associated standard error.
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Figure 3.9 Percentage change (%) of identified metabolites from the t-test-filtered 2-D nuclear magnetic resonance difference spectra of endosulfan exposed Eisenia fetida tissue extracts. The percentage changes that were significantly different from the control (P<0.05) are labelled with an asterisk. Each percentage change is shown with their associated standard error.
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Figure 3.10 Mean principal component analysis (PCA) score plots of PC1 v. PC2 of trifluralin- and endosulfan-exposed Eisenia fetida aqueous tissue extracts using: (a) 1-D presaturation utilising relaxation gradients and echos (PURGE) and (b) 2-D 1H–13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance spectra. Each point represents the mean PCA score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the exposure concentrations for each point. The asterisk represents mean concentrations that are significantly different from that of the control (P<0.05) using Dunnett’s multiple comparison test. The arrows indicate the trajectory of exposure by trifluralin or endosulfan.
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Figure 4.1 1-D and 2-D NMR spectra of control earthworm CF acquired using A) 1-D PURGE and B) 2-D 1H–13C HSQC NMR spectroscopy
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Figure 4.2 1-D and 2-D NMR spectra of control worm tissue extracts acquired using A) 1-D PURGE and B) 2-D 1H–13C HSQC NMR spectroscopy
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Figure 4.3 Mean PCA scores plots of 1H NMR spectra of endosulfan exposed E. fetida using their A) CF (PC 1 vs PC 2) and B) aqueous tissue extract (PC 1 vs PC 3). Each point represents the mean PC score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the endosulfan exposure concentration for each point. The ‘‘*’’ represents the mean concentrations that were significantly different from the control (p<0.05) using Dunnett’s multiple comparison test.
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Figure 4.4 t-test filtered 1H NMR difference spectra of E. fetida CF were obtained by subtracting the mean buckets of each endosulfan-exposed earthworm concentration: (a) 0.5 µg cm-2, (b) 1.0 µg cm-2 and (c) 2.0 µg cm-2 with the mean buckets of the control earthworms. Signals that were significantly different from the control (p<0.05) were retained while others are excluded. Only the major metabolites are labeled for clarity.
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Figure 4.5 t-test filtered 1H NMR difference spectra of E. fetida tissue extracts were obtained by subtracting the mean buckets of each endosulfan-exposed earthworm: (a) 0.5 µg cm-2, (b) 1.0 µg cm-2 and (c) 2.0 µg cm-2 with the mean buckets of the control earthworms. Signals that were significantly different from the control (p<0.05) were retained while everything else were excluded. Only the major metabolites are labeled for clarity.
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Figure 4.6
Percent change (%) of identified metabolites from the t test filtered 1-D NMR difference spectra of endosulfan-exposed E. fetida CF. Percent changes that were significantly different from the control (p<0.05) were labelled with ‘‘*’’. Each percent change is shown with their associated standard error.
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Figure 4.7 Percent change (%) of identified metabolites from the t test filtered 1-D NMR difference spectra of endosulfan-exposed E. fetida tissue extracts. Percent changes that were significantly different from the control (p<0.05) were labelled with ‘‘*’’.
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Figure 5.1 Mean PCA scores plots of PC1 versus PC2 of endosulfan-exposed E. fetida: A) coelomic fluid using 1-D NMR, B) tissue extracts using 1-D NMR, and C) tissue extracts using 2-D HSQC NMR spectra. Each point represents the mean PC score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the exposure concentrations for each point. The “*” represents mean concentrations that were significantly different from the control (p<0.05) using Dunnett’s multiple comparison test.
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Figure 5.2 Mean PCA scores plots of PC1 versus PC2 of endosulfan sulfate-exposed E. fetida: A) coelomic fluid using 1-D NMR, B) tissue extracts using 1-D NMR, and C) tissue extracts using 2-D HSQC NMR spectra. Each point represents the mean PC score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the exposure concentrations for each point. The “*” represents mean concentrations that were significantly different from the control (p<0.05) using Dunnett’s multiple comparison test.
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Figure 5.3 Percent change (%) of all identified metabolites from the t-test filtered 1-D NMR
difference spectra of endosulfan-exposed E. fetida coelomic fluid. Percent changes that were significantly different from the control (p<0.05) are labelled with “*”. Each percent change is shown with their associated standard error.
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Figure 5.4 Percent change (%) of all identified metabolites from the t-test filtered A) 1-D NMR and B) 2-D NMR difference spectra of endosulfan-exposed E. fetida tissue extracts. Percent changes that were significantly different from the control (p<0.05) are labelled with “*”. Each percent change is shown with their associated standard error.
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Figure 5.5 Percent change (%) of all identified metabolites from the t-test filtered 1-D NMR difference spectra of endosulfan sulfate-exposed E. fetida coelomic fluid. Percent changes that were significantly different from the control (p<0.05) are labelled with “*”. Each percent change is shown with their associated standard error.
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Figure 5.6 Percent change (%) of all identified metabolites from the t-test filtered A) 1-D NMR and B) 2-D NMR difference spectra of endosulfan sulfate-exposed E. fetida
tissue extracts. Percent changes that were significantly different from the control (p<0.05) are labelled with “*”. Each percent change is shown with their associated standard error.
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Figure 5.7 Mean PCA scores plots of PC1 versus PC2 of endosulfan- and endosulfan sulfate-exposed E. fetida: A) coelomic fluid using 1-D NMR, B) tissue extracts using 1-D NMR, and C) tissue extracts using 2-D HSQC NMR spectra. Each point represents the mean PC score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the exposure concentrations for each point. The “*” represents mean concentrations that were significantly different from the control (p<0.05) using Dunnett’s multiple comparison test. The arrows indicate the trajectory of exposure of endosulfan and endosulfan sulfate.
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LIST OF ABBREVIATIONS
% GSSG: Percentage of Oxidized Glutathione
1-D One dimensional
2-D Two-dimensional
ACRs: Acute-to-Chronic Ratios
AMIX: Analysis of Mixtures
ANOVA: Analysis of Variance
ATP: Adenosine Triphosphate
CAC: Citric Acid Cycle
CANUP:
Candian Atmospheric Network for Currently Used Pesticides
CF: Coelomic fluid
ChEs: Cholinesterases
COSY Correlation Spectroscopy
CPMG: Carr-Purcell-Meliboom-Gill
Cyt Cytochrome
DNP: 2,4-dinitrophenol
DSS: 2, 2-dimethyl-2-silapentane-5-sulfonate sodium salt
EC50: Half maximal effective concentrations
EE2: Ethinylestradiol
EEC: European Economic Community
EJF: Environmental Justice Foundation
GABA: Gamma-Aminobutyric Acid
GC: Gas Chromatography
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GR: Glutathione Reductase
GSH: Glutathione
GST: Glutathione-S-Transferase
HPLC: High Performance Liquid Chromatography
HSQC: Heteronuclear single quantum coherence
J-RES: J-resolved spectroscopy
Kow: Log n-Octanol/Water Partition Coefficient
LC50: Half maximal lethality concentration
Log Koc: High Log Organic Carbon Adsorption Coefficient
LP: Lipid Peroxides
LP1: Peroxidizable Lipids
MANOVA: Multivariate analysis of variance
MOA Mode Of Action
MROD: Methoxyresorufin-O-deethlylase
MS: Mass Spectrometry
NADH Red: Nicotinamide Adenine Dinucleotide Cytochrome Reductase
NADPH Red:
Nicotinamide Adenine Dinucleotide Phosphate Cytochrome Reductase
NMR: Nuclear Magnetic Resonance
NOEC: No-Observed Effect Concentration
NOESYPRESAT: Nuclear Overhauser Effect Spectroscopy Presaturation
OECD: Organization for Economic Co-operation and Development
PAH: Polyaromatic Hydrocarbon
xix
PBT: Persistent, Bioaccumulative and Toxic
PC: Principal Components
PCA: Principal Component Analysis
PLS-DA: Partial Least Squares Discriminant Analysis
PNEC: Predicted No-Effect Concentration
PRESAT: Presaturation
QSAR: Quantitative Structure Activity Relationships
QXI: Quadruple Inverse
REACH:
Registration, Evaluation, Authorization and Restriction of Chemicals
S/N: Signal-To-Noise
TCE: Trichloroethylene
TOCSY: Total Correlation Spectroscopy
Total GSH: Total Glutathione
UNEP: United Nations Environment Program
xx
PREFACE
The present dissertation is a collection of published manuscripts (Chapters 2-4) in peer-
reviewed journals and one manuscript that has been submitted (Chapter 5). As a result, this
thesis contains some repetition. Contributions from authors are as follows:
CHAPTER 1:
Introduction: Organohalogenated argochemicals, ecotoxicology, earthworms in
ecotoxicology and environmental NMR-based metabolomics
Contributions: Written by Jimmy Yuk with critical comments from André J. Simpson
CHAPTER 2:
Comparison of 1-D and 2-D NMR techniques for screening earthworm responses to sub-
lethal endosulfan exposure.
Published as: Yuk J, McKelvie JR, Simpson MJ, Spraul M, and Simpson AJ (2010). Comparison of 1-D and 2-D NMR techniques for screening earthworm responses to sub-lethal endosulfan exposure. Environ Chem. 7 (6):524-536
Contributions: The experiment was designed by Jimmy Yuk and André J. Simpson. The data
collection and analysis were performed by Jimmy Yuk and Jennifer R. McKelvie with guidance and critical comments from Manfred Spraul. The manuscript was written by Jimmy Yuk with critical comments from Jennifer R. McKelvie, Manfred Spraul, Myrna J. Simpson and André J. Simpson.
CHAPTER 3:
1-D and 2-D NMR metabolomics of earthworm responses to sub-lethal trifluralin and
endosulfan exposure
Published as: Yuk J, Simpson MJ, Simpson AJ (2011) 1-D and 2-D NMR metabolomics of earthworm responses to sub-lethal trifluralin and endosulfan exposure. Environ Chem. 8 (3):281-294
Contributions: The experiment was designed by Jimmy Yuk, Myrna J. Simpson and André J.
Simpson. The data collection and analysis were performed by Jimmy Yuk. The manuscript was written by Jimmy Yuk with critical comments from Myrna J. Simpson and André J. Simpson.
xxi
CHAPTER 4:
Coelomic fluid: A complimentary biological medium to assess sub-lethal endosulfan
exposure using 1H NMR-based earthworm metabolomics
Published as: Yuk J, Simpson MJ, Simpson AJ (2012) Coelomic fluid: A complimentary biological medium to assess sub-lethal endosulfan exposure using 1H NMR-based earthworm metabolomics. Ecotoxicology: In Press
Contributions: The experiment was designed by Jimmy Yuk, Myrna J. Simpson and André J.
Simpson. The data collection and analysis were performed by Jimmy Yuk. The manuscript was written by Jimmy Yuk with critical comments from Myrna J. Simpson and André J. Simpson.
CHAPTER 5:
1-D and 2-D NMR metabolomics of earthworm responses to sub-lethal endosulfan and
endosulfan sulfate exposure in soil
Content in this chapter has been submitted in: Environmental Pollution
Contributions: The experiment was designed by Jimmy Yuk, Myrna J. Simpson and André J.
Simpson. The data collection and analysis were performed by Jimmy Yuk. The manuscript was written by Jimmy Yuk with critical comments from Myrna J. Simpson and André J. Simpson.
CHAPTER 6:
Conclusions and future research
Contributions: Written by Jimmy Yuk with critical comments from André J. Simpson.
1
CHAPTER ONE
Introduction: Organohalogenated argochemicals, ecotoxicology, earthworms in ecotoxicology and environmental NMR-based
metabolomics
2
1.1 Organhalogenated agrochemicals
The Neolithic revolution is referred to as the agricultural revolution when humans began
to transition from hunting and gathering to cultivating crops and starting settlements [1].
Nomadic groups began settling into permanent areas, allowing them to utilize their surrounding
natural environment to cultivate crops as their main source of food. The surplus of food allowed
villages and towns to be built as populations began to dramatically increase. To sustain
themselves, these communities relied heavily on maintaining and increasing food production. A
major obstacle for farmers was the destruction of their crops by weeds and pests. The solution
was the invention of agrochemicals. Agrochemicals date back to pre-Roman times when farmers
used elemental sulphur to remove predatory organisms [2]. As time passed, farmers utilized
other chemical applications such as burning bitumen, cow manure, tobacco and mercury [3].
However, these early agrochemicals failed to control or eliminate the wide diversity of pests.
The 1940s ushered in the modernization of the agrochemical industry when organohalogenated
pesticides, synthetic chemicals with halides as a substituent, were invented [3]. Over the next 30
years, scientists were on a quest to discover the perfect agrochemical with maximum potency,
efficacy and economic viability. This saw a significant increase of organohalogenated pesticides
with different functional groups using various halides (Figure 1.1) [4].
As depicted in Figure 1.1, the majority of the agrochemicals in the 1940’s were
organochlorinated. It was not until the 1990’s when the number of chlorinated products started to
decrease. During the 1960s, scientists started to document numerous adverse impacts such as
declining wildlife populations and increasing human health problems, all linked to the use of
organochlorinated pesticides [5].
3
Figure 1.1: The emergence of organohalogenated agrochemicals from the period of 1940-2008 from Jeschke, 2009 [4]. A well-known naturalist and scientist, Rachel Carson, published a book called “Silent
Spring” in 1962 that criticized the agrochemical industry and their production of deadly
pesticides [6]. At the time, most farms and households already applied some form of
halogenated pesticides on their crops, home, yards and gardens [6]. Her book brought major
awareness to the dangerous impacts of agrochemicals on wildlife and human health. One
particular agrochemical invented in the early 1950s, endosulfan (6,7,8,10,10-hexachloro-
1,5,5a,6,9,9a hexahydro-6-9-methano-2,3,4,benzodioxathiepin-3-oxide), is a cyclodiene
insecticide widely used throughout the world (structure shown in Figure 1.2A). Endosulfan has
been registered in many European and Central American countries as well as, among others,
India, Indonesia, Australia, Canada, United States, Mexico, Brazil and China [7]. Researchers
developed endosulfan as a potent agrochemical to combat a wide variety of insect pests and
mites and to apply to different crops such as cotton, cereals, fruit trees, tea and coffee [8].
4
Figure 1.2: Structure of: A) Endosulfan and B) Endosulfan sulfate
Ever since its introduction in 1953, endosulfan has been one of the leading agrochemicals
used throughout the world with close to 308,000 tonnes applied from 1950 to 2000 [8].
However, in the past 30 years, endosulfan has received considerable attention from many
international environmental agencies including the United Nations Environment Program
(UNEP), the World Health Organization (WHO) and the Environmental Justice Foundation
(EJF) [9, 10]. Many studies have reported genotoxic, tertagenic, mutagenic, and neurotoxic
properties to surrounding wildlife such as mammals, birds, fishes and bees [9, 11-13]. In
addition, endosulfan is considered a persistent organic pollutant that sorbs strongly to soils and
sediments after application. Due to its longer degradation times and volatility, endosulfan has
the potential to migrate to locations far away from the direct site of usage [9-13]. In the
atmosphere, endosulfan is considered to be one of the most ubiquitous organochlorine pesticides
[10]. In a recent atmospheric surveillance program by the Canadian Atmospheric Network for
Currently Used Pesticides (CANUP), endosulfan was the most commonly detected
organochlorinated pesticide across the country with high wet deposition fluxes, suggesting major
input by the atmosphere [14]. Endosulfan breaks down to many degradation products such as
endosulfan sulfate, diol, ether, -hydroxy ether and –lactone [11]. Endosulfan sulfate (shown in
Figure 1.2B) is considered the most problematic degradation product as it is even more persistent
O
S
O O
Cl
Cl
Cl
Cl
Cl Cl
H
H
O
S
O
O
O
Cl
Cl
Cl Cl
Cl
Cl
A) B)
5
than endosulfan in soil and sediments [15]. Endosulfan has a degradation half-life of one to
three months but endosulfan sulfate can have a degradation half-life of two to six years. In
addition, past toxicity tests applied to aquatic species have found endosulfan sulfate to be just as
toxic as the parent compound [10, 16, 17]. Due to the ubiquity of endosulfan in the environment
and the lack of studies on endosulfan sulfate, it is important to investigate the potential risks of
these past pesticides that continue to contaminate many areas today.
The early 1980’s saw growth and in the popularity of organofluorinated agrochemicals
because of their increased biological potency compared to other agrochemicals. This meant less
material could be used in the field. The decrease in application rates theoretically meant less
impact on the environment and cost for production. The use of organofluorinated pesticides in
the agrochemical industry dramatically increased from 23 compounds in 1977 to 126 compounds
by 2000 [18]. Fluorine has a significant effect on biological activity when used in herbicides,
insecticides, and fungicides [19]. Trifluralin (Figure 1.3) is one of oldest organofluorinated
herbicides and belongs to the dinitroaniline family, which was the first fluorine-added group to
to be marketed. Eli Lily pushed trifluralin onto the market in the 1960s, and today, it is still one
of the most widely used herbicides for the control of annual grasses and broadleaf weeds [18].
Trifluralin is estimated to be produced annually at 20 to 25 thousand tons worldwide [20].
Figure 1.3: Structure of Trifluralin
N+
N+
N
O-
O-
O
O
F
F
F
CH3
CH3
6
Recently, there have been numerous reports of trifluralin having genotoxic potential in
various organisms [21, 22] and being a human carcinogen [23]. A study of the Canadian
Prairies identified trifluralin as one of the most frequently detected organofluorinated herbicide
in the air (observed in 79% of samples) [24]. In addition, trifluralin is the most frequently
detected pesticide on the hands (60%) of non-occupational exposed adults [25]. Trifluralin is
mostly immobile in the soil environment but can be transported out by volatilisation or surface
run-off when it moves into particulate matter [26]. Since trifluralin has a degradation half-life
of >1 year in soils, it is imperative to understand more about the fate of this widely used
herbicide especially to terrestrial organisms.
1.1.1 Soil contamination and assessing environmental risk
The soil environment is the primary sink for organohalogenated pesticides due to direct
application, leakage or spillage, and atmospheric deposition [27]. Since many
organohalogenated pesticides are designed to be resistant to biological or chemical degradation,
they can remain in the soil for years. Their presence raises the risk to surrounding areas as they
have a higher residence time to exert their potential toxicity. Understanding the fate and
behaviour of pesticides in the soil is complex. Many factors are involved such as the soil type
(mineral and organic matter content) and the physico-chemical properties (e.g. aqueous
solubility, polarity, hydrophobicity, lipophilicity, and molecular structure) of the pesticides [28].
Most organohalogenated pesticides are highly non-polar and lipophilic and this allows them to
bind strongly to organic matter in the soil and sediments [29]. Understanding the bioavailability
of contaminants in the soil to surrounding organisms is a major challenge as studies have shown
the decrease of hydrophobic pesticides over time as the soil ages or weathers [30, 31].
7
Bioavailability, in an environmental context, is the accessibility of the contaminant for
assimilation or possible toxicity [29]. Many modern assessment methods such as Soxhlet,
ultrasonic, shake-flask and super-critical fluid extraction use various solvent systems to assess
bioavailability [32]. However, recent research has shown that these types of chemical proxies
are not able to directly link the toxicity especially with long-term exposure to contaminated soils
[33]. In addition, the results from chemical extractions focus on the total contaminant
concentration and do not provide insight into the difference between free and bound forms of the
pesticide [34]. From an ecological risk assessment standpoint, this is a major concern for
remediating and setting policies for contaminated soil sites as inadequate assessments are made
using potentially inaccurate data. Therefore, a direct measure of soil quality is required with an
understanding of the health of surrounding organisms.
1.2 Ecotoxicology
Ecology is the study of the interactions between organisms and their environment at all
levels, from individual organisms to the ecosystem [35]. In addition, ecology includes
understanding an organism’s distributions, abundances and its functioning in biological
population and communities and all the parameters that mediate these factors [16]. Toxicology
is the understanding of the adverse response to an organism by exposure to a chemical, physical
or biological agent [36]. This field includes a diverse range of pertubations including mild
biochemical perturbations to permanent organ damage and death. From these definitions,
ecotoxicology is the integration of both ecology and toxicology with the objective of not only
understanding the environmental stress response of the organism to the ecosystem but also
predicting the response of chemicals on the natural communities under realistic conditions [36].
8
1.2.1 Regulatory approaches to ecotoxicological testing
Every year, new organohalogenated pesticides are created and this becomes a major
challenge in assessing the potential environmental risks associated with these chemicals on
various organisms and their ecosystems [37]. To alleviate this concern, authorities developed a
general risk assessment scheme in which a predicted no-effect concentration (PNEC) was
derived from a set of acute and semichronic ecotoxicological data using a few representative
species [38, 39]. These methods were then extrapolated to be used for different species. For
chemicals that only contain acute toxicity data, acute-to-chronic ratios (ACRs) were made for
environmental risk assessment to estimate the chronic no-observed effect concentration (NOEC)
[38]. The acute tests were mostly based on survival lethality concentration for 50% of the
population (LC50) [40].
However, for chemicals that were on the market before 1982 (e.g. endosulfan and
trifluralin) and are already released into the environment, these chemicals had to be addressed
using the Existing Chemicals Regulation [41]. From the >100,000 chemicals listed in the
European Inventory of Existing Commercial Chemical Substances, certain priority substances
were chosen for risk assessment using information given from the manufacturers and importers.
The assessment results showed large data gaps as the available information ranged from very low
to very high making it difficult to compare old chemicals with the new ones, which were already
standardized. In addition, many formerly applied chemicals had test results that were
inconsistent because many were not performed using defined test conditions. This added a large
complexity for understanding the overall risk. Therefore, only a small number of existing
chemicals are currently assessed and regulated to date [42].
9
Due to the large discrepancies in establishing a standardized regulatory system for
existing and new chemicals, in June 2007, a European initiative called the Registration,
Evaluation, Authorization and Restriction of Chemicals (REACH) was created [40]. REACH’s
main objective is to set minimum data requirements for all chemicals to ensure the highest level
of protection for human health and the environment. In addition, the responsibility for chemical
assessment has been shifted from government authorities to agrochemical industry [43].
According to REACH, this allows higher standards to be made by enhancing competition and
innovation in the market [44]. All existing and new chemicals are included in REACH.
Flexibility in the system is given to industry if modifications are needed for certain standard
tests. However, the substitution of another test must have equivalent results that will produce
adequate information to draw a conclusion for classification and labelling, for persistent,
bioaccumulative and toxic (PBT) potential and for PNEC calculation [44]. A weight of evidence
approach is taken to ensure all information for a particular chemical is equivalent or adequate for
making a proper assessment. The overall goal is then to understand what information might be
lacking and to develop various strategies for an overall conclusion on the toxicity of a certain
chemical.
1.2.2 Limitations in current approaches
Current regulatory approaches for assessing chemicals have focused on data derived from
the physical chemical information for quantitative structure activity relationships (QSAR) and
from laboratory-based toxicity tests [45]. However, one concern for risk assessments of existing
and new chemicals is the necessary requirements before tests are to be done. Tests are
performed based on the production volume of chemicals rather than their estimated risk. For
10
REACH, substances produced or imported that are less than 1 metric ton (1,000 kg) do not need
to be registered and the extent of toxicological tests varies according to tonnage [43]. For
example, guidelines made from the Organization for Economic Co-operation and Development
(OECD) for REACH set the threhold for testing acute inhalation and dermal toxicity at 10
tonnes/ year, developmental toxicity and 90 day subchronic toxicity at 100 tonnes/ year and 2-
generation reproductive toxicity and carcinogenicity at 1000 tonnes/ year [43]. With new
organohalogenated pesticides being produced every year having higher biological efficiency
[18], the potential for higher toxicity can be present at lower concentrations – this danger might
be overlooked due to the production volume guidelines.
For laboratory toxicity tests, many regulatory agencies rely on whole animal exposures
and end-points of toxicity focusing on mortality, survival and reproductive effects [40].
However, there is a bias for conducting acute lethality tests as time and resources are expensive
when moving from acute to chronic tests. This is problematic as acute endpoints extrapolated
into chronic endpoints are not often accurate predictors and create uncertainty to toxicity
assessment [46]. In addition, these tests do not directly evaluate the sub-lethal toxicity of
chemicals to organisms [45]. Based on the above, the challenge is to develop a method that
understands the mechanism of molecular and cellular interactions between the contaminant and
the organism. This will then generate more accurate data for predictive simulation models and
potentially show a link to pathophysiological “endpoints” [47]. During the past 20 years, major
interest has been devoted to the development of finding biomarkers for ecological risk
assessment [48]. From the United States National Academy of Sciences Committee on
Biological Markers, biomarkers are defined and categorized into 3 groups: biomarkers of
exposure, response or susceptibility [49]. All three types are inter-related but vary in their
11
biological endpoint. Biomarkers of exposure are used to determine the level of chemical in the
organism and can be the compound or the metabolite of the chemical. These are considered the
exogenous metabolites that do not originally exist in the organism. This type allows the
understanding of the intervening processes of absorption, distribution metabolism and excretion
to measure the extent of exposure of the organism to the contaminant. When exposure has been
clearly established, biomarkers of response are then determined to detect any reversible or
irreversible pathological effects to the organism by the contaminant. These are mainly
endogenous metabolites produced by the organism that are significantly fluctuating and could be
related to the mechanism of toxicity or the mode of action (MOA) by the contaminant. Finally,
biomarker of susceptibility are detected which potentially can affect specific species or
individuals within a species and can cause more of a response by the contaminant than others
(i.e. genetic factors). In ecotoxicology, biomarkers are defined as a combination of the three
with the main goal of detecting early biochemical indicators related to exposure of xenobiotic
chemicals [50].
When an organohalogenated pesticide is registered in REACH, there is usually
information on a probable toxic MOA due to its function against different pests. Therefore,
current pesticide regulators require various types of tests using different representative species
and endpoints of toxicity experiments to ensure all potential MOA were anticipated and not
overlooked [40] . However, these tests can be time-consuming and expensive when making risk
assessment decisions [40].
Ecological risk assessments of organohalogenated pesticides need to be improved –
especially with the continuing increase of new pesticides developed and registered every year, as
well as the re-registration of existing pesticides [40]. As one of the goals for REACH is to
12
register 30,000 existing chemicals by 2018, a new approach is required to delineate toxic MOAs.
This new approach needs to be high-throughput, cost-effective, and able to be implemented
immediately.
1.3 Metabolomics
Metabolomics is the study of low molecular weight organic molecules within a cell,
tissue or biofluid and how their composition varies with an external stressor [51]. These organic
molecules include endogenous metabolites (referred to as the organism’s metabonome) which
consist of their amino and fatty acids, carbohydrates, vitamins and lipids. New metabolites, such
as those linked with disease or a genetic modification may also be observed. Metabolomics is
closely related to other “omic” technologies (Figure 1.4) such as: genomics, the study of the
organism’s DNA (genome); transcriptomics, the study of their RNA (transcriptome); and
proteomics, the study of their proteins (proteome).
Figure 1.4: The different types of omic technologies
The metabolic information from organisms complements the proteomic information as
the metabolites are often cellular respiration products from proteins or substrates used for
biochemical reactions in specific proteins such as enzymes [52]. The advantage of
metabolomics, in comparison with genomics or proteomics, is that it allows an instant overall
picture of the physiology of a cell at any time. In addition, exogenous metabolites can also be
13
detected as well. These are another class of metabolites which include food products, drugs,
environmental contaminants and microbial‐derived metabolites which only metabolomics can
detect. This facilitates the understanding of various biochemical cycles affected and specific
metabolites produced: both of which may be a consequence of a stressor on the organism. The
concept of metabolite profiling started in the late 1940s by Roger Williams and his associates
[53]. Analyzing different types of biological fluids using thin-layer chromatography, his group
was able to determine unique excretion patterns for a variety of substances in different
individuals. This study on the distinctiveness of the metabolic profile was the beginning of the
metabolomics era. The advancement of analytical platforms for metabolomics soon followed as
the quantitative metabolite profiling of volatilizable compounds such as urinary organic acids by
Gas Chromatography (GC) was published in the 1970s [53] and the application of Nuclear
Magnetic Resonance (NMR)-based metabolomic studies for toxicological classification was
published in the 1990s [54]. In addition, the role of computer processing in metabolomics
research was introduced which allowed unparallel strength in data and statistical analysis for
large metabolomic datasets [54]. Currently, metabolomics is an emerging field in a wide variety
of studies such as toxicology [54], drug discovery [55], nutrition [56], cancer [57], diabetes [58],
natural product discovery [59] and environmental stress [51].
1.3.1 Environmental metabolomics
Environmental metabolomics is a sub-discipline of metabolomics and is defined as the
characterization of the metabolite profile of organisms and their interactions with their
environment [34]. This approach allows an in-depth analysis to investigate organism-
environment interactions and give insight into an organism’s health at a molecular level [51].
14
One advantage of environmental metabolomics over traditional toxicity tests is that it provides
mechanistic information on the functional status of an organism; this mechanistic information
can be connected to an organism’s phenotype [51]. Another advantage is that environmental
metabolomics can potentially uncover unknown biochemical relationships through the
metabolite profile for hypothesis generation [60].
The emergence of environmental metabolomics in environmental sciences is seen with
studies investigating an organism’s response to natural stressors, related to, for example,
temperature and anthropogenic stressors [51]. There is huge potential for metabolomics to be
used in ecological risk assessment. However, there is debate on how this may be achieved [61].
The main advantages for applying environmental metabolomics to risk assessment include: early
and rapid screening for contaminants that may cause an adverse chronic response in the
environment, and the ability to understand the toxic MOA of a contaminant. Environmental
monitoring by environmental metabolomics will complement other traditional methods, such as
chemical residue analysis. The ability to detect other metabolite biomarkers in various species
will be critical in understanding which contaminant classes are exerting a toxic response.
Delineating the toxic MOA of contaminants by metabolomics will be important to reduce
uncertainty in risk assessment [40]. A better understanding of the toxic MOA increases
confidence in extrapolating the data to various species[62]. The metabolome of a living
organism (plant or animal) can contain ~2000-20,000 components [63] which can contain a vast
amount of information to provide better insight into the biochemical MOA. Environmental
metabolomics will assist in customizing the test designs and endpoints such that specific assays
can be optimized for a toxic compound. This method can potentially cut costs and time spent on
unnecessary tests which have little impact on risk assessment.
15
Currently, researchers are applying environmental metabolomics to study aquatic
environments. Environmental metabolomics has been used to examine the endocrine disruption
ability of the synthetic contraceptive estrogen, ethinylestradiol (EE2), on various fish species
such as the juvenile rainbow trout (Oncorhynchus mykiss) [64], adult fathead minnow
(Pimephales promelas) [65, 66] and three-spined stickleback (Gasterosteus aculeatus) [51].
Using Nuclear Magnetic Resonance (NMR) spectroscopy to analyze the blood plasma and liver
extracts of each fish species, researchers have identified various specific biomarkers from
exposure to EE2. For example, the studies found vitellogenenin, alanine, phospholipids and
cholesterol for juvenile rainbow trout [64]; glycogen, glucose, lactate, creatine and bile acids for
adult fathead minnow[65, 66]; and glucose for the adult fathead minnow [65, 66]. Fish embryos
from the Japanese medaka have been used in metabolomics to understand their developmental
change after exposure to a well-known herbicide, dinoseb, an uncoupler of oxidative
phosphorylation [67]. The results of this study correlated with traditional biological assays by
identifying reduced growth rate and heart rate, abnormal development and mortality. In addition,
NMR metabolomics confirmed results from a past study using in vivo 31P NMR and High
Performance Liquid Chromatography (HPLC), and identified phosphocreatine utilisation in the
embryo as an indicator for medaka embryotoxicity [68]. Environmental metabolomics has been
used on other aquatic species: the water flea (Daphnia Magna) [69] to examine the response to
various toxicants (cadmium, fenvalerate, dinitrophenol, and propranolol), and crustaceans, like
the Atlantic blue crab (Callinectes sapidus), to investigate their exposure to 2,4-dinitrophenol
and a bacterium, Vibrio campbellii [70]. In both studies, the toxic MOA was uncovered with
distinct differences in the metabolic profiles of the exposed versus the control groups.
16
Compared to aquatic environments, the development and application of environmental
metabolomics in soil environments is currently in its infancy [51]. Most metabolomics studies
currently focus on vertebrate toxicology with the main interest in disease diagnosis [71]. As
noted before, the soil environment is the primary sink for organohalogenated pesticides and has
the highest risk for exposure to the surrounding wildlife. Given the large potential of
metabolomics as a potential environmental monitoring tool and its ability to determine the toxic
MOA of contaminants, the goal of this thesis is to develop metabolomic methods using a
“sentinel” organism in the soil environment to understand sub-lethal toxicity. In this thesis, the
earthworm, Eisenia fetida, will be the representative organism for the soil environment and is the
recommended OECD species [72].
1.4 Earthworms in Ecotoxicity tests
Invertebrates account for 95% of all animal species and are a vital part of understanding
the ecosystem structure and function [73]. Ecotoxicologists are more concerned about the
increased use of pesticides, and the adverse effects on soil fertility in agricultural lands, and
potential toxicity on the surrounding plants and animals. The need for a sensitive indicator
organism is crucial for research, monitoring, and regulatory testing [74]. Earthworms are ideal
organisms to assess soil quality [75]. Earthworms process decaying organic matter into the soil,
improve aeration and water transport [76]. In addition, they comprise the most biomass in soils
and they help maintain soil structure and microbial communities [74, 77].
The earthworm does not have a skeleton but instead a thinly pigmented cuticle with an
outer layer of circular muscle and an inner layer of longitudinal muscles. During maturation,
earthworms develop a swollen area of the epidermis called a clitellum where the formation of
17
cocoon occurs for the eggs and ova to be deposited as it is then passed over to the anterior
segment [76]. The young develop within the eggs without a clear larvae stage and newly
hatched earthworms resemble young adults. Earthworms are hermaphrodites with various
gonads situated at specific segmental positions. Structurally, the earthworm has large coelomic
cavities containing coelomic fluid with a closed vascular system and a dorsal and ventral trunk
connecting to a nerve cord. Most waste and nutrient cycling involves the coelomic fluid which
contain specialized cells such as the chlorgagon cells and yellow granules called chloragosomes.
These cells assist in removing wastes from the blood and transport the nutrients between the
tissues and organs. Respiration is mainly through the cuticles with no specialized respiratory
organs and an alimentary canal runs from the anterior to the posterior of the earthworm. This
helps circulation of materials coming in and also excretion through the anus or through
specialized organs called nephridia [76]. The earthworm has a large ventral nerve cord with
three longitudinal giant fibers that surround the whole body. Organic matter is the main source
of nutrition for the earthworm but also depends on the other microorganisms such as bacteria,
fungi, and decomposed nematodes for their nutrients [76]. Earthworms are very sensitive to the
surrounding environment and eject coelomic fluid at the dorsal pores if threatened. They are
quite resilient in various environments such as long periods in H2O and can survive in anaerobic
conditions.
Earthworms are commonly used in ecotoxicology studies because they are ubiquitous in
soil environments, easy to sample and to identify [78]. They are relatively immobile, travelling
only a small soil area throughout their lifetime and are in full contact of any potential stressors
that may be present. Earthworms could be considered as the terrestrial equivalent of aquatic
filter-feeders [76]. E. fetida is a common species used in ecotoxicological studies as it is
18
dominant in many soil fauna, has a wide temperature and moisture tolerance and is the OECD’s
recommended species [72, 76]. In addition, it is an epigeic earthworm, meaning it lives and
feeds on the top soil, forms no permanent burrows and moves around in a small area; this allows
insight into a particular contaminated area [78].
Early ecotoxicology testing included acute toxicity tests in the laboratory using E. fetida
and were developed by the OECD [72]. These standardized earthworm toxicity experiments
were mainly conducted on contact filter paper tests or artificial soil tests. The contact filter paper
test allows a direct method in exposing a single earthworm to a standard filter paper with the test
chemical dissolved in water or organic solvent (such as acetone, hexane and chloroform). This
test was an excellent screening method to understand the relative toxicity of a wide spectrum of
chemicals at various concentrations. However, the contact filter paper test does not account for
the differential sorption of chemicals that may occur in soil [76]. To have a better comparison to
natural soil environments, an artificial soil test was developed by Edwards [79] and was quickly
adopted by the European Economic Community (EEC) and OECD. This standardized artificial
soil had an adsorptive capacity of a common loam soil (25 meq) and had the following mixture
of components: 10% finely ground sphagnum peat (pH 5.5-6.0), 20% kaolinite clay, 70%
industrial quartz sand and an approximate moisture content of 30 to 45% [72].
Most ecotoxicity tests using E.fetida are focused on main endpoints to assess the adverse
effects of potentially dangerous chemicals. Mortality was established as a main endpoint in
acute toxicity tests with E. fetida from the OECD and ECC [72]. For standardization of
mortality tests, the earthworms are usually bred in the laboratory so the age and past history of
the species are known. Mortality is determined by the identification of the LC50. Exposure times
varied in specific conditions chosen for the tests. For contact filter paper tests, the chemical
19
exposure time was 48 hours, while for the artificial soil, 7 and 14 days of exposure were suitable
[80]. From the OECD criteria, the upper limit was set for a chemical concentration of 1000 mg
active ingredient/kg of dry substrate. Any concentration above that limit was believed to be safe
for the earthworms [72]. However, there has been criticism about mortality tests as earthworms
that are alive after the tests can have irreversible damage and would most likely die if the tests
were prolonged. As such, a better method is required to assess the sub-lethal toxicity of
chemicals after exposure and the physiological state of the organism.
As previously discussed (section 1.2.2), molecular biomarker tests have been developed
to complement acute and chronic toxicity tests. Detecting biomarkers can give clear evidence of
a cause-effect relationship of the contaminant in the soil environment and adverse responses at
the individual level using earthworms [78]. A broad group of biomarkers have been developed
for E. fetida such as cholinesterases (ChEs), cytochrome P450-dependent monooxygenases,
DNA breakage, or enzymes of oxidative stress. However, most earthworm studies have focused
on the exposure to metals and other chemicals but not on persistent organohalogenated pesticides
(Table 1.1). In addition, as previously discussed, the entire process of gathering all the
experimental data for biomarkers can be time consuming and expensive [81].
20
Table 1.1. Summary of the E. fetida earthworm biomarker responses experimentally exposed to environmental contaminants
AChE, acetylcholinesterase; MROD, methoxyresorufin-O-deethlylase; NADH Red, NADH cytochrome reductase; NADPH Red, NADPH cytochrome reductase; GR, glutathione reductase; GST, glutathione-S-transferase; LP, lipid peroxides; LPI, peroxidizable lipids; total GSH, total glutathione; % GSSG, percentage of oxidized glutathione
Test Concentration and time of exposure
Test conditions Biomarkers Summary of results Reference
Carbaryl 12,25, and 50 mg kg-1
2,7 and 14 days
Artificial soil (OECD)
AChE, MROD, NADH Red, NADPH Red, Catalase, GR, GST, LP and LPI, Total GSH and %GSSG
Depression of MROD, NADH Red, and NADPH RED enzymes for biotransformation of enzymes. Inhibition of AChE activity. Lack of concentration dependent relationship.
[82]
Chlorpyrifos
2.96 ±0.39, and 2.33 ±0.39 mg ml-1
12,24,36 and 48 hours
Filter paper contact test (OECD)
AChE Significant inhibition of AChE activity in both concentrations. Many abnormal morphologies
[83]
Pb: Lead acetate 30,60, 120, and 250 mg kg-1 2,7,14, and 28 days
Artificial soil (OECD)
AChE, MROD, NADH Red, NADPH Red, Catalase, GR, GST, LP and LPI, Total GSH and %GSSG
Lipid peroxidation detected by not by Pb or time of exposure. Depression of GST after 2 days of exposure. No clear relationships by Pb exposure on variations in enzyme fluctuations. Reactive oxygen species detected but difficult to determine if due to Pb exposure
[84]
Benzo(a)pyrene 0.05,1,100 and 1,000 mg kg-1
1,2,7, and 14 days
Artificial soil (OECD)
AChE, MROD, NADH Red, NADPH Red, Catalase, GR, GST, LP and LPI, Total GSH and %GSSG
Low to high inhibition of MROD as the concentration of benzo(a)pyrene increases
[85]
21
1.5 Nuclear Magnetic Resonance-based metabolomics using earthworms
Since earthworms are widely used to study soil ecotoxicology of environmental
contaminants, the development of environmental metabolomics using earthworms is rapidly
emerging [51]. Studies have already used earthworms in environmental metabolomics to
investigate their exposure to polyaromatic aromatic hydrocarbons [5, 86-89], metals [90] and
fluorinated contaminants such as 4-fluoroaniline, 3,4-difluoroaniline and 2-fluoro-4-methylanailine
[91]. However, when this thesis project was started, no one had used metabolomics to understand
the toxic MOA of organohalogenated pesticides using the earthworm, E. fetida.
Environmental metabolomics is a powerful tool to monitor the fluctuations of an
organism’s metabolic profile or metabolome after exposure to an environmental contaminant.
However, the metabolome is a highly complex heterogeneous mixture of endogenous metabolites
consisting of organic acids, amino acids, sugars and other cellular respiratory products. Therefore,
to properly and accurately analyze these samples, a versatile analytical technique is necessary.
Environmental metabolomic studies on earthworms have primarily used NMR spectroscopy [86,
87, 90-95]. 1H NMR has high reproducibility, less sample bias and indiscriminate in compound
detection compared to other analytical techniques such as Mass Spectrometry (MS), Gas
Chromatography (GC) and Liquid Chromatography (LC). Due to the vast amount of compounds
in each sample, certain techniques, such as LC or MS, usually require a pre- or post column
derivatization of the analytes; others, like GC or MS, require sample derivatization since most of
the metabolites of interest are non-volatile [96]. Even though selectivity can be advantageous by
choosing metabolites of interest and simplifying the complexity of the mixture, extra preparation
steps and time are required before analysis. In addition, the usage of MS, as the detector, requires
the ionisation of the sample for mass spectral analysis and therefore destroys the original sample.
22
In contrast, NMR spectroscopy only uses radio frequency radiation to analyze the samples. Radio
frequency radiation does not destroy samples and allows re-analyzing of the same samples. NMR
spectroscopy does not require derivatization and requires very minimal sample preparation. As
noted above, NMR spectroscopy allows comprehensive analysis of all metabolites in the sample
and requires less preparation steps. This, in turn, leads to a high throughput of samples analyzed
and a reduction of costs per sample. In this thesis, a brief overview and experimental design of
NMR-based metabolomics will be explained. For more information, many texts comprehensively
and thoroughly explain the theory and application of NMR spectroscopy in other fields [97, 98].
NMR spectroscopy is the exploitation of the spin and magnetic property of certain atom
nuclei as they precess or resonate when placed in a strong magnetic field. When a structure is
placed in a magnetic field, each nuclei resonates at a specific frequency characteristic of its
chemical environment. The specific frequency is termed the “chemical shift” and identifies the
different types of nuclei in the molecule and their bonding environment, while the peak area, in a
properly acquired spectrum, is fully quantitative [98]. Subtle variations of the bonds and structures
of compounds create very different shielding effects and thus a unique NMR spectrum is generated
for each compound. Not all atom nuclei are NMR visible; however, for metabolomic analysis of
metabolites, the important nuclei, 1H, 13C, 31P and 15N are detectable. In addition, metabolomic
studies commonly use 1H NMR, which is most prevalent in biological molecules, and with a
natural abundance of 99.8%, it has the 2nd highest sensitivity compared to any other atom active
spin nuclei [99] after Tritium (3H). However, readers should be aware that compared to other
analytical techniques such as MS, NMR spectroscopy is the least sensitive. For 1H NMR
techniques, metabolites can only be detected in the low mg L-1 to high µg L-1 [100]. With MS,
however, metabolites can be detected in the low femtomole range [101]. Currently, researchers are
23
attempting to increase the sensitivity for NMR but this requires more sophisticated hardware such
as microcoil, flow or cryogenically cooled probes[102]. These have not been commonly used for
earthworm metabolomic studies.
Many earthworm metabolomic studies conducting with E. fetida have used the OECD
contact filter paper test exposure (48 hours) or OECD soil exposure (2, 7 and 14 days) methods;
both show potential for risk assessment and screening of environmental contaminants [51].
Researchers generally analyze the whole earthworm tissue polar extract after exposure because this
allows the detection of a wide diversity of metabolites [34, 103]. The sample preparation of the
earthworm tissue extract is relatively simple as the earthworms are flash-frozen in liquid nitrogen
after exposure, lyophilized and then homogenized using a homogenizer or spatula [86].
However, some studies have analyzed specific earthworm segments (the head, testes, crop,
clitellium, gut and oesophagus) [104] or other biological fluids, such as the coelomic fluid (CF)
[105], to identify potential biomarkers that might be obscured when the whole tissue homogenate
is analyzed. Potential pH shifts from varying earthworm samples must also be minimized to avoid
the detection of false positives due to changes in NMR chemical shifts with protonation and
deprotonation of metabolites. A common sodium phosphate buffer solution (NaH2PO4.H2O) is
used to control any fluctuations and was found to extract a wide range of polar metabolites [87].
Non-polar metabolites have also been extracted using chloroform from a three solvent
(methanol/water/chloroform) system [106]. Commonly, 1 ml volume of earthworm tissue sample
is used for 1H NMR metabolomics analysis [86]. In this thesis, the earthworm tissue extract will
be used as the main biological medium for metabolomic analysis but the CF will also be explored
(Chapter 4 and 5) to understand if additional metabolites can be detected after their exposure to
organohalogenated pesticides.
24
1.5.1 One-dimensional NMR techniques
The selection of the NMR technique plays an important role in detecting and visualizing
the compounds of interest. A majority of environmental metabolomic studies for earthworms use
one dimensional (1-D) 1H NMR techniques due to their faster acquisition time (~<15 mins) [34,
62, 90-92, 103]. A common 1H NMR technique for metabolomic studies involves solvent
suppression. As previously discussed, the solvent used for sample preparation will typically
contain some residual H2O even if mostly D2O (NMR invisible) solvent are used. Since the
concentration of H2O can be as high as 50 mol L-1 compared to millimolar metabolite
concentrations [97] in the sample, H2O can saturate the NMR receiver and become the dominant
peak present. Among the many solvent suppression methods, the main methods for earthworm
metabolomic studies are presaturation (PRESAT), Nuclear overhauser effect spectroscopy
presaturation (NOESYPRESAT) and Presaturation utilizing relaxation gradients and echos
(PURGE) [34, 87, 91, 105, 107]. The key to solvent suppression is saturating the proton signal at
the solvent frequency of, for example H2O, during the NMR experiment so the solvent peak will
be minimized when the NMR spectrum is acquired [98]. Each unique solvent suppression
technique is done to maximize the removal of the solvent signal, while simultaneously reducing
baseline distortions and maximizing signal detection before the excitation pulse. For more
information on the wide variety of solvent suppression methods, readers should consult a review
by Mckay (2009) [108] which thoroughly review current NMR solvent suppression methods.
Another challenge when acquiring 1-D NMR spectra of biological samples is their
complexity due to the large mixture of metabolites present in the sample. This can cause
difficultly in identifying a specific metabolite as one sample can contain multiple peak signals in
the NMR spectrum and cause large spectral overlap. Various NMR techniques have been
25
developed to mitigate spectral congestion or improve spectral resolution between peaks.
Currently, two commonly used 1-D NMR techniques for metabolomics are Carr-Purcell-
Meliboom-Gill (CPMG) [109] and J-resolved spectroscopy (J-RES) projections [93]. Biological
samples for metabolomics studies typically contain various molecular weight compounds. Thus,
during the radiofrequency pulse in the NMR experiment, large differences in relaxation rates can
cause broadness in the signal peaks [110]. For large molecular weight macromolecules, their
relaxation times are faster compared to lower weight metabolites and this discrepancy is one of the
origins of line broadening in an NMR spectra. CPMG is a spectral editing filter technique used
during acquisition. It removes broad resonances associated with high molecular metabolites or
motion constrained compounds, and it allows clearer observation of low molecular weight
metabolites [110]. Past studies have found that CPMG removed broad signals from triglycerides,
residue proteins, cholesterols and phospholipids to better detect small-molecule metabolites [109,
111]. Pre-treatment of the biological samples such as using spin filters is another option to remove
macromolecules but contamination by the filters themselves such as glyercol has been detected in
the past [112]. In a 1H NMR spectrum, splitting of signal peaks occur due to the coupling spins of
adjacent protons. This is one of the main reasons why large spectral overlap is present especially
with complex mixtures. J-RES spectroscopy is a two-dimensional (2-D) technique that allows the
separation of the chemical shift information on one axis and the spin-spin coupling information
along another [113]. By doing a projection of only the chemical shift axis, a 1-D proton decoupled
spectrum is displayed and this reduces spectral congestion and allows higher detection of a specific
metabolite [110]. Neither CPMG and J-RES projections have been used or compared to other
NMR techniques, such as PURGE, in environmental metabolomics using earthworms. The study
26
presented in Chapter 2 compares their application to E. fetida after exposure to a commonly used
organochlorine pesticide, endosulfan.
1.5.2 Two-dimensional NMR techniques
Even with the wide variety of 1-D NMR techniques, spectral overlap cannot always be
avoided. Luckily, there are numerous 2-D NMR methods that disperse the signals into another
dimension for structure determination and are slowly being developed for metabolomic studies
[34]. Some of the main 2-D NMR techniques currently used in metabolomics studies include: 1H-
1H correlation spectroscopy (COSY), 1H-13C heteronuclear single quantum coherence (HSQC) and
1H- JRES NMR spectroscopy [102, 113, 114]. 1H-1H COSY is a NMR technique that correlates
the 1H nuclei to another adjacent 1H via a single J-coupling (spin). This allows the display of the
proton-proton couplings over two dimensions to alleviate and identify specific metabolites. 1H-13C
HSQC is an NMR experiment similar to COSY; however, unlike COSY, it displays correlations
between 1H couplings and adjacent 13C nuclei. The 13C will be on the indirect dimension axis
while the 1H will be on the direct dimension (F2) axis [98]. The 1H-JRES, as previously discussed,
separates the chemical shift and the proton coupling to two different axes and allows the
identification of metabolites using the J-coupling constants between protons. Even with the 2-D
NMR’s greater identification ability, only a few studies have utilized this potential because most of
its techniques are less sensitive and require longer acquisition times (3 to 4 times more than 1-D
experiments) [98]. Prior to this thesis, no 2-D NMR techniques have been used in earthworm
metabolomic studies or on their exposure to organohalogenated pesticides. Chapters 2 and 3 focus
on results that compare various 1-D and 2-D NMR techniques to identify metabolites in the
earthworm, E. fetida, and to examine exposure to sub-lethal concentrations of an organochlorine,
endosulfan and organofluorine pesticide, trifluralin.
27
1.5.3 Statistical methods for metabolomic analysis
In metabolomics, the generation of large datasets is common as studies will include many
variables to characterize different observations (samples, time points, etc). Generally, the dataset
can be analyzed through the arrangement of the data into a table where the rows will constitute an
observation (sample) and the columns will represent the variable or factor that was measured from
an analytical instrument (e.g. wavelength, mass number, chemical shift) [115]. For NMR-based
environmental metabolomics, each NMR spectrum of the organism contains a unique metabolic
fingerprint that can contain thousands of overlapping resonances. If the health condition of the
organism fluctuates due to an environmental contaminant, the metabolic profile will reflect that
change [116]. By a detailed inspection of the NMR spectrum and an integration of individual
peaks, dominant biochemical changes can be quantified. However, due to the large number of
samples, subtle changes may be overlooked and it will be difficult to correlate them to other
factors, such as concentration or time [71]. Multivariate statistical methods are one of the most
important steps for metabolomic analysis to maximize the understanding of complex NMR
spectral datasets [71]. Pattern recognition methods allow the interpretation of the NMR spectra
into a low dimensional space to visualize the data. The clustering detected in the samples will then
represent their similarities in their metabolic profile and the pattern in their biochemical changes
can be understood for samples that were not the same.
Before any statistical analysis is done, the NMR spectral data is first divided into different
regions along the chemical shift axis and each area is summed into integrals called binning. This
creates a table of data where the observations and samples are shown in rows and the spectral
integrals are shown in columns of the defined bins across the spectral width [117]. The
28
identification of patterns and the classification of the data table for each sample are achieved using
unsupervised and supervised pattern recognition techniques.
Principal component analysis (PCA) is frequently used in metabolomic studies and is
considered an unsupervised approach. This means that no prior information is added to the model
for classifying the samples and the inherent clustering of the samples is due to the natural
similarities in their metabolic profile [117]. PCA highlights the overall variation in the data and
the variations are summarized in different principal components (PC) axes. PCs describe the
variation in the data with PC1 having the highest variance and PC2 having the next highest and so
on [118]. Through the PCs, scores plot (such as PC1 versus PC2) are formed to display the
clustering patterns of the NMR samples. This is a convenient way to reduce the complexity of the
data, to visualize and to classify different groups [71]. A loadings plot is also used in conjunction
with the scores plot. The loadings plot allows further analysis and depicts which spectral variables
in the NMR samples contribute to their position or separation to other samples in the scores plot.
Partial least squares discriminant analysis (PLS-DA) is another commonly used pattern
recognition tool for metabolomic studies and is considered to be a supervised approach. In this
method, prior knowledge of the sample classes are known (e.g. exposed vs control) and pre-
defined variables are added to the model (e.g. zeros and ones) to maximize the separation between
the sample groups and to develop predictive models on the original data set [119]. This approach
allows one to understand which dominant spectral variables are the ones that created the separation
between the various groups. A PLS-DA scores plot is performed using PLS components, which
are similar to PCs, to visualize the clustering of the samples in a 2-D axes plot (typically PLS1
versus PLS2). A loading plot can also be developed to determine the peaks responsible for the
separation between the classification groups [71]. Due to the input of classification values in the
29
model supervised analysis tests, it is highly recommended that cross-validation tests be done to
prevent over-fitting of the data and to determine if the prediction of each sample classes is
accurate. Cross-validation is usually done by eliminating a portion of the data (test set) while the
remaining samples (the training set) is analyzed using PLS-DA. The Q2 (goodness of prediction)
can then be calculated by using the model to predict the test set which then can help determine the
model’s robustness [119]. A strong model typically will have a Q2 value higher than >0.4 [103].
Even through, multivariate statistical analysis is key for metabolomic analysis, univariate
analysis is commonly used as well. Analysis of variance (ANOVA) and t-tests are used to
calculate the significance of the separation between the groups in PCA or PLS-DA scores plots. In
addition, these tests can be used to understand the significance of the peaks of interest detected
from the PCA or PLS-DA loading plots. A t-test filtered difference NMR spectrum can also be
constructed to identify increases or decreases in the peaks when comparing different sample
groups [65, 66]. This NMR spectrum (1-D or 2-D) is generated by subtracting the average bin
intensities of the different groups to a control and determine which areas are significant (p-value <
0.05). Any other intensity values that are higher are replaced with a zero and this allows one to
determine which potential metabolites are significant in conjunction with the loading plot results
[65, 66]. In this thesis, PCA, PLS-DA and univariate statistical tests are all employed for
understanding the significant perturbations in the metabolic profile of E. fetida after its exposure to
various organohalogenated pesticides.
30
1.6 Study Objectives Currently, the understanding of the toxic MOA of organohalogenated pesticides especially
with respect to soil organisms such as earthworms at sub-lethal concentrations, has not been
thoroughly investigated [40, 51]. NMR-based metabolomics has proven to be a powerful
biochemical/screening tool in elucidating potential molecular biomarkers due to external stressors
in medicine[54] and drug discovery [55]. However, metabolomics is slowly emerging in
environmental studies but has not been employed in understanding organohalogenated pesticide
exposure. This dissertation aims to provide insight into the potential of environmental
metabolomics to delineate the toxic MOA of various organohalogenated pesticides to an OECD
recommended earthworm species, E. fetida, using 1-D and 2-D NMR spectroscopy. This will be
achieved through the following objectives:
1) To compare various 1-D and 2-D NMR spectroscopy techniques commonly used in
metabolomic studies, and to determine which methods are most effective in discriminating
and identifying potential biomarkers of response between exposed and non-exposed E.
fetida earthworms using an organochlorine pesticide, endosulfan, in an OECD contact test
experiment;
2) To utilize a 1-D and 2-D NMR spectroscopy-based metabolomic approach using E.fetida in
an OECD contact test experiment to understand the toxic MOA of various sub-lethal
concentrations of an organochlorine pesticide, endosulfan, and organofluorine pesticide,
trifluralin;
3) To investigate the potential of using the CF and tissue extract of E. fetida in a NMR-based
metabolomic study, and to evaluate the toxic MOA of endosulfan using various sub-lethal
concentrations in an OECD contact test experiment; and
31
4) To explore the toxic MOA of an organochlorine pesticide, endosulfan and its main
degradation product, endosulfan sulfate, using 1-D and 2-D NMR-based metabolomic
approaches on E. fetida in soil environments. The tissue extract and the CF will both be
used as the biological media for analysis to maximize the identification of the biomarkers
due to contaminant exposure.
These four objectives will be addressed in chapters 2, 3, 4 and 5 respectively. A final note to
readers that all organhalogenated pesticides used in this thesis were all handled with extreme
care and safety precautions (lab coats, goggles and disposable gloves) were used at all times
whenever the chemicals were present.
1.7 Thesis Summary
CHAPTER 1: Introduction: Organohalogenated argochemicals, ecotoxicology, earthworms in
ecotoxicology and environmental NMR-based metabolomics
CHAPTER 2: Comparison of 1-D and 2-D NMR techniques for screening earthworm responses
to sub-lethal endosulfan exposure.
This chapter has been published in Environmental Chemistry and addresses objective 1. In
this study, three 1-D NMR techniques (PURGE, CPMG and J-RES projections) and three 2-D
NMR techniques (1H-1H COSY, 1H-13C HSQC and 1H-J-RES) were investigated in a metabolomic
study to compare their discrimination abilities of an earthworm, E. fetida, control group to an
exposed group using a sub-lethal concentration of an organochlorine pesticide, endosulfan. PLS-
DA was used as the multivariate analysis and results showed PURGE and 1H-13C HSQC had the
highest discrimination ability for the 1-D and 2-D NMR experiments respectively. 1H-13C HSQC
identified the most metabolites of response (alanine, leucine, lysine, glutamate, glucose and
maltose) due to endosulfan exposure. The results conclude that 1H-13C HSQC in combination with
32
a shorter 1-D NMR experiment such as PURGE can be an effective tool in discriminating and
identifying significant metabolites in earthworms under environmental stress.
CHAPTER 3: 1-D and 2-D NMR metabolomics of earthworm responses to sub-lethal trifluralin
and endosulfan exposure
This chapter has been published in Environmental Chemistry and addresses objective 2. In
this study, 1-D and 2-D NMR-based metabolomics were used to investigate the metabolic response
of the earthworm, E. fetida, to an organofluorine pesticide, trifluralin, and organochlorine
pesticide, endosulfan, using three sub-lethal concentrations in an OECD contact test experiment.
PCA analysis was used on the trifluralin and endosulfan NMR datasets and distinct separation was
seen between the unexposed and exposed earthworm groups. A non-polar narcosis toxic MOA
was delineated for trifluralin exposure as alanine, glycine, maltose and adenosine triphosphate
(ATP) was detected as the metabolites of response. A neurotoxic toxic MOA was postulated for
endosulfan exposure as leucine, phenylalanine, tryptophan, lysine, glutamate, valine, glycine,
isoleucine, methionine, glutamine, alanine, maltose, glucose, meibiose, malate, fumarate and ATP
were detected as the significant metabolites of response. This study highlights the potential of 1-D
and 2-D NMR-based metabolomics for understanding the biochemical response of native soil
organisms such as earthworms to organohalogenated pesticides.
CHAPTER 4: Coelomic fluid: a complimentary biological medium to assess sub-lethal
endosulfan exposure using 1H NMR-based earthworm metabolomics
This chapter has been published in Ecotoxicology and addresses objective 3. This work
explored the potential of using the earthworm, E. fetida’s, CF in conjunction with the earthworm
tissue extract in a 1-D NMR-based metabolomic study to investigate the response to three sub-
33
lethal concentrations of an organochlorine pesticide, endosulfan. The 1-D NMR spectrum of the
CF identified a plethora of metabolites that were masked in the earthworm tissue extract due its
large spectral overlap of certain metabolites such as sugars, which can contain multiple resonances.
The PCA results revealed significant separation between the exposed and control earthworms at
various sub-lethal concentrations due to endosulfan exposure for both biological mediums.
Alanine, glycine, malate, a-ketoglutarate, succinate, betaine, myo-inositol, lactate and spermidine
in the earthworm’s CF and alanine, glutamine, fumarate, glutamate, maltose, melibiose, ATP and
lactate in earthworm tissue extract were all detected as significant metabolties of response due to
endosulfan exposure. In addition to confirming the neurotoxic MOA identified in Chapter 3, the
additional biomarkers detected in the CF enabled the recognition of an apoptotic MOA, which
could be the earthworm’s defensive mechanism in response to endosulfan stress. This study
highlights the potential of using both the earthworm’s CF and tissue extract to maximize the
identification of biomarkers to delineate potential MOA by environmental contaminants at sub-
lethal concentrations.
CHAPTER 5: 1-D and 2-D NMR-based metabolomics of earthworms exposed to endosulfan and
endosulfan sulfate in soil
This study has been submitted to Environmental Pollution and addresses objective 4. In
this research, 1-D and 2-D NMR-based metabolomics was used to understand the toxic MOA of an
organochlorine pesticide, endosulfan, and its main degradation product, endosulfan sulfate, to the
earthworm, E. fetida, in soil environments. This study utilizes knowledge obtained from Chapters
3 and 4 by analyzing the earthworm’s CF and tissue extract using 1H and 1H-13C NMR techniques.
The PCA results showed that the toxicity of endosulfan and endosulfan sulfate were similar as the
34
scores plot of both biological mediums showed similar separations of the exposed and control
earthworms at various soil concentrations. This was further confirmed as similar metabolites of
response were detected for both contaminants and neurotoxic and apoptotic MOA were delineated.
The results from this study highlight the potential of NMR-based metabolomics using earthworms
as biological probes to understand the toxic MOA of organohalogenated pesticides and their
degradation product in soil environments.
CHAPTER 6: Conclusions and future research
This chapter will focus on summarizing the findings from this dissertation and discuss the
future applications of NMR-based metabolomics.
35
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49
CHAPTER TWO
Comparison of 1-D and 2-D NMR techniques for screening earthworm responses to sub-lethal endosulfan exposure
Published as: Yuk J, McKelvie JR, Simpson MJ, Spraul M, and Simpson AJ (2010). Comparison of 1-D and 2-D NMR techniques for screening earthworm responses to sub-lethal endosulfan exposure. Environ Chem. 7 (6):524-536
Reproduced with permission from Environmental Chemistry, 2010, 6:524-536 (http://www.publish.csiro.au/paper/EN10084). © Copyright CSIRO Publishing
50
2.1 Abstract
Nuclear Magnetic Resonance (NMR) based metabolomics is a powerful approach to
monitoring an organism’s metabolic response to environmental exposure. However, the
discrimination between exposed and control groups, depends largely on the NMR technique
chosen. Here, three 1-D NMR and three 2-D NMR techniques were investigated for their ability to
discriminate between control earthworms (Eisenia fetida) and those exposed to a sub-lethal
concentration of a commonly occurring organochlorine pesticide, endosulfan. Partial least-squares
discriminant analysis found 1H–13C Heteronuclear Single Quantum Coherence (HSQC)
spectroscopy to have the highest discrimination with a MANOVA value (degree of separation)
three orders lower than any of the 1-D and 2-D NMR techniques. HSQC spectroscopy identified
alanine, leucine, lysine, glutamate, glucose and maltose as the major metabolites of exposure to
endosulfan, more than all the other techniques combined. HSQC spectroscopy in combination with
a shorter 1-D experiment may prove to be an effective tool for the discrimination and identification
of significant metabolites in organisms under environmental stress.
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2.2 Introduction
There has been an increasing concern over the prevalent agricultural usage of pesticides
worldwide and their potential adverse effects on the soil environment and native biota [1]. Many
agrochemicals exhibit toxicity at low concentrations and, due to their high hydrophobicity, have
high persistence in the soil environment and bioaccumulative potential [2]. Recent literature has
demonstrated that the chemical analysis of the soil, such as Soxhlet extractions, are not able to
characterise soil health and do not always relate the toxicity to the native organisms [3]. Past
reviews have stressed the need for a direct method in understanding the toxic mechanisms at a
molecular level and how these relate to functional changes at the organism and population level
[2,4]. Environmental metabolomics is an emerging field that analyses the metabolic response of
environmentally relevant organisms exposed to conditions that are potentially encountered in the
environment [2]. Earthworms have been used in metabolomic studies as they are important
indicators of soil health through their contribution to the soil decomposition activity, they have
high biological activity in the soil and they represent one of the major biomasses in the soil [5].
Recent earthworm metabolomic studies have examined their response to polyaromatic
hydrocarbons (PAHs) [6], metal contaminated sites [7], and fluorine toxicants [8] such as 4-
fluoroaniline, 3,5-difluoroaniline and 2-fluoro-4-methylaniline. Nuclear Magnetic Resonance
(NMR) has been frequently used in earthworm metabolomic studies as it is non-selective, rapid
and non-destructive with minimal sample preparation [9,10]. However, most of organic molecules
produce numerous signals in 1-D 1H–NMR, therefore, with complex mixtures such as biofluids
and tissues, spectral overlap is problematic. This complicates both the identification and
quantification of major metabolites and can lead to signals from minor metabolites being obscured
by more intense resonances [11]. There are numerous 1-D NMR experiments currently being used
52
in metabolomic studies. The simplest experiments aim to retain most metabolites while
suppressing residual water, which can dominate the spectra especially in biological samples. Some
common water suppression NMR techniques include simple presaturation [12], 1-D Nuclear
Overhauser Effect Spectroscopy (NOESY) presaturation, which further reduces the water signal
through chemical exchange [13], and Presaturation Utilising Relaxation Gradients and Echos
(PURGE), which utilises both presaturation and continuous refocusing cassettes with gradients,
which offers excellent water suppression and less influence on neighbouring resonances [14]. In
addition to presaturation NMR experiments, there are other 1-D NMR sequences that act as
spectral filters and aim to reduce the complexity of the metabolite profile. Two common examples
are J-resolved spectroscopy (J-RES) projections [15] and Carr–Purcell–Meiboom–Gill (CPMG)
[16]. J-RES employs a 2-D sequence during acquisition but the data are commonly projected into
a 1-D plot for interpretation and analysis. The resulting 1-D projection contains less overlap as
contributions from the J-couplings (proton–proton splitting) are suppressed. Due to these
advantages, J-RES projections have become popular in metabolomics especially in environmental
studies [11,15]. A study by Lin et al. [15] analysed the metabolites of Chinook salmon liver tissue
using both 1H and J-RES projections spectroscopy. Signals from lipids and macromolecules in the
liver were considerably reduced and the results showed sharper and more resolved peaks on a
flatter baseline when using J-RES projections compared to a 1H presaturation NMR technique.
Comparatively, CPMG is a 1-D experiment that filters molecules based on their T2 relaxation.
This experiment can significantly improve spectral baseline and remove broad signals from large
molecules such as proteins and peptides, but does little to reduce the overlap from the metabolites
themselves. Wishart et al. [17] described CPMG as a useful tool to eliminate most broad signals
from triglycerides, proteins and cholesterols thereby allowing better detection of small molecule
53
metabolites. Alternatively, another approach to reducing overlap is to utilise 2-D NMR
acquisitions to disperse the signals over an additional dimension allowing more information to be
extracted. The combination of 2-D NMR acquisition and pattern recognition methods has been
used previously to identify metabolites and is relatively new in the metabolomics field [18–20]. 2-
D NMR techniques that are used in metabolomics studies are 1H–J-RES Spectroscopy, 1H–1H
Correlation Spectroscopy (COSY), and 1H–13C Single Quantum Coherence (HSQC) spectroscopy.
These techniques are advantageous over 1-D NMR methods in that they reduce spectral overlap,
through dispersion provided by the second dimension as well as providing additional
‘connectivity’ information. In J-RES, the second dimension provides J-coupling information
between protons with the connection to their proton chemical shift in the other dimension [21].
This offers the advantage of higher discrimination between metabolites in complex biological
samples as spin–spin coupling measurements are less sensitive to fluctuations compared to using
chemical shift measurements [22]. Even though 1-D J-RES projections are more commonly used
in metabolomics compared to the full 2-D J-RES dataset, the statistical analysis and dataset size
are simplified by utilising only a single dimension. However, the 1-D projection removes the
additional dispersion by the second dimension, which could potentially contain additional
information in metabolomics datasets [21,22]. Therefore, in the present study, both J-RES and the
J-RES projections are analysed. In the case of COSY, the experiment correlates proton signals
based on 1–4J 1H–1H coupling. For HSQC spectroscopy, the 1H chemical shift is correlated with
the 13C chemical shift through the one bond coupling constant (1J 1H–13C) and identifies which
protons are bound to which carbon in a molecule. However, the added advantage of additional
structural information and dispersion in 2-D experiments is offset by the longer acquisition times
and lower sensitivity per unit time in comparison to 1-D experiments [18]. In this study, three 1-D
54
NMR approaches (PURGE, J-RES projections and CPMG) and three 2-D NMR methods (1H–J-
RES, 1H–1H COSY, and 1H–13C HSQC spectroscopy) are compared in terms of their ability to
measure earthworm responses (Eisenia fetida) to a sub-lethal concentration of endosulfan applied
via contact tests [23]. PURGE was chosen in this study as an example of a simple water
suppression sequence to be consistent with our previous studies [2,10,23], but readers should note
that other sequences such as NOESY presaturation can be utilised as well. Contact tests are
advantageous in monitoring the metabolic response of earthworms to chemical exposure using
direct contact of the earthworm on a filter paper with the dissolved contaminant. Even though
contact tests are not indicative of an earthworm in a soil environment (it neglects pesticide
exposure through ingestion and pesticide–soil interactions [24]), it allows an initial basis for direct
analysis of pesticide exposure especially for method development [23]. Endosulfan is a pesticide
used to prevent vector-borne diseases [25], is classified as a persistent organic pollutant and has
been detected in the atmosphere, soil, sediments, water and food materials [26]. Endosulfan is
highly toxic to fish and been confirmed as a genotoxicant and neurotoxicant [25]. These
environmental and health concerns have led to a major interest in the fate of endosulfan in the
environment. McKelvie et al. [23] identified alanine, leucine and maltose as response indicators of
endosulfan and DDT exposure to E. fetida using PURGE 1H NMR and Gas Chromatography/Mass
Spectrometry (GC/MS) metabolomics. The present study introduces two additional 1-D NMR
techniques and three 2-D NMR techniques and compares their ability with PURGE NMR to
discriminate between exposed and control earthworms as well as exploring their potential to
identify additional indicators of endosulfan exposure in E. fetida tissue extracts. Many earthworm
metabolomic studies utilise statistical methods such as principal component analysis (PCA) or
partial least-squares discriminant analysis (PLS-DA) to examine 1H NMR spectra to identify
55
significant metabolites of exposure due to the contaminant [2,6,7,9,10,23,27]. The identification
of metabolites of exposure is important in understanding the response of the earthworm to the
contaminant and could potentially serve in the future as an indicator of environmental exposure or
pollution [7,27]. In the present study, PLS-DA was used as the discriminatory model due to its
ability to describe maximum separation between two different classes (control v. exposed). PLS-
DA is well suited for identifying specific discriminating variations in the data when compared to
unsupervised approaches such as PCA [28]. As PCA explains the maximum variance in the data
using different principal components, it has the potential of overlooking minor, but biologically
significant, metabolic changes that are specific to each group [7]. To the authors’ knowledge,
there has been no study that compares various 1-D and 2-D NMR techniques for metabolomic
applications in an environmental context. Therefore, it is important to investigate the potential of
these NMR techniques here in an earthworm metabolomic study using an environmentally relevant
pesticide, such as endosulfan, to further our understanding on the discriminating potential of 1-D
and 2-D NMR to detect subtle metabolic fluxes induced by an environmental stressor.
2.3 Experimental methods
2.3.1 Earthworm maintenance and contact tests
E. fetida specimens were purchased from The Worm Factory (Perth, ON, Canada) and
raised in earthworm bins containing sphagnum peat bedding (Magic Worm bedding, Magic
Products, Amherst Junction, WI, USA) with a water content of ~67% water by weight.
Earthworms were fed Magic Worm Food (Magic Products) and were allowed to acclimate for a
period of at least 1 month to ensure stability of the basal metabolic profile [9]. Mature earthworms
with a visible clitellum and weight of 0.570 ± 0.07 g were depurated in groups of five in the dark
56
for 96 h on Whatman 4 Qualitative filter paper with a diameter of 9 cm (Fisher Scientific,
Waltham, MA, USA) in 500-mL jars to remove any residues from their intestinal tracts [9].
Earthworms (10 replicates for each the control and exposed group) were then transferred to
individual 120-mL amber glass jars containing pre-treated Whatman GF/A 4.25-cm diameter glass
filter paper (Fisher Scientific). Endosulfan (99% purity; Sigma Aldrich, St Louis, MO, USA) was
applied to the filter paper at a concentration of 2 mg cm-2 using 1 mL of chloroform (HPLC grade;
Fisher Scientific) as the carrier solvent. This concentration was chosen to be sub-lethal and is at
35% of the LC50 based on values reported by Heimbach [41] for E. fetida. One millilitre of
chloroform was applied to filter papers for control earthworms to mimic identical environments
with the exception of the endosulfan itself. In all cases, the chloroform was allowed to evaporate
and 1 mL of distilled water was added before the addition of earthworms. After being placed on
filter papers, earthworms were kept in the dark for 48 h, as recommended by the Organization for
Economic Co-operation and Development (OECD) LC50 contact test guideline [42]. Earthworms
were then flash frozen in liquid nitrogen, lyophilised and stored frozen until extraction.
2.3.2 Earthworm tissue extraction
The lyophilised earthworms were homogenised in a 1.5-mL centrifuge tube. Samples were
then extracted using 1 mL of a 0.2-M monobasic sodium phosphate buffer solution
(NaH2PO4.2H2O; 99.3%; Fisher Scientific) containing 0.1% (w/v) sodium azide (99.5% purity;
Sigma Aldrich) as a preservative [9]. Buffer solution was made with D2O (99.9% purity,
Cambridge Isotope Laboratories Inc., Andover, MA, USA) and adjusted to a pD of 7.4 using
NaOD (30% w/w in 99.5% D2O, Cambridge Isotope Laboratories Inc.). The buffer solution also
contained 10 mg L-1 of 2,2-dimethyl-2-silapentane-5-sulfonate sodium salt (DSS; 97%, Sigma
Aldrich) as an internal standard. Samples were vortexed for 30 s using a VX 100 vortexer (Labnet,
57
Edison, NJ, USA) and then sonicated for 15 min using a FS60 sonicator (Fisher Scientific) to aid
with the extraction. Samples were then centrifuged at 10 000 g at 20oC using a Hanil Micro-12
centrifuge (Rose Scientific, Edmonton, AB, Canada) and the supernatant was transferred into a
new 1.5-mL centrifuge tube. The samples were centrifuged for an additional 5 min to remove any
additional precipitates and were transferred into 5-mm High Throughputplus NMRtubes (Norell
Inc., Landisville ,NJ,USA). All samples were frozen immediately after preparation and each
sample was thawed before NMR analysis.
2.3.3 NMR spectroscopy
2.3.3.1 1-D NMR spectroscopy
All NMR spectra were acquired using a Bruker Avance 500-MHz spectrometer with a 1H–
BB–13C triple-resonance broadband inverse probe fitted with an actively shielded Z gradient
(Bruker BioSpin). The 1H 90o pulse was calibrated for each sample in the study. PURGE
experiments [29] were performed with 128 scans, a recycle delay of 3 s, and 16 384 time domain
points. CPMG experiments were performed with an interpulse delay τ of 500 ms and 100 echoes,
corresponding to a relaxation filter of 100 ms. Water suppression for CPMG was performed using
presaturation. J-RES projections were obtained from 2-D J-RES datasets as described later. All 1-
D spectra were manually phased and calibrated to the DSS internal reference methyl singlet, set to
a chemical shift (δ) of 0.00 ppm.
2.3.3.2 2-D NMR spectroscopy
All 2-D experiments were optimised experimentally in terms of the relaxation delay (d1)
and the number of increments in the indirect dimension (F1). For COSY, 256 F1 increments were
required to produce high quality data, whereas 196 increments combined with 32 coefficients of
58
linear prediction were found to be sufficient for HSQC spectroscopy. For J-RES, 32 increments
were used as previously reported by Lin et al [15]. A series of 2-D NMR experiments were
acquired with relaxation times of 2, 1, 0.5 and 0.2 s. Each experiment was tested such that in a
given period of time, the number and intensity of 2-D cross peaks was maximised. For example,
an HSQC spectroscopy with two scans and a 2-s recycle delay took ~47 min to complete, which is
the same amount of time for an HSQC spectroscopy with 4 scans and a 1 s recycle delay. In each
experiment, sufficient scans were collected to reach the minimum phase cycle requirements. In
HSQC spectroscopy and COSY, it was found that a relaxation delay of 0.5 s with 20 and 8 scans
respectively produced the most intense and numerous correlations in an acceptable amount of time.
The reduction in the relaxation delay from 2 to 0.5 s shortened the acquisition time in COSY and
HSQC spectroscopy from 1 h 19 min and 2 h 26 min to 27 min and 47 min respectively (Table
2.1). In J-RES, reducing the relaxation delay below 1 s resulted in the loss of signals in the 1-D
projections therefore a relaxation delay of 1 s was employed.
J-RES spectra were acquired using a gradient echo for inversion, a relaxation delay of 1 s
and 16 dummy scans. Eight scans and 16 384 data points were collected for each of the 32
increments in the F1 dimension for a total experiment time of 12 min and 58 s. Datasets were
zero-filled to 128 points in F1, both dimensions multiplied by an unshifted sine-bell window
function with forward linear prediction using 16 coefficients applied in the F1 dimension. Spectra
were tilted by 45o, symmetrised around F1, calibrated (DSS, 0.0 ppm), and the proton-decoupled
skyline J-RES projections was obtained using TopspinTM version 2.1 (Bruker BioSpin).
Gradient enhanced COSY experiments were acquired with an excitation pulse of 90o and a
relaxation delay of 0.5 s. Eight scans and 2048 data points were collected for each of the 256
increments in F1. Both dimensions were processed using an unshifted sine-squared function in
59
both dimensions and a zero filling factor of two. Various numbers of coefficients for linear
prediction were tried but increased resolution was only marginal (given the relatively high number
of points collected in F1) and accompanied by a loss in intensity of the weaker and broader
resonances. Due to this, linear prediction of the COSY data was not employed.
HSQC spectra were collected in phase-sensitive mode using echo–anti-echo gradient
selection, a 1J 1H–13C (145 Hz) and a relaxation delay of 0.5 s. Twenty scans and 2048 data points
were collected for each of the 196 increments in the F1 dimension. The F2 dimension was
processed using an exponential function corresponding to a line broadening of 15 Hz whereas the
F1 dimension was processed using a sine-squared function with a π/2 phase shift. Both dimensions
were zero filled by a factor of two whereas forward linear prediction using 32 coefficients was
applied in the F1 dimension. The HSQC spectroscopy 2-D datasets were carefully inspected and
no additional artefacts were observed by shortening the relaxation delay. All 2-D spectra were
manually phased and calibrated to the DSS internal reference methyl singlet, set to a chemical shift
(δ) of 0.00 ppm.
2.3.4 Data analysis
Average spectra were calculated using the mean function in the algebra module in Bruker
Biospin’s Analysis of Mixtures (AMIX) package software version 3.8 (Bruker Biospin). Partial
least square discriminant analysis (PLS-DA) was performed using AMIX software 3.8 (Bruker
Biospin). All 1-D spectra (PURGE, CPMG and J-RES projections) were divided into buckets with
widths of 0.01 ppm for a total of 861 buckets from 0.25 to 9.0 ppm. The region from 4.7 to 4.85
ppm was not analysed due to residual H2O/HOD signals present in this region. The sum of
intensities was used as the integration mode and the scaling was set to total intensity for each
spectrum. This permits each metabolite spectrum to contribute equally to the average spectrum. In
60
the J-RES experiments, 0.25–9.0 ppm was examined on the 1H spectrum (F2) and split into 0.01-
ppm buckets. For the J-coupling axis (F1), the regions -15 to 15 Hz were observed and bucket
widths of 1 Hz were used. The total number of buckets for J-RES was 12 930.
For the COSY experiments, the spectra were bucketed at 0.03 ppm on both the F1 and F2
axis from 0.25 to 6.0 ppm. Due to the weak signals (in many spectra, there were no signals) in the
6.0–9.0 ppm region of the COSY, this area was not analysed in this study. Simple rectangle
bucketing was used with a total of 14 365 buckets. In the HSQC spectroscopy experiments, the
13C spectrum (F1) was evaluated from region of 10.0 to 110.0 ppm, which was divided into 0.50-
ppm buckets. For the 1H spectrum (F2), the region of 0.25 to 6.0 ppm was investigated and was
divided into 0.03-ppm buckets for a total of 37 000 buckets. The aromatic resonances were weak
in HSQC spectroscopy and were not analysed in this study. Due to the abundance of spectral
signals in the sugar regions (3.0–4.5 ppm on the 1H spectrum), a second PLS-DA analysis on
HSQC spectroscopy was conducted to focus on the aliphatic regions from 0.25 to 2.5 ppm on the
1H spectrum (F1) and from 10.0 to 50.0 ppm on the 13C spectrum (F2) for a total of 6000 buckets.
Cross validation was used to verify the number of PLS components and to explain the
significance of the model [43]. R2X and R2Y (goodness of fit) is the explained variance in the X
or Y matrix respectively and describes how representative the data are to the model. Cross
validation also calculates the Q2 (goodness of prediction) that describes the predictive ability of the
model, which assists in explaining the model’s robustness and to show that the data were not
overfitted [44]. This method is useful in validating a fitted model when the available sample set is
small [45]. All PLS models were validated using leave one out cross-validation and were
performed using AMIX software 3.8 (Bruker Biospin). A strong model typically will have an R2
value higher than >0.5 and a Q2 value higher than >0.4 [28,35]. All models described in this paper
61
had R2 values and Q2 values greater than these threshold values with most values >0.9 and >0.4
respectively. MANOVA was used to explain the significance of the separation in the PLS-DA
scores plot between the control and exposed groups. MANOVA using Wilks’ lambda [46]
provides a method to analyse the overall significance between two or more groups of data [47]. A
value of P < 0.05 was used as the threshold value to indicate if the separation between the two
groups is significant. MANOVA was performed using SPSS Statistics version 17.0 (IBM, Somers,
NY, USA). Influential peak signals identified in the 1-D and 2-D NMR spectra from the PLS-DA
loadings plots were matched with metabolite signals from a previous study that identified the
major metabolites in E. fetida [9] and were also compared to the Bruker Biofluid Reference
Compound Database version 2-0-0 (Bruker Biospin). Bucket intensities for these regions were
exported and two-sample t-tests were conducted to compare the control and exposed bucket
intensities. Metabolites were identified as significant through a confidence interval of 95%
(P<0.05).
2.4 Results and Discussion
2.4.1 1H NMR spectroscopic characterization
Three different 1-D NMR techniques were used to classify potential metabolites in E.
fetida after exposure to endosulfan. Figure 2.1 shows the average of the 10 earthworm control
spectra for each of the three 1-D methods (PURGE, CPMG and J-RES projections). PURGE
(Figure 2.1a) represents a simple water suppression approach and was used here as it provides
excellent and selective suppression. As the spectral profile produced by PURGE is sensitive to
pulse calibration [29], the 90o 1H pulse was calibrated per sample. As a comparison to PURGE,
CPMG (Figure 2.1b), which utilises T2 filters to reduce broad signals from faster relaxing
62
macromolecules, was used and, in turn, provided a flat baseline. Although CPMG produced
similar results to PURGE except with a slightly flatter baseline, PURGE produced peaks and
multiplets that were generally better defined, most clearly seen in the aromatic region. The slightly
lower definition in CPMG likely resulted from radio-frequency heating induced by the train of
hard pulses (100 pulses, in this study) employed in sequence [30]. However, the present study
may underestimate the potential of CPMG as the extracts studied here contained only small
contributions from macromolecular components that CPMG effectively suppresses. In studies that
have intense protein backgrounds, CPMG can be a key tool as the suppression of broader
macromolecular signals enhances the metabolic profiles in complex samples [16]. In comparison
to the two other methods (PURGE and CPMG), the J-RES projections spectra (Figure 2.1c) were
greatly simplified as a result of suppressing the macromolecular background via T1/T2
discrimination and the strong attenuation of the homonuclear couplings. In the J-RES projections,
the overlap of sugar and amino acid resonances usually seen in the 3.0–4.5-ppm region was
particularly reduced. However, this was accompanied by a significant loss in the signal-to-noise
(S/N) ratio that led to a considerable signal loss in the aromatic (δ=7.0–8.5 ppm) and aliphatic
regions (δ=1.0–2.5 ppm). This lower S/N ratio arised due to the inherently lower sensitivity of the
2-D acquisition required to produce J-RES projections [21].
63
Figure 2.1: Average 1H NMR spectra (n=10) of control worm tissue extracts acquired using: (a) Presaturation Utilising Relaxation Gradients and Echos (PURGE); (b) Carr–Purcell–Meiboom–Gill (CPMG); and (c) J-Resolved (J-RES) projections. An asterisk represents the residual H2O/HOD.
2.4.2 Partial least square discriminant analysis (PLS-DA) on 1-D NMR spectra
The PLS-DA scores plots for the three 1-D NMR methods (Figure 2.2) show distinct
separation using MANOVA (p<0.05) between control and endosulfan-exposed earthworms. From
PLS 1, most of the controls have positive values in the scores plot while the exposed earthworms
generally have negative values. PURGE (Figure 2.2a) and J-RES projections showed the highest
separation by MANOVA (p=2.82 x 10-7 and p =1.5x 10-7 respectively) compared to CPMG
2345678 ppm
*
*
*
C) J-RES projections
A) PURGE
B) CPMG
64
(p=1.02 x 10-5). To examine which specific NMR resonances caused differentiation between the
control and exposed worms, the PLS 1 vs PLS 2 (2D) loadings plots were examined.
Figure 2.2: Partial least-squares discriminant analysis (PLS-DA) scores plot of data-reduced 1H NMR spectra for control Eisenia fetida (●) and E. fetida exposed to endosulfan (▲) using 1-D NMR techniques. (a) Presaturation Utilising Relaxation Gradients and Echos (PURGE); (b) Carr–Purcell–Meiboom–Gill (CPMG); and (c) J-Resolved (J-RES) projections. The P value for MANOVA (Wilks’ lambda) of control and exposed earthworms for the PLS components is reported. Scores for control and endosulfan-exposed earthworms are significantly different at the P <0.05 level.
In Figure 2.3a and Figure 2.3b, the 2D loadings plots for PURGE and CPMG indicate
which spectral components identified are potentially significant. Points located closer to the center
(0,0) are spectral components that are common in both control and exposed groups but further are
ones responsible for the separation between the two groups in the scores plot. Alanine (δ=1.46-
1.48 ppm), leucine (δ=0.94-0.96 ppm) and maltose (δ=5.39-5.40 ppm) were identified as important
metabolites from both PURGE and CPMG loadings plots and, were confirmed as significant
through a two sample t-test of their bucket intensities (p<0.05) (Figure 2.3). These findings
confirm the results from McKelvie et al. [23], where these two amino acids and one sugar were
indicated as important variables in the PCA analysis of the PURGE 1H NMR data. It has been
suggested that alanine has a potential role in osmolite replacement of other metabolites that are
used during stress conditions [31]. Leucine is vital in the development of sterols in adipose and
-0.3 -0.2 -0.1 0.0 0.1 0.2-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
A) PURGE C) J-RES projectionsB) CPMG
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8
-0.4
-0.2
0.0
0.2
0.4
0.6
-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
PLS 1 (52.92% variance)
PL
S 2
(26
.86%
vari
an
ce
)
PLS 1 (58.44% variance)
PL
S 2
(16
.57%
vari
an
ce
) p= 2.82E-07* p =1.02E-05* p =1.5E-07*
PLS 1 (40.84% variance)
PLS
2 (1
7.2
8%
va
ria
nce)
65
muscle tissue and increasing the concentration in organisms has been studied to decrease muscle
degradation [32]. For the sugar maltose, earthworms follow the Embden-Meyerhof pathway [33]
for carbohydrate metabolism and the detection of sugars during exposure to endosulfan could be
stressed-induced. Maltose has been detected as a stress-related biomarker in various earthworm
exposure studies such as 4-fluoroaniline [8], Pb/Zn contaminated sites [27] and polyaromatic
hydrocarbons (PAHs) [10]. In the sugar and aliphatic regions (δ=3.0-4.7ppm), the extensive
overlap in the NMR spectra in both PURGE and CPMG complicated the identification of complete
metabolites. From the loadings plots of PURGE and CPMG, selected signals at 3.26, 3.76 and
3.83ppm were identified in this region (note that the later two signals were only clearly
differentiated in the PURGE data). Since many amino acids and sugars correlate with these
spectral regions, it is difficult to elucidate their origin from the 1-D signal alone.
Figure 2.3: Partial least-squares discriminant analysis (PLS-DA) 2-D loadings plots of data-reduced 1HNMR spectra for control and endosulfan-exposed Eisenia fetida using three 1-D NMR techniques: (a) Presaturation Utilising Relaxation Gradients and Echos (PURGE); (b) Carr–Purcell–Meiboom–Gill (CPMG); and (c) J-Resolved (J-RES) projections. Each oval region represents areas of significance determined using a two-sample t-test with a confidence interval of 95% (P<0.05). The asterisk indicates chemical shifts unidentified by the 1-D NMR technique alone (these regions are later identified using 2-D NMR).
The loadings plot results for J-RES projections (Figure 2.3c) were quite different than
PURGE and CPMG. Alanine (δ=1.47ppm) and maltose (δ=5.40ppm) was also detected as major
-0.2 -0.1 0.0 0.1 0.2 0.3
-0.4
-0.2
0.0
0.2
0.4
8.998.988.978.968.958.948.938.928.918.98.898.888.878.868.858.848.838.828.818.88.798.788.778.768.758.748.738.728.718.78.698.688.678.668.658.648.638.628.618.68.598.588.578.568.558.548.538.528.518.58.498.488.478.468.458.448.438.428.418.48.398.388.378.368.358.348.338.328.318.38.298.288.278.268.258.248.238.228.218.28.198.188.178.168.158.148.138.128.118.18.098.088.078.068.058.048.038.028.0187.997.987.977.967.957.947.937.927.917.97.897.887.877.867.857.847.837.827.817.87.797.787.777.767.757.747.737.727.717.77.697.687.677.667.657.647.637.627.617.67.597.587.577.567.557.547.537.527.517.57.497.487.477.467.457.447.437.427.417.47.397.387.377.367.357.347.33
7.327.317.37.297.287.277.267.257.247.237.227.217.27.197.187.177.167.157.147.137.127.117.17.097.087.077.067.057.047.037.027.0176.996.986.976.966.956.946.936.926.916.96.896.886.876.866.856.846.836.826.816.86.796.786.776.766.756.746.736.726.716.76.696.686.676.666.656.646.636.626.616.66.596.586.576.566.556.546.536.526.516.56.496.486.476.466.456.446.436.426.416.46.396.386.376.366.356.346.336.326.316.36.296.286.276.266.256.246.236.226.216.26.196.186.176.166.156.146.136.126.116.16.096.086.076.066.056.046.036.026.0165.995.985.975.965.955.945.935.925.915.95.895.885.875.865.855.845.835.825.815.85.795.785.775.765.755.745.735.725.715.75.695.685.675.665.655.645.635.625.615.65.595.585.575.565.555.545.535.525.515.55.495.485.475.465.455.445.435.42
5.41
5.45.39
5.385.375.365.355.345.335.325.315.35.295.285.275.265.255.24
5.23
5.225.215.25.195.185.175.165.155.145.135.125.115.15.095.085.075.065.055.045.035.025.0154.994.984.974.964.954.944.934.924.914.94.894.884.874.864.854.74.694.684.67 4.664.65
4.64
4.63
4.624.614.64.594.584.574.564.554.544.534.524.514.54.494.484.474.464.454.444.434.424.414.44.394.384.374.364.354.344.334.324.314.34.294.284.274.264.254.244.234.224.214.24.194.184.174.164.154.144.134.124.114.14.094.084.074.064.054.044.034.024.0143.993.983.97
3.963.95
3.943.933.92
3.91
3.9
3.893.883.873.86
3.85
3.84
3.83
3.82
3.813.8
3.79 3.783.77
3.76
3.75
3.74
3.733.723.713.73.69
3.683.673.66
3.65
3.64
3.63
3.62
3.613.6
3.593.583.57
3.56
3.553.54
3.533.52
3.513.53.493.48
3.473.463.453.44
3.43
3.42
3.41
3.4
3.393.383.373.363.353.34
3.333.323.313.33.293.283.27
3.26
3.253.24
3.23
3.22
3.213.23.193.183.173.163.153.143.133.12
3.11
3.13.093.083.073.063.053.043.033.023.0132.992.982.972.962.952.942.932.922.912.92.892.882.872.862.852.842.832.822.812.82.792.782.772.762.752.742.732.722.712.72.692.682.672.662.652.642.632.622.612.62.592.582.572.562.552.542.532.522.512.52.492.482.472.462.452.442.432.422.412.42.392.382.372.362.352.342.332.322.312.32.292.282.272.262.252.242.232.222.212.22.192.182.172.162.152.142.13
2.122.112.12.092.082.072.062.052.042.032.022.0121.991.981.971.961.951.941.931.921.911.91.891.881.871.861.851.841.831.821.811.81.791.781.771.761.751.741.731.721.711.71.691.681.671.661.651.641.631.621.611.61.591.581.571.561.551.541.531.521.511.51.49
1.481.47
1.46
1.451.441.431.421.411.41.391.381.371.361.351.341.331.321.311.31.291.281.271.261.251.241.231.221.211.21.191.181.171.161.151.141.131.121.111.11.091.081.071.061.051.04
1.031.021.0110.990.98
0.97
0.96
0.950.94
0.93
0.920.910.90.890.880.870.86
0.850.840.830.820.810.80.790.780.770.760.750.740.730.720.710.70.690.680.670.660.650.640.630.620.610.60.590.580.570.560.550.540.530.520.510.50.490.480.470.460.450.440.430.420.410.40.390.380.370.360.350.340.330.320.310.30.290.280.270.260.25
-0.3 -0.2 -0.1 0.0 0.1 0.2-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
8.998.988.978.968.958.948.938.928.918.98.898.888.878.868.858.848.838.828.818.88.798.788.778.768.758.748.738.728.718.78.698.688.678.668.658.648.638.628.618.68.598.588.578.568.558.548.538.528.518.58.498.488.478.468.458.448.438.428.418.48.398.388.378.368.358.348.338.328.318.38.298.288.278.268.258.248.238.228.218.28.198.188.178.168.158.148.138.128.118.18.098.088.078.068.058.048.038.028.0187.997.987.977.967.957.947.937.927.917.97.897.887.877.867.857.847.837.827.817.87.797.787.777.767.757.747.737.727.717.77.697.687.677.667.657.647.637.627.617.67.597.587.577.567.557.547.537.527.517.57.497.487.477.467.457.447.437.427.417.47.397.387.377.367.357.347.337.327.317.37.297.287.277.267.257.247.237.227.217.27.197.187.177.167.157.147.137.127.117.17.097.087.077.067.057.047.037.027.0176.996.986.976.966.956.946.936.926.916.96.896.886.876.866.856.846.836.826.816.86.796.786.776.766.756.746.736.726.716.76.696.686.676.666.656.646.636.626.616.66.596.586.576.566.556.546.536.526.516.56.496.486.476.466.456.446.436.426.416.46.396.386.376.366.356.346.336.326.316.36.296.286.276.266.256.246.236.226.216.26.196.186.176.166.156.146.136.126.116.16.096.086.076.066.056.046.036.026.0165.995.985.975.965.955.945.935.925.915.95.895.885.875.865.855.845.835.825.815.85.795.785.775.765.755.745.735.725.715.75.695.685.675.665.655.645.635.625.615.65.595.585.575.565.555.545.535.525.515.55.495.485.475.465.455.445.435.42 5.41
5.45.39
5.385.375.365.355.345.335.325.315.35.295.285.275.265.255.24
5.235.22
5.215.25.195.185.175.165.155.145.135.125.115.15.095.085.075.065.055.045.035.025.0154.994.984.974.964.954.944.934.924.914.94.894.884.874.864.854.74.694.684.674.66
4.65
4.64
4.63
4.624.614.64.594.584.574.564.554.544.534.524.514.54.494.484.474.464.454.444.434.424.414.44.394.384.374.364.354.344.334.324.314.34.294.284.274.264.254.244.234.224.214.24.194.184.174.164.154.144.134.124.114.14.094.084.07
4.064.054.044.034.024.0143.993.983.97
3.963.953.943.933.92
3.91
3.9
3.893.88
3.87
3.863.85
3.84
3.83
3.82
3.81
3.83.793.78
3.773.763.75
3.74
3.733.723.71
3.73.69
3.683.673.663.65
3.643.63
3.623.613.6 3.593.583.57
3.56
3.553.54
3.533.523.513.5
3.493.48
3.473.463.45
3.44
3.43
3.42
3.413.43.39
3.383.37
3.363.35
3.34
3.333.323.313.33.293.283.27
3.26
3.253.24
3.23
3.22
3.213.23.193.183.173.163.153.143.133.12
3.11
3.13.093.083.073.063.053.043.033.023.0132.992.982.972.962.952.942.932.922.912.92.892.882.872.862.852.842.832.822.812.82.792.782.772.762.752.742.732.722.712.72.692.682.672.662.652.642.632.622.612.62.592.582.572.562.552.542.532.522.512.52.492.482.472.462.452.442.432.422.412.42.392.382.372.362.352.342.332.322.312.32.292.282.272.262.252.242.232.222.212.22.192.182.172.162.152.142.132.122.112.12.092.082.072.062.052.042.032.022.0121.991.981.971.961.951.941.931.921.911.91.891.881.871.861.851.841.831.821.811.81.791.781.771.761.751.741.731.721.711.71.691.681.671.661.651.641.631.621.611.61.591.581.571.561.551.54
1.531.521.511.51.491.481.471.46
1.451.441.431.421.411.41.391.381.371.361.351.34
1.331.321.311.31.291.281.271.261.251.241.231.221.211.21.191.181.171.161.151.141.131.121.111.11.091.081.071.061.051.041.031.021.0110.990.980.97
0.960.95
0.940.93
0.920.910.90.890.880.870.86
0.850.840.830.820.810.80.790.780.770.760.750.740.730.720.710.70.690.680.670.660.650.640.630.620.610.60.590.580.570.560.550.540.530.520.510.50.490.480.470.460.450.440.430.420.410.40.390.380.370.360.350.340.330.320.310.30.290.280.270.260.25
-0.6 -0.4 -0.2 0.0 0.2-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
8.998.988.978.968.958.948.938.928.918.98.898.888.878.868.858.848.838.828.818.88.798.788.778.768.758.748.738.728.718.78.698.688.678.668.658.648.638.628.618.68.598.588.578.568.558.548.538.528.518.58.498.488.478.468.458.448.438.428.418.48.398.388.378.368.358.348.338.328.318.38.298.288.278.268.258.248.238.228.218.28.198.188.178.168.158.148.138.128.118.18.098.088.078.068.058.048.038.028.0187.997.987.977.967.957.947.937.927.917.97.897.887.877.867.857.847.837.827.817.87.797.787.777.767.757.747.737.727.717.77.697.687.677.667.657.647.637.627.617.67.597.587.577.567.557.547.537.527.517.57.497.487.477.467.457.447.437.427.417.47.397.387.377.367.357.347.337.327.317.37.297.287.277.267.257.247.237.227.217.27.197.187.177.167.157.147.137.127.117.17.097.087.077.067.057.047.037.027.0176.996.986.976.966.956.946.936.926.916.96.896.886.876.866.856.846.836.826.816.86.796.786.776.766.756.746.736.726.716.76.696.686.676.666.656.646.636.626.616.66.596.586.576.566.556.546.536.526.516.56.496.486.476.466.456.446.436.426.416.46.396.386.376.366.356.346.336.326.316.36.296.286.276.266.256.246.236.226.216.26.196.186.176.166.156.146.136.126.116.16.096.086.076.066.056.046.036.026.0165.995.985.975.965.955.945.935.925.915.95.895.885.875.865.855.845.835.825.815.85.795.785.775.765.755.745.735.725.715.75.695.685.675.665.655.645.635.625.615.65.595.585.575.565.555.545.535.525.515.55.495.485.475.465.455.445.435.425.415.45.39
5.385.375.365.355.345.335.325.315.35.295.285.275.265.255.245.235.22
5.21
5.25.195.185.175.165.155.145.135.125.115.15.095.085.075.065.055.045.035.025.0154.994.984.974.964.954.944.934.924.914.94.894.884.874.864.854.74.694.684.674.664.65 4.644.63
4.62
4.614.64.594.584.574.564.554.544.534.524.514.54.494.484.474.464.454.444.434.424.414.44.394.384.374.364.354.344.334.324.314.34.294.284.274.264.254.244.234.224.214.24.194.184.174.164.154.144.134.124.114.14.094.084.074.064.054.044.034.024.0143.993.983.973.963.953.943.933.923.913.93.893.88
3.87
3.863.85
3.843.83
3.823.813.83.793.783.77
3.763.753.74
3.733.723.713.73.693.683.673.663.653.64 3.633.623.613.63.593.58 3.573.563.553.543.533.523.51
3.53.493.48
3.47
3.463.453.443.433.423.41
3.43.393.38
3.373.363.353.34
3.33
3.323.313.33.293.283.273.26
3.25
3.24
3.23
3.223.213.23.193.183.173.163.153.143.133.123.11
3.13.093.083.073.063.053.043.033.023.0132.992.982.972.962.952.942.932.922.912.92.892.882.872.862.852.842.832.822.812.82.792.782.772.762.752.742.732.722.712.72.692.682.672.662.652.642.632.622.612.62.592.582.572.562.552.542.532.522.512.52.492.482.472.462.452.442.432.422.412.42.392.382.372.362.352.342.332.322.312.32.292.282.272.262.252.242.232.222.212.22.192.182.172.162.152.142.13
2.122.112.12.092.082.072.062.052.042.032.022.0121.991.981.971.961.951.941.931.921.911.91.891.881.871.861.851.841.831.821.811.81.791.781.771.761.751.741.731.721.711.71.691.681.671.661.651.641.631.621.611.61.591.581.571.561.551.541.531.521.511.51.491.48
1.471.46
1.451.441.431.421.411.41.391.381.371.361.351.341.331.321.311.31.291.281.271.261.251.241.231.221.211.21.191.181.171.161.151.141.131.121.111.11.091.081.071.061.051.041.031.02
1.0110.990.980.970.960.950.940.93
0.920.910.90.890.880.870.860.850.840.830.820.810.80.790.780.770.760.750.740.730.720.710.70.690.680.670.660.650.640.630.620.610.60.590.580.570.560.550.540.530.520.510.50.490.480.470.460.450.440.430.420.410.40.390.380.370.360.350.340.330.320.310.30.290.280.270.260.25
5.40
P[1]
P[2
]
P[1]
P[2
]
P[1]
P[2
]
Leucine
Alanine
MaltoseLeucine
Alanine
Maltose
A) PURGE C) J-RES projectionsB) CPMG
Alanine
Maltose
Unknown*
Unknown*Unknown*
66
metabolites contributing to the separation of the two groups in the J-RES projections but due to
decreases in signal to noise in some areas, leucine (δ=0.94-0.96ppm) was not distinguished. In the
3.0-5.0ppm region (the overlapping sugar and amino acid resonances), the simplified spectra from
J-RES projections were only able to detect two signals at 3.25 and 3.26ppm as significant. Using
the 1-D NMR data alone it was difficult to assign these specific chemical shifts [9]. For example,
3.25 and 3.26ppm were identified as an important spectral signal by all three methods. However,
from the 1-D data alone, this signal could arise from several metabolites including arginine,
phenylalanine, maltose and glucose. These resonances are assigned later in the paper using the
additional information from 2-D NMR spectroscopy.
2.4.3 2-D NMR spectroscopic characterization
Three different 2-D NMR techniques were used to investigate potential metabolites in E.
fetida after exposure to endosulfan. Figure 2.4 shows one representative spectrum for each of the
2D NMR techniques (J-RES, COSY and HSQC). For J-RES (Figure 2.4a), the 1H dimension is
the J-RES projections (Figure 2.3c) and each peak is correlated to its respective J-coupling
constant on the indirect dimension (F1). This is advantageous as multiple couplings are detected
from a single peak which could correlate to different metabolites. However, complications could
arise as intense couplings could overlap with each other. In Figure 2.4a, the sugar region (3.0-
4.0ppm in the 1H spectrum) contained many small and large J-couplings which caused difficulty in
their differentiation. Ludwig and Viant [21] cautioned that strong couplings in the JRES spectra
can lead to extra signals in the spectra after tilting and projection of the data. This can potentially
lead to misinterpretation of the spectra if not examined. In our study, artefacts were minimized
through acquiring the recommended 32 datapoints in the indirect dimension (F1) [21] and by
careful checking against the Bruker Biofluid Reference Compound Database (Bruker Biospin) to
67
ensure matches of both chemical shift and J-coupling constants. The COSY spectrum (Figure
2.4b) displayed the 1-4J 1H-1H coupling connections from both the proton indirect (F1) and direct
dimensions (F2). Overlap was observed in the crowded sugar region (3.0-4.0 ppm in both
dimensions) but in regions further from the diagonal couplings by other metabolites, they were
well resolved. In HSQC (Figure 2.4c), the spectrum represents the chemical shift of both the
carbon and protons in a 1H-13C unit, with the direct dimension (F2) displaying the 1H chemical
shift and the indirect dimension (F1) displaying the 13C chemical shift. Additional dispersion of
the peaks is seen in HSQC due to the relatively large carbon chemical shift range (10.0-110.0 ppm)
in the indirect dimension (F1) compared to COSY where the indirect dimension is the 1H
chemical shift (0.25- 6.0 ppm).
68
Figure 2.4: 2-D NMR spectra of control worm tissue extract using: (a) 1H–J-RES, (b) 1H–1H COSY, and (c) 1H–13C Single Quantum Coherence (HSQC) spectroscopy.
2.4.4 Partial least square discriminant analysis (PLS-DA) on 2D NMR spectra
The scores plots for the 2D NMR spectra of the control and exposed worms are shown in
Figures 2.5 and 2.6. In the scores plot, the control and exposed worms analyzed by J-RES were
well separated (p= 2.82 x 10-5), as were the corresponding clusters in the COSY data (p=5.5x 10-4).
However, neither showed a significant improvement in the degree of separation when compared to
the PURGE and 1-D J-RES projections. This was an interesting result as the J-RES datasets
should contain more spectral information than there corresponding 1-D projections, but the full 2D
5 4 3 2 1ppm
20
30
40
50
60
70
80
90
100
ppm
(13C
)
(1H)
5 4 3 2 1ppm
1
2
3
4
5
pp
m
8 6 4 2ppm
-10
-5
0
5
10H
z
A) 1H-J-RES
B) 1H-1H COSY
C) 1H-13C HSQC
69
datasets resulted in a lower discrimination between the control and exposed worms. This may be
due to the lower S/N in each individual slice of the 2-D, compared to the projection where the
slices are summed to improve the total signal. In addition, the J-RES datasets were acquired using
parameters adapted from previously reported studies optimized for the collection of J-RES
projections for metabolic mixtures [15, 34]. While these produce excellent J-RES projections in a
very short amount of time (~13 mins, comparable to 1-D NMR, see Table 2.1), and allow
comparison to other 1-D techniques, only 32 points are collected in the second dimension. As such
the J-RES datasets were analyzed statistically and included here only for completeness and to
investigate if these datasets can provide additional discrimination compared to its 1-D projections.
Figure 2.5: Partial least-squares discriminant analysis (PLS-DA) scores plot of data-reduced 1H NMR spectra for control Eisenia fetida (●) and E. fetida exposed to endosulfan (▲) (n=10) using two 2-D NMR techniques: (a) 1H–J-RES and (b) 1H–1H COSY. The P value for MANOVA (Wilks’ lambda) of control and exposed earthworms for the PLS components is reported. Scores for control and endosulfan-exposed earthworms are significantly different at the P<0.05 level.
PLS 1 (41.70% variance)
PL
S 2
(2
3.7
1%
va
ria
nce
)
p =5.5E-04
B) 1H-1H COSY
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
PLS 1 (44.21% variance)
PL
S 2
(2
1.0
5%
va
ria
nce
)
p =2.82E-05
A) 1H-J-RES
-0.6 -0.4 -0.2 0.0 0.2 0.4
-0.4
-0.2
0.0
0.2
0.4
70
Figure 2.6: Partial least-squares discriminant analysis (PLS-DA) scores plot of data-reduced 1H NMR spectra for control Eisenia fetida (●) and E. fetida exposed to endosulfan (▲) (n=10) using 1H–13C Single Quantum Coherence (HSQC) spectroscopy in the chemical shift range of: (a) 1H=6.0–0.25 ppm; 13C=110.0–10.0 ppm and (b) 1H=2.5–0.25 ppm; 13C=50.0–10.0 ppm. The P
value for MANOVA (Wilks’ lambda) of control and exposed earthworms for the PLS components is reported. Scores for control and endosulfan-exposed earthworms are significantly different at the P<0.05 level.
It would be interesting for future studies to investigate the discriminating ability of J-RES by
increasing the number of scans and points in the F1 dimension to optimize this technique for
metabolomic studies. A recent publication by Ludwig et al. [21] provides an excellent
introduction of 2-D J-RES spectroscopy in metabolomic studies and interested readers should refer
to this.
In contrast, the HSQC data showed excellent discrimination between the exposed and
control groups with a MANOVA value of p=9.16x 10-10 which was three orders lower compared to
all the other 1-D and 2-D NMR techniques. The high separation of the two sample groups in the
scores plot of the HSQC data likely results from the additional dispersion provided by the carbon
dimension which has a higher chemical shift range (considered here, 10.0-110.0 ppm) compared to
proton (0.25-9.0ppm) which alleviates spectral overlap and allows key resonances to be more
clearly discerned.
-0.4 -0.2 0.0 0.2 0.4 0.6-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
PLS 1 (12.86% variance)
PLS
2 (
10
.76
% v
ari
an
ce)
PLS 1 (37.96% variance)
PLS
2 (
9.6
6%
va
ria
nce
) p =9.16E-10 p =1.16E-11
A) B)
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
71
Figure 2.7 shows the 2D loadings plots for J-RES and COSY corresponding to the scores
plots in Figure 2.5. Each point in the 2D loadings plot represents a cross peak in the J-RES or
COSY spectra and is indicated by two values beside it. For J-RES, the first value describes the
proton chemical shift information in the direct dimension (F2 axis) and the second value describes
the J-coupling information in the indirect dimension (F1 axis). For COSY, the first value describes
the proton chemical shift in the indirect dimension (F1 axis) and the second value describes the
proton chemical shift in the direct dimension (F2 axis). As with the 2-D loadings plots for the 1-D
NMR results, points located further from the central cluster (0,0) are ones responsible for the
separation between the two groups in the scores plot. For the J-RES loadings plot (Figure 2.7a),
three main areas were detected as significant using a two sample t- test (p<0.05).
Figure 2.7: Partial least-squares discriminant analysis (PLS-DA) 2-D loadings plots of 2-D NMR spectra for control and endosulfan-exposed Eisenia fetida: (a) 1H–J-RES and (b) 1H–1H COSY. Each oval region represents areas of significance determined using a two-sample t-test with a confidence interval of 95% (P<0.05) (see Figure 5).
The first main area located in the lower left at (1.47 ppm, ~3.5 Hz) was deduced as alanine using
the Bruker Biofluid Reference Compound Database version 2-0-0 (Bruker Biospin). Note that
several similar signals were detected because the contours in the 2-D dataset are relatively broad.
-0.4 -0.2 0.0 0.2
0.0
0.2
0.4
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3.72,8.53.71,8.5
3.70,8.53.69,8.53.68,8.53.67,8.53.66,8.53.65,8.5
3.64,8.53.63,8.53.62,8.53.61,8.53.60,8.53.59,8.53.58,8.53.57,8.53.56,8.53.55,8.53.54,8.53.53,8.53.52,8.53.51,8.53.50,8.5
3.49,8.53.48,8.5
3.47,8.53.46,8.53.45,8.53.44,8.53.43,8.5
3.42,8.53.41,8.53.40,8.53.39,8.5
3.38,8.53.37,8.53.36,8.53.35,8.53.34,8.53.33,8.53.32,8.53.31,8.53.30,8.53.29,8.53.28,8.53.27,8.53.26,8.53.25,8.53.24,8.5
3.23,8.53.22,8.53.21,8.53.20,8.53.19,8.53.18,8.53.17,8.53.16,8.53.15,8.53.14,8.53.13,8.53.12,8.53.11,8.53.10,8.53.09,8.53.08,8.53.07,8.53.06,8.53.05,8.53.04,8.53.03,8.53.02,8.53.01,8.53.00,8.52.99,8.52.98,8.52.97,8.52.96,8.52.95,8.52.94,8.52.93,8.52.92,8.52.91,8.52.90,8.52.89,8.52.88,8.52.87,8.52.86,8.52.85,8.52.84,8.52.83,8.52.82,8.52.81,8.52.80,8.52.79,8.52.78,8.52.77,8.52.76,8.52.75,8.52.74,8.52.73,8.52.72,8.52.71,8.52.70,8.52.69,8.52.68,8.52.67,8.52.66,8.52.65,8.52.64,8.52.63,8.52.62,8.52.61,8.52.60,8.52.59,8.52.58,8.52.57,8.52.56,8.52.55,8.52.54,8.52.53,8.52.52,8.52.51,8.52.50,8.52.49,8.52.48,8.52.47,8.52.46,8.52.45,8.52.44,8.52.43,8.52.42,8.52.41,8.52.40,8.52.39,8.52.38,8.52.37,8.52.36,8.52.35,8.52.34,8.52.33,8.52.32,8.52.31,8.52.30,8.52.29,8.52.28,8.52.27,8.52.26,8.52.25,8.52.24,8.52.23,8.52.22,8.52.21,8.52.20,8.52.19,8.52.18,8.52.17,8.52.16,8.52.15,8.52.14,8.52.13,8.52.12,8.52.11,8.52.10,8.52.09,8.52.08,8.52.07,8.52.06,8.52.05,8.52.04,8.52.03,8.52.02,8.52.01,8.52.00,8.51.99,8.51.98,8.51.97,8.51.96,8.51.95,8.51.94,8.51.93,8.51.92,8.51.91,8.51.90,8.51.89,8.51.88,8.51.87,8.51.86,8.51.85,8.51.84,8.51.83,8.51.82,8.51.81,8.51.80,8.51.79,8.51.78,8.51.77,8.51.76,8.51.75,8.51.74,8.51.73,8.51.72,8.51.71,8.51.70,8.51.69,8.51.68,8.51.67,8.51.66,8.51.65,8.51.64,8.51.63,8.51.62,8.51.61,8.51.60,8.51.59,8.51.58,8.51.57,8.51.56,8.51.55,8.51.54,8.51.53,8.51.52,8.51.51,8.51.50,8.51.49,8.51.48,8.51.47,8.51.46,8.51.45,8.51.44,8.51.43,8.51.42,8.51.41,8.51.40,8.51.39,8.51.38,8.51.37,8.51.36,8.51.35,8.51.34,8.51.33,8.51.32,8.51.31,8.51.30,8.51.29,8.51.28,8.51.27,8.51.26,8.51.25,8.51.24,8.51.23,8.51.22,8.51.21,8.51.20,8.51.19,8.51.18,8.51.17,8.51.16,8.51.15,8.51.14,8.51.13,8.51.12,8.51.11,8.51.10,8.51.09,8.51.08,8.51.07,8.51.06,8.51.05,8.51.04,8.51.03,8.51.02,8.51.01,8.51.00,8.50.99,8.50.98,8.50.97,8.50.96,8.50.95,8.50.94,8.50.93,8.50.92,8.50.91,8.50.90,8.50.89,8.50.88,8.50.87,8.50.86,8.50.85,8.50.84,8.50.83,8.50.82,8.50.81,8.50.80,8.50.79,8.50.78,8.50.77,8.50.76,8.50.75,8.50.74,8.50.73,8.50.72,8.50.71,8.50.70,8.50.69,8.50.68,8.50.67,8.50.66,8.50.65,8.50.64,8.50.63,8.50.62,8.50.61,8.50.60,8.50.59,8.50.58,8.50.57,8.50.56,8.50.55,8.50.54,8.50.53,8.50.52,8.50.51,8.50.50,8.50.49,8.50.48,8.50.47,8.50.46,8.50.45,8.50.44,8.50.43,8.50.42,8.50.41,8.50.40,8.50.39,8.50.38,8.50.37,8.50.36,8.50.35,8.50.34,8.50.33,8.50.32,8.50.31,8.50.30,8.50.29,8.50.28,8.50.27,8.50.26,8.59.00,7.58.99,7.58.98,7.58.97,7.58.96,7.58.95,7.58.94,7.58.93,7.58.92,7.58.91,7.58.90,7.58.89,7.58.88,7.58.87,7.58.86,7.58.85,7.58.84,7.58.83,7.58.82,7.58.81,7.58.80,7.58.79,7.58.78,7.58.77,7.58.76,7.58.75,7.58.74,7.58.73,7.58.72,7.58.71,7.58.70,7.58.69,7.58.68,7.58.67,7.58.66,7.58.65,7.58.64,7.58.63,7.58.62,7.58.61,7.58.60,7.58.59,7.58.58,7.58.57,7.58.56,7.58.55,7.58.54,7.58.53,7.58.52,7.58.51,7.58.50,7.58.49,7.58.48,7.58.47,7.58.46,7.58.45,7.58.44,7.58.43,7.58.42,7.58.41,7.58.40,7.58.39,7.58.38,7.58.37,7.58.36,7.58.35,7.58.34,7.58.33,7.58.32,7.58.31,7.58.30,7.58.29,7.58.28,7.58.27,7.58.26,7.58.25,7.58.24,7.58.23,7.58.22,7.58.21,7.58.20,7.58.19,7.58.18,7.58.17,7.58.16,7.58.15,7.58.14,7.58.13,7.58.12,7.58.11,7.58.10,7.58.09,7.58.08,7.58.07,7.58.06,7.58.05,7.58.04,7.58.03,7.58.02,7.58.01,7.58.00,7.57.99,7.57.98,7.57.97,7.57.96,7.57.95,7.57.94,7.57.93,7.57.92,7.57.91,7.57.90,7.57.89,7.57.88,7.57.87,7.57.86,7.57.85,7.57.84,7.57.83,7.57.82,7.57.81,7.57.80,7.57.79,7.57.78,7.57.77,7.57.76,7.57.75,7.57.74,7.57.73,7.57.72,7.57.71,7.57.70,7.57.69,7.57.68,7.57.67,7.57.66,7.57.65,7.57.64,7.57.63,7.57.62,7.57.61,7.57.60,7.57.59,7.57.58,7.57.57,7.57.56,7.57.55,7.57.54,7.57.53,7.57.52,7.57.51,7.57.50,7.57.49,7.57.48,7.57.47,7.57.46,7.57.45,7.57.44,7.57.43,7.57.42,7.57.41,7.57.40,7.57.39,7.57.38,7.57.37,7.57.36,7.57.35,7.57.34,7.57.33,7.57.32,7.57.31,7.57.30,7.57.29,7.57.28,7.57.27,7.57.26,7.57.25,7.57.24,7.57.23,7.57.22,7.57.21,7.57.20,7.57.19,7.57.18,7.57.17,7.57.16,7.57.15,7.57.14,7.57.13,7.57.12,7.57.11,7.57.10,7.57.09,7.57.08,7.57.07,7.57.06,7.57.05,7.57.04,7.57.03,7.57.02,7.57.01,7.57.00,7.56.99,7.56.98,7.56.97,7.56.96,7.56.95,7.56.94,7.56.93,7.56.92,7.56.91,7.56.90,7.56.89,7.56.88,7.56.87,7.56.86,7.56.85,7.56.84,7.56.83,7.56.82,7.56.81,7.56.80,7.56.79,7.56.78,7.56.77,7.56.76,7.56.75,7.56.74,7.56.73,7.56.72,7.56.71,7.56.70,7.56.69,7.56.68,7.56.67,7.56.66,7.56.65,7.56.64,7.56.63,7.56.62,7.56.61,7.56.60,7.56.59,7.56.58,7.56.57,7.56.56,7.56.55,7.56.54,7.56.53,7.56.52,7.56.51,7.56.50,7.56.49,7.56.48,7.56.47,7.56.46,7.56.45,7.56.44,7.56.43,7.56.42,7.56.41,7.56.40,7.56.39,7.56.38,7.56.37,7.56.36,7.56.35,7.56.34,7.56.33,7.56.32,7.56.31,7.56.30,7.56.29,7.56.28,7.56.27,7.56.26,7.56.25,7.56.24,7.56.23,7.56.22,7.56.21,7.56.20,7.56.19,7.56.18,7.56.17,7.56.16,7.56.15,7.56.14,7.56.13,7.56.12,7.56.11,7.56.10,7.56.09,7.56.08,7.56.07,7.56.06,7.56.05,7.56.04,7.56.03,7.56.02,7.56.01,7.56.00,7.55.99,7.55.98,7.55.97,7.55.96,7.55.95,7.55.94,7.55.93,7.55.92,7.55.91,7.55.90,7.55.89,7.55.88,7.55.87,7.55.86,7.55.85,7.55.84,7.55.83,7.55.82,7.55.81,7.55.80,7.55.79,7.55.78,7.55.77,7.55.76,7.55.75,7.55.74,7.55.73,7.55.72,7.55.71,7.55.70,7.55.69,7.55.68,7.55.67,7.55.66,7.55.65,7.55.64,7.55.63,7.55.62,7.55.61,7.55.60,7.55.59,7.55.58,7.55.57,7.55.56,7.55.55,7.55.54,7.55.53,7.55.52,7.55.51,7.55.50,7.55.49,7.55.48,7.55.47,7.55.46,7.55.45,7.55.44,7.55.43,7.55.42,7.55.41,7.55.40,7.55.39,7.55.38,7.55.37,7.55.36,7.55.35,7.55.34,7.55.33,7.55.32,7.55.31,7.55.30,7.55.29,7.55.28,7.55.27,7.55.26,7.55.25,7.55.24,7.55.23,7.55.22,7.55.21,7.55.20,7.55.19,7.55.18,7.55.17,7.55.16,7.55.15,7.55.14,7.55.13,7.55.12,7.55.11,7.55.10,7.55.09,7.55.08,7.55.07,7.55.06,7.55.05,7.55.04,7.55.03,7.55.02,7.55.01,7.55.00,7.54.99,7.54.98,7.54.97,7.54.96,7.54.95,7.54.94,7.54.93,7.54.92,7.54.91,7.54.90,7.54.89,7.54.88,7.54.87,7.54.86,7.54.85,7.54.70,7.54.69,7.54.68,7.54.67,7.54.66,7.54.65,7.54.64,7.54.63,7.54.62,7.54.61,7.54.60,7.54.59,7.54.58,7.54.57,7.54.56,7.54.55,7.54.54,7.54.53,7.54.52,7.54.51,7.54.50,7.54.49,7.54.48,7.54.47,7.54.46,7.54.45,7.54.44,7.54.43,7.54.42,7.54.41,7.54.40,7.54.39,7.54.38,7.54.37,7.54.36,7.54.35,7.54.34,7.54.33,7.54.32,7.54.31,7.54.30,7.54.29,7.54.28,7.54.27,7.54.26,7.54.25,7.54.24,7.54.23,7.54.22,7.54.21,7.54.20,7.54.19,7.54.18,7.54.17,7.54.16,7.54.15,7.54.14,7.54.13,7.54.12,7.54.11,7.54.10,7.54.09,7.54.08,7.54.07,7.54.06,7.54.05,7.54.04,7.54.03,7.54.02,7.54.01,7.54.00,7.53.99,7.53.98,7.53.97,7.53.96,7.53.95,7.53.94,7.53.93,7.53.92,7.53.91,7.53.90,7.53.89,7.5
3.88,7.53.87,7.53.86,7.53.85,7.53.84,7.5
3.83,7.53.82,7.53.81,7.53.80,7.53.79,7.53.78,7.5
3.77,7.5
3.76,7.53.75,7.53.74,7.53.73,7.53.72,7.5
3.71,7.53.70,7.53.69,7.53.68,7.53.67,7.53.66,7.53.65,7.53.64,7.53.63,7.53.62,7.53.61,7.53.60,7.53.59,7.53.58,7.53.57,7.5
3.56,7.53.55,7.53.54,7.53.53,7.53.52,7.53.51,7.53.50,7.5
3.49,7.53.48,7.53.47,7.5
3.46,7.5
3.45,7.53.44,7.53.43,7.53.42,7.53.41,7.53.40,7.53.39,7.53.38,7.53.37,7.53.36,7.53.35,7.53.34,7.53.33,7.53.32,7.53.31,7.53.30,7.53.29,7.5
3.28,7.53.27,7.53.26,7.5
3.25,7.5
3.24,7.5
3.23,7.53.22,7.53.21,7.53.20,7.53.19,7.53.18,7.53.17,7.53.16,7.53.15,7.53.14,7.53.13,7.53.12,7.53.11,7.53.10,7.53.09,7.53.08,7.53.07,7.53.06,7.53.05,7.53.04,7.53.03,7.53.02,7.53.01,7.53.00,7.52.99,7.52.98,7.52.97,7.52.96,7.52.95,7.52.94,7.52.93,7.52.92,7.52.91,7.52.90,7.52.89,7.52.88,7.52.87,7.52.86,7.52.85,7.52.84,7.52.83,7.52.82,7.52.81,7.52.80,7.52.79,7.52.78,7.52.77,7.52.76,7.52.75,7.52.74,7.52.73,7.52.72,7.52.71,7.52.70,7.52.69,7.52.68,7.52.67,7.52.66,7.52.65,7.52.64,7.52.63,7.52.62,7.52.61,7.52.60,7.52.59,7.52.58,7.52.57,7.52.56,7.52.55,7.52.54,7.52.53,7.52.52,7.52.51,7.52.50,7.52.49,7.52.48,7.52.47,7.52.46,7.52.45,7.52.44,7.52.43,7.52.42,7.52.41,7.52.40,7.52.39,7.52.38,7.52.37,7.52.36,7.52.35,7.52.34,7.52.33,7.52.32,7.52.31,7.52.30,7.52.29,7.52.28,7.52.27,7.52.26,7.52.25,7.52.24,7.52.23,7.52.22,7.52.21,7.52.20,7.52.19,7.52.18,7.52.17,7.52.16,7.52.15,7.52.14,7.52.13,7.52.12,7.52.11,7.52.10,7.52.09,7.52.08,7.52.07,7.52.06,7.52.05,7.52.04,7.52.03,7.52.02,7.52.01,7.52.00,7.51.99,7.51.98,7.51.97,7.51.96,7.51.95,7.51.94,7.51.93,7.51.92,7.51.91,7.51.90,7.51.89,7.51.88,7.51.87,7.51.86,7.51.85,7.51.84,7.51.83,7.51.82,7.51.81,7.51.80,7.51.79,7.51.78,7.51.77,7.51.76,7.51.75,7.51.74,7.51.73,7.51.72,7.51.71,7.51.70,7.51.69,7.51.68,7.51.67,7.51.66,7.51.65,7.51.64,7.51.63,7.51.62,7.51.61,7.51.60,7.51.59,7.51.58,7.51.57,7.51.56,7.51.55,7.51.54,7.51.53,7.51.52,7.51.51,7.51.50,7.51.49,7.51.48,7.51.47,7.51.46,7.51.45,7.51.44,7.51.43,7.51.42,7.51.41,7.51.40,7.51.39,7.51.38,7.51.37,7.51.36,7.51.35,7.51.34,7.51.33,7.51.32,7.51.31,7.51.30,7.51.29,7.51.28,7.51.27,7.51.26,7.51.25,7.51.24,7.51.23,7.51.22,7.51.21,7.51.20,7.51.19,7.51.18,7.51.17,7.51.16,7.51.15,7.51.14,7.51.13,7.51.12,7.51.11,7.51.10,7.51.09,7.51.08,7.51.07,7.51.06,7.51.05,7.51.04,7.51.03,7.51.02,7.51.01,7.51.00,7.50.99,7.50.98,7.50.97,7.50.96,7.50.95,7.50.94,7.50.93,7.50.92,7.50.91,7.50.90,7.50.89,7.50.88,7.50.87,7.50.86,7.50.85,7.50.84,7.50.83,7.50.82,7.50.81,7.50.80,7.50.79,7.50.78,7.50.77,7.50.76,7.50.75,7.50.74,7.50.73,7.50.72,7.50.71,7.50.70,7.50.69,7.50.68,7.50.67,7.50.66,7.50.65,7.50.64,7.50.63,7.50.62,7.50.61,7.50.60,7.50.59,7.50.58,7.50.57,7.50.56,7.50.55,7.50.54,7.50.53,7.50.52,7.50.51,7.50.50,7.50.49,7.50.48,7.50.47,7.50.46,7.50.45,7.50.44,7.50.43,7.50.42,7.50.41,7.50.40,7.50.39,7.50.38,7.50.37,7.50.36,7.50.35,7.50.34,7.50.33,7.50.32,7.50.31,7.50.30,7.50.29,7.50.28,7.50.27,7.50.26,7.59.00,6.58.99,6.58.98,6.58.97,6.58.96,6.58.95,6.58.94,6.58.93,6.58.92,6.58.91,6.58.90,6.58.89,6.58.88,6.58.87,6.58.86,6.58.85,6.58.84,6.58.83,6.58.82,6.58.81,6.58.80,6.58.79,6.58.78,6.58.77,6.58.76,6.58.75,6.58.74,6.58.73,6.58.72,6.58.71,6.58.70,6.58.69,6.58.68,6.58.67,6.58.66,6.58.65,6.58.64,6.58.63,6.58.62,6.58.61,6.58.60,6.58.59,6.58.58,6.58.57,6.58.56,6.58.55,6.58.54,6.58.53,6.58.52,6.58.51,6.58.50,6.58.49,6.58.48,6.58.47,6.58.46,6.58.45,6.58.44,6.58.43,6.58.42,6.58.41,6.58.40,6.58.39,6.58.38,6.58.37,6.58.36,6.58.35,6.58.34,6.58.33,6.58.32,6.58.31,6.58.30,6.58.29,6.58.28,6.58.27,6.58.26,6.58.25,6.58.24,6.58.23,6.58.22,6.58.21,6.58.20,6.58.19,6.58.18,6.58.17,6.58.16,6.58.15,6.58.14,6.58.13,6.58.12,6.58.11,6.58.10,6.58.09,6.58.08,6.58.07,6.58.06,6.58.05,6.58.04,6.58.03,6.58.02,6.58.01,6.58.00,6.57.99,6.57.98,6.57.97,6.57.96,6.57.95,6.57.94,6.57.93,6.57.92,6.57.91,6.57.90,6.57.89,6.57.88,6.57.87,6.57.86,6.57.85,6.57.84,6.57.83,6.57.82,6.57.81,6.57.80,6.57.79,6.57.78,6.57.77,6.57.76,6.57.75,6.57.74,6.57.73,6.57.72,6.57.71,6.57.70,6.57.69,6.57.68,6.57.67,6.57.66,6.57.65,6.57.64,6.57.63,6.57.62,6.57.61,6.57.60,6.57.59,6.57.58,6.57.57,6.57.56,6.57.55,6.57.54,6.57.53,6.57.52,6.57.51,6.57.50,6.57.49,6.57.48,6.57.47,6.57.46,6.57.45,6.57.44,6.57.43,6.57.42,6.57.41,6.57.40,6.57.39,6.57.38,6.57.37,6.57.36,6.57.35,6.57.34,6.57.33,6.57.32,6.57.31,6.57.30,6.57.29,6.57.28,6.57.27,6.57.26,6.57.25,6.57.24,6.57.23,6.57.22,6.57.21,6.57.20,6.57.19,6.57.18,6.57.17,6.57.16,6.57.15,6.57.14,6.57.13,6.57.12,6.57.11,6.57.10,6.57.09,6.57.08,6.57.07,6.57.06,6.57.05,6.57.04,6.57.03,6.57.02,6.57.01,6.57.00,6.56.99,6.56.98,6.56.97,6.56.96,6.56.95,6.56.94,6.56.93,6.56.92,6.56.91,6.56.90,6.56.89,6.56.88,6.56.87,6.56.86,6.56.85,6.56.84,6.56.83,6.56.82,6.56.81,6.56.80,6.56.79,6.56.78,6.56.77,6.56.76,6.56.75,6.56.74,6.56.73,6.56.72,6.56.71,6.56.70,6.56.69,6.56.68,6.56.67,6.56.66,6.56.65,6.56.64,6.56.63,6.56.62,6.56.61,6.56.60,6.56.59,6.56.58,6.56.57,6.56.56,6.56.55,6.56.54,6.56.53,6.56.52,6.56.51,6.56.50,6.56.49,6.56.48,6.56.47,6.56.46,6.56.45,6.56.44,6.56.43,6.56.42,6.56.41,6.56.40,6.56.39,6.56.38,6.56.37,6.56.36,6.56.35,6.56.34,6.56.33,6.56.32,6.56.31,6.56.30,6.56.29,6.56.28,6.56.27,6.56.26,6.56.25,6.56.24,6.56.23,6.56.22,6.56.21,6.56.20,6.56.19,6.56.18,6.56.17,6.56.16,6.56.15,6.56.14,6.56.13,6.56.12,6.56.11,6.56.10,6.56.09,6.56.08,6.56.07,6.56.06,6.56.05,6.56.04,6.56.03,6.56.02,6.56.01,6.56.00,6.55.99,6.55.98,6.55.97,6.55.96,6.55.95,6.55.94,6.55.93,6.55.92,6.55.91,6.55.90,6.55.89,6.55.88,6.55.87,6.55.86,6.55.85,6.55.84,6.55.83,6.55.82,6.55.81,6.55.80,6.55.79,6.55.78,6.55.77,6.55.76,6.55.75,6.55.74,6.55.73,6.55.72,6.55.71,6.55.70,6.55.69,6.55.68,6.55.67,6.55.66,6.55.65,6.55.64,6.55.63,6.55.62,6.55.61,6.55.60,6.55.59,6.55.58,6.55.57,6.55.56,6.55.55,6.55.54,6.55.53,6.55.52,6.55.51,6.55.50,6.55.49,6.55.48,6.55.47,6.55.46,6.55.45,6.55.44,6.55.43,6.55.42,6.55.41,6.55.40,6.55.39,6.55.38,6.55.37,6.55.36,6.55.35,6.55.34,6.55.33,6.55.32,6.55.31,6.55.30,6.55.29,6.55.28,6.55.27,6.55.26,6.55.25,6.55.24,6.55.23,6.55.22,6.55.21,6.55.20,6.55.19,6.55.18,6.55.17,6.55.16,6.55.15,6.55.14,6.55.13,6.55.12,6.55.11,6.55.10,6.55.09,6.55.08,6.55.07,6.55.06,6.55.05,6.55.04,6.55.03,6.55.02,6.55.01,6.55.00,6.54.99,6.54.98,6.54.97,6.54.96,6.54.95,6.54.94,6.54.93,6.54.92,6.54.91,6.54.90,6.54.89,6.54.88,6.54.87,6.54.86,6.54.85,6.54.70,6.54.69,6.54.68,6.54.67,6.54.66,6.54.65,6.54.64,6.54.63,6.54.62,6.54.61,6.54.60,6.54.59,6.54.58,6.54.57,6.54.56,6.54.55,6.54.54,6.54.53,6.54.52,6.54.51,6.54.50,6.54.49,6.54.48,6.54.47,6.54.46,6.54.45,6.54.44,6.54.43,6.54.42,6.54.41,6.54.40,6.54.39,6.54.38,6.54.37,6.54.36,6.54.35,6.54.34,6.54.33,6.54.32,6.54.31,6.54.30,6.54.29,6.54.28,6.54.27,6.54.26,6.54.25,6.54.24,6.54.23,6.54.22,6.54.21,6.54.20,6.54.19,6.54.18,6.54.17,6.54.16,6.54.15,6.54.14,6.54.13,6.54.12,6.54.11,6.54.10,6.54.09,6.54.08,6.54.07,6.54.06,6.54.05,6.54.04,6.54.03,6.54.02,6.54.01,6.54.00,6.53.99,6.53.98,6.53.97,6.53.96,6.53.95,6.53.94,6.53.93,6.53.92,6.53.91,6.53.90,6.5
3.89,6.5
3.88,6.53.87,6.53.86,6.5
3.85,6.5
3.84,6.53.83,6.5
3.82,6.53.81,6.53.80,6.53.79,6.53.78,6.53.77,6.53.76,6.53.75,6.53.74,6.53.73,6.53.72,6.53.71,6.5
3.70,6.53.69,6.53.68,6.53.67,6.53.66,6.53.65,6.53.64,6.53.63,6.53.62,6.53.61,6.53.60,6.53.59,6.5
3.58,6.53.57,6.5
3.56,6.53.55,6.53.54,6.5
3.53,6.5
3.52,6.53.51,6.53.50,6.53.49,6.53.48,6.53.47,6.5
3.46,6.5
3.45,6.53.44,6.53.43,6.53.42,6.53.41,6.53.40,6.53.39,6.53.38,6.53.37,6.53.36,6.53.35,6.53.34,6.53.33,6.53.32,6.53.31,6.53.30,6.53.29,6.53.28,6.5
3.27,6.53.26,6.53.25,6.53.24,6.53.23,6.53.22,6.53.21,6.53.20,6.53.19,6.53.18,6.53.17,6.53.16,6.53.15,6.53.14,6.53.13,6.53.12,6.53.11,6.53.10,6.53.09,6.53.08,6.53.07,6.53.06,6.53.05,6.53.04,6.53.03,6.53.02,6.53.01,6.53.00,6.52.99,6.52.98,6.52.97,6.52.96,6.52.95,6.52.94,6.52.93,6.52.92,6.52.91,6.52.90,6.52.89,6.52.88,6.52.87,6.52.86,6.52.85,6.52.84,6.52.83,6.52.82,6.52.81,6.52.80,6.52.79,6.52.78,6.52.77,6.52.76,6.52.75,6.52.74,6.52.73,6.52.72,6.52.71,6.52.70,6.52.69,6.52.68,6.52.67,6.52.66,6.52.65,6.52.64,6.52.63,6.52.62,6.52.61,6.52.60,6.52.59,6.52.58,6.52.57,6.52.56,6.52.55,6.52.54,6.52.53,6.52.52,6.52.51,6.52.50,6.52.49,6.52.48,6.52.47,6.52.46,6.52.45,6.52.44,6.52.43,6.52.42,6.52.41,6.52.40,6.52.39,6.52.38,6.52.37,6.52.36,6.52.35,6.52.34,6.52.33,6.52.32,6.52.31,6.52.30,6.52.29,6.52.28,6.52.27,6.52.26,6.52.25,6.52.24,6.52.23,6.52.22,6.52.21,6.52.20,6.52.19,6.52.18,6.52.17,6.52.16,6.52.15,6.52.14,6.52.13,6.52.12,6.52.11,6.52.10,6.52.09,6.52.08,6.52.07,6.52.06,6.52.05,6.52.04,6.52.03,6.52.02,6.52.01,6.52.00,6.51.99,6.51.98,6.51.97,6.51.96,6.51.95,6.51.94,6.51.93,6.51.92,6.51.91,6.51.90,6.51.89,6.51.88,6.51.87,6.51.86,6.51.85,6.51.84,6.51.83,6.51.82,6.51.81,6.51.80,6.51.79,6.51.78,6.51.77,6.51.76,6.51.75,6.51.74,6.51.73,6.51.72,6.51.71,6.51.70,6.51.69,6.51.68,6.51.67,6.51.66,6.51.65,6.51.64,6.51.63,6.51.62,6.51.61,6.51.60,6.51.59,6.51.58,6.51.57,6.51.56,6.51.55,6.51.54,6.51.53,6.51.52,6.51.51,6.51.50,6.51.49,6.51.48,6.51.47,6.51.46,6.51.45,6.51.44,6.51.43,6.51.42,6.51.41,6.51.40,6.51.39,6.51.38,6.51.37,6.51.36,6.51.35,6.51.34,6.51.33,6.51.32,6.51.31,6.51.30,6.51.29,6.51.28,6.51.27,6.51.26,6.51.25,6.51.24,6.51.23,6.51.22,6.51.21,6.51.20,6.51.19,6.51.18,6.51.17,6.51.16,6.51.15,6.51.14,6.51.13,6.51.12,6.51.11,6.51.10,6.51.09,6.51.08,6.51.07,6.51.06,6.51.05,6.51.04,6.51.03,6.51.02,6.51.01,6.51.00,6.50.99,6.50.98,6.50.97,6.50.96,6.50.95,6.50.94,6.50.93,6.50.92,6.50.91,6.50.90,6.50.89,6.50.88,6.50.87,6.50.86,6.50.85,6.50.84,6.50.83,6.50.82,6.50.81,6.50.80,6.50.79,6.50.78,6.50.77,6.50.76,6.50.75,6.50.74,6.50.73,6.50.72,6.50.71,6.50.70,6.50.69,6.50.68,6.50.67,6.50.66,6.50.65,6.50.64,6.50.63,6.50.62,6.50.61,6.50.60,6.50.59,6.50.58,6.50.57,6.50.56,6.50.55,6.50.54,6.50.53,6.50.52,6.50.51,6.50.50,6.50.49,6.50.48,6.50.47,6.50.46,6.50.45,6.50.44,6.50.43,6.50.42,6.50.41,6.50.40,6.50.39,6.50.38,6.50.37,6.50.36,6.50.35,6.50.34,6.50.33,6.50.32,6.50.31,6.50.30,6.50.29,6.50.28,6.50.27,6.50.26,6.59.00,5.58.99,5.58.98,5.58.97,5.58.96,5.58.95,5.58.94,5.58.93,5.58.92,5.58.91,5.58.90,5.58.89,5.58.88,5.58.87,5.58.86,5.58.85,5.58.84,5.58.83,5.58.82,5.58.81,5.58.80,5.58.79,5.58.78,5.58.77,5.58.76,5.58.75,5.58.74,5.58.73,5.58.72,5.58.71,5.58.70,5.58.69,5.58.68,5.58.67,5.58.66,5.58.65,5.58.64,5.58.63,5.58.62,5.58.61,5.58.60,5.58.59,5.58.58,5.58.57,5.58.56,5.58.55,5.58.54,5.58.53,5.58.52,5.58.51,5.58.50,5.58.49,5.58.48,5.58.47,5.58.46,5.58.45,5.58.44,5.58.43,5.58.42,5.58.41,5.58.40,5.58.39,5.58.38,5.58.37,5.58.36,5.58.35,5.58.34,5.58.33,5.58.32,5.58.31,5.58.30,5.58.29,5.58.28,5.58.27,5.58.26,5.58.25,5.58.24,5.58.23,5.58.22,5.58.21,5.58.20,5.58.19,5.58.18,5.58.17,5.58.16,5.58.15,5.58.14,5.58.13,5.58.12,5.58.11,5.58.10,5.58.09,5.58.08,5.58.07,5.58.06,5.58.05,5.58.04,5.58.03,5.58.02,5.58.01,5.58.00,5.57.99,5.57.98,5.57.97,5.57.96,5.57.95,5.57.94,5.57.93,5.57.92,5.57.91,5.57.90,5.57.89,5.57.88,5.57.87,5.57.86,5.57.85,5.57.84,5.57.83,5.57.82,5.57.81,5.57.80,5.57.79,5.57.78,5.57.77,5.57.76,5.57.75,5.57.74,5.57.73,5.57.72,5.57.71,5.57.70,5.57.69,5.57.68,5.57.67,5.57.66,5.57.65,5.57.64,5.57.63,5.57.62,5.57.61,5.57.60,5.57.59,5.57.58,5.57.57,5.57.56,5.57.55,5.57.54,5.57.53,5.57.52,5.57.51,5.57.50,5.57.49,5.57.48,5.57.47,5.57.46,5.57.45,5.57.44,5.57.43,5.57.42,5.57.41,5.57.40,5.57.39,5.57.38,5.57.37,5.57.36,5.57.35,5.57.34,5.57.33,5.57.32,5.57.31,5.57.30,5.57.29,5.57.28,5.57.27,5.57.26,5.57.25,5.57.24,5.57.23,5.57.22,5.57.21,5.57.20,5.57.19,5.57.18,5.57.17,5.57.16,5.57.15,5.57.14,5.57.13,5.57.12,5.57.11,5.57.10,5.57.09,5.57.08,5.57.07,5.57.06,5.57.05,5.57.04,5.57.03,5.57.02,5.57.01,5.57.00,5.56.99,5.56.98,5.56.97,5.56.96,5.56.95,5.56.94,5.56.93,5.56.92,5.56.91,5.56.90,5.56.89,5.56.88,5.56.87,5.56.86,5.56.85,5.56.84,5.56.83,5.56.82,5.56.81,5.56.80,5.56.79,5.56.78,5.56.77,5.56.76,5.56.75,5.56.74,5.56.73,5.56.72,5.56.71,5.56.70,5.56.69,5.56.68,5.56.67,5.56.66,5.56.65,5.56.64,5.56.63,5.56.62,5.56.61,5.56.60,5.56.59,5.56.58,5.56.57,5.56.56,5.56.55,5.56.54,5.56.53,5.56.52,5.56.51,5.56.50,5.56.49,5.56.48,5.56.47,5.56.46,5.56.45,5.56.44,5.56.43,5.56.42,5.56.41,5.56.40,5.56.39,5.56.38,5.56.37,5.56.36,5.56.35,5.56.34,5.56.33,5.56.32,5.56.31,5.56.30,5.56.29,5.56.28,5.56.27,5.56.26,5.56.25,5.56.24,5.56.23,5.56.22,5.56.21,5.56.20,5.56.19,5.56.18,5.56.17,5.56.16,5.56.15,5.56.14,5.56.13,5.56.12,5.56.11,5.56.10,5.56.09,5.56.08,5.56.07,5.56.06,5.56.05,5.56.04,5.56.03,5.56.02,5.56.01,5.56.00,5.55.99,5.55.98,5.55.97,5.55.96,5.55.95,5.55.94,5.55.93,5.55.92,5.55.91,5.55.90,5.55.89,5.55.88,5.55.87,5.55.86,5.55.85,5.55.84,5.55.83,5.55.82,5.55.81,5.55.80,5.55.79,5.55.78,5.55.77,5.55.76,5.55.75,5.55.74,5.55.73,5.55.72,5.55.71,5.55.70,5.55.69,5.55.68,5.55.67,5.55.66,5.55.65,5.55.64,5.55.63,5.55.62,5.55.61,5.55.60,5.55.59,5.55.58,5.55.57,5.55.56,5.55.55,5.55.54,5.55.53,5.55.52,5.55.51,5.55.50,5.55.49,5.55.48,5.55.47,5.55.46,5.55.45,5.55.44,5.55.43,5.55.42,5.55.41,5.55.40,5.55.39,5.55.38,5.55.37,5.55.36,5.55.35,5.55.34,5.55.33,5.55.32,5.55.31,5.55.30,5.55.29,5.55.28,5.55.27,5.55.26,5.55.25,5.55.24,5.55.23,5.55.22,5.55.21,5.55.20,5.55.19,5.55.18,5.55.17,5.55.16,5.55.15,5.55.14,5.55.13,5.55.12,5.55.11,5.55.10,5.55.09,5.55.08,5.55.07,5.55.06,5.55.05,5.55.04,5.55.03,5.55.02,5.55.01,5.55.00,5.54.99,5.54.98,5.54.97,5.54.96,5.54.95,5.54.94,5.54.93,5.54.92,5.54.91,5.54.90,5.54.89,5.54.88,5.54.87,5.54.86,5.54.85,5.54.70,5.54.69,5.54.68,5.54.67,5.54.66,5.54.65,5.54.64,5.54.63,5.54.62,5.54.61,5.54.60,5.54.59,5.54.58,5.54.57,5.54.56,5.54.55,5.54.54,5.54.53,5.54.52,5.54.51,5.54.50,5.54.49,5.54.48,5.54.47,5.54.46,5.54.45,5.54.44,5.54.43,5.54.42,5.54.41,5.54.40,5.54.39,5.54.38,5.54.37,5.54.36,5.54.35,5.54.34,5.54.33,5.54.32,5.54.31,5.54.30,5.54.29,5.54.28,5.54.27,5.54.26,5.54.25,5.54.24,5.54.23,5.54.22,5.54.21,5.54.20,5.54.19,5.54.18,5.54.17,5.54.16,5.54.15,5.54.14,5.54.13,5.54.12,5.54.11,5.54.10,5.54.09,5.54.08,5.54.07,5.54.06,5.54.05,5.54.04,5.54.03,5.54.02,5.54.01,5.54.00,5.53.99,5.53.98,5.53.97,5.53.96,5.53.95,5.53.94,5.53.93,5.53.92,5.53.91,5.53.90,5.5
3.89,5.5
3.88,5.53.87,5.53.86,5.5
3.85,5.5
3.84,5.53.83,5.5
3.82,5.53.81,5.53.80,5.53.79,5.53.78,5.53.77,5.53.76,5.53.75,5.53.74,5.53.73,5.53.72,5.53.71,5.53.70,5.53.69,5.53.68,5.53.67,5.53.66,5.53.65,5.53.64,5.53.63,5.53.62,5.53.61,5.53.60,5.53.59,5.5
3.58,5.53.57,5.53.56,5.53.55,5.53.54,5.53.53,5.5
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4.65,4.54.64,4.5
4.63,4.54.62,4.54.61,4.54.60,4.54.59,4.54.58,4.54.57,4.54.56,4.54.55,4.54.54,4.54.53,4.54.52,4.54.51,4.54.50,4.54.49,4.54.48,4.54.47,4.54.46,4.54.45,4.54.44,4.54.43,4.54.42,4.54.41,4.54.40,4.54.39,4.54.38,4.54.37,4.54.36,4.54.35,4.54.34,4.54.33,4.54.32,4.54.31,4.54.30,4.54.29,4.54.28,4.54.27,4.54.26,4.54.25,4.54.24,4.54.23,4.54.22,4.54.21,4.54.20,4.54.19,4.54.18,4.54.17,4.54.16,4.54.15,4.54.14,4.54.13,4.54.12,4.54.11,4.54.10,4.54.09,4.54.08,4.54.07,4.54.06,4.54.05,4.54.04,4.54.03,4.54.02,4.54.01,4.54.00,4.53.99,4.53.98,4.53.97,4.53.96,4.53.95,4.53.94,4.53.93,4.53.92,4.5
3.91,4.53.90,4.5
3.89,4.5
3.88,4.5
3.87,4.53.86,4.5
3.85,4.5
3.84,4.5
3.83,4.5
3.82,4.53.81,4.53.80,4.53.79,4.53.78,4.53.77,4.53.76,4.53.75,4.53.74,4.53.73,4.5
3.72,4.53.71,4.53.70,4.53.69,4.53.68,4.53.67,4.53.66,4.53.65,4.53.64,4.53.63,4.53.62,4.53.61,4.53.60,4.53.59,4.5
3.58,4.53.57,4.53.56,4.53.55,4.53.54,4.5
3.53,4.5
3.52,4.53.51,4.53.50,4.5
3.49,4.53.48,4.53.47,4.53.46,4.5
3.45,4.53.44,4.53.43,4.53.42,4.53.41,4.53.40,4.53.39,4.53.38,4.53.37,4.53.36,4.53.35,4.53.34,4.53.33,4.53.32,4.53.31,4.53.30,4.53.29,4.53.28,4.53.27,4.53.26,4.5
3.25,4.53.24,4.53.23,4.53.22,4.53.21,4.53.20,4.53.19,4.53.18,4.53.17,4.53.16,4.53.15,4.53.14,4.53.13,4.53.12,4.53.11,4.53.10,4.53.09,4.53.08,4.53.07,4.53.06,4.53.05,4.53.04,4.53.03,4.53.02,4.53.01,4.53.00,4.52.99,4.52.98,4.52.97,4.52.96,4.52.95,4.52.94,4.52.93,4.52.92,4.52.91,4.52.90,4.52.89,4.52.88,4.52.87,4.52.86,4.52.85,4.52.84,4.52.83,4.52.82,4.52.81,4.52.80,4.52.79,4.52.78,4.52.77,4.52.76,4.52.75,4.52.74,4.52.73,4.52.72,4.52.71,4.52.70,4.52.69,4.52.68,4.52.67,4.52.66,4.52.65,4.52.64,4.52.63,4.52.62,4.52.61,4.52.60,4.52.59,4.52.58,4.52.57,4.52.56,4.52.55,4.52.54,4.52.53,4.52.52,4.52.51,4.52.50,4.52.49,4.52.48,4.52.47,4.52.46,4.52.45,4.52.44,4.52.43,4.52.42,4.52.41,4.52.40,4.52.39,4.52.38,4.52.37,4.52.36,4.52.35,4.52.34,4.52.33,4.52.32,4.52.31,4.52.30,4.52.29,4.52.28,4.52.27,4.52.26,4.52.25,4.52.24,4.52.23,4.52.22,4.52.21,4.52.20,4.52.19,4.52.18,4.52.17,4.52.16,4.52.15,4.52.14,4.52.13,4.52.12,4.52.11,4.52.10,4.52.09,4.52.08,4.52.07,4.52.06,4.52.05,4.52.04,4.52.03,4.52.02,4.52.01,4.52.00,4.51.99,4.51.98,4.51.97,4.51.96,4.51.95,4.51.94,4.51.93,4.51.92,4.51.91,4.51.90,4.51.89,4.51.88,4.51.87,4.51.86,4.51.85,4.51.84,4.51.83,4.51.82,4.51.81,4.51.80,4.51.79,4.51.78,4.51.77,4.51.76,4.51.75,4.51.74,4.51.73,4.51.72,4.51.71,4.51.70,4.51.69,4.51.68,4.51.67,4.51.66,4.51.65,4.51.64,4.51.63,4.51.62,4.51.61,4.51.60,4.51.59,4.51.58,4.51.57,4.51.56,4.51.55,4.51.54,4.51.53,4.51.52,4.51.51,4.51.50,4.51.49,4.51.48,4.51.47,4.51.46,4.51.45,4.51.44,4.51.43,4.51.42,4.51.41,4.51.40,4.51.39,4.51.38,4.51.37,4.51.36,4.51.35,4.51.34,4.51.33,4.51.32,4.51.31,4.51.30,4.51.29,4.51.28,4.51.27,4.51.26,4.51.25,4.51.24,4.51.23,4.51.22,4.51.21,4.51.20,4.51.19,4.51.18,4.51.17,4.51.16,4.51.15,4.51.14,4.51.13,4.51.12,4.51.11,4.51.10,4.51.09,4.51.08,4.51.07,4.51.06,4.51.05,4.51.04,4.51.03,4.51.02,4.51.01,4.51.00,4.50.99,4.50.98,4.50.97,4.50.96,4.50.95,4.50.94,4.50.93,4.50.92,4.50.91,4.50.90,4.50.89,4.50.88,4.50.87,4.50.86,4.50.85,4.50.84,4.50.83,4.50.82,4.50.81,4.50.80,4.50.79,4.50.78,4.50.77,4.50.76,4.50.75,4.50.74,4.50.73,4.50.72,4.50.71,4.50.70,4.50.69,4.50.68,4.50.67,4.50.66,4.50.65,4.50.64,4.50.63,4.50.62,4.50.61,4.50.60,4.50.59,4.50.58,4.50.57,4.50.56,4.50.55,4.50.54,4.50.53,4.50.52,4.50.51,4.50.50,4.50.49,4.50.48,4.50.47,4.50.46,4.50.45,4.50.44,4.50.43,4.50.42,4.50.41,4.50.40,4.50.39,4.50.38,4.50.37,4.50.36,4.50.35,4.50.34,4.50.33,4.50.32,4.50.31,4.50.30,4.50.29,4.50.28,4.50.27,4.50.26,4.59.00,3.58.99,3.58.98,3.58.97,3.58.96,3.58.95,3.58.94,3.58.93,3.58.92,3.58.91,3.58.90,3.58.89,3.58.88,3.58.87,3.58.86,3.58.85,3.58.84,3.58.83,3.58.82,3.58.81,3.58.80,3.58.79,3.58.78,3.58.77,3.58.76,3.58.75,3.58.74,3.58.73,3.58.72,3.58.71,3.58.70,3.58.69,3.58.68,3.58.67,3.58.66,3.58.65,3.58.64,3.58.63,3.58.62,3.58.61,3.58.60,3.58.59,3.58.58,3.58.57,3.58.56,3.58.55,3.58.54,3.58.53,3.58.52,3.58.51,3.58.50,3.58.49,3.58.48,3.58.47,3.58.46,3.58.45,3.58.44,3.58.43,3.58.42,3.58.41,3.58.40,3.58.39,3.58.38,3.58.37,3.58.36,3.58.35,3.58.34,3.58.33,3.58.32,3.58.31,3.58.30,3.58.29,3.58.28,3.58.27,3.58.26,3.58.25,3.58.24,3.58.23,3.58.22,3.58.21,3.58.20,3.58.19,3.58.18,3.58.17,3.58.16,3.58.15,3.58.14,3.58.13,3.58.12,3.58.11,3.58.10,3.58.09,3.58.08,3.58.07,3.58.06,3.58.05,3.58.04,3.58.03,3.58.02,3.58.01,3.58.00,3.57.99,3.57.98,3.57.97,3.57.96,3.57.95,3.57.94,3.57.93,3.57.92,3.57.91,3.57.90,3.57.89,3.57.88,3.57.87,3.57.86,3.57.85,3.57.84,3.57.83,3.57.82,3.57.81,3.57.80,3.57.79,3.57.78,3.57.77,3.57.76,3.57.75,3.57.74,3.57.73,3.57.72,3.57.71,3.57.70,3.57.69,3.57.68,3.57.67,3.57.66,3.57.65,3.57.64,3.57.63,3.57.62,3.57.61,3.57.60,3.57.59,3.57.58,3.57.57,3.57.56,3.57.55,3.57.54,3.57.53,3.57.52,3.57.51,3.57.50,3.57.49,3.57.48,3.57.47,3.57.46,3.57.45,3.57.44,3.57.43,3.57.42,3.57.41,3.57.40,3.57.39,3.57.38,3.57.37,3.57.36,3.57.35,3.57.34,3.57.33,3.57.32,3.57.31,3.57.30,3.57.29,3.57.28,3.57.27,3.57.26,3.57.25,3.57.24,3.57.23,3.57.22,3.57.21,3.57.20,3.57.19,3.57.18,3.57.17,3.57.16,3.57.15,3.57.14,3.57.13,3.57.12,3.57.11,3.57.10,3.57.09,3.57.08,3.57.07,3.57.06,3.57.05,3.57.04,3.57.03,3.57.02,3.57.01,3.57.00,3.56.99,3.56.98,3.56.97,3.56.96,3.56.95,3.56.94,3.56.93,3.56.92,3.56.91,3.56.90,3.56.89,3.56.88,3.56.87,3.56.86,3.56.85,3.56.84,3.56.83,3.56.82,3.56.81,3.56.80,3.56.79,3.56.78,3.56.77,3.56.76,3.56.75,3.56.74,3.56.73,3.56.72,3.56.71,3.56.70,3.56.69,3.56.68,3.56.67,3.56.66,3.56.65,3.56.64,3.56.63,3.56.62,3.56.61,3.56.60,3.56.59,3.56.58,3.56.57,3.56.56,3.56.55,3.56.54,3.56.53,3.56.52,3.56.51,3.56.50,3.56.49,3.56.48,3.56.47,3.56.46,3.56.45,3.56.44,3.56.43,3.56.42,3.56.41,3.56.40,3.56.39,3.56.38,3.56.37,3.56.36,3.56.35,3.56.34,3.56.33,3.56.32,3.56.31,3.56.30,3.56.29,3.56.28,3.56.27,3.56.26,3.56.25,3.56.24,3.56.23,3.56.22,3.56.21,3.56.20,3.56.19,3.56.18,3.56.17,3.56.16,3.56.15,3.56.14,3.56.13,3.56.12,3.56.11,3.56.10,3.56.09,3.56.08,3.56.07,3.56.06,3.56.05,3.56.04,3.56.03,3.56.02,3.56.01,3.56.00,3.55.99,3.55.98,3.55.97,3.55.96,3.55.95,3.55.94,3.55.93,3.55.92,3.55.91,3.55.90,3.55.89,3.55.88,3.55.87,3.55.86,3.55.85,3.55.84,3.55.83,3.55.82,3.55.81,3.55.80,3.55.79,3.55.78,3.55.77,3.55.76,3.55.75,3.55.74,3.55.73,3.55.72,3.55.71,3.55.70,3.55.69,3.55.68,3.55.67,3.55.66,3.55.65,3.55.64,3.55.63,3.55.62,3.55.61,3.55.60,3.55.59,3.55.58,3.55.57,3.55.56,3.55.55,3.55.54,3.55.53,3.55.52,3.55.51,3.55.50,3.55.49,3.55.48,3.55.47,3.55.46,3.55.45,3.55.44,3.55.43,3.55.42,3.55.41,3.55.40,3.55.39,3.55.38,3.55.37,3.55.36,3.55.35,3.55.34,3.55.33,3.55.32,3.55.31,3.55.30,3.55.29,3.55.28,3.55.27,3.55.26,3.55.25,3.55.24,3.55.23,3.55.22,3.55.21,3.55.20,3.55.19,3.55.18,3.55.17,3.55.16,3.55.15,3.55.14,3.55.13,3.55.12,3.55.11,3.55.10,3.55.09,3.55.08,3.55.07,3.55.06,3.55.05,3.55.04,3.55.03,3.55.02,3.55.01,3.55.00,3.54.99,3.54.98,3.54.97,3.54.96,3.54.95,3.54.94,3.54.93,3.54.92,3.54.91,3.54.90,3.54.89,3.54.88,3.54.87,3.54.86,3.54.85,3.54.70,3.54.69,3.54.68,3.54.67,3.5
4.66,3.5
4.65,3.5
4.64,3.5
4.63,3.54.62,3.54.61,3.54.60,3.54.59,3.54.58,3.54.57,3.54.56,3.54.55,3.54.54,3.54.53,3.54.52,3.54.51,3.54.50,3.54.49,3.54.48,3.54.47,3.54.46,3.54.45,3.54.44,3.54.43,3.54.42,3.54.41,3.54.40,3.54.39,3.54.38,3.54.37,3.54.36,3.54.35,3.54.34,3.54.33,3.54.32,3.54.31,3.54.30,3.54.29,3.54.28,3.54.27,3.54.26,3.54.25,3.54.24,3.54.23,3.54.22,3.54.21,3.54.20,3.54.19,3.54.18,3.54.17,3.54.16,3.54.15,3.54.14,3.54.13,3.54.12,3.54.11,3.54.10,3.54.09,3.54.08,3.54.07,3.54.06,3.54.05,3.54.04,3.54.03,3.54.02,3.54.01,3.54.00,3.53.99,3.53.98,3.53.97,3.53.96,3.53.95,3.53.94,3.53.93,3.53.92,3.53.91,3.53.90,3.5
3.89,3.53.88,3.5
3.87,3.53.86,3.5
3.85,3.5
3.84,3.53.83,3.5
3.82,3.53.81,3.53.80,3.53.79,3.53.78,3.53.77,3.53.76,3.5
3.75,3.53.74,3.53.73,3.5
3.72,3.53.71,3.53.70,3.53.69,3.53.68,3.53.67,3.53.66,3.53.65,3.53.64,3.53.63,3.53.62,3.53.61,3.53.60,3.53.59,3.5
3.58,3.53.57,3.53.56,3.53.55,3.53.54,3.5
3.53,3.5
3.52,3.53.51,3.53.50,3.5
3.49,3.53.48,3.53.47,3.53.46,3.5
3.45,3.53.44,3.53.43,3.53.42,3.53.41,3.53.40,3.53.39,3.53.38,3.53.37,3.53.36,3.53.35,3.53.34,3.53.33,3.53.32,3.53.31,3.53.30,3.53.29,3.53.28,3.53.27,3.53.26,3.5
3.25,3.53.24,3.53.23,3.53.22,3.53.21,3.53.20,3.53.19,3.53.18,3.53.17,3.53.16,3.53.15,3.53.14,3.53.13,3.53.12,3.53.11,3.53.10,3.53.09,3.53.08,3.53.07,3.53.06,3.53.05,3.53.04,3.53.03,3.53.02,3.53.01,3.53.00,3.52.99,3.52.98,3.52.97,3.52.96,3.52.95,3.52.94,3.52.93,3.52.92,3.52.91,3.52.90,3.52.89,3.52.88,3.52.87,3.52.86,3.52.85,3.52.84,3.52.83,3.52.82,3.52.81,3.52.80,3.52.79,3.52.78,3.52.77,3.52.76,3.52.75,3.52.74,3.52.73,3.52.72,3.52.71,3.52.70,3.52.69,3.52.68,3.52.67,3.52.66,3.52.65,3.52.64,3.52.63,3.52.62,3.52.61,3.52.60,3.52.59,3.52.58,3.52.57,3.52.56,3.52.55,3.52.54,3.52.53,3.52.52,3.52.51,3.52.50,3.52.49,3.52.48,3.52.47,3.52.46,3.52.45,3.52.44,3.52.43,3.52.42,3.52.41,3.52.40,3.52.39,3.52.38,3.52.37,3.52.36,3.52.35,3.52.34,3.52.33,3.52.32,3.52.31,3.52.30,3.52.29,3.52.28,3.52.27,3.52.26,3.52.25,3.52.24,3.52.23,3.52.22,3.52.21,3.52.20,3.52.19,3.52.18,3.52.17,3.52.16,3.52.15,3.52.14,3.52.13,3.52.12,3.52.11,3.52.10,3.52.09,3.52.08,3.52.07,3.52.06,3.52.05,3.52.04,3.52.03,3.52.02,3.52.01,3.52.00,3.51.99,3.51.98,3.51.97,3.51.96,3.51.95,3.51.94,3.51.93,3.51.92,3.51.91,3.51.90,3.51.89,3.51.88,3.51.87,3.51.86,3.51.85,3.51.84,3.51.83,3.51.82,3.51.81,3.51.80,3.51.79,3.51.78,3.51.77,3.51.76,3.51.75,3.51.74,3.51.73,3.51.72,3.51.71,3.51.70,3.51.69,3.51.68,3.51.67,3.51.66,3.51.65,3.51.64,3.51.63,3.51.62,3.51.61,3.51.60,3.51.59,3.51.58,3.51.57,3.51.56,3.51.55,3.51.54,3.51.53,3.51.52,3.51.51,3.51.50,3.51.49,3.51.48,3.51.47,3.51.46,3.51.45,3.51.44,3.51.43,3.51.42,3.51.41,3.51.40,3.51.39,3.51.38,3.51.37,3.51.36,3.51.35,3.51.34,3.51.33,3.5
1.32,3.5
1.31,3.51.30,3.51.29,3.51.28,3.51.27,3.51.26,3.51.25,3.51.24,3.51.23,3.51.22,3.51.21,3.51.20,3.51.19,3.51.18,3.51.17,3.51.16,3.51.15,3.51.14,3.51.13,3.51.12,3.51.11,3.51.10,3.51.09,3.51.08,3.51.07,3.51.06,3.51.05,3.51.04,3.51.03,3.51.02,3.51.01,3.51.00,3.50.99,3.50.98,3.50.97,3.50.96,3.50.95,3.50.94,3.50.93,3.50.92,3.50.91,3.50.90,3.50.89,3.50.88,3.50.87,3.50.86,3.50.85,3.50.84,3.50.83,3.50.82,3.50.81,3.50.80,3.50.79,3.50.78,3.50.77,3.50.76,3.50.75,3.50.74,3.50.73,3.50.72,3.50.71,3.50.70,3.50.69,3.50.68,3.50.67,3.50.66,3.50.65,3.50.64,3.50.63,3.50.62,3.50.61,3.50.60,3.50.59,3.50.58,3.50.57,3.50.56,3.50.55,3.50.54,3.50.53,3.50.52,3.50.51,3.50.50,3.50.49,3.50.48,3.50.47,3.50.46,3.50.45,3.50.44,3.50.43,3.50.42,3.50.41,3.50.40,3.50.39,3.50.38,3.50.37,3.50.36,3.50.35,3.50.34,3.50.33,3.50.32,3.50.31,3.50.30,3.50.29,3.50.28,3.50.27,3.50.26,3.59.00,2.58.99,2.58.98,2.58.97,2.58.96,2.58.95,2.58.94,2.58.93,2.58.92,2.58.91,2.58.90,2.58.89,2.58.88,2.58.87,2.58.86,2.58.85,2.58.84,2.58.83,2.58.82,2.58.81,2.58.80,2.58.79,2.58.78,2.58.77,2.58.76,2.58.75,2.58.74,2.58.73,2.58.72,2.58.71,2.58.70,2.58.69,2.58.68,2.58.67,2.58.66,2.58.65,2.58.64,2.58.63,2.58.62,2.58.61,2.58.60,2.58.59,2.58.58,2.58.57,2.58.56,2.58.55,2.58.54,2.58.53,2.58.52,2.58.51,2.58.50,2.58.49,2.58.48,2.58.47,2.58.46,2.58.45,2.58.44,2.58.43,2.58.42,2.58.41,2.58.40,2.58.39,2.58.38,2.58.37,2.58.36,2.58.35,2.58.34,2.58.33,2.58.32,2.58.31,2.58.30,2.58.29,2.58.28,2.58.27,2.58.26,2.58.25,2.58.24,2.58.23,2.58.22,2.58.21,2.58.20,2.58.19,2.58.18,2.58.17,2.58.16,2.58.15,2.58.14,2.58.13,2.58.12,2.58.11,2.58.10,2.58.09,2.58.08,2.58.07,2.58.06,2.58.05,2.58.04,2.58.03,2.58.02,2.58.01,2.58.00,2.57.99,2.57.98,2.57.97,2.57.96,2.57.95,2.57.94,2.57.93,2.57.92,2.57.91,2.57.90,2.57.89,2.57.88,2.57.87,2.57.86,2.57.85,2.57.84,2.57.83,2.57.82,2.57.81,2.57.80,2.57.79,2.57.78,2.57.77,2.57.76,2.57.75,2.57.74,2.57.73,2.57.72,2.57.71,2.57.70,2.57.69,2.57.68,2.57.67,2.57.66,2.57.65,2.57.64,2.57.63,2.57.62,2.57.61,2.57.60,2.57.59,2.57.58,2.57.57,2.57.56,2.57.55,2.57.54,2.57.53,2.57.52,2.57.51,2.57.50,2.57.49,2.57.48,2.57.47,2.57.46,2.57.45,2.57.44,2.57.43,2.57.42,2.57.41,2.57.40,2.57.39,2.57.38,2.57.37,2.57.36,2.57.35,2.57.34,2.57.33,2.57.32,2.57.31,2.57.30,2.57.29,2.57.28,2.57.27,2.57.26,2.57.25,2.57.24,2.57.23,2.57.22,2.57.21,2.57.20,2.57.19,2.57.18,2.57.17,2.57.16,2.57.15,2.57.14,2.57.13,2.57.12,2.57.11,2.57.10,2.57.09,2.57.08,2.57.07,2.57.06,2.57.05,2.57.04,2.57.03,2.57.02,2.57.01,2.57.00,2.56.99,2.56.98,2.56.97,2.56.96,2.56.95,2.56.94,2.56.93,2.56.92,2.56.91,2.56.90,2.56.89,2.56.88,2.56.87,2.56.86,2.56.85,2.56.84,2.56.83,2.56.82,2.56.81,2.56.80,2.56.79,2.56.78,2.56.77,2.56.76,2.56.75,2.56.74,2.56.73,2.56.72,2.56.71,2.56.70,2.56.69,2.56.68,2.56.67,2.56.66,2.56.65,2.56.64,2.56.63,2.56.62,2.56.61,2.56.60,2.56.59,2.56.58,2.56.57,2.56.56,2.56.55,2.56.54,2.56.53,2.56.52,2.56.51,2.56.50,2.56.49,2.56.48,2.56.47,2.56.46,2.56.45,2.56.44,2.56.43,2.56.42,2.56.41,2.56.40,2.56.39,2.56.38,2.56.37,2.56.36,2.56.35,2.56.34,2.56.33,2.56.32,2.56.31,2.56.30,2.56.29,2.56.28,2.56.27,2.56.26,2.56.25,2.56.24,2.56.23,2.56.22,2.56.21,2.56.20,2.56.19,2.56.18,2.56.17,2.56.16,2.56.15,2.56.14,2.56.13,2.56.12,2.56.11,2.56.10,2.56.09,2.56.08,2.56.07,2.56.06,2.56.05,2.56.04,2.56.03,2.56.02,2.56.01,2.56.00,2.55.99,2.55.98,2.55.97,2.55.96,2.55.95,2.55.94,2.55.93,2.55.92,2.55.91,2.55.90,2.55.89,2.55.88,2.55.87,2.55.86,2.55.85,2.55.84,2.55.83,2.55.82,2.55.81,2.55.80,2.55.79,2.55.78,2.55.77,2.55.76,2.55.75,2.55.74,2.55.73,2.55.72,2.55.71,2.55.70,2.55.69,2.55.68,2.55.67,2.55.66,2.55.65,2.55.64,2.55.63,2.55.62,2.55.61,2.55.60,2.55.59,2.55.58,2.55.57,2.55.56,2.55.55,2.55.54,2.55.53,2.55.52,2.55.51,2.55.50,2.55.49,2.55.48,2.55.47,2.55.46,2.55.45,2.55.44,2.55.43,2.55.42,2.55.41,2.55.40,2.55.39,2.55.38,2.55.37,2.55.36,2.55.35,2.55.34,2.55.33,2.55.32,2.55.31,2.55.30,2.55.29,2.55.28,2.55.27,2.55.26,2.55.25,2.55.24,2.55.23,2.55.22,2.55.21,2.55.20,2.55.19,2.55.18,2.55.17,2.55.16,2.55.15,2.55.14,2.55.13,2.55.12,2.55.11,2.55.10,2.55.09,2.55.08,2.55.07,2.55.06,2.55.05,2.55.04,2.55.03,2.55.02,2.55.01,2.55.00,2.54.99,2.54.98,2.54.97,2.54.96,2.54.95,2.54.94,2.54.93,2.54.92,2.54.91,2.54.90,2.54.89,2.54.88,2.54.87,2.54.86,2.54.85,2.54.70,2.54.69,2.54.68,2.54.67,2.5
4.66,2.54.65,2.5
4.64,2.5
4.63,2.54.62,2.54.61,2.54.60,2.54.59,2.54.58,2.54.57,2.54.56,2.54.55,2.54.54,2.54.53,2.54.52,2.54.51,2.54.50,2.54.49,2.54.48,2.54.47,2.54.46,2.54.45,2.54.44,2.54.43,2.54.42,2.54.41,2.54.40,2.54.39,2.54.38,2.54.37,2.54.36,2.54.35,2.54.34,2.54.33,2.54.32,2.54.31,2.54.30,2.54.29,2.54.28,2.54.27,2.54.26,2.54.25,2.54.24,2.54.23,2.54.22,2.54.21,2.54.20,2.54.19,2.54.18,2.54.17,2.54.16,2.54.15,2.54.14,2.54.13,2.54.12,2.54.11,2.54.10,2.54.09,2.54.08,2.54.07,2.54.06,2.54.05,2.54.04,2.54.03,2.54.02,2.54.01,2.54.00,2.53.99,2.53.98,2.53.97,2.53.96,2.53.95,2.53.94,2.53.93,2.53.92,2.53.91,2.53.90,2.53.89,2.53.88,2.53.87,2.53.86,2.53.85,2.53.84,2.53.83,2.53.82,2.53.81,2.53.80,2.53.79,2.53.78,2.53.77,2.53.76,2.53.75,2.53.74,2.53.73,2.53.72,2.53.71,2.53.70,2.53.69,2.53.68,2.53.67,2.53.66,2.53.65,2.53.64,2.53.63,2.53.62,2.53.61,2.53.60,2.53.59,2.53.58,2.53.57,2.53.56,2.53.55,2.53.54,2.5
3.53,2.5
3.52,2.53.51,2.53.50,2.53.49,2.53.48,2.53.47,2.53.46,2.5
3.45,2.53.44,2.53.43,2.53.42,2.53.41,2.53.40,2.53.39,2.53.38,2.53.37,2.53.36,2.53.35,2.53.34,2.53.33,2.53.32,2.53.31,2.53.30,2.53.29,2.53.28,2.53.27,2.53.26,2.53.25,2.53.24,2.53.23,2.53.22,2.53.21,2.53.20,2.53.19,2.53.18,2.53.17,2.53.16,2.53.15,2.53.14,2.53.13,2.53.12,2.53.11,2.53.10,2.53.09,2.53.08,2.53.07,2.53.06,2.53.05,2.53.04,2.53.03,2.53.02,2.53.01,2.53.00,2.52.99,2.52.98,2.52.97,2.52.96,2.52.95,2.52.94,2.52.93,2.52.92,2.52.91,2.52.90,2.52.89,2.52.88,2.52.87,2.52.86,2.52.85,2.52.84,2.52.83,2.52.82,2.52.81,2.52.80,2.52.79,2.52.78,2.52.77,2.52.76,2.52.75,2.52.74,2.52.73,2.52.72,2.52.71,2.52.70,2.52.69,2.52.68,2.52.67,2.52.66,2.52.65,2.52.64,2.52.63,2.52.62,2.52.61,2.52.60,2.52.59,2.52.58,2.52.57,2.52.56,2.52.55,2.52.54,2.52.53,2.52.52,2.52.51,2.52.50,2.52.49,2.52.48,2.52.47,2.52.46,2.52.45,2.52.44,2.52.43,2.52.42,2.52.41,2.52.40,2.52.39,2.52.38,2.52.37,2.52.36,2.52.35,2.52.34,2.52.33,2.52.32,2.52.31,2.52.30,2.52.29,2.52.28,2.52.27,2.52.26,2.52.25,2.52.24,2.52.23,2.52.22,2.52.21,2.52.20,2.52.19,2.52.18,2.52.17,2.52.16,2.52.15,2.52.14,2.52.13,2.52.12,2.52.11,2.52.10,2.52.09,2.52.08,2.52.07,2.52.06,2.52.05,2.52.04,2.52.03,2.52.02,2.52.01,2.52.00,2.51.99,2.51.98,2.51.97,2.51.96,2.51.95,2.51.94,2.51.93,2.51.92,2.51.91,2.51.90,2.51.89,2.51.88,2.51.87,2.51.86,2.51.85,2.51.84,2.51.83,2.51.82,2.51.81,2.51.80,2.51.79,2.51.78,2.51.77,2.51.76,2.51.75,2.51.74,2.51.73,2.51.72,2.51.71,2.51.70,2.51.69,2.51.68,2.51.67,2.51.66,2.51.65,2.51.64,2.51.63,2.51.62,2.51.61,2.51.60,2.51.59,2.51.58,2.51.57,2.51.56,2.51.55,2.51.54,2.51.53,2.51.52,2.51.51,2.51.50,2.51.49,2.51.48,2.51.47,2.51.46,2.51.45,2.51.44,2.51.43,2.51.42,2.51.41,2.51.40,2.51.39,2.51.38,2.51.37,2.51.36,2.51.35,2.51.34,2.51.33,2.51.32,2.5
1.31,2.51.30,2.51.29,2.51.28,2.51.27,2.51.26,2.51.25,2.51.24,2.51.23,2.51.22,2.51.21,2.51.20,2.51.19,2.51.18,2.51.17,2.51.16,2.51.15,2.51.14,2.51.13,2.51.12,2.51.11,2.51.10,2.51.09,2.51.08,2.51.07,2.51.06,2.51.05,2.51.04,2.51.03,2.51.02,2.51.01,2.51.00,2.50.99,2.50.98,2.50.97,2.50.96,2.50.95,2.50.94,2.50.93,2.50.92,2.50.91,2.50.90,2.50.89,2.50.88,2.50.87,2.50.86,2.50.85,2.50.84,2.50.83,2.50.82,2.50.81,2.50.80,2.50.79,2.50.78,2.50.77,2.50.76,2.50.75,2.50.74,2.50.73,2.50.72,2.50.71,2.50.70,2.50.69,2.50.68,2.50.67,2.50.66,2.50.65,2.50.64,2.50.63,2.50.62,2.50.61,2.50.60,2.50.59,2.50.58,2.50.57,2.50.56,2.50.55,2.50.54,2.50.53,2.50.52,2.50.51,2.50.50,2.50.49,2.50.48,2.50.47,2.50.46,2.50.45,2.50.44,2.50.43,2.50.42,2.50.41,2.50.40,2.50.39,2.50.38,2.50.37,2.50.36,2.50.35,2.50.34,2.50.33,2.50.32,2.50.31,2.50.30,2.50.29,2.50.28,2.50.27,2.50.26,2.59.00,1.58.99,1.58.98,1.58.97,1.58.96,1.58.95,1.58.94,1.58.93,1.58.92,1.58.91,1.58.90,1.58.89,1.58.88,1.58.87,1.58.86,1.58.85,1.58.84,1.58.83,1.58.82,1.58.81,1.58.80,1.58.79,1.58.78,1.58.77,1.58.76,1.58.75,1.58.74,1.58.73,1.58.72,1.58.71,1.58.70,1.58.69,1.58.68,1.58.67,1.58.66,1.58.65,1.58.64,1.58.63,1.58.62,1.58.61,1.58.60,1.58.59,1.58.58,1.58.57,1.58.56,1.58.55,1.58.54,1.58.53,1.58.52,1.58.51,1.58.50,1.58.49,1.58.48,1.58.47,1.58.46,1.58.45,1.58.44,1.58.43,1.58.42,1.58.41,1.58.40,1.58.39,1.58.38,1.58.37,1.58.36,1.58.35,1.58.34,1.58.33,1.58.32,1.58.31,1.58.30,1.58.29,1.58.28,1.58.27,1.58.26,1.58.25,1.58.24,1.58.23,1.58.22,1.58.21,1.58.20,1.58.19,1.58.18,1.58.17,1.58.16,1.58.15,1.58.14,1.58.13,1.58.12,1.58.11,1.58.10,1.58.09,1.58.08,1.58.07,1.58.06,1.58.05,1.58.04,1.58.03,1.58.02,1.58.01,1.58.00,1.57.99,1.57.98,1.57.97,1.57.96,1.57.95,1.57.94,1.57.93,1.57.92,1.57.91,1.57.90,1.57.89,1.57.88,1.57.87,1.57.86,1.57.85,1.57.84,1.57.83,1.57.82,1.57.81,1.57.80,1.57.79,1.57.78,1.57.77,1.57.76,1.57.75,1.57.74,1.57.73,1.57.72,1.57.71,1.57.70,1.57.69,1.57.68,1.57.67,1.57.66,1.57.65,1.57.64,1.57.63,1.57.62,1.57.61,1.57.60,1.57.59,1.57.58,1.57.57,1.57.56,1.57.55,1.57.54,1.57.53,1.57.52,1.57.51,1.57.50,1.57.49,1.57.48,1.57.47,1.57.46,1.57.45,1.57.44,1.57.43,1.57.42,1.57.41,1.57.40,1.57.39,1.57.38,1.57.37,1.57.36,1.57.35,1.57.34,1.57.33,1.57.32,1.57.31,1.57.30,1.57.29,1.57.28,1.57.27,1.57.26,1.57.25,1.57.24,1.57.23,1.57.22,1.57.21,1.57.20,1.57.19,1.57.18,1.57.17,1.57.16,1.57.15,1.57.14,1.57.13,1.57.12,1.57.11,1.57.10,1.57.09,1.57.08,1.57.07,1.57.06,1.57.05,1.57.04,1.57.03,1.57.02,1.57.01,1.57.00,1.56.99,1.56.98,1.56.97,1.56.96,1.56.95,1.56.94,1.56.93,1.56.92,1.56.91,1.56.90,1.56.89,1.56.88,1.56.87,1.56.86,1.56.85,1.56.84,1.56.83,1.56.82,1.56.81,1.56.80,1.56.79,1.56.78,1.56.77,1.56.76,1.56.75,1.56.74,1.56.73,1.56.72,1.56.71,1.56.70,1.56.69,1.56.68,1.56.67,1.56.66,1.56.65,1.56.64,1.56.63,1.56.62,1.56.61,1.56.60,1.56.59,1.56.58,1.56.57,1.56.56,1.56.55,1.56.54,1.56.53,1.56.52,1.56.51,1.56.50,1.56.49,1.56.48,1.56.47,1.56.46,1.56.45,1.56.44,1.56.43,1.56.42,1.56.41,1.56.40,1.56.39,1.56.38,1.56.37,1.56.36,1.56.35,1.56.34,1.56.33,1.56.32,1.56.31,1.56.30,1.56.29,1.56.28,1.56.27,1.56.26,1.56.25,1.56.24,1.56.23,1.56.22,1.56.21,1.56.20,1.56.19,1.56.18,1.56.17,1.56.16,1.56.15,1.56.14,1.56.13,1.56.12,1.56.11,1.56.10,1.56.09,1.56.08,1.56.07,1.56.06,1.56.05,1.56.04,1.56.03,1.56.02,1.56.01,1.56.00,1.55.99,1.55.98,1.55.97,1.55.96,1.55.95,1.55.94,1.55.93,1.55.92,1.55.91,1.55.90,1.55.89,1.55.88,1.55.87,1.55.86,1.55.85,1.55.84,1.55.83,1.55.82,1.55.81,1.55.80,1.55.79,1.55.78,1.55.77,1.55.76,1.55.75,1.55.74,1.55.73,1.55.72,1.55.71,1.55.70,1.55.69,1.55.68,1.55.67,1.55.66,1.55.65,1.55.64,1.55.63,1.55.62,1.55.61,1.55.60,1.55.59,1.55.58,1.55.57,1.55.56,1.55.55,1.55.54,1.55.53,1.55.52,1.55.51,1.55.50,1.55.49,1.55.48,1.55.47,1.55.46,1.55.45,1.55.44,1.55.43,1.55.42,1.5
5.41,1.5
5.40,1.5
5.39,1.55.38,1.55.37,1.55.36,1.55.35,1.55.34,1.55.33,1.55.32,1.55.31,1.55.30,1.55.29,1.55.28,1.55.27,1.55.26,1.55.25,1.55.24,1.5
5.23,1.5
5.22,1.55.21,1.55.20,1.55.19,1.55.18,1.55.17,1.55.16,1.55.15,1.55.14,1.55.13,1.55.12,1.55.11,1.55.10,1.55.09,1.55.08,1.55.07,1.55.06,1.55.05,1.55.04,1.55.03,1.55.02,1.55.01,1.55.00,1.54.99,1.54.98,1.54.97,1.54.96,1.54.95,1.54.94,1.54.93,1.54.92,1.54.91,1.54.90,1.54.89,1.54.88,1.54.87,1.54.86,1.54.85,1.54.70,1.54.69,1.54.68,1.54.67,1.5
4.66,1.54.65,1.5
4.64,1.54.63,1.54.62,1.54.61,1.54.60,1.54.59,1.54.58,1.54.57,1.54.56,1.54.55,1.54.54,1.54.53,1.54.52,1.54.51,1.54.50,1.54.49,1.54.48,1.54.47,1.54.46,1.54.45,1.54.44,1.54.43,1.54.42,1.54.41,1.54.40,1.54.39,1.54.38,1.54.37,1.54.36,1.54.35,1.54.34,1.54.33,1.54.32,1.54.31,1.54.30,1.54.29,1.54.28,1.54.27,1.54.26,1.54.25,1.54.24,1.54.23,1.54.22,1.54.21,1.54.20,1.54.19,1.54.18,1.54.17,1.54.16,1.54.15,1.54.14,1.54.13,1.54.12,1.54.11,1.54.10,1.54.09,1.54.08,1.54.07,1.54.06,1.54.05,1.54.04,1.54.03,1.54.02,1.54.01,1.54.00,1.53.99,1.53.98,1.53.97,1.53.96,1.53.95,1.53.94,1.53.93,1.53.92,1.53.91,1.53.90,1.5
3.89,1.53.88,1.53.87,1.53.86,1.53.85,1.53.84,1.53.83,1.53.82,1.53.81,1.53.80,1.53.79,1.53.78,1.53.77,1.53.76,1.53.75,1.53.74,1.53.73,1.53.72,1.53.71,1.53.70,1.53.69,1.53.68,1.53.67,1.53.66,1.53.65,1.53.64,1.53.63,1.53.62,1.53.61,1.53.60,1.53.59,1.5
3.58,1.53.57,1.53.56,1.53.55,1.53.54,1.53.53,1.5
3.52,1.53.51,1.53.50,1.53.49,1.53.48,1.53.47,1.5
3.46,1.5
3.45,1.53.44,1.53.43,1.53.42,1.53.41,1.53.40,1.53.39,1.53.38,1.53.37,1.53.36,1.53.35,1.53.34,1.53.33,1.53.32,1.53.31,1.53.30,1.53.29,1.53.28,1.53.27,1.53.26,1.53.25,1.53.24,1.53.23,1.53.22,1.53.21,1.53.20,1.53.19,1.53.18,1.53.17,1.53.16,1.53.15,1.53.14,1.53.13,1.53.12,1.53.11,1.53.10,1.53.09,1.53.08,1.53.07,1.53.06,1.53.05,1.53.04,1.53.03,1.53.02,1.53.01,1.53.00,1.52.99,1.52.98,1.52.97,1.52.96,1.52.95,1.52.94,1.52.93,1.52.92,1.52.91,1.52.90,1.52.89,1.52.88,1.52.87,1.52.86,1.52.85,1.52.84,1.52.83,1.52.82,1.52.81,1.52.80,1.52.79,1.52.78,1.52.77,1.52.76,1.52.75,1.52.74,1.52.73,1.52.72,1.52.71,1.52.70,1.52.69,1.52.68,1.52.67,1.52.66,1.52.65,1.52.64,1.52.63,1.52.62,1.52.61,1.52.60,1.52.59,1.52.58,1.52.57,1.52.56,1.52.55,1.52.54,1.52.53,1.52.52,1.52.51,1.52.50,1.52.49,1.52.48,1.52.47,1.52.46,1.52.45,1.52.44,1.52.43,1.52.42,1.52.41,1.52.40,1.52.39,1.52.38,1.52.37,1.52.36,1.52.35,1.52.34,1.52.33,1.52.32,1.52.31,1.52.30,1.52.29,1.52.28,1.52.27,1.52.26,1.52.25,1.52.24,1.52.23,1.52.22,1.52.21,1.52.20,1.52.19,1.52.18,1.52.17,1.52.16,1.52.15,1.52.14,1.52.13,1.52.12,1.52.11,1.52.10,1.52.09,1.52.08,1.52.07,1.52.06,1.52.05,1.52.04,1.52.03,1.52.02,1.52.01,1.52.00,1.51.99,1.51.98,1.51.97,1.51.96,1.51.95,1.51.94,1.51.93,1.51.92,1.51.91,1.51.90,1.51.89,1.51.88,1.51.87,1.51.86,1.51.85,1.51.84,1.51.83,1.51.82,1.51.81,1.51.80,1.51.79,1.51.78,1.51.77,1.51.76,1.51.75,1.51.74,1.51.73,1.51.72,1.51.71,1.51.70,1.51.69,1.51.68,1.51.67,1.51.66,1.51.65,1.51.64,1.51.63,1.51.62,1.51.61,1.51.60,1.51.59,1.51.58,1.51.57,1.51.56,1.51.55,1.51.54,1.51.53,1.51.52,1.51.51,1.51.50,1.51.49,1.51.48,1.51.47,1.51.46,1.51.45,1.51.44,1.51.43,1.51.42,1.51.41,1.51.40,1.51.39,1.51.38,1.51.37,1.51.36,1.51.35,1.51.34,1.51.33,1.51.32,1.51.31,1.51.30,1.51.29,1.51.28,1.51.27,1.51.26,1.51.25,1.51.24,1.51.23,1.51.22,1.51.21,1.51.20,1.51.19,1.51.18,1.51.17,1.51.16,1.51.15,1.51.14,1.51.13,1.51.12,1.51.11,1.51.10,1.51.09,1.51.08,1.51.07,1.51.06,1.51.05,1.51.04,1.51.03,1.51.02,1.51.01,1.51.00,1.50.99,1.50.98,1.50.97,1.50.96,1.50.95,1.50.94,1.50.93,1.50.92,1.50.91,1.50.90,1.50.89,1.50.88,1.50.87,1.50.86,1.50.85,1.50.84,1.50.83,1.50.82,1.50.81,1.50.80,1.50.79,1.50.78,1.50.77,1.50.76,1.50.75,1.50.74,1.50.73,1.50.72,1.50.71,1.50.70,1.50.69,1.50.68,1.50.67,1.50.66,1.50.65,1.50.64,1.50.63,1.50.62,1.50.61,1.50.60,1.50.59,1.50.58,1.50.57,1.50.56,1.50.55,1.50.54,1.50.53,1.50.52,1.50.51,1.50.50,1.50.49,1.50.48,1.50.47,1.50.46,1.50.45,1.50.44,1.50.43,1.50.42,1.50.41,1.50.40,1.50.39,1.50.38,1.50.37,1.50.36,1.50.35,1.50.34,1.50.33,1.50.32,1.50.31,1.50.30,1.50.29,1.50.28,1.50.27,1.50.26,1.59.00,0.58.99,0.58.98,0.58.97,0.58.96,0.58.95,0.58.94,0.58.93,0.58.92,0.58.91,0.58.90,0.58.89,0.58.88,0.58.87,0.58.86,0.58.85,0.58.84,0.58.83,0.58.82,0.58.81,0.58.80,0.58.79,0.58.78,0.58.77,0.58.76,0.58.75,0.58.74,0.58.73,0.58.72,0.58.71,0.58.70,0.58.69,0.58.68,0.58.67,0.58.66,0.58.65,0.58.64,0.58.63,0.58.62,0.58.61,0.58.60,0.58.59,0.58.58,0.58.57,0.58.56,0.58.55,0.58.54,0.58.53,0.58.52,0.58.51,0.58.50,0.58.49,0.58.48,0.58.47,0.58.46,0.58.45,0.58.44,0.58.43,0.58.42,0.58.41,0.58.40,0.58.39,0.58.38,0.58.37,0.58.36,0.58.35,0.58.34,0.58.33,0.58.32,0.58.31,0.58.30,0.58.29,0.58.28,0.58.27,0.58.26,0.58.25,0.58.24,0.58.23,0.58.22,0.58.21,0.58.20,0.58.19,0.58.18,0.58.17,0.58.16,0.58.15,0.58.14,0.58.13,0.58.12,0.58.11,0.58.10,0.58.09,0.58.08,0.58.07,0.58.06,0.58.05,0.58.04,0.58.03,0.58.02,0.58.01,0.58.00,0.57.99,0.57.98,0.57.97,0.57.96,0.57.95,0.57.94,0.57.93,0.57.92,0.57.91,0.57.90,0.57.89,0.57.88,0.57.87,0.57.86,0.57.85,0.57.84,0.57.83,0.57.82,0.57.81,0.57.80,0.57.79,0.57.78,0.57.77,0.57.76,0.57.75,0.57.74,0.57.73,0.57.72,0.57.71,0.57.70,0.57.69,0.57.68,0.57.67,0.57.66,0.57.65,0.57.64,0.57.63,0.57.62,0.57.61,0.57.60,0.57.59,0.57.58,0.57.57,0.57.56,0.57.55,0.57.54,0.57.53,0.57.52,0.57.51,0.57.50,0.57.49,0.57.48,0.57.47,0.57.46,0.57.45,0.57.44,0.57.43,0.57.42,0.57.41,0.57.40,0.57.39,0.57.38,0.57.37,0.57.36,0.57.35,0.57.34,0.57.33,0.57.32,0.57.31,0.57.30,0.57.29,0.57.28,0.57.27,0.57.26,0.57.25,0.57.24,0.57.23,0.57.22,0.57.21,0.57.20,0.57.19,0.57.18,0.57.17,0.57.16,0.57.15,0.57.14,0.57.13,0.57.12,0.57.11,0.57.10,0.57.09,0.57.08,0.57.07,0.57.06,0.57.05,0.57.04,0.57.03,0.57.02,0.57.01,0.57.00,0.56.99,0.56.98,0.56.97,0.56.96,0.56.95,0.56.94,0.56.93,0.56.92,0.56.91,0.56.90,0.56.89,0.56.88,0.56.87,0.56.86,0.56.85,0.56.84,0.56.83,0.56.82,0.56.81,0.56.80,0.56.79,0.56.78,0.56.77,0.56.76,0.56.75,0.56.74,0.56.73,0.56.72,0.56.71,0.56.70,0.56.69,0.56.68,0.56.67,0.56.66,0.56.65,0.56.64,0.56.63,0.56.62,0.56.61,0.56.60,0.56.59,0.56.58,0.56.57,0.56.56,0.56.55,0.56.54,0.56.53,0.56.52,0.56.51,0.56.50,0.56.49,0.56.48,0.56.47,0.56.46,0.56.45,0.56.44,0.56.43,0.56.42,0.56.41,0.56.40,0.56.39,0.56.38,0.56.37,0.56.36,0.56.35,0.56.34,0.56.33,0.56.32,0.56.31,0.56.30,0.56.29,0.56.28,0.56.27,0.56.26,0.56.25,0.56.24,0.56.23,0.56.22,0.56.21,0.56.20,0.56.19,0.56.18,0.56.17,0.56.16,0.56.15,0.56.14,0.56.13,0.56.12,0.56.11,0.56.10,0.56.09,0.56.08,0.56.07,0.56.06,0.56.05,0.56.04,0.56.03,0.56.02,0.56.01,0.56.00,0.55.99,0.55.98,0.55.97,0.55.96,0.55.95,0.55.94,0.55.93,0.55.92,0.55.91,0.55.90,0.55.89,0.55.88,0.55.87,0.55.86,0.55.85,0.55.84,0.55.83,0.55.82,0.55.81,0.55.80,0.55.79,0.55.78,0.55.77,0.55.76,0.55.75,0.55.74,0.55.73,0.55.72,0.55.71,0.55.70,0.55.69,0.55.68,0.55.67,0.55.66,0.55.65,0.55.64,0.55.63,0.55.62,0.55.61,0.55.60,0.55.59,0.55.58,0.55.57,0.55.56,0.55.55,0.55.54,0.55.53,0.55.52,0.55.51,0.55.50,0.55.49,0.55.48,0.55.47,0.55.46,0.55.45,0.55.44,0.55.43,0.55.42,0.55.41,0.55.40,0.55.39,0.55.38,0.55.37,0.55.36,0.55.35,0.55.34,0.55.33,0.55.32,0.55.31,0.55.30,0.55.29,0.55.28,0.55.27,0.55.26,0.55.25,0.55.24,0.55.23,0.55.22,0.55.21,0.55.20,0.55.19,0.55.18,0.55.17,0.55.16,0.55.15,0.55.14,0.55.13,0.55.12,0.55.11,0.55.10,0.55.09,0.55.08,0.55.07,0.55.06,0.55.05,0.55.04,0.55.03,0.55.02,0.55.01,0.55.00,0.54.99,0.54.98,0.54.97,0.54.96,0.54.95,0.54.94,0.54.93,0.54.92,0.54.91,0.54.90,0.54.89,0.54.88,0.54.87,0.54.86,0.54.85,0.54.70,0.54.69,0.54.68,0.54.67,0.54.66,0.54.65,0.5
4.64,0.54.63,0.54.62,0.54.61,0.54.60,0.54.59,0.54.58,0.54.57,0.54.56,0.54.55,0.54.54,0.54.53,0.54.52,0.54.51,0.54.50,0.54.49,0.54.48,0.54.47,0.54.46,0.54.45,0.54.44,0.54.43,0.54.42,0.54.41,0.54.40,0.54.39,0.54.38,0.54.37,0.54.36,0.54.35,0.54.34,0.54.33,0.54.32,0.54.31,0.54.30,0.54.29,0.54.28,0.54.27,0.54.26,0.54.25,0.54.24,0.54.23,0.54.22,0.54.21,0.54.20,0.54.19,0.54.18,0.54.17,0.54.16,0.54.15,0.54.14,0.54.13,0.54.12,0.54.11,0.54.10,0.54.09,0.54.08,0.54.07,0.54.06,0.54.05,0.54.04,0.54.03,0.54.02,0.54.01,0.54.00,0.53.99,0.53.98,0.53.97,0.53.96,0.53.95,0.53.94,0.53.93,0.53.92,0.53.91,0.53.90,0.53.89,0.53.88,0.53.87,0.53.86,0.53.85,0.53.84,0.53.83,0.53.82,0.53.81,0.53.80,0.53.79,0.53.78,0.53.77,0.53.76,0.53.75,0.53.74,0.53.73,0.53.72,0.53.71,0.53.70,0.53.69,0.53.68,0.53.67,0.53.66,0.53.65,0.53.64,0.53.63,0.53.62,0.53.61,0.53.60,0.53.59,0.53.58,0.53.57,0.53.56,0.53.55,0.53.54,0.53.53,0.53.52,0.53.51,0.53.50,0.53.49,0.53.48,0.53.47,0.53.46,0.5
3.45,0.53.44,0.53.43,0.53.42,0.53.41,0.53.40,0.53.39,0.53.38,0.53.37,0.53.36,0.53.35,0.53.34,0.53.33,0.53.32,0.53.31,0.53.30,0.53.29,0.53.28,0.5
3.27,0.53.26,0.53.25,0.5
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5.21,3.565.18,3.565.15,3.565.12,3.565.09,3.565.06,3.565.03,3.565.00,3.564.97,3.564.94,3.564.91,3.564.88,3.564.85,3.564.82,3.564.79,3.564.76,3.564.73,3.564.70,3.564.67,3.564.64,3.564.61,3.564.58,3.564.55,3.564.52,3.564.49,3.564.46,3.564.43,3.564.40,3.564.37,3.564.34,3.564.31,3.564.28,3.564.25,3.564.22,3.564.19,3.565.99,3.535.96,3.535.93,3.535.90,3.535.87,3.535.84,3.535.81,3.535.78,3.535.75,3.535.72,3.535.69,3.535.66,3.535.63,3.535.60,3.535.57,3.535.54,3.535.51,3.535.48,3.535.45,3.535.42,3.535.39,3.535.36,3.535.33,3.535.30,3.535.27,3.535.24,3.535.21,3.535.18,3.535.15,3.535.12,3.535.09,3.535.06,3.535.03,3.535.00,3.534.97,3.534.94,3.534.91,3.534.88,3.534.85,3.534.82,3.534.79,3.534.76,3.534.73,3.534.70,3.534.67,3.534.64,3.534.61,3.534.58,3.534.55,3.534.52,3.534.49,3.534.46,3.534.43,3.534.40,3.534.37,3.534.34,3.534.31,3.534.28,3.534.25,3.534.22,3.534.19,3.534.16,3.535.99,3.505.96,3.505.93,3.505.90,3.505.87,3.505.84,3.505.81,3.505.78,3.505.75,3.505.72,3.505.69,3.505.66,3.505.63,3.505.60,3.505.57,3.505.54,3.505.51,3.505.48,3.505.45,3.505.42,3.505.39,3.505.36,3.505.33,3.505.30,3.505.27,3.505.24,3.505.21,3.505.18,3.505.15,3.505.12,3.505.09,3.505.06,3.505.03,3.505.00,3.504.97,3.504.94,3.504.91,3.504.88,3.504.85,3.504.82,3.504.79,3.504.76,3.504.73,3.504.70,3.504.67,3.504.64,3.504.61,3.504.58,3.504.55,3.504.52,3.504.49,3.504.46,3.504.43,3.504.40,3.504.37,3.504.34,3.504.31,3.504.28,3.504.25,3.504.22,3.504.19,3.504.16,3.504.13,3.505.99,3.475.96,3.475.93,3.475.90,3.475.87,3.475.84,3.475.81,3.475.78,3.475.75,3.475.72,3.475.69,3.475.66,3.475.63,3.475.60,3.475.57,3.475.54,3.475.51,3.475.48,3.475.45,3.475.42,3.475.39,3.475.36,3.475.33,3.475.30,3.475.27,3.475.24,3.475.21,3.475.18,3.475.15,3.475.12,3.475.09,3.475.06,3.475.03,3.475.00,3.474.97,3.474.94,3.474.91,3.474.88,3.474.85,3.474.82,3.474.79,3.474.76,3.474.73,3.474.70,3.474.67,3.474.64,3.474.61,3.474.58,3.474.55,3.474.52,3.474.49,3.474.46,3.474.43,3.474.40,3.474.37,3.474.34,3.474.31,3.474.28,3.474.25,3.474.22,3.474.19,3.474.16,3.474.13,3.474.10,3.475.99,3.445.96,3.445.93,3.445.90,3.445.87,3.445.84,3.445.81,3.445.78,3.445.75,3.445.72,3.445.69,3.445.66,3.445.63,3.445.60,3.445.57,3.445.54,3.445.51,3.445.48,3.445.45,3.445.42,3.445.39,3.445.36,3.445.33,3.445.30,3.445.27,3.445.24,3.445.21,3.445.18,3.445.15,3.445.12,3.445.09,3.445.06,3.445.03,3.445.00,3.444.97,3.444.94,3.444.91,3.444.88,3.444.85,3.444.82,3.444.79,3.444.76,3.444.73,3.444.70,3.444.67,3.444.64,3.444.61,3.444.58,3.444.55,3.444.52,3.444.49,3.444.46,3.444.43,3.444.40,3.444.37,3.444.34,3.444.31,3.444.28,3.444.25,3.444.22,3.444.19,3.444.16,3.444.13,3.444.10,3.444.07,3.445.99,3.415.96,3.415.93,3.415.90,3.415.87,3.415.84,3.415.81,3.415.78,3.415.75,3.415.72,3.415.69,3.415.66,3.415.63,3.415.60,3.415.57,3.415.54,3.415.51,3.415.48,3.415.45,3.415.42,3.415.39,3.415.36,3.415.33,3.415.30,3.415.27,3.415.24,3.415.21,3.415.18,3.415.15,3.415.12,3.415.09,3.415.06,3.415.03,3.415.00,3.414.97,3.414.94,3.414.91,3.414.88,3.414.85,3.414.82,3.414.79,3.414.76,3.414.73,3.414.70,3.414.67,3.414.64,3.414.61,3.414.58,3.414.55,3.414.52,3.414.49,3.414.46,3.414.43,3.414.40,3.414.37,3.414.34,3.414.31,3.414.28,3.414.25,3.414.22,3.414.19,3.414.16,3.414.13,3.414.10,3.414.07,3.414.04,3.415.99,3.385.96,3.385.93,3.385.90,3.385.87,3.385.84,3.385.81,3.385.78,3.385.75,3.385.72,3.385.69,3.385.66,3.385.63,3.385.60,3.385.57,3.385.54,3.385.51,3.385.48,3.385.45,3.385.42,3.385.39,3.385.36,3.385.33,3.385.30,3.385.27,3.385.24,3.385.21,3.385.18,3.385.15,3.385.12,3.385.09,3.385.06,3.385.03,3.385.00,3.384.97,3.384.94,3.384.91,3.384.88,3.384.85,3.384.82,3.384.79,3.384.76,3.384.73,3.384.70,3.384.67,3.384.64,3.384.61,3.384.58,3.384.55,3.384.52,3.384.49,3.384.46,3.384.43,3.384.40,3.384.37,3.384.34,3.384.31,3.384.28,3.384.25,3.384.22,3.384.19,3.384.16,3.384.13,3.384.10,3.384.07,3.384.04,3.384.01,3.385.99,3.355.96,3.355.93,3.355.90,3.355.87,3.355.84,3.355.81,3.355.78,3.355.75,3.355.72,3.355.69,3.355.66,3.355.63,3.355.60,3.355.57,3.355.54,3.355.51,3.355.48,3.355.45,3.355.42,3.355.39,3.355.36,3.355.33,3.355.30,3.355.27,3.355.24,3.355.21,3.355.18,3.355.15,3.355.12,3.355.09,3.355.06,3.355.03,3.355.00,3.354.97,3.354.94,3.354.91,3.354.88,3.354.85,3.354.82,3.354.79,3.354.76,3.354.73,3.354.70,3.354.67,3.354.64,3.354.61,3.354.58,3.354.55,3.354.52,3.354.49,3.354.46,3.354.43,3.354.40,3.354.37,3.354.34,3.354.31,3.354.28,3.354.25,3.354.22,3.354.19,3.354.16,3.354.13,3.354.10,3.354.07,3.354.04,3.354.01,3.353.98,3.355.99,3.325.96,3.325.93,3.325.90,3.325.87,3.325.84,3.325.81,3.325.78,3.325.75,3.325.72,3.325.69,3.325.66,3.325.63,3.325.60,3.325.57,3.325.54,3.325.51,3.325.48,3.325.45,3.325.42,3.325.39,3.325.36,3.325.33,3.325.30,3.325.27,3.325.24,3.325.21,3.325.18,3.325.15,3.325.12,3.325.09,3.325.06,3.325.03,3.325.00,3.324.97,3.324.94,3.324.91,3.324.88,3.324.85,3.324.82,3.324.79,3.324.76,3.324.73,3.324.70,3.324.67,3.324.64,3.324.61,3.324.58,3.324.55,3.324.52,3.324.49,3.324.46,3.324.43,3.324.40,3.324.37,3.324.34,3.324.31,3.324.28,3.324.25,3.324.22,3.324.19,3.324.16,3.324.13,3.324.10,3.324.07,3.324.04,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4.67,3.294.64,3.29
4.61,3.294.58,3.294.55,3.294.52,3.294.49,3.294.46,3.294.43,3.294.40,3.294.37,3.294.34,3.294.31,3.294.28,3.294.25,3.294.22,3.294.19,3.294.16,3.294.13,3.294.10,3.294.07,3.294.04,3.294.01,3.293.98,3.293.95,3.293.92,3.295.99,3.265.96,3.265.93,3.265.90,3.265.87,3.265.84,3.265.81,3.265.78,3.265.75,3.265.72,3.265.69,3.265.66,3.265.63,3.265.60,3.265.57,3.265.54,3.265.51,3.265.48,3.265.45,3.265.42,3.265.39,3.265.36,3.265.33,3.265.30,3.265.27,3.265.24,3.265.21,3.265.18,3.265.15,3.265.12,3.265.09,3.265.06,3.265.03,3.265.00,3.264.97,3.264.94,3.264.91,3.264.88,3.264.85,3.264.82,3.264.79,3.264.76,3.264.73,3.264.70,3.26
4.67,3.264.64,3.26
4.61,3.264.58,3.264.55,3.264.52,3.264.49,3.264.46,3.264.43,3.264.40,3.264.37,3.264.34,3.264.31,3.264.28,3.264.25,3.264.22,3.264.19,3.264.16,3.264.13,3.264.10,3.264.07,3.264.04,3.264.01,3.263.98,3.263.95,3.263.92,3.263.89,3.265.99,3.235.96,3.235.93,3.235.90,3.235.87,3.235.84,3.235.81,3.235.78,3.235.75,3.235.72,3.235.69,3.235.66,3.235.63,3.235.60,3.235.57,3.235.54,3.235.51,3.235.48,3.235.45,3.235.42,3.235.39,3.235.36,3.235.33,3.235.30,3.235.27,3.235.24,3.235.21,3.235.18,3.235.15,3.235.12,3.235.09,3.235.06,3.235.03,3.235.00,3.234.97,3.234.94,3.234.91,3.234.88,3.234.85,3.234.82,3.234.79,3.234.76,3.234.73,3.234.70,3.23
4.67,3.23
4.64,3.23
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3.02,1.73
P[1]
P[2
]
Glucose
Glucose
Maltose
Alanine
B) 1H-1H COSY A) 1H-J-RES
Glucose
Alanine Maltose
P[1]
P[2
]
Lysine
72
For the other two areas, maltose at (5.41 ppm, 0.5 Hz) and a new metabolite, glucose at (3.25, -0.5
Hz) and (4.63 ppm, 3.5-4.5 Hz) were identified as significant. Glucose has been linked to stress in
many different organisms [5]. Especially in earthworms, glucose has been known to be a major
metabolite of response in gluconeogensis due to sub-lethal pyrene exposure [35]. For the COSY
loadings plot (Figure 2.7b), five main areas have been identified as significant using two-sample t-
tests (P<0.05). The first area located in the lower left, with values of (3.77, 1.46 ppm) and (3.80,
1.46 ppm), result from alanine. The second area located near the center with the value of (3.02,
1.73 ppm) was found to be significant and through comparison of the 2-D NMR of standards
previously identified as major metabolites in E. fetida [9], was deduced as lysine. Lysine is an
essential amino acid for the production of acetyl-CoA which is has a major role in the citric acid
cycle for energy production [36]. Specifically in E. fetida, studies have shown that lysine was
detected as an important metabolite of response during the exposure to sub-lethal concentrations of
naphthalene, phenanthrene, and pyrene [10]. For the last three areas in Figure 2.7b, all signals are
from sugars. Maltose was identified as the dominant metabolite with signal at (5.42, 3.59 ppm).
Glucose was also detected and the spectral signals were all in the ppm range of (δ=3.26-4.66 ppm).
Most of the above spectral signals were detected in the 1-D NMR techniques but the additional
proton connectivity and J-coupling information provided by J-RES and COSY respectively gave
higher confirmation of their presence. For example, the spectral signals at 3.25 and 3.26 ppm,
which were detected in all three 1-D NMR techniques were deduced through J-RES and COSY to
be glucose. In addition, the proton correlation by COSY provided higher dispersion of spectral
signals which allowed higher resolution for the identification of lysine. The 1-D NMR spectra,
including J-RES projections, were unable to resolve lysine due to the extensive overlap of sugar
signal peaks within the 2.8-5.0ppm region.
73
In the HSQC 2-D loadings plots (Figures 2.8a and 2.8b), each data point displayed contains
two values with the first being the 1H chemical shift and the second the 13C chemical shift of each
C-H unit. In Figure 2.8a, a large spectral area (1H 0.25-6.0 ppm, 13C 10.0-110.0 ppm) was
investigated and four spectral areas were found to be significantly different using a two-sample t-
test (p<0.05) in the exposed earthworms compared to the control. The spectral signals (2.99,
41.75-42.25 ppm) were found to be matching the amino acid lysine. Maltose at various signals
(3.80-3.89, 62.75-64.25 ppm) and glucose (3.47-3.50, 78.75 ppm) were also observed which
further confirms the identity of these metabolites in the COSY and J-RES spectra.
Figure 2.8: Partial least-squares discriminant analysis (PLS-DA) 2-D loadings plots of 2-D NMR spectra for control and endosulfan-exposed Eisenia fetida using 1H–13C Single Quantum Coherence (HSQC) spectroscopy in the chemical shift range of: (a) 1H=6.0–0.25 ppm; 13C=110.0–10.0 ppm and (b) 1H=2.5–0.25 ppm; 13C=50.0–10.0 ppm. Each oval region represents areas of significance determined using a two-sample t-test with a confidence interval of 95% (P<0.05).
When a large spectral region is considered (1H 0.25-6.0 ppm, 13C 10.0-110.0 ppm), the 2-D
loadings plot is dominated by the most intense signals in the 2-D datasets (3.0-4.5 ppm, in the 1H
spectrum) in large part from sugars. To investigate whether complimentary information is
contained in the less intense aliphatic region which will likely contain many signals from amino
-0.1 0.0 0.1 0.2 0.3 0.4 0.5-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
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3.41,79.253.38,79.253.35,79.253.32,79.253.29,79.253.26,79.253.23,79.253.20,79.253.17,79.253.14,79.253.11,79.253.08,79.253.05,79.253.02,79.252.99,79.252.96,79.252.93,79.252.90,79.252.87,79.252.84,79.252.81,79.252.78,79.252.75,79.252.72,79.252.69,79.252.66,79.252.63,79.252.60,79.252.57,79.252.54,79.252.51,79.252.48,79.252.45,79.252.42,79.252.39,79.252.36,79.252.33,79.252.30,79.252.27,79.252.24,79.252.21,79.252.18,79.252.15,79.252.12,79.252.09,79.252.06,79.252.03,79.252.00,79.251.97,79.251.94,79.251.91,79.251.88,79.251.85,79.251.82,79.251.79,79.251.76,79.251.73,79.251.70,79.251.67,79.251.64,79.251.61,79.251.58,79.251.55,79.251.52,79.251.49,79.251.46,79.251.43,79.251.40,79.251.37,79.251.34,79.251.31,79.251.28,79.251.25,79.251.22,79.251.19,79.251.16,79.251.13,79.251.10,79.251.07,79.251.04,79.251.01,79.250.98,79.250.95,79.250.92,79.250.89,79.250.86,79.250.83,79.250.80,79.250.77,79.250.74,79.250.71,79.250.68,79.250.65,79.250.62,79.250.59,79.250.56,79.250.53,79.250.50,79.250.47,79.250.44,79.250.41,79.250.38,79.250.35,79.250.32,79.250.29,79.250.26,79.255.99,78.755.96,78.755.93,78.755.90,78.755.87,78.755.84,78.755.81,78.755.78,78.755.75,78.755.72,78.755.69,78.755.66,78.755.63,78.755.60,78.755.57,78.755.54,78.755.51,78.755.48,78.755.45,78.755.42,78.755.39,78.755.36,78.755.33,78.755.30,78.755.27,78.755.24,78.755.21,78.755.18,78.755.15,78.755.12,78.755.09,78.755.06,78.755.03,78.755.00,78.754.97,78.754.94,78.754.91,78.754.88,78.754.85,78.754.61,78.754.58,78.754.55,78.754.52,78.754.49,78.754.46,78.754.43,78.754.40,78.754.37,78.754.34,78.754.31,78.754.28,78.754.25,78.754.22,78.754.19,78.754.16,78.754.13,78.754.10,78.754.07,78.754.04,78.754.01,78.753.98,78.753.95,78.753.92,78.753.89,78.753.86,78.753.83,78.753.80,78.753.77,78.753.74,78.753.71,78.753.68,78.75
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3.50,78.753.47,78.75
3.44,78.75
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3.56,77.253.53,77.253.50,77.253.47,77.253.44,77.253.41,77.253.38,77.253.35,77.253.32,77.253.29,77.253.26,77.253.23,77.25
3.20,77.253.17,77.253.14,77.253.11,77.253.08,77.253.05,77.253.02,77.252.99,77.252.96,77.252.93,77.252.90,77.252.87,77.252.84,77.252.81,77.252.78,77.252.75,77.252.72,77.252.69,77.252.66,77.252.63,77.252.60,77.252.57,77.252.54,77.252.51,77.252.48,77.252.45,77.252.42,77.252.39,77.252.36,77.252.33,77.252.30,77.252.27,77.252.24,77.252.21,77.252.18,77.252.15,77.252.12,77.252.09,77.252.06,77.252.03,77.252.00,77.251.97,77.251.94,77.251.91,77.251.88,77.251.85,77.251.82,77.251.79,77.251.76,77.251.73,77.251.70,77.251.67,77.251.64,77.251.61,77.251.58,77.251.55,77.251.52,77.251.49,77.251.46,77.251.43,77.251.40,77.251.37,77.251.34,77.251.31,77.251.28,77.251.25,77.251.22,77.251.19,77.251.16,77.251.13,77.251.10,77.251.07,77.251.04,77.251.01,77.250.98,77.250.95,77.250.92,77.250.89,77.250.86,77.250.83,77.250.80,77.250.77,77.250.74,77.250.71,77.250.68,77.250.65,77.250.62,77.250.59,77.250.56,77.250.53,77.250.50,77.250.47,77.250.44,77.250.41,77.250.38,77.250.35,77.250.32,77.250.29,77.250.26,77.255.99,76.755.96,76.755.93,76.755.90,76.755.87,76.755.84,76.755.81,76.755.78,76.755.75,76.755.72,76.755.69,76.755.66,76.755.63,76.755.60,76.755.57,76.755.54,76.755.51,76.755.48,76.755.45,76.755.42,76.755.39,76.755.36,76.755.33,76.755.30,76.755.27,76.755.24,76.755.21,76.755.18,76.755.15,76.755.12,76.755.09,76.755.06,76.755.03,76.755.00,76.754.97,76.754.94,76.754.91,76.754.88,76.754.85,76.754.61,76.754.58,76.754.55,76.754.52,76.754.49,76.754.46,76.754.43,76.754.40,76.754.37,76.754.34,76.754.31,76.754.28,76.754.25,76.754.22,76.754.19,76.754.16,76.754.13,76.754.10,76.754.07,76.754.04,76.754.01,76.753.98,76.753.95,76.753.92,76.753.89,76.753.86,76.753.83,76.753.80,76.753.77,76.75
3.74,76.753.71,76.753.68,76.753.65,76.753.62,76.753.59,76.753.56,76.753.53,76.753.50,76.753.47,76.753.44,76.753.41,76.753.38,76.753.35,76.753.32,76.75
3.29,76.75
3.26,76.753.23,76.75
3.20,76.753.17,76.753.14,76.753.11,76.753.08,76.753.05,76.753.02,76.752.99,76.752.96,76.752.93,76.752.90,76.752.87,76.752.84,76.752.81,76.752.78,76.752.75,76.752.72,76.752.69,76.752.66,76.752.63,76.752.60,76.752.57,76.752.54,76.752.51,76.752.48,76.752.45,76.752.42,76.752.39,76.752.36,76.752.33,76.752.30,76.752.27,76.752.24,76.752.21,76.752.18,76.752.15,76.752.12,76.752.09,76.752.06,76.752.03,76.752.00,76.751.97,76.751.94,76.751.91,76.751.88,76.751.85,76.751.82,76.751.79,76.751.76,76.751.73,76.751.70,76.751.67,76.751.64,76.751.61,76.751.58,76.751.55,76.751.52,76.751.49,76.751.46,76.751.43,76.751.40,76.751.37,76.751.34,76.751.31,76.751.28,76.751.25,76.751.22,76.751.19,76.751.16,76.751.13,76.751.10,76.751.07,76.751.04,76.751.01,76.750.98,76.750.95,76.750.92,76.750.89,76.750.86,76.750.83,76.750.80,76.750.77,76.750.74,76.750.71,76.750.68,76.750.65,76.750.62,76.750.59,76.750.56,76.750.53,76.750.50,76.750.47,76.750.44,76.750.41,76.750.38,76.750.35,76.750.32,76.750.29,76.750.26,76.755.99,76.255.96,76.255.93,76.255.90,76.255.87,76.255.84,76.255.81,76.255.78,76.255.75,76.255.72,76.255.69,76.255.66,76.255.63,76.255.60,76.255.57,76.255.54,76.255.51,76.255.48,76.255.45,76.255.42,76.255.39,76.255.36,76.255.33,76.255.30,76.255.27,76.255.24,76.255.21,76.255.18,76.255.15,76.255.12,76.255.09,76.255.06,76.255.03,76.255.00,76.254.97,76.254.94,76.254.91,76.254.88,76.254.85,76.254.61,76.254.58,76.254.55,76.254.52,76.254.49,76.254.46,76.254.43,76.254.40,76.254.37,76.254.34,76.254.31,76.254.28,76.254.25,76.254.22,76.254.19,76.254.16,76.254.13,76.254.10,76.254.07,76.254.04,76.254.01,76.253.98,76.253.95,76.25
3.92,76.253.89,76.253.86,76.253.83,76.253.80,76.253.77,76.253.74,76.25
3.71,76.253.68,76.25
3.65,76.253.62,76.253.59,76.253.56,76.253.53,76.253.50,76.253.47,76.253.44,76.253.41,76.253.38,76.253.35,76.25
3.32,76.253.29,76.253.26,76.253.23,76.253.20,76.253.17,76.253.14,76.253.11,76.253.08,76.253.05,76.253.02,76.252.99,76.252.96,76.252.93,76.252.90,76.252.87,76.252.84,76.252.81,76.252.78,76.252.75,76.252.72,76.252.69,76.252.66,76.252.63,76.252.60,76.252.57,76.252.54,76.252.51,76.252.48,76.252.45,76.252.42,76.252.39,76.252.36,76.252.33,76.252.30,76.252.27,76.252.24,76.252.21,76.252.18,76.252.15,76.252.12,76.252.09,76.252.06,76.252.03,76.252.00,76.251.97,76.251.94,76.251.91,76.251.88,76.251.85,76.251.82,76.251.79,76.251.76,76.251.73,76.251.70,76.251.67,76.251.64,76.251.61,76.251.58,76.251.55,76.251.52,76.251.49,76.251.46,76.251.43,76.251.40,76.251.37,76.251.34,76.251.31,76.251.28,76.251.25,76.251.22,76.251.19,76.251.16,76.251.13,76.251.10,76.251.07,76.251.04,76.251.01,76.250.98,76.250.95,76.250.92,76.250.89,76.250.86,76.250.83,76.250.80,76.250.77,76.250.74,76.250.71,76.250.68,76.250.65,76.250.62,76.250.59,76.250.56,76.250.53,76.250.50,76.250.47,76.250.44,76.250.41,76.250.38,76.250.35,76.250.32,76.250.29,76.250.26,76.255.99,75.755.96,75.755.93,75.755.90,75.755.87,75.755.84,75.755.81,75.755.78,75.755.75,75.755.72,75.755.69,75.755.66,75.755.63,75.755.60,75.755.57,75.755.54,75.755.51,75.755.48,75.755.45,75.755.42,75.755.39,75.755.36,75.755.33,75.755.30,75.755.27,75.755.24,75.755.21,75.755.18,75.755.15,75.755.12,75.755.09,75.755.06,75.755.03,75.755.00,75.754.97,75.754.94,75.754.91,75.754.88,75.754.85,75.754.61,75.754.58,75.754.55,75.754.52,75.754.49,75.754.46,75.754.43,75.754.40,75.754.37,75.754.34,75.754.31,75.754.28,75.754.25,75.754.22,75.754.19,75.754.16,75.754.13,75.754.10,75.754.07,75.754.04,75.754.01,75.753.98,75.753.95,75.753.92,75.753.89,75.753.86,75.753.83,75.753.80,75.753.77,75.75
3.74,75.75
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3.74,75.25
3.71,75.253.68,75.253.65,75.253.62,75.253.59,75.253.56,75.25
3.53,75.253.50,75.253.47,75.253.44,75.253.41,75.253.38,75.253.35,75.253.32,75.253.29,75.253.26,75.253.23,75.253.20,75.253.17,75.253.14,75.253.11,75.253.08,75.253.05,75.253.02,75.252.99,75.252.96,75.252.93,75.252.90,75.252.87,75.252.84,75.252.81,75.252.78,75.252.75,75.252.72,75.252.69,75.252.66,75.252.63,75.252.60,75.252.57,75.252.54,75.252.51,75.252.48,75.252.45,75.252.42,75.252.39,75.252.36,75.252.33,75.252.30,75.252.27,75.252.24,75.252.21,75.252.18,75.252.15,75.252.12,75.252.09,75.252.06,75.252.03,75.252.00,75.251.97,75.251.94,75.251.91,75.251.88,75.251.85,75.251.82,75.251.79,75.251.76,75.251.73,75.251.70,75.251.67,75.251.64,75.251.61,75.251.58,75.251.55,75.251.52,75.251.49,75.251.46,75.251.43,75.251.40,75.251.37,75.251.34,75.251.31,75.251.28,75.251.25,75.251.22,75.251.19,75.251.16,75.251.13,75.251.10,75.251.07,75.251.04,75.251.01,75.250.98,75.250.95,75.250.92,75.250.89,75.250.86,75.250.83,75.250.80,75.250.77,75.250.74,75.250.71,75.250.68,75.250.65,75.250.62,75.250.59,75.250.56,75.250.53,75.250.50,75.250.47,75.250.44,75.250.41,75.250.38,75.250.35,75.250.32,75.250.29,75.250.26,75.255.99,74.755.96,74.755.93,74.755.90,74.755.87,74.755.84,74.755.81,74.755.78,74.755.75,74.755.72,74.755.69,74.755.66,74.755.63,74.755.60,74.755.57,74.755.54,74.755.51,74.755.48,74.755.45,74.755.42,74.755.39,74.755.36,74.755.33,74.755.30,74.755.27,74.755.24,74.755.21,74.755.18,74.755.15,74.755.12,74.755.09,74.755.06,74.755.03,74.755.00,74.754.97,74.754.94,74.754.91,74.754.88,74.754.85,74.754.61,74.754.58,74.754.55,74.754.52,74.754.49,74.754.46,74.754.43,74.754.40,74.754.37,74.754.34,74.754.31,74.754.28,74.754.25,74.754.22,74.754.19,74.754.16,74.754.13,74.754.10,74.754.07,74.754.04,74.754.01,74.753.98,74.753.95,74.753.92,74.753.89,74.753.86,74.753.83,74.753.80,74.753.77,74.753.74,74.75
3.71,74.753.68,74.753.65,74.75
3.62,74.75
3.59,74.753.56,74.75
3.53,74.753.50,74.753.47,74.753.44,74.753.41,74.753.38,74.753.35,74.753.32,74.753.29,74.753.26,74.753.23,74.753.20,74.753.17,74.753.14,74.753.11,74.753.08,74.753.05,74.753.02,74.752.99,74.752.96,74.752.93,74.752.90,74.752.87,74.752.84,74.752.81,74.752.78,74.752.75,74.752.72,74.752.69,74.752.66,74.752.63,74.752.60,74.752.57,74.752.54,74.752.51,74.752.48,74.752.45,74.752.42,74.752.39,74.752.36,74.752.33,74.752.30,74.752.27,74.752.24,74.752.21,74.752.18,74.752.15,74.752.12,74.752.09,74.752.06,74.752.03,74.752.00,74.751.97,74.751.94,74.751.91,74.751.88,74.751.85,74.751.82,74.751.79,74.751.76,74.751.73,74.751.70,74.751.67,74.751.64,74.751.61,74.751.58,74.751.55,74.751.52,74.751.49,74.751.46,74.751.43,74.751.40,74.751.37,74.751.34,74.751.31,74.751.28,74.751.25,74.751.22,74.751.19,74.751.16,74.751.13,74.751.10,74.751.07,74.751.04,74.751.01,74.750.98,74.750.95,74.750.92,74.750.89,74.750.86,74.750.83,74.750.80,74.750.77,74.750.74,74.750.71,74.750.68,74.750.65,74.750.62,74.750.59,74.750.56,74.750.53,74.750.50,74.750.47,74.750.44,74.750.41,74.750.38,74.750.35,74.750.32,74.750.29,74.750.26,74.755.99,74.255.96,74.255.93,74.255.90,74.255.87,74.255.84,74.255.81,74.255.78,74.255.75,74.255.72,74.255.69,74.255.66,74.255.63,74.255.60,74.255.57,74.255.54,74.255.51,74.255.48,74.255.45,74.255.42,74.255.39,74.255.36,74.255.33,74.255.30,74.255.27,74.255.24,74.255.21,74.255.18,74.255.15,74.255.12,74.255.09,74.255.06,74.255.03,74.255.00,74.254.97,74.254.94,74.254.91,74.254.88,74.254.85,74.254.61,74.254.58,74.254.55,74.254.52,74.254.49,74.254.46,74.254.43,74.254.40,74.254.37,74.254.34,74.254.31,74.254.28,74.254.25,74.254.22,74.254.19,74.254.16,74.254.13,74.254.10,74.254.07,74.254.04,74.254.01,74.253.98,74.253.95,74.253.92,74.25
3.89,74.253.86,74.25
3.83,74.253.80,74.25
3.77,74.253.74,74.253.71,74.253.68,74.253.65,74.25
3.62,74.25
3.59,74.253.56,74.25
3.53,74.25
3.50,74.253.47,74.253.44,74.253.41,74.253.38,74.253.35,74.253.32,74.253.29,74.253.26,74.253.23,74.253.20,74.253.17,74.253.14,74.253.11,74.253.08,74.253.05,74.253.02,74.252.99,74.252.96,74.252.93,74.252.90,74.252.87,74.252.84,74.252.81,74.252.78,74.252.75,74.252.72,74.252.69,74.252.66,74.252.63,74.252.60,74.252.57,74.252.54,74.252.51,74.252.48,74.252.45,74.252.42,74.252.39,74.252.36,74.252.33,74.252.30,74.252.27,74.252.24,74.252.21,74.252.18,74.252.15,74.252.12,74.252.09,74.252.06,74.252.03,74.252.00,74.251.97,74.251.94,74.251.91,74.251.88,74.251.85,74.251.82,74.251.79,74.251.76,74.251.73,74.251.70,74.251.67,74.251.64,74.251.61,74.251.58,74.251.55,74.251.52,74.251.49,74.251.46,74.251.43,74.251.40,74.251.37,74.251.34,74.251.31,74.251.28,74.251.25,74.251.22,74.251.19,74.251.16,74.251.13,74.251.10,74.251.07,74.251.04,74.251.01,74.250.98,74.250.95,74.250.92,74.250.89,74.250.86,74.250.83,74.250.80,74.250.77,74.250.74,74.250.71,74.250.68,74.250.65,74.250.62,74.250.59,74.250.56,74.250.53,74.250.50,74.250.47,74.250.44,74.250.41,74.250.38,74.250.35,74.250.32,74.250.29,74.250.26,74.255.99,73.755.96,73.755.93,73.755.90,73.755.87,73.755.84,73.755.81,73.755.78,73.755.75,73.755.72,73.755.69,73.755.66,73.755.63,73.755.60,73.755.57,73.755.54,73.755.51,73.755.48,73.755.45,73.755.42,73.755.39,73.755.36,73.755.33,73.755.30,73.755.27,73.755.24,73.755.21,73.755.18,73.755.15,73.755.12,73.755.09,73.755.06,73.755.03,73.755.00,73.754.97,73.754.94,73.754.91,73.754.88,73.754.85,73.754.61,73.754.58,73.754.55,73.754.52,73.754.49,73.754.46,73.754.43,73.754.40,73.754.37,73.754.34,73.754.31,73.754.28,73.754.25,73.754.22,73.754.19,73.754.16,73.754.13,73.754.10,73.754.07,73.754.04,73.754.01,73.753.98,73.753.95,73.753.92,73.753.89,73.753.86,73.753.83,73.753.80,73.753.77,73.753.74,73.753.71,73.753.68,73.753.65,73.753.62,73.753.59,73.753.56,73.753.53,73.753.50,73.753.47,73.753.44,73.753.41,73.753.38,73.753.35,73.753.32,73.753.29,73.753.26,73.753.23,73.753.20,73.753.17,73.753.14,73.753.11,73.753.08,73.753.05,73.753.02,73.752.99,73.752.96,73.752.93,73.752.90,73.752.87,73.752.84,73.752.81,73.752.78,73.752.75,73.752.72,73.752.69,73.752.66,73.752.63,73.752.60,73.752.57,73.752.54,73.752.51,73.752.48,73.752.45,73.752.42,73.752.39,73.752.36,73.752.33,73.752.30,73.752.27,73.752.24,73.752.21,73.752.18,73.752.15,73.752.12,73.752.09,73.752.06,73.752.03,73.752.00,73.751.97,73.751.94,73.751.91,73.751.88,73.751.85,73.751.82,73.751.79,73.751.76,73.751.73,73.751.70,73.751.67,73.751.64,73.751.61,73.751.58,73.751.55,73.751.52,73.751.49,73.751.46,73.751.43,73.751.40,73.751.37,73.751.34,73.751.31,73.751.28,73.751.25,73.751.22,73.751.19,73.751.16,73.751.13,73.751.10,73.751.07,73.751.04,73.751.01,73.750.98,73.750.95,73.750.92,73.750.89,73.750.86,73.750.83,73.750.80,73.750.77,73.750.74,73.750.71,73.750.68,73.750.65,73.750.62,73.750.59,73.750.56,73.750.53,73.750.50,73.750.47,73.750.44,73.750.41,73.750.38,73.750.35,73.750.32,73.750.29,73.750.26,73.755.99,73.255.96,73.255.93,73.255.90,73.255.87,73.255.84,73.255.81,73.255.78,73.255.75,73.255.72,73.255.69,73.255.66,73.255.63,73.255.60,73.255.57,73.255.54,73.255.51,73.255.48,73.255.45,73.255.42,73.255.39,73.255.36,73.255.33,73.255.30,73.255.27,73.255.24,73.255.21,73.255.18,73.255.15,73.255.12,73.255.09,73.255.06,73.255.03,73.255.00,73.254.97,73.254.94,73.254.91,73.254.88,73.254.85,73.254.61,73.254.58,73.254.55,73.254.52,73.254.49,73.254.46,73.254.43,73.254.40,73.254.37,73.254.34,73.254.31,73.254.28,73.254.25,73.254.22,73.254.19,73.254.16,73.254.13,73.254.10,73.254.07,73.254.04,73.254.01,73.253.98,73.253.95,73.253.92,73.253.89,73.253.86,73.253.83,73.25
3.80,73.253.77,73.253.74,73.253.71,73.253.68,73.253.65,73.253.62,73.253.59,73.253.56,73.253.53,73.25
3.50,73.253.47,73.253.44,73.253.41,73.25
3.38,73.253.35,73.253.32,73.253.29,73.253.26,73.253.23,73.253.20,73.253.17,73.253.14,73.253.11,73.253.08,73.253.05,73.253.02,73.252.99,73.252.96,73.252.93,73.252.90,73.252.87,73.252.84,73.252.81,73.252.78,73.252.75,73.252.72,73.252.69,73.252.66,73.252.63,73.252.60,73.252.57,73.252.54,73.252.51,73.252.48,73.252.45,73.252.42,73.252.39,73.252.36,73.252.33,73.252.30,73.252.27,73.252.24,73.252.21,73.252.18,73.252.15,73.252.12,73.252.09,73.252.06,73.252.03,73.252.00,73.251.97,73.251.94,73.251.91,73.251.88,73.251.85,73.251.82,73.251.79,73.251.76,73.251.73,73.251.70,73.251.67,73.251.64,73.251.61,73.251.58,73.251.55,73.251.52,73.251.49,73.251.46,73.251.43,73.251.40,73.251.37,73.251.34,73.251.31,73.251.28,73.251.25,73.251.22,73.251.19,73.251.16,73.251.13,73.251.10,73.251.07,73.251.04,73.251.01,73.250.98,73.250.95,73.250.92,73.250.89,73.250.86,73.250.83,73.250.80,73.250.77,73.250.74,73.250.71,73.250.68,73.250.65,73.250.62,73.250.59,73.250.56,73.250.53,73.250.50,73.250.47,73.250.44,73.250.41,73.250.38,73.250.35,73.250.32,73.250.29,73.250.26,73.255.99,72.755.96,72.755.93,72.755.90,72.755.87,72.755.84,72.755.81,72.755.78,72.755.75,72.755.72,72.755.69,72.755.66,72.755.63,72.755.60,72.755.57,72.755.54,72.755.51,72.755.48,72.755.45,72.755.42,72.755.39,72.755.36,72.755.33,72.755.30,72.755.27,72.755.24,72.755.21,72.755.18,72.755.15,72.755.12,72.755.09,72.755.06,72.755.03,72.755.00,72.754.97,72.754.94,72.754.91,72.754.88,72.754.85,72.754.61,72.754.58,72.754.55,72.754.52,72.754.49,72.754.46,72.754.43,72.754.40,72.754.37,72.754.34,72.754.31,72.754.28,72.754.25,72.754.22,72.754.19,72.754.16,72.754.13,72.754.10,72.754.07,72.754.04,72.754.01,72.753.98,72.753.95,72.75
3.92,72.753.89,72.753.86,72.753.83,72.753.80,72.753.77,72.753.74,72.753.71,72.753.68,72.753.65,72.753.62,72.753.59,72.753.56,72.753.53,72.753.50,72.753.47,72.753.44,72.753.41,72.75
3.38,72.75
3.35,72.753.32,72.753.29,72.753.26,72.753.23,72.753.20,72.753.17,72.753.14,72.753.11,72.753.08,72.753.05,72.753.02,72.752.99,72.752.96,72.752.93,72.752.90,72.752.87,72.752.84,72.752.81,72.752.78,72.752.75,72.752.72,72.752.69,72.752.66,72.752.63,72.752.60,72.752.57,72.752.54,72.752.51,72.752.48,72.752.45,72.752.42,72.752.39,72.752.36,72.752.33,72.752.30,72.752.27,72.752.24,72.752.21,72.752.18,72.752.15,72.752.12,72.752.09,72.752.06,72.752.03,72.752.00,72.751.97,72.751.94,72.751.91,72.751.88,72.751.85,72.751.82,72.751.79,72.751.76,72.751.73,72.751.70,72.751.67,72.751.64,72.751.61,72.751.58,72.751.55,72.751.52,72.751.49,72.751.46,72.751.43,72.751.40,72.751.37,72.751.34,72.751.31,72.751.28,72.751.25,72.751.22,72.751.19,72.751.16,72.751.13,72.751.10,72.751.07,72.751.04,72.751.01,72.750.98,72.750.95,72.750.92,72.750.89,72.750.86,72.750.83,72.750.80,72.750.77,72.750.74,72.750.71,72.750.68,72.750.65,72.750.62,72.750.59,72.750.56,72.750.53,72.750.50,72.750.47,72.750.44,72.750.41,72.750.38,72.750.35,72.750.32,72.750.29,72.750.26,72.755.99,72.255.96,72.255.93,72.255.90,72.255.87,72.255.84,72.255.81,72.255.78,72.255.75,72.255.72,72.255.69,72.255.66,72.255.63,72.255.60,72.255.57,72.255.54,72.255.51,72.255.48,72.255.45,72.255.42,72.255.39,72.255.36,72.255.33,72.255.30,72.255.27,72.255.24,72.255.21,72.255.18,72.255.15,72.255.12,72.255.09,72.255.06,72.255.03,72.255.00,72.254.97,72.254.94,72.254.91,72.254.88,72.254.85,72.254.61,72.254.58,72.254.55,72.254.52,72.254.49,72.254.46,72.254.43,72.254.40,72.254.37,72.254.34,72.254.31,72.254.28,72.254.25,72.254.22,72.254.19,72.254.16,72.254.13,72.254.10,72.254.07,72.254.04,72.254.01,72.253.98,72.253.95,72.253.92,72.253.89,72.253.86,72.253.83,72.253.80,72.253.77,72.253.74,72.253.71,72.253.68,72.253.65,72.253.62,72.253.59,72.253.56,72.253.53,72.253.50,72.253.47,72.253.44,72.25
3.41,72.25
3.38,72.25
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1.77,49.751.74,49.751.71,49.751.68,49.751.65,49.751.62,49.751.59,49.751.56,49.751.53,49.751.50,49.751.47,49.751.44,49.751.41,49.75
1.38,49.751.35,49.751.32,49.751.29,49.751.26,49.751.23,49.751.20,49.751.17,49.751.14,49.751.11,49.751.08,49.751.05,49.751.02,49.750.99,49.750.96,49.750.93,49.750.90,49.750.87,49.750.84,49.750.81,49.750.78,49.750.75,49.750.72,49.750.69,49.750.66,49.750.63,49.750.60,49.750.57,49.750.54,49.750.51,49.750.48,49.750.45,49.75
0.42,49.750.39,49.750.36,49.750.33,49.750.30,49.750.27,49.752.49,49.252.46,49.252.43,49.252.40,49.252.37,49.252.34,49.252.31,49.252.28,49.252.25,49.252.22,49.252.19,49.252.16,49.252.13,49.252.10,49.252.07,49.252.04,49.252.01,49.251.98,49.251.95,49.251.92,49.251.89,49.251.86,49.251.83,49.251.80,49.251.77,49.251.74,49.251.71,49.251.68,49.251.65,49.251.62,49.251.59,49.251.56,49.251.53,49.251.50,49.251.47,49.251.44,49.251.41,49.251.38,49.251.35,49.251.32,49.251.29,49.251.26,49.251.23,49.251.20,49.251.17,49.251.14,49.251.11,49.251.08,49.251.05,49.251.02,49.250.99,49.250.96,49.250.93,49.250.90,49.250.87,49.250.84,49.250.81,49.250.78,49.250.75,49.250.72,49.250.69,49.250.66,49.250.63,49.250.60,49.250.57,49.250.54,49.250.51,49.250.48,49.250.45,49.250.42,49.250.39,49.250.36,49.250.33,49.250.30,49.250.27,49.252.49,48.752.46,48.752.43,48.752.40,48.752.37,48.752.34,48.752.31,48.752.28,48.752.25,48.752.22,48.752.19,48.752.16,48.752.13,48.752.10,48.752.07,48.752.04,48.752.01,48.751.98,48.751.95,48.751.92,48.751.89,48.751.86,48.75
1.83,48.751.80,48.751.77,48.751.74,48.751.71,48.751.68,48.751.65,48.751.62,48.751.59,48.751.56,48.751.53,48.751.50,48.751.47,48.751.44,48.751.41,48.751.38,48.751.35,48.751.32,48.751.29,48.751.26,48.751.23,48.751.20,48.751.17,48.751.14,48.751.11,48.751.08,48.751.05,48.751.02,48.75
0.99,48.750.96,48.750.93,48.750.90,48.750.87,48.750.84,48.750.81,48.750.78,48.75
0.75,48.750.72,48.750.69,48.750.66,48.750.63,48.750.60,48.750.57,48.750.54,48.750.51,48.750.48,48.750.45,48.750.42,48.750.39,48.750.36,48.750.33,48.750.30,48.750.27,48.752.49,48.252.46,48.252.43,48.252.40,48.252.37,48.252.34,48.25
2.31,48.252.28,48.252.25,48.252.22,48.252.19,48.252.16,48.252.13,48.252.10,48.252.07,48.252.04,48.252.01,48.251.98,48.251.95,48.251.92,48.25
1.89,48.251.86,48.251.83,48.251.80,48.251.77,48.251.74,48.251.71,48.251.68,48.251.65,48.251.62,48.251.59,48.251.56,48.251.53,48.25
1.50,48.251.47,48.251.44,48.251.41,48.251.38,48.251.35,48.251.32,48.251.29,48.25
1.26,48.251.23,48.251.20,48.251.17,48.251.14,48.251.11,48.251.08,48.251.05,48.25
1.02,48.250.99,48.25
0.96,48.250.93,48.250.90,48.250.87,48.250.84,48.250.81,48.250.78,48.250.75,48.250.72,48.250.69,48.250.66,48.250.63,48.250.60,48.250.57,48.250.54,48.250.51,48.250.48,48.250.45,48.250.42,48.250.39,48.250.36,48.250.33,48.250.30,48.250.27,48.252.49,47.752.46,47.752.43,47.752.40,47.752.37,47.752.34,47.752.31,47.752.28,47.752.25,47.752.22,47.75
2.19,47.752.16,47.752.13,47.752.10,47.752.07,47.752.04,47.752.01,47.751.98,47.751.95,47.751.92,47.751.89,47.751.86,47.751.83,47.75
1.80,47.751.77,47.751.74,47.751.71,47.751.68,47.751.65,47.751.62,47.75
1.59,47.751.56,47.751.53,47.751.50,47.751.47,47.75
1.44,47.751.41,47.751.38,47.751.35,47.751.32,47.751.29,47.751.26,47.751.23,47.751.20,47.751.17,47.751.14,47.751.11,47.751.08,47.751.05,47.751.02,47.750.99,47.750.96,47.750.93,47.750.90,47.750.87,47.750.84,47.750.81,47.750.78,47.750.75,47.75
0.72,47.750.69,47.750.66,47.750.63,47.750.60,47.750.57,47.750.54,47.750.51,47.750.48,47.75
0.45,47.750.42,47.750.39,47.750.36,47.750.33,47.750.30,47.750.27,47.752.49,47.252.46,47.252.43,47.252.40,47.252.37,47.252.34,47.252.31,47.252.28,47.252.25,47.252.22,47.252.19,47.252.16,47.252.13,47.252.10,47.252.07,47.252.04,47.252.01,47.251.98,47.251.95,47.251.92,47.251.89,47.251.86,47.251.83,47.251.80,47.251.77,47.25
1.74,47.251.71,47.251.68,47.251.65,47.251.62,47.251.59,47.251.56,47.251.53,47.251.50,47.251.47,47.251.44,47.251.41,47.251.38,47.251.35,47.251.32,47.251.29,47.251.26,47.251.23,47.251.20,47.251.17,47.251.14,47.251.11,47.251.08,47.251.05,47.251.02,47.250.99,47.250.96,47.250.93,47.250.90,47.250.87,47.250.84,47.250.81,47.250.78,47.250.75,47.250.72,47.250.69,47.250.66,47.250.63,47.250.60,47.250.57,47.250.54,47.250.51,47.250.48,47.250.45,47.250.42,47.250.39,47.250.36,47.250.33,47.250.30,47.250.27,47.252.49,46.752.46,46.752.43,46.752.40,46.752.37,46.752.34,46.752.31,46.752.28,46.75
2.25,46.752.22,46.752.19,46.752.16,46.752.13,46.752.10,46.752.07,46.752.04,46.752.01,46.751.98,46.751.95,46.75
1.92,46.751.89,46.751.86,46.751.83,46.751.80,46.751.77,46.751.74,46.751.71,46.751.68,46.751.65,46.751.62,46.751.59,46.751.56,46.75
1.53,46.751.50,46.751.47,46.751.44,46.751.41,46.751.38,46.751.35,46.751.32,46.751.29,46.751.26,46.751.23,46.751.20,46.751.17,46.751.14,46.751.11,46.751.08,46.751.05,46.751.02,46.75
0.99,46.750.96,46.750.93,46.750.90,46.750.87,46.750.84,46.750.81,46.750.78,46.750.75,46.750.72,46.750.69,46.750.66,46.750.63,46.750.60,46.75
0.57,46.750.54,46.750.51,46.75
0.48,46.750.45,46.750.42,46.750.39,46.750.36,46.750.33,46.750.30,46.750.27,46.752.49,46.252.46,46.25
2.43,46.252.40,46.252.37,46.252.34,46.252.31,46.252.28,46.252.25,46.252.22,46.25
2.19,46.252.16,46.252.13,46.252.10,46.252.07,46.252.04,46.252.01,46.251.98,46.251.95,46.251.92,46.251.89,46.251.86,46.251.83,46.251.80,46.251.77,46.251.74,46.251.71,46.251.68,46.251.65,46.25
1.62,46.251.59,46.251.56,46.251.53,46.251.50,46.251.47,46.251.44,46.251.41,46.251.38,46.251.35,46.251.32,46.251.29,46.251.26,46.251.23,46.251.20,46.251.17,46.25
1.14,46.251.11,46.251.08,46.251.05,46.251.02,46.250.99,46.250.96,46.250.93,46.250.90,46.250.87,46.25
0.84,46.250.81,46.250.78,46.250.75,46.250.72,46.250.69,46.250.66,46.250.63,46.250.60,46.250.57,46.250.54,46.250.51,46.250.48,46.250.45,46.250.42,46.250.39,46.250.36,46.250.33,46.250.30,46.250.27,46.252.49,45.75
2.46,45.752.43,45.752.40,45.75
2.37,45.752.34,45.752.31,45.75
2.28,45.752.25,45.752.22,45.752.19,45.752.16,45.752.13,45.752.10,45.752.07,45.752.04,45.752.01,45.751.98,45.751.95,45.751.92,45.751.89,45.751.86,45.751.83,45.751.80,45.751.77,45.751.74,45.751.71,45.751.68,45.751.65,45.751.62,45.751.59,45.751.56,45.751.53,45.751.50,45.751.47,45.751.44,45.75
1.41,45.751.38,45.751.35,45.751.32,45.751.29,45.751.26,45.75
1.23,45.751.20,45.751.17,45.751.14,45.751.11,45.751.08,45.751.05,45.751.02,45.750.99,45.750.96,45.750.93,45.750.90,45.750.87,45.750.84,45.750.81,45.750.78,45.750.75,45.750.72,45.750.69,45.750.66,45.750.63,45.750.60,45.75
0.57,45.750.54,45.750.51,45.750.48,45.750.45,45.750.42,45.750.39,45.75
0.36,45.750.33,45.750.30,45.750.27,45.752.49,45.252.46,45.252.43,45.252.40,45.25
2.37,45.252.34,45.252.31,45.252.28,45.252.25,45.252.22,45.252.19,45.252.16,45.252.13,45.252.10,45.252.07,45.252.04,45.252.01,45.251.98,45.251.95,45.251.92,45.251.89,45.251.86,45.251.83,45.251.80,45.251.77,45.251.74,45.251.71,45.251.68,45.251.65,45.251.62,45.251.59,45.251.56,45.251.53,45.251.50,45.251.47,45.251.44,45.251.41,45.251.38,45.251.35,45.251.32,45.25
1.29,45.251.26,45.251.23,45.251.20,45.251.17,45.251.14,45.251.11,45.251.08,45.251.05,45.251.02,45.250.99,45.250.96,45.250.93,45.250.90,45.250.87,45.250.84,45.250.81,45.250.78,45.250.75,45.250.72,45.250.69,45.250.66,45.250.63,45.250.60,45.250.57,45.250.54,45.250.51,45.250.48,45.250.45,45.250.42,45.250.39,45.250.36,45.250.33,45.250.30,45.250.27,45.25
2.49,44.752.46,44.752.43,44.75
2.40,44.752.37,44.752.34,44.752.31,44.752.28,44.752.25,44.752.22,44.752.19,44.752.16,44.75
2.13,44.752.10,44.752.07,44.752.04,44.752.01,44.751.98,44.751.95,44.751.92,44.751.89,44.751.86,44.751.83,44.751.80,44.751.77,44.751.74,44.751.71,44.751.68,44.751.65,44.751.62,44.751.59,44.751.56,44.751.53,44.751.50,44.751.47,44.751.44,44.751.41,44.751.38,44.751.35,44.751.32,44.75
1.29,44.751.26,44.751.23,44.751.20,44.751.17,44.751.14,44.751.11,44.751.08,44.751.05,44.751.02,44.750.99,44.750.96,44.750.93,44.750.90,44.750.87,44.750.84,44.750.81,44.750.78,44.750.75,44.750.72,44.750.69,44.75
0.66,44.750.63,44.750.60,44.750.57,44.750.54,44.750.51,44.750.48,44.750.45,44.750.42,44.750.39,44.750.36,44.750.33,44.750.30,44.750.27,44.752.49,44.252.46,44.252.43,44.252.40,44.252.37,44.25
2.34,44.252.31,44.252.28,44.252.25,44.252.22,44.252.19,44.252.16,44.252.13,44.252.10,44.252.07,44.252.04,44.252.01,44.251.98,44.25
1.95,44.251.92,44.251.89,44.251.86,44.251.83,44.251.80,44.25
1.77,44.251.74,44.251.71,44.251.68,44.251.65,44.251.62,44.25
1.59,44.251.56,44.251.53,44.251.50,44.251.47,44.251.44,44.251.41,44.25
1.38,44.251.35,44.251.32,44.251.29,44.251.26,44.251.23,44.251.20,44.25
1.17,44.251.14,44.251.11,44.251.08,44.251.05,44.251.02,44.250.99,44.250.96,44.250.93,44.250.90,44.250.87,44.250.84,44.250.81,44.250.78,44.250.75,44.250.72,44.250.69,44.250.66,44.250.63,44.250.60,44.250.57,44.250.54,44.25
0.51,44.250.48,44.250.45,44.250.42,44.250.39,44.25
0.36,44.250.33,44.250.30,44.250.27,44.252.49,43.752.46,43.752.43,43.752.40,43.75
2.37,43.752.34,43.752.31,43.752.28,43.752.25,43.752.22,43.75
2.19,43.752.16,43.752.13,43.752.10,43.752.07,43.752.04,43.752.01,43.751.98,43.751.95,43.751.92,43.751.89,43.751.86,43.751.83,43.751.80,43.751.77,43.751.74,43.751.71,43.751.68,43.75
1.65,43.75
1.62,43.75
1.59,43.75
1.56,43.75
1.53,43.751.50,43.751.47,43.751.44,43.751.41,43.751.38,43.751.35,43.751.32,43.751.29,43.751.26,43.751.23,43.751.20,43.75
1.17,43.751.14,43.751.11,43.751.08,43.751.05,43.751.02,43.750.99,43.750.96,43.750.93,43.750.90,43.750.87,43.750.84,43.750.81,43.750.78,43.750.75,43.750.72,43.750.69,43.750.66,43.750.63,43.750.60,43.750.57,43.750.54,43.750.51,43.750.48,43.750.45,43.750.42,43.750.39,43.750.36,43.750.33,43.750.30,43.750.27,43.752.49,43.252.46,43.252.43,43.252.40,43.252.37,43.252.34,43.252.31,43.252.28,43.252.25,43.252.22,43.252.19,43.252.16,43.252.13,43.252.10,43.252.07,43.252.04,43.252.01,43.251.98,43.251.95,43.251.92,43.251.89,43.251.86,43.251.83,43.251.80,43.251.77,43.251.74,43.251.71,43.251.68,43.251.65,43.25
1.62,43.251.59,43.25
1.56,43.251.53,43.251.50,43.251.47,43.251.44,43.251.41,43.251.38,43.251.35,43.251.32,43.251.29,43.251.26,43.251.23,43.251.20,43.251.17,43.251.14,43.251.11,43.251.08,43.251.05,43.251.02,43.250.99,43.250.96,43.250.93,43.250.90,43.250.87,43.250.84,43.250.81,43.250.78,43.250.75,43.250.72,43.250.69,43.250.66,43.250.63,43.250.60,43.250.57,43.250.54,43.250.51,43.250.48,43.250.45,43.250.42,43.250.39,43.250.36,43.250.33,43.250.30,43.250.27,43.252.49,42.752.46,42.752.43,42.752.40,42.752.37,42.752.34,42.752.31,42.752.28,42.752.25,42.752.22,42.752.19,42.752.16,42.752.13,42.752.10,42.752.07,42.752.04,42.752.01,42.751.98,42.751.95,42.751.92,42.751.89,42.751.86,42.751.83,42.75
1.80,42.751.77,42.75
1.74,42.751.71,42.75
1.68,42.751.65,42.751.62,42.75
1.59,42.751.56,42.751.53,42.751.50,42.751.47,42.751.44,42.751.41,42.751.38,42.751.35,42.751.32,42.751.29,42.75
1.26,42.751.23,42.751.20,42.751.17,42.751.14,42.751.11,42.751.08,42.751.05,42.751.02,42.750.99,42.750.96,42.750.93,42.750.90,42.750.87,42.750.84,42.750.81,42.750.78,42.750.75,42.750.72,42.75
0.69,42.750.66,42.750.63,42.750.60,42.750.57,42.750.54,42.750.51,42.750.48,42.750.45,42.750.42,42.750.39,42.750.36,42.750.33,42.750.30,42.750.27,42.752.49,42.252.46,42.252.43,42.252.40,42.25
2.37,42.252.34,42.252.31,42.252.28,42.252.25,42.252.22,42.252.19,42.252.16,42.252.13,42.252.10,42.252.07,42.252.04,42.252.01,42.251.98,42.251.95,42.251.92,42.25
1.89,42.251.86,42.251.83,42.251.80,42.251.77,42.25
1.74,42.251.71,42.25
1.68,42.251.65,42.25
1.62,42.251.59,42.251.56,42.251.53,42.251.50,42.251.47,42.251.44,42.251.41,42.25
1.38,42.251.35,42.25
1.32,42.251.29,42.251.26,42.251.23,42.251.20,42.25
1.17,42.251.14,42.25
1.11,42.251.08,42.251.05,42.251.02,42.250.99,42.250.96,42.250.93,42.250.90,42.250.87,42.250.84,42.250.81,42.250.78,42.250.75,42.250.72,42.25
0.69,42.250.66,42.250.63,42.250.60,42.250.57,42.250.54,42.250.51,42.250.48,42.250.45,42.250.42,42.25
0.39,42.250.36,42.250.33,42.250.30,42.250.27,42.25
2.49,41.752.46,41.752.43,41.752.40,41.752.37,41.752.34,41.752.31,41.752.28,41.752.25,41.752.22,41.752.19,41.752.16,41.752.13,41.752.10,41.752.07,41.752.04,41.75
2.01,41.751.98,41.751.95,41.751.92,41.751.89,41.751.86,41.751.83,41.751.80,41.751.77,41.751.74,41.751.71,41.75
1.68,41.751.65,41.751.62,41.751.59,41.751.56,41.751.53,41.751.50,41.751.47,41.751.44,41.751.41,41.751.38,41.751.35,41.751.32,41.751.29,41.751.26,41.751.23,41.75
1.20,41.751.17,41.751.14,41.751.11,41.751.08,41.751.05,41.751.02,41.75
0.99,41.750.96,41.750.93,41.750.90,41.750.87,41.750.84,41.750.81,41.750.78,41.750.75,41.750.72,41.75
0.69,41.750.66,41.75
0.63,41.750.60,41.750.57,41.750.54,41.750.51,41.750.48,41.750.45,41.750.42,41.75
0.39,41.750.36,41.750.33,41.750.30,41.750.27,41.752.49,41.252.46,41.252.43,41.252.40,41.252.37,41.252.34,41.252.31,41.252.28,41.252.25,41.252.22,41.252.19,41.252.16,41.252.13,41.252.10,41.252.07,41.252.04,41.252.01,41.251.98,41.251.95,41.251.92,41.251.89,41.251.86,41.251.83,41.251.80,41.251.77,41.251.74,41.251.71,41.251.68,41.251.65,41.251.62,41.251.59,41.251.56,41.251.53,41.251.50,41.251.47,41.251.44,41.251.41,41.251.38,41.251.35,41.251.32,41.251.29,41.251.26,41.251.23,41.251.20,41.251.17,41.251.14,41.251.11,41.251.08,41.251.05,41.251.02,41.250.99,41.250.96,41.250.93,41.250.90,41.250.87,41.250.84,41.250.81,41.250.78,41.250.75,41.250.72,41.250.69,41.250.66,41.250.63,41.250.60,41.250.57,41.250.54,41.250.51,41.250.48,41.250.45,41.250.42,41.250.39,41.250.36,41.250.33,41.250.30,41.250.27,41.252.49,40.75
2.46,40.752.43,40.752.40,40.752.37,40.752.34,40.75
2.31,40.752.28,40.752.25,40.752.22,40.752.19,40.752.16,40.752.13,40.752.10,40.752.07,40.752.04,40.752.01,40.751.98,40.751.95,40.751.92,40.751.89,40.751.86,40.751.83,40.751.80,40.75
1.77,40.751.74,40.751.71,40.751.68,40.751.65,40.751.62,40.751.59,40.751.56,40.751.53,40.751.50,40.751.47,40.751.44,40.751.41,40.751.38,40.751.35,40.751.32,40.751.29,40.751.26,40.751.23,40.75
1.20,40.751.17,40.751.14,40.751.11,40.751.08,40.751.05,40.751.02,40.75
0.99,40.750.96,40.750.93,40.750.90,40.750.87,40.750.84,40.750.81,40.750.78,40.75
0.75,40.750.72,40.750.69,40.750.66,40.750.63,40.750.60,40.750.57,40.750.54,40.750.51,40.750.48,40.750.45,40.750.42,40.750.39,40.750.36,40.750.33,40.750.30,40.750.27,40.752.49,40.252.46,40.252.43,40.252.40,40.252.37,40.252.34,40.252.31,40.25
2.28,40.252.25,40.252.22,40.252.19,40.252.16,40.252.13,40.252.10,40.252.07,40.252.04,40.252.01,40.251.98,40.25
1.95,40.251.92,40.251.89,40.251.86,40.251.83,40.251.80,40.251.77,40.251.74,40.251.71,40.251.68,40.251.65,40.251.62,40.251.59,40.251.56,40.251.53,40.251.50,40.251.47,40.251.44,40.251.41,40.251.38,40.251.35,40.251.32,40.251.29,40.25
1.26,40.251.23,40.251.20,40.251.17,40.251.14,40.251.11,40.251.08,40.251.05,40.251.02,40.250.99,40.250.96,40.250.93,40.250.90,40.250.87,40.250.84,40.250.81,40.250.78,40.250.75,40.250.72,40.25
0.69,40.250.66,40.250.63,40.250.60,40.250.57,40.250.54,40.250.51,40.250.48,40.250.45,40.250.42,40.250.39,40.250.36,40.250.33,40.25
0.30,40.250.27,40.252.49,39.752.46,39.752.43,39.752.40,39.752.37,39.75
2.34,39.752.31,39.752.28,39.752.25,39.752.22,39.752.19,39.752.16,39.752.13,39.752.10,39.752.07,39.752.04,39.752.01,39.75
1.98,39.751.95,39.751.92,39.751.89,39.751.86,39.751.83,39.751.80,39.751.77,39.751.74,39.751.71,39.751.68,39.751.65,39.751.62,39.751.59,39.751.56,39.751.53,39.751.50,39.751.47,39.751.44,39.751.41,39.751.38,39.751.35,39.75
1.32,39.751.29,39.751.26,39.751.23,39.751.20,39.751.17,39.751.14,39.751.11,39.751.08,39.75
1.05,39.751.02,39.750.99,39.750.96,39.750.93,39.750.90,39.750.87,39.750.84,39.750.81,39.750.78,39.750.75,39.750.72,39.750.69,39.75
0.66,39.750.63,39.750.60,39.750.57,39.750.54,39.750.51,39.750.48,39.750.45,39.750.42,39.750.39,39.750.36,39.750.33,39.75
0.30,39.750.27,39.752.49,39.252.46,39.252.43,39.252.40,39.252.37,39.25
2.34,39.252.31,39.252.28,39.252.25,39.252.22,39.252.19,39.252.16,39.252.13,39.252.10,39.252.07,39.252.04,39.252.01,39.251.98,39.251.95,39.251.92,39.251.89,39.251.86,39.251.83,39.251.80,39.251.77,39.251.74,39.251.71,39.251.68,39.251.65,39.251.62,39.251.59,39.251.56,39.251.53,39.251.50,39.251.47,39.251.44,39.251.41,39.251.38,39.251.35,39.251.32,39.251.29,39.251.26,39.251.23,39.251.20,39.251.17,39.251.14,39.251.11,39.251.08,39.251.05,39.251.02,39.250.99,39.250.96,39.250.93,39.250.90,39.250.87,39.250.84,39.250.81,39.250.78,39.250.75,39.250.72,39.250.69,39.250.66,39.250.63,39.250.60,39.250.57,39.250.54,39.250.51,39.250.48,39.250.45,39.250.42,39.250.39,39.250.36,39.250.33,39.250.30,39.250.27,39.252.49,38.752.46,38.752.43,38.752.40,38.752.37,38.75
2.34,38.752.31,38.752.28,38.752.25,38.752.22,38.752.19,38.752.16,38.752.13,38.752.10,38.752.07,38.752.04,38.752.01,38.751.98,38.751.95,38.751.92,38.751.89,38.75
1.86,38.751.83,38.751.80,38.751.77,38.751.74,38.751.71,38.751.68,38.751.65,38.751.62,38.751.59,38.751.56,38.751.53,38.751.50,38.751.47,38.75
1.44,38.751.41,38.75
1.38,38.751.35,38.751.32,38.751.29,38.751.26,38.751.23,38.751.20,38.751.17,38.751.14,38.751.11,38.751.08,38.751.05,38.751.02,38.750.99,38.750.96,38.750.93,38.750.90,38.750.87,38.75
0.84,38.750.81,38.750.78,38.750.75,38.750.72,38.750.69,38.750.66,38.750.63,38.750.60,38.750.57,38.750.54,38.750.51,38.75
0.48,38.750.45,38.750.42,38.750.39,38.750.36,38.750.33,38.750.30,38.750.27,38.752.49,38.252.46,38.252.43,38.252.40,38.252.37,38.252.34,38.252.31,38.252.28,38.252.25,38.252.22,38.252.19,38.252.16,38.252.13,38.252.10,38.252.07,38.252.04,38.252.01,38.251.98,38.251.95,38.251.92,38.251.89,38.251.86,38.251.83,38.251.80,38.251.77,38.251.74,38.251.71,38.251.68,38.251.65,38.251.62,38.251.59,38.251.56,38.251.53,38.251.50,38.251.47,38.251.44,38.251.41,38.25
1.38,38.251.35,38.251.32,38.251.29,38.251.26,38.251.23,38.251.20,38.251.17,38.251.14,38.251.11,38.251.08,38.251.05,38.25
1.02,38.250.99,38.25
0.96,38.250.93,38.250.90,38.250.87,38.250.84,38.25
0.81,38.250.78,38.250.75,38.250.72,38.250.69,38.250.66,38.250.63,38.250.60,38.250.57,38.25
0.54,38.250.51,38.250.48,38.250.45,38.25
0.42,38.250.39,38.250.36,38.250.33,38.250.30,38.25
0.27,38.252.49,37.752.46,37.752.43,37.752.40,37.75
2.37,37.752.34,37.752.31,37.752.28,37.752.25,37.752.22,37.752.19,37.752.16,37.752.13,37.752.10,37.752.07,37.752.04,37.752.01,37.751.98,37.751.95,37.751.92,37.751.89,37.751.86,37.751.83,37.751.80,37.751.77,37.751.74,37.751.71,37.751.68,37.751.65,37.751.62,37.751.59,37.75
1.56,37.751.53,37.751.50,37.751.47,37.751.44,37.751.41,37.751.38,37.751.35,37.751.32,37.751.29,37.751.26,37.751.23,37.751.20,37.751.17,37.751.14,37.751.11,37.751.08,37.751.05,37.75
1.02,37.750.99,37.750.96,37.750.93,37.750.90,37.750.87,37.750.84,37.750.81,37.750.78,37.750.75,37.750.72,37.750.69,37.750.66,37.750.63,37.750.60,37.750.57,37.750.54,37.750.51,37.750.48,37.750.45,37.750.42,37.750.39,37.75
0.36,37.750.33,37.750.30,37.750.27,37.75
2.49,37.252.46,37.252.43,37.252.40,37.252.37,37.252.34,37.252.31,37.252.28,37.252.25,37.252.22,37.252.19,37.252.16,37.252.13,37.252.10,37.252.07,37.252.04,37.252.01,37.251.98,37.251.95,37.251.92,37.251.89,37.251.86,37.251.83,37.251.80,37.251.77,37.251.74,37.251.71,37.251.68,37.251.65,37.251.62,37.251.59,37.251.56,37.251.53,37.251.50,37.251.47,37.251.44,37.251.41,37.251.38,37.251.35,37.251.32,37.251.29,37.251.26,37.251.23,37.251.20,37.251.17,37.251.14,37.251.11,37.251.08,37.251.05,37.251.02,37.250.99,37.250.96,37.250.93,37.250.90,37.250.87,37.250.84,37.250.81,37.250.78,37.250.75,37.250.72,37.250.69,37.250.66,37.250.63,37.250.60,37.250.57,37.250.54,37.250.51,37.250.48,37.250.45,37.250.42,37.250.39,37.250.36,37.250.33,37.250.30,37.250.27,37.25
2.49,36.752.46,36.752.43,36.752.40,36.752.37,36.752.34,36.75
2.31,36.752.28,36.75
2.25,36.75
2.22,36.752.19,36.752.16,36.75
2.13,36.752.10,36.75
2.07,36.752.04,36.752.01,36.751.98,36.751.95,36.751.92,36.751.89,36.751.86,36.751.83,36.751.80,36.75
1.77,36.751.74,36.751.71,36.751.68,36.751.65,36.751.62,36.751.59,36.751.56,36.751.53,36.75
1.50,36.751.47,36.751.44,36.751.41,36.751.38,36.751.35,36.751.32,36.751.29,36.751.26,36.751.23,36.751.20,36.751.17,36.75
1.14,36.751.11,36.751.08,36.751.05,36.751.02,36.750.99,36.750.96,36.750.93,36.750.90,36.750.87,36.750.84,36.750.81,36.750.78,36.750.75,36.750.72,36.750.69,36.750.66,36.750.63,36.750.60,36.750.57,36.750.54,36.75
0.51,36.750.48,36.750.45,36.750.42,36.750.39,36.750.36,36.750.33,36.750.30,36.750.27,36.752.49,36.252.46,36.252.43,36.252.40,36.252.37,36.25
2.34,36.252.31,36.252.28,36.25
2.25,36.252.22,36.252.19,36.252.16,36.252.13,36.252.10,36.252.07,36.252.04,36.252.01,36.251.98,36.251.95,36.251.92,36.251.89,36.251.86,36.251.83,36.251.80,36.251.77,36.251.74,36.251.71,36.251.68,36.251.65,36.251.62,36.251.59,36.251.56,36.251.53,36.251.50,36.25
1.47,36.251.44,36.251.41,36.251.38,36.251.35,36.251.32,36.251.29,36.251.26,36.251.23,36.251.20,36.251.17,36.251.14,36.251.11,36.251.08,36.251.05,36.251.02,36.250.99,36.250.96,36.250.93,36.250.90,36.250.87,36.250.84,36.250.81,36.250.78,36.25
0.75,36.250.72,36.250.69,36.250.66,36.250.63,36.250.60,36.250.57,36.250.54,36.250.51,36.250.48,36.250.45,36.250.42,36.250.39,36.250.36,36.250.33,36.250.30,36.250.27,36.252.49,35.752.46,35.752.43,35.752.40,35.75
2.37,35.75
2.34,35.752.31,35.75
2.28,35.752.25,35.752.22,35.752.19,35.752.16,35.752.13,35.752.10,35.752.07,35.752.04,35.752.01,35.751.98,35.751.95,35.751.92,35.751.89,35.751.86,35.751.83,35.751.80,35.751.77,35.751.74,35.751.71,35.751.68,35.751.65,35.751.62,35.751.59,35.751.56,35.751.53,35.751.50,35.751.47,35.751.44,35.75
1.41,35.751.38,35.751.35,35.751.32,35.751.29,35.751.26,35.751.23,35.751.20,35.751.17,35.751.14,35.751.11,35.751.08,35.751.05,35.751.02,35.750.99,35.750.96,35.750.93,35.750.90,35.750.87,35.750.84,35.750.81,35.750.78,35.75
0.75,35.750.72,35.750.69,35.750.66,35.750.63,35.750.60,35.750.57,35.750.54,35.750.51,35.750.48,35.750.45,35.750.42,35.750.39,35.750.36,35.750.33,35.750.30,35.750.27,35.752.49,35.252.46,35.252.43,35.25
2.40,35.252.37,35.252.34,35.252.31,35.252.28,35.252.25,35.252.22,35.252.19,35.252.16,35.252.13,35.252.10,35.252.07,35.252.04,35.252.01,35.251.98,35.251.95,35.251.92,35.251.89,35.251.86,35.251.83,35.251.80,35.251.77,35.251.74,35.251.71,35.251.68,35.251.65,35.251.62,35.251.59,35.251.56,35.251.53,35.251.50,35.251.47,35.251.44,35.251.41,35.251.38,35.251.35,35.251.32,35.251.29,35.251.26,35.251.23,35.251.20,35.251.17,35.251.14,35.251.11,35.251.08,35.251.05,35.251.02,35.250.99,35.250.96,35.250.93,35.250.90,35.250.87,35.250.84,35.250.81,35.250.78,35.250.75,35.250.72,35.250.69,35.250.66,35.250.63,35.250.60,35.250.57,35.250.54,35.250.51,35.250.48,35.250.45,35.250.42,35.250.39,35.250.36,35.250.33,35.250.30,35.250.27,35.252.49,34.752.46,34.752.43,34.75
2.40,34.752.37,34.75
2.34,34.752.31,34.75
2.28,34.752.25,34.752.22,34.752.19,34.752.16,34.752.13,34.752.10,34.752.07,34.752.04,34.752.01,34.751.98,34.751.95,34.751.92,34.751.89,34.751.86,34.75
1.83,34.751.80,34.751.77,34.751.74,34.751.71,34.751.68,34.751.65,34.751.62,34.751.59,34.751.56,34.751.53,34.751.50,34.75
1.47,34.751.44,34.751.41,34.751.38,34.751.35,34.751.32,34.751.29,34.751.26,34.751.23,34.751.20,34.751.17,34.751.14,34.751.11,34.751.08,34.751.05,34.751.02,34.750.99,34.750.96,34.750.93,34.750.90,34.750.87,34.750.84,34.750.81,34.75
0.78,34.750.75,34.750.72,34.750.69,34.750.66,34.750.63,34.750.60,34.750.57,34.750.54,34.750.51,34.750.48,34.750.45,34.750.42,34.750.39,34.750.36,34.750.33,34.750.30,34.750.27,34.75
2.49,34.252.46,34.252.43,34.252.40,34.25
2.37,34.25
2.34,34.252.31,34.252.28,34.25
2.25,34.252.22,34.252.19,34.252.16,34.252.13,34.252.10,34.252.07,34.252.04,34.252.01,34.251.98,34.251.95,34.251.92,34.251.89,34.25
1.86,34.251.83,34.251.80,34.251.77,34.251.74,34.251.71,34.251.68,34.251.65,34.251.62,34.251.59,34.251.56,34.251.53,34.251.50,34.251.47,34.251.44,34.251.41,34.251.38,34.251.35,34.251.32,34.251.29,34.251.26,34.25
1.23,34.251.20,34.251.17,34.251.14,34.251.11,34.251.08,34.251.05,34.251.02,34.250.99,34.250.96,34.250.93,34.250.90,34.250.87,34.250.84,34.250.81,34.25
0.78,34.250.75,34.250.72,34.250.69,34.250.66,34.250.63,34.250.60,34.250.57,34.250.54,34.250.51,34.250.48,34.250.45,34.250.42,34.250.39,34.250.36,34.250.33,34.250.30,34.250.27,34.25
2.49,33.752.46,33.75
2.43,33.752.40,33.752.37,33.752.34,33.752.31,33.752.28,33.752.25,33.752.22,33.75
2.19,33.752.16,33.752.13,33.752.10,33.752.07,33.752.04,33.752.01,33.751.98,33.751.95,33.751.92,33.751.89,33.751.86,33.75
1.83,33.751.80,33.751.77,33.751.74,33.75
1.71,33.751.68,33.751.65,33.751.62,33.751.59,33.751.56,33.751.53,33.751.50,33.751.47,33.751.44,33.751.41,33.751.38,33.751.35,33.751.32,33.751.29,33.751.26,33.751.23,33.751.20,33.751.17,33.751.14,33.751.11,33.751.08,33.751.05,33.75
1.02,33.750.99,33.750.96,33.750.93,33.750.90,33.750.87,33.750.84,33.750.81,33.750.78,33.750.75,33.750.72,33.750.69,33.750.66,33.750.63,33.75
0.60,33.750.57,33.750.54,33.750.51,33.750.48,33.750.45,33.750.42,33.750.39,33.750.36,33.750.33,33.750.30,33.750.27,33.752.49,33.252.46,33.252.43,33.252.40,33.252.37,33.252.34,33.252.31,33.252.28,33.252.25,33.252.22,33.252.19,33.252.16,33.252.13,33.252.10,33.252.07,33.252.04,33.252.01,33.251.98,33.251.95,33.251.92,33.251.89,33.251.86,33.251.83,33.251.80,33.251.77,33.251.74,33.251.71,33.251.68,33.25
1.65,33.251.62,33.251.59,33.251.56,33.251.53,33.251.50,33.251.47,33.251.44,33.251.41,33.251.38,33.251.35,33.251.32,33.251.29,33.251.26,33.251.23,33.251.20,33.251.17,33.251.14,33.251.11,33.251.08,33.251.05,33.251.02,33.250.99,33.250.96,33.250.93,33.250.90,33.250.87,33.250.84,33.250.81,33.250.78,33.250.75,33.250.72,33.250.69,33.250.66,33.250.63,33.250.60,33.250.57,33.250.54,33.250.51,33.250.48,33.250.45,33.250.42,33.250.39,33.250.36,33.250.33,33.250.30,33.250.27,33.25
2.49,32.752.46,32.752.43,32.752.40,32.752.37,32.752.34,32.752.31,32.752.28,32.752.25,32.752.22,32.752.19,32.752.16,32.752.13,32.75
2.10,32.752.07,32.752.04,32.752.01,32.75
1.98,32.751.95,32.751.92,32.75
1.89,32.751.86,32.751.83,32.751.80,32.751.77,32.751.74,32.75
1.71,32.75
1.68,32.75
1.65,32.751.62,32.751.59,32.751.56,32.75
1.53,32.751.50,32.751.47,32.751.44,32.751.41,32.751.38,32.751.35,32.751.32,32.751.29,32.751.26,32.751.23,32.751.20,32.751.17,32.751.14,32.751.11,32.75
1.08,32.751.05,32.751.02,32.750.99,32.750.96,32.750.93,32.750.90,32.750.87,32.750.84,32.750.81,32.750.78,32.750.75,32.750.72,32.750.69,32.750.66,32.750.63,32.75
0.60,32.750.57,32.750.54,32.750.51,32.750.48,32.750.45,32.750.42,32.750.39,32.750.36,32.750.33,32.75
0.30,32.750.27,32.752.49,32.252.46,32.252.43,32.252.40,32.252.37,32.252.34,32.252.31,32.252.28,32.25
2.25,32.252.22,32.252.19,32.252.16,32.252.13,32.25
2.10,32.252.07,32.252.04,32.25
2.01,32.251.98,32.251.95,32.251.92,32.251.89,32.251.86,32.251.83,32.251.80,32.25
1.77,32.251.74,32.251.71,32.251.68,32.251.65,32.25
1.62,32.251.59,32.251.56,32.251.53,32.25
1.50,32.251.47,32.251.44,32.251.41,32.251.38,32.251.35,32.25
1.32,32.251.29,32.251.26,32.251.23,32.251.20,32.251.17,32.251.14,32.251.11,32.251.08,32.251.05,32.25
1.02,32.250.99,32.250.96,32.250.93,32.250.90,32.250.87,32.250.84,32.250.81,32.250.78,32.250.75,32.250.72,32.25
0.69,32.250.66,32.250.63,32.25
0.60,32.250.57,32.250.54,32.250.51,32.250.48,32.250.45,32.250.42,32.250.39,32.250.36,32.250.33,32.250.30,32.250.27,32.252.49,31.752.46,31.752.43,31.752.40,31.752.37,31.752.34,31.752.31,31.752.28,31.752.25,31.752.22,31.752.19,31.752.16,31.752.13,31.752.10,31.752.07,31.75
2.04,31.75
2.01,31.751.98,31.751.95,31.751.92,31.751.89,31.751.86,31.75
1.83,31.751.80,31.751.77,31.751.74,31.751.71,31.751.68,31.751.65,31.751.62,31.751.59,31.751.56,31.751.53,31.751.50,31.751.47,31.751.44,31.751.41,31.751.38,31.75
1.35,31.751.32,31.751.29,31.751.26,31.751.23,31.751.20,31.75
1.17,31.751.14,31.751.11,31.751.08,31.751.05,31.751.02,31.750.99,31.750.96,31.750.93,31.750.90,31.750.87,31.750.84,31.750.81,31.750.78,31.750.75,31.750.72,31.750.69,31.750.66,31.750.63,31.750.60,31.750.57,31.750.54,31.75
0.51,31.750.48,31.750.45,31.750.42,31.750.39,31.750.36,31.750.33,31.75
0.30,31.750.27,31.752.49,31.252.46,31.252.43,31.252.40,31.252.37,31.252.34,31.252.31,31.252.28,31.252.25,31.252.22,31.252.19,31.252.16,31.252.13,31.252.10,31.252.07,31.252.04,31.252.01,31.251.98,31.251.95,31.251.92,31.251.89,31.251.86,31.251.83,31.251.80,31.251.77,31.251.74,31.251.71,31.251.68,31.251.65,31.251.62,31.251.59,31.251.56,31.251.53,31.251.50,31.251.47,31.251.44,31.251.41,31.251.38,31.251.35,31.251.32,31.251.29,31.251.26,31.251.23,31.251.20,31.251.17,31.251.14,31.251.11,31.251.08,31.251.05,31.251.02,31.250.99,31.250.96,31.250.93,31.250.90,31.250.87,31.250.84,31.250.81,31.250.78,31.250.75,31.250.72,31.250.69,31.250.66,31.250.63,31.250.60,31.250.57,31.250.54,31.250.51,31.250.48,31.250.45,31.250.42,31.250.39,31.250.36,31.250.33,31.250.30,31.250.27,31.252.49,30.752.46,30.752.43,30.752.40,30.752.37,30.752.34,30.752.31,30.75
2.28,30.752.25,30.752.22,30.752.19,30.75
2.16,30.752.13,30.752.10,30.752.07,30.75
2.04,30.75
2.01,30.751.98,30.75
1.95,30.751.92,30.75
1.89,30.751.86,30.751.83,30.751.80,30.751.77,30.751.74,30.751.71,30.75
1.68,30.751.65,30.751.62,30.751.59,30.751.56,30.751.53,30.751.50,30.751.47,30.751.44,30.751.41,30.751.38,30.751.35,30.75
1.32,30.751.29,30.751.26,30.751.23,30.751.20,30.751.17,30.751.14,30.751.11,30.751.08,30.751.05,30.751.02,30.750.99,30.750.96,30.750.93,30.750.90,30.750.87,30.750.84,30.750.81,30.750.78,30.75
0.75,30.750.72,30.750.69,30.750.66,30.750.63,30.750.60,30.750.57,30.750.54,30.750.51,30.750.48,30.750.45,30.750.42,30.75
0.39,30.750.36,30.750.33,30.750.30,30.750.27,30.75
2.49,30.252.46,30.252.43,30.252.40,30.252.37,30.252.34,30.252.31,30.252.28,30.252.25,30.252.22,30.252.19,30.25
2.16,30.25
2.13,30.25
2.10,30.252.07,30.25
2.04,30.252.01,30.251.98,30.25
1.95,30.251.92,30.25
1.89,30.25
1.86,30.251.83,30.251.80,30.251.77,30.251.74,30.251.71,30.251.68,30.251.65,30.251.62,30.251.59,30.251.56,30.251.53,30.251.50,30.251.47,30.251.44,30.251.41,30.251.38,30.251.35,30.251.32,30.251.29,30.251.26,30.251.23,30.251.20,30.251.17,30.251.14,30.251.11,30.251.08,30.251.05,30.251.02,30.25
0.99,30.250.96,30.250.93,30.250.90,30.250.87,30.250.84,30.25
0.81,30.250.78,30.250.75,30.250.72,30.250.69,30.250.66,30.250.63,30.250.60,30.250.57,30.250.54,30.250.51,30.250.48,30.250.45,30.250.42,30.250.39,30.250.36,30.250.33,30.250.30,30.250.27,30.252.49,29.752.46,29.752.43,29.752.40,29.752.37,29.752.34,29.752.31,29.752.28,29.752.25,29.752.22,29.75
2.19,29.752.16,29.752.13,29.75
2.10,29.75
2.07,29.752.04,29.752.01,29.751.98,29.751.95,29.751.92,29.75
1.89,29.751.86,29.751.83,29.751.80,29.751.77,29.75
1.74,29.75
1.71,29.751.68,29.751.65,29.75
1.62,29.751.59,29.751.56,29.751.53,29.751.50,29.751.47,29.751.44,29.751.41,29.751.38,29.751.35,29.751.32,29.751.29,29.751.26,29.751.23,29.751.20,29.751.17,29.751.14,29.751.11,29.751.08,29.751.05,29.751.02,29.75
0.99,29.750.96,29.750.93,29.750.90,29.750.87,29.750.84,29.75
0.81,29.750.78,29.750.75,29.750.72,29.750.69,29.750.66,29.750.63,29.750.60,29.750.57,29.750.54,29.750.51,29.750.48,29.750.45,29.750.42,29.750.39,29.750.36,29.750.33,29.750.30,29.750.27,29.752.49,29.252.46,29.252.43,29.252.40,29.252.37,29.252.34,29.252.31,29.252.28,29.252.25,29.252.22,29.25
2.19,29.252.16,29.252.13,29.252.10,29.252.07,29.252.04,29.25
2.01,29.251.98,29.251.95,29.251.92,29.251.89,29.251.86,29.251.83,29.251.80,29.251.77,29.25
1.74,29.25
1.71,29.251.68,29.251.65,29.251.62,29.251.59,29.251.56,29.251.53,29.251.50,29.251.47,29.251.44,29.251.41,29.251.38,29.251.35,29.251.32,29.251.29,29.251.26,29.251.23,29.251.20,29.251.17,29.251.14,29.251.11,29.251.08,29.251.05,29.251.02,29.250.99,29.250.96,29.25
0.93,29.250.90,29.250.87,29.250.84,29.250.81,29.250.78,29.250.75,29.250.72,29.250.69,29.250.66,29.250.63,29.250.60,29.250.57,29.250.54,29.250.51,29.250.48,29.250.45,29.250.42,29.250.39,29.250.36,29.250.33,29.250.30,29.250.27,29.252.49,28.752.46,28.752.43,28.752.40,28.752.37,28.752.34,28.752.31,28.752.28,28.752.25,28.752.22,28.75
2.19,28.752.16,28.752.13,28.752.10,28.75
2.07,28.752.04,28.752.01,28.751.98,28.751.95,28.751.92,28.751.89,28.751.86,28.751.83,28.75
1.80,28.751.77,28.75
1.74,28.75
1.71,28.75
1.68,28.751.65,28.751.62,28.751.59,28.751.56,28.751.53,28.751.50,28.751.47,28.751.44,28.751.41,28.751.38,28.751.35,28.75
1.32,28.751.29,28.751.26,28.751.23,28.751.20,28.751.17,28.751.14,28.751.11,28.75
1.08,28.751.05,28.751.02,28.750.99,28.750.96,28.75
0.93,28.750.90,28.750.87,28.750.84,28.750.81,28.750.78,28.750.75,28.750.72,28.750.69,28.750.66,28.750.63,28.750.60,28.750.57,28.75
0.54,28.750.51,28.750.48,28.750.45,28.750.42,28.750.39,28.750.36,28.750.33,28.750.30,28.750.27,28.75
2.49,28.252.46,28.252.43,28.252.40,28.252.37,28.252.34,28.252.31,28.252.28,28.25
2.25,28.252.22,28.252.19,28.252.16,28.25
2.13,28.252.10,28.252.07,28.252.04,28.252.01,28.251.98,28.251.95,28.251.92,28.251.89,28.251.86,28.251.83,28.251.80,28.251.77,28.25
1.74,28.251.71,28.251.68,28.251.65,28.251.62,28.251.59,28.251.56,28.251.53,28.251.50,28.251.47,28.251.44,28.251.41,28.251.38,28.251.35,28.251.32,28.25
1.29,28.251.26,28.251.23,28.251.20,28.25
1.17,28.251.14,28.251.11,28.251.08,28.25
1.05,28.251.02,28.250.99,28.25
0.96,28.250.93,28.250.90,28.250.87,28.25
0.84,28.250.81,28.250.78,28.250.75,28.250.72,28.250.69,28.250.66,28.250.63,28.250.60,28.250.57,28.250.54,28.250.51,28.250.48,28.250.45,28.250.42,28.250.39,28.250.36,28.250.33,28.250.30,28.250.27,28.252.49,27.752.46,27.752.43,27.752.40,27.752.37,27.752.34,27.752.31,27.752.28,27.75
2.25,27.752.22,27.752.19,27.75
2.16,27.752.13,27.752.10,27.752.07,27.752.04,27.75
2.01,27.751.98,27.751.95,27.751.92,27.751.89,27.751.86,27.751.83,27.751.80,27.751.77,27.751.74,27.751.71,27.751.68,27.75
1.65,27.75
1.62,27.751.59,27.75
1.56,27.751.53,27.751.50,27.75
1.47,27.751.44,27.751.41,27.751.38,27.751.35,27.751.32,27.751.29,27.751.26,27.751.23,27.751.20,27.751.17,27.751.14,27.751.11,27.751.08,27.751.05,27.751.02,27.750.99,27.75
0.96,27.750.93,27.750.90,27.75
0.87,27.750.84,27.750.81,27.750.78,27.750.75,27.750.72,27.750.69,27.750.66,27.750.63,27.750.60,27.750.57,27.750.54,27.750.51,27.750.48,27.750.45,27.750.42,27.750.39,27.750.36,27.750.33,27.750.30,27.750.27,27.75
2.49,27.252.46,27.252.43,27.252.40,27.252.37,27.252.34,27.252.31,27.252.28,27.252.25,27.252.22,27.252.19,27.252.16,27.252.13,27.252.10,27.252.07,27.252.04,27.252.01,27.251.98,27.251.95,27.251.92,27.251.89,27.251.86,27.251.83,27.251.80,27.251.77,27.25
1.74,27.251.71,27.251.68,27.251.65,27.25
1.62,27.251.59,27.251.56,27.251.53,27.251.50,27.251.47,27.251.44,27.251.41,27.251.38,27.251.35,27.251.32,27.251.29,27.251.26,27.251.23,27.251.20,27.251.17,27.251.14,27.251.11,27.251.08,27.251.05,27.251.02,27.250.99,27.250.96,27.25
0.93,27.250.90,27.250.87,27.250.84,27.250.81,27.250.78,27.250.75,27.250.72,27.250.69,27.250.66,27.250.63,27.250.60,27.250.57,27.250.54,27.250.51,27.250.48,27.250.45,27.250.42,27.250.39,27.250.36,27.250.33,27.250.30,27.250.27,27.252.49,26.752.46,26.752.43,26.752.40,26.752.37,26.75
2.34,26.752.31,26.752.28,26.752.25,26.752.22,26.75
2.19,26.752.16,26.752.13,26.752.10,26.752.07,26.752.04,26.752.01,26.75
1.98,26.751.95,26.751.92,26.751.89,26.751.86,26.751.83,26.751.80,26.751.77,26.75
1.74,26.751.71,26.75
1.68,26.75
1.65,26.751.62,26.751.59,26.751.56,26.751.53,26.751.50,26.751.47,26.751.44,26.751.41,26.751.38,26.751.35,26.751.32,26.751.29,26.751.26,26.751.23,26.751.20,26.751.17,26.751.14,26.751.11,26.751.08,26.751.05,26.751.02,26.750.99,26.750.96,26.75
0.93,26.750.90,26.750.87,26.750.84,26.750.81,26.750.78,26.750.75,26.750.72,26.750.69,26.750.66,26.750.63,26.750.60,26.750.57,26.750.54,26.75
0.51,26.750.48,26.750.45,26.750.42,26.750.39,26.750.36,26.750.33,26.750.30,26.750.27,26.752.49,26.25
2.46,26.252.43,26.252.40,26.252.37,26.252.34,26.252.31,26.252.28,26.252.25,26.252.22,26.252.19,26.252.16,26.252.13,26.252.10,26.252.07,26.252.04,26.252.01,26.251.98,26.251.95,26.251.92,26.251.89,26.251.86,26.251.83,26.251.80,26.251.77,26.25
1.74,26.251.71,26.251.68,26.251.65,26.25
1.62,26.251.59,26.251.56,26.251.53,26.251.50,26.251.47,26.251.44,26.251.41,26.25
1.38,26.251.35,26.251.32,26.251.29,26.251.26,26.251.23,26.251.20,26.251.17,26.251.14,26.251.11,26.251.08,26.251.05,26.251.02,26.25
0.99,26.25
0.96,26.25
0.93,26.250.90,26.250.87,26.250.84,26.250.81,26.250.78,26.250.75,26.250.72,26.250.69,26.250.66,26.250.63,26.250.60,26.25
0.57,26.250.54,26.250.51,26.250.48,26.250.45,26.250.42,26.250.39,26.250.36,26.250.33,26.250.30,26.250.27,26.25
2.49,25.752.46,25.752.43,25.75
2.40,25.752.37,25.752.34,25.752.31,25.752.28,25.752.25,25.752.22,25.752.19,25.75
2.16,25.752.13,25.752.10,25.752.07,25.752.04,25.752.01,25.751.98,25.751.95,25.751.92,25.751.89,25.751.86,25.751.83,25.751.80,25.751.77,25.751.74,25.75
1.71,25.751.68,25.751.65,25.751.62,25.751.59,25.751.56,25.751.53,25.751.50,25.751.47,25.751.44,25.75
1.41,25.751.38,25.751.35,25.751.32,25.751.29,25.751.26,25.751.23,25.75
1.20,25.751.17,25.751.14,25.751.11,25.751.08,25.751.05,25.751.02,25.750.99,25.75
0.96,25.75
0.93,25.75
0.90,25.75
0.87,25.750.84,25.750.81,25.750.78,25.750.75,25.750.72,25.750.69,25.750.66,25.750.63,25.750.60,25.750.57,25.750.54,25.750.51,25.750.48,25.750.45,25.750.42,25.750.39,25.750.36,25.75
0.33,25.750.30,25.750.27,25.752.49,25.252.46,25.252.43,25.252.40,25.252.37,25.252.34,25.252.31,25.252.28,25.252.25,25.252.22,25.252.19,25.252.16,25.252.13,25.252.10,25.252.07,25.252.04,25.252.01,25.251.98,25.251.95,25.251.92,25.251.89,25.251.86,25.251.83,25.251.80,25.251.77,25.251.74,25.251.71,25.251.68,25.251.65,25.251.62,25.251.59,25.251.56,25.251.53,25.251.50,25.251.47,25.251.44,25.25
1.41,25.251.38,25.251.35,25.251.32,25.251.29,25.251.26,25.251.23,25.251.20,25.251.17,25.251.14,25.251.11,25.251.08,25.251.05,25.251.02,25.250.99,25.25
0.96,25.25
0.93,25.25
0.90,25.25
0.87,25.250.84,25.250.81,25.250.78,25.250.75,25.250.72,25.250.69,25.250.66,25.250.63,25.250.60,25.250.57,25.250.54,25.250.51,25.250.48,25.250.45,25.250.42,25.250.39,25.250.36,25.250.33,25.250.30,25.250.27,25.252.49,24.752.46,24.752.43,24.752.40,24.752.37,24.752.34,24.752.31,24.75
2.28,24.752.25,24.752.22,24.75
2.19,24.752.16,24.752.13,24.752.10,24.752.07,24.752.04,24.75
2.01,24.751.98,24.751.95,24.751.92,24.751.89,24.751.86,24.75
1.83,24.751.80,24.751.77,24.751.74,24.751.71,24.751.68,24.751.65,24.751.62,24.751.59,24.75
1.56,24.751.53,24.751.50,24.751.47,24.751.44,24.75
1.41,24.751.38,24.75
1.35,24.75
1.32,24.751.29,24.751.26,24.751.23,24.751.20,24.75
1.17,24.751.14,24.751.11,24.751.08,24.751.05,24.751.02,24.75
0.99,24.75
0.96,24.75
0.93,24.75
0.90,24.75
0.87,24.750.84,24.750.81,24.750.78,24.750.75,24.750.72,24.750.69,24.750.66,24.750.63,24.75
0.60,24.750.57,24.75
0.54,24.750.51,24.750.48,24.750.45,24.750.42,24.750.39,24.750.36,24.750.33,24.750.30,24.750.27,24.752.49,24.252.46,24.252.43,24.252.40,24.252.37,24.252.34,24.252.31,24.252.28,24.252.25,24.252.22,24.252.19,24.252.16,24.252.13,24.252.10,24.252.07,24.252.04,24.252.01,24.251.98,24.251.95,24.251.92,24.251.89,24.251.86,24.251.83,24.251.80,24.25
1.77,24.251.74,24.251.71,24.251.68,24.251.65,24.251.62,24.251.59,24.25
1.56,24.251.53,24.25
1.50,24.25
1.47,24.251.44,24.251.41,24.251.38,24.251.35,24.251.32,24.251.29,24.251.26,24.25
1.23,24.251.20,24.251.17,24.251.14,24.251.11,24.251.08,24.251.05,24.25
1.02,24.250.99,24.25
0.96,24.250.93,24.25
0.90,24.25
0.87,24.25
0.84,24.250.81,24.250.78,24.250.75,24.250.72,24.250.69,24.250.66,24.250.63,24.250.60,24.250.57,24.250.54,24.250.51,24.250.48,24.250.45,24.250.42,24.250.39,24.250.36,24.25
0.33,24.250.30,24.250.27,24.252.49,23.752.46,23.752.43,23.752.40,23.752.37,23.752.34,23.75
2.31,23.752.28,23.752.25,23.752.22,23.752.19,23.752.16,23.75
2.13,23.752.10,23.75
2.07,23.752.04,23.752.01,23.751.98,23.751.95,23.751.92,23.75
1.89,23.751.86,23.751.83,23.75
1.80,23.751.77,23.751.74,23.751.71,23.751.68,23.751.65,23.751.62,23.75
1.59,23.751.56,23.751.53,23.751.50,23.751.47,23.751.44,23.751.41,23.751.38,23.751.35,23.751.32,23.751.29,23.751.26,23.751.23,23.75
1.20,23.751.17,23.751.14,23.751.11,23.751.08,23.751.05,23.75
1.02,23.750.99,23.75
0.96,23.750.90,23.75
0.87,23.75
0.84,23.750.81,23.750.78,23.750.75,23.750.72,23.750.69,23.750.66,23.750.63,23.750.60,23.750.57,23.750.54,23.75
0.51,23.750.48,23.750.45,23.750.42,23.75
0.39,23.750.36,23.750.33,23.750.30,23.750.27,23.75
2.49,23.252.46,23.252.43,23.252.40,23.252.37,23.252.34,23.252.31,23.252.28,23.252.25,23.252.22,23.252.19,23.252.16,23.252.13,23.252.10,23.252.07,23.252.04,23.252.01,23.251.98,23.251.95,23.251.92,23.251.89,23.25
1.86,23.251.83,23.251.80,23.251.77,23.251.74,23.251.71,23.25
1.68,23.251.65,23.251.62,23.251.59,23.251.56,23.251.53,23.251.50,23.251.47,23.251.44,23.251.41,23.251.38,23.251.35,23.251.32,23.251.29,23.251.26,23.251.23,23.251.20,23.251.17,23.251.14,23.251.11,23.25
1.08,23.251.05,23.251.02,23.250.99,23.25
0.96,23.25
0.93,23.250.90,23.250.87,23.25
0.84,23.250.81,23.250.78,23.250.75,23.250.72,23.250.69,23.250.66,23.250.63,23.250.60,23.250.57,23.250.54,23.250.51,23.250.48,23.250.45,23.250.42,23.250.39,23.250.36,23.250.33,23.250.30,23.250.27,23.252.49,22.752.46,22.752.43,22.752.40,22.752.37,22.752.34,22.752.31,22.752.28,22.752.25,22.752.22,22.752.19,22.752.16,22.752.13,22.752.10,22.752.07,22.752.04,22.752.01,22.751.98,22.751.95,22.751.92,22.751.89,22.751.86,22.751.83,22.751.80,22.751.77,22.751.74,22.751.71,22.751.68,22.751.65,22.751.62,22.751.59,22.751.56,22.751.53,22.751.50,22.751.47,22.751.44,22.751.41,22.751.38,22.751.35,22.751.32,22.751.29,22.751.26,22.75
1.23,22.751.20,22.751.17,22.751.14,22.751.11,22.751.08,22.751.05,22.751.02,22.750.99,22.75
0.96,22.75
0.93,22.750.90,22.750.87,22.750.84,22.750.81,22.750.78,22.750.75,22.750.72,22.750.69,22.750.66,22.750.63,22.750.60,22.750.57,22.750.54,22.750.51,22.750.48,22.750.45,22.750.42,22.750.39,22.750.36,22.750.33,22.750.30,22.750.27,22.75
2.49,22.252.46,22.252.43,22.252.40,22.252.37,22.252.34,22.252.31,22.252.28,22.252.25,22.25
2.22,22.252.19,22.252.16,22.252.13,22.25
2.10,22.252.07,22.252.04,22.252.01,22.251.98,22.251.95,22.251.92,22.251.89,22.25
1.86,22.251.83,22.251.80,22.25
1.77,22.251.74,22.251.71,22.251.68,22.251.65,22.251.62,22.251.59,22.251.56,22.251.53,22.251.50,22.251.47,22.251.44,22.25
1.41,22.251.38,22.25
1.35,22.251.32,22.25
1.29,22.251.26,22.251.23,22.25
1.20,22.251.17,22.251.14,22.251.11,22.251.08,22.25
1.05,22.251.02,22.25
0.99,22.25
0.96,22.25
0.93,22.250.90,22.250.87,22.250.84,22.250.81,22.250.78,22.25
0.75,22.250.72,22.250.69,22.250.66,22.250.63,22.250.60,22.250.57,22.250.54,22.250.51,22.25
0.48,22.250.45,22.250.42,22.25
0.39,22.250.36,22.250.33,22.250.30,22.250.27,22.252.49,21.752.46,21.752.43,21.752.40,21.752.37,21.752.34,21.752.31,21.752.28,21.752.25,21.75
2.22,21.752.19,21.752.16,21.752.13,21.752.10,21.752.07,21.752.04,21.75
2.01,21.751.98,21.751.95,21.751.92,21.751.89,21.75
1.86,21.751.83,21.751.80,21.751.77,21.751.74,21.751.71,21.751.68,21.751.65,21.751.62,21.751.59,21.751.56,21.751.53,21.751.50,21.751.47,21.751.44,21.75
1.41,21.751.38,21.751.35,21.75
1.32,21.751.29,21.751.26,21.751.23,21.751.20,21.751.17,21.751.14,21.75
1.11,21.751.08,21.751.05,21.751.02,21.750.99,21.750.96,21.75
0.93,21.750.90,21.75
0.87,21.750.84,21.75
0.81,21.750.78,21.750.75,21.750.72,21.750.69,21.750.66,21.750.63,21.750.60,21.750.57,21.75
0.54,21.750.51,21.750.48,21.750.45,21.750.42,21.750.39,21.750.36,21.750.33,21.750.30,21.750.27,21.75
2.49,21.252.46,21.252.43,21.252.40,21.252.37,21.252.34,21.252.31,21.252.28,21.252.25,21.252.22,21.252.19,21.25
2.16,21.252.13,21.252.10,21.252.07,21.252.04,21.252.01,21.251.98,21.251.95,21.251.92,21.251.89,21.251.86,21.251.83,21.251.80,21.251.77,21.251.74,21.251.71,21.251.68,21.25
1.65,21.251.62,21.251.59,21.251.56,21.251.53,21.251.50,21.251.47,21.251.44,21.25
1.41,21.251.38,21.251.35,21.25
1.32,21.251.29,21.251.26,21.251.23,21.251.20,21.251.17,21.251.14,21.251.11,21.251.08,21.251.05,21.251.02,21.250.99,21.250.96,21.250.93,21.25
0.90,21.250.87,21.250.84,21.250.81,21.250.78,21.250.75,21.250.72,21.250.69,21.250.66,21.250.63,21.250.60,21.250.57,21.25
0.54,21.250.51,21.250.48,21.250.45,21.250.42,21.250.39,21.250.36,21.250.33,21.25
0.30,21.250.27,21.25
2.49,20.752.46,20.752.43,20.752.40,20.752.37,20.752.34,20.752.31,20.752.28,20.752.25,20.752.22,20.752.19,20.752.16,20.752.13,20.752.10,20.752.07,20.752.04,20.752.01,20.751.98,20.751.95,20.751.92,20.751.89,20.751.86,20.751.83,20.751.80,20.751.77,20.751.74,20.751.71,20.751.68,20.751.65,20.751.62,20.751.59,20.751.56,20.751.53,20.751.50,20.751.47,20.751.44,20.751.41,20.751.38,20.751.35,20.751.32,20.751.29,20.751.26,20.751.23,20.751.20,20.751.17,20.75
1.14,20.751.11,20.751.08,20.751.05,20.751.02,20.750.99,20.750.96,20.750.93,20.750.90,20.750.87,20.750.84,20.750.81,20.750.78,20.750.75,20.750.72,20.750.69,20.750.66,20.750.63,20.750.60,20.750.57,20.750.54,20.750.51,20.750.48,20.750.45,20.750.42,20.750.39,20.750.36,20.750.33,20.750.30,20.750.27,20.752.49,20.252.46,20.252.43,20.252.40,20.252.37,20.252.34,20.252.31,20.252.28,20.252.25,20.25
2.22,20.252.19,20.252.16,20.252.13,20.252.10,20.252.07,20.252.04,20.252.01,20.251.98,20.251.95,20.251.92,20.25
1.89,20.251.86,20.251.83,20.251.80,20.251.77,20.251.74,20.251.71,20.251.68,20.251.65,20.251.62,20.251.59,20.251.56,20.251.53,20.251.50,20.251.47,20.251.44,20.251.41,20.251.38,20.25
1.35,20.25
1.32,20.25
1.29,20.251.26,20.25
1.23,20.251.20,20.251.17,20.251.14,20.251.11,20.251.08,20.25
1.05,20.251.02,20.25
0.99,20.250.96,20.25
0.93,20.250.90,20.250.87,20.25
0.84,20.250.81,20.250.78,20.250.75,20.250.72,20.250.69,20.250.66,20.250.63,20.250.60,20.250.57,20.250.54,20.250.51,20.250.48,20.250.45,20.25
0.42,20.250.39,20.250.36,20.250.33,20.250.30,20.250.27,20.252.49,19.752.46,19.752.43,19.752.40,19.752.37,19.752.34,19.752.31,19.752.28,19.75
2.25,19.752.22,19.752.19,19.752.16,19.752.13,19.752.10,19.752.07,19.752.04,19.752.01,19.751.98,19.751.95,19.75
1.92,19.751.89,19.751.86,19.751.83,19.751.80,19.751.77,19.751.74,19.751.71,19.751.68,19.751.65,19.751.62,19.751.59,19.75
1.56,19.751.53,19.75
1.50,19.751.47,19.75
1.44,19.751.41,19.75
1.38,19.75
1.35,19.751.32,19.751.29,19.751.26,19.751.23,19.75
1.20,19.751.17,19.751.14,19.751.11,19.751.08,19.751.05,19.75
1.02,19.750.99,19.750.96,19.75
0.93,19.750.90,19.75
0.87,19.750.84,19.750.81,19.750.78,19.750.75,19.750.72,19.750.69,19.750.66,19.750.63,19.750.60,19.750.57,19.75
0.54,19.750.51,19.750.48,19.750.45,19.750.42,19.750.39,19.750.36,19.750.33,19.750.30,19.750.27,19.752.49,19.252.46,19.252.43,19.252.40,19.252.37,19.252.34,19.252.31,19.252.28,19.252.25,19.252.22,19.252.19,19.252.16,19.252.13,19.252.10,19.252.07,19.252.04,19.252.01,19.251.98,19.251.95,19.251.92,19.25
1.89,19.251.86,19.251.83,19.251.80,19.251.77,19.251.74,19.251.71,19.251.68,19.251.65,19.25
1.62,19.251.59,19.25
1.56,19.251.53,19.251.50,19.25
1.47,19.25
1.44,19.25
1.41,19.251.38,19.251.35,19.251.32,19.251.29,19.251.26,19.251.23,19.25
1.20,19.251.17,19.251.14,19.251.11,19.251.08,19.251.05,19.251.02,19.250.99,19.25
0.96,19.250.93,19.250.90,19.250.87,19.250.84,19.250.81,19.250.78,19.250.75,19.250.72,19.250.69,19.250.66,19.250.63,19.250.60,19.250.57,19.25
0.54,19.250.51,19.250.48,19.250.45,19.250.42,19.250.39,19.250.36,19.250.33,19.250.30,19.250.27,19.252.49,18.752.46,18.752.43,18.752.40,18.752.37,18.752.34,18.752.31,18.752.28,18.752.25,18.752.22,18.752.19,18.752.16,18.752.13,18.752.10,18.752.07,18.752.04,18.752.01,18.751.98,18.751.95,18.751.92,18.751.89,18.751.86,18.751.83,18.751.80,18.751.77,18.751.74,18.751.71,18.751.68,18.751.65,18.751.62,18.751.59,18.751.56,18.751.53,18.751.50,18.751.47,18.75
1.44,18.751.41,18.751.38,18.751.35,18.751.32,18.751.29,18.751.26,18.751.23,18.751.20,18.751.17,18.751.14,18.751.11,18.751.08,18.751.05,18.751.02,18.750.99,18.750.96,18.750.93,18.750.90,18.750.87,18.750.84,18.750.81,18.750.78,18.750.75,18.750.72,18.750.69,18.750.66,18.750.63,18.750.60,18.750.57,18.750.54,18.750.51,18.750.48,18.750.45,18.750.42,18.750.39,18.750.36,18.750.33,18.750.30,18.750.27,18.752.49,18.252.46,18.252.43,18.252.40,18.252.37,18.252.34,18.252.31,18.252.28,18.252.25,18.252.22,18.252.19,18.252.16,18.252.13,18.252.10,18.252.07,18.252.04,18.25
2.01,18.251.98,18.251.95,18.251.92,18.251.89,18.251.86,18.251.83,18.251.80,18.251.77,18.251.74,18.251.71,18.251.68,18.251.65,18.251.62,18.251.59,18.251.56,18.251.53,18.251.50,18.25
1.47,18.251.44,18.251.41,18.25
1.38,18.251.35,18.25
1.32,18.251.29,18.251.26,18.251.23,18.251.20,18.251.17,18.25
1.14,18.251.11,18.251.08,18.251.05,18.25
1.02,18.250.99,18.250.96,18.25
0.93,18.250.90,18.250.87,18.250.84,18.250.81,18.250.78,18.250.75,18.250.72,18.250.69,18.250.66,18.250.63,18.250.60,18.25
0.57,18.250.54,18.250.51,18.25
0.48,18.250.45,18.250.42,18.250.39,18.250.36,18.250.33,18.25
0.30,18.250.27,18.252.49,17.752.46,17.752.43,17.752.40,17.752.37,17.752.34,17.752.31,17.752.28,17.752.25,17.752.22,17.752.19,17.75
2.16,17.752.13,17.752.10,17.752.07,17.752.04,17.752.01,17.751.98,17.751.95,17.751.92,17.751.89,17.751.86,17.751.83,17.751.80,17.751.77,17.751.74,17.751.71,17.751.68,17.751.65,17.751.62,17.751.59,17.751.56,17.751.53,17.751.50,17.751.47,17.751.44,17.751.41,17.751.38,17.75
1.35,17.751.32,17.751.29,17.751.26,17.751.23,17.751.20,17.751.17,17.75
1.14,17.751.11,17.751.08,17.751.05,17.75
1.02,17.750.99,17.750.96,17.750.93,17.75
0.90,17.750.87,17.750.84,17.75
0.81,17.750.78,17.750.75,17.750.72,17.750.69,17.750.66,17.750.63,17.750.60,17.750.57,17.75
0.54,17.750.51,17.750.48,17.750.45,17.750.42,17.750.39,17.750.36,17.750.33,17.750.30,17.750.27,17.752.49,17.252.46,17.252.43,17.25
2.40,17.252.37,17.252.34,17.252.31,17.252.28,17.252.25,17.252.22,17.252.19,17.252.16,17.252.13,17.252.10,17.25
2.07,17.252.04,17.252.01,17.251.98,17.251.95,17.251.92,17.251.89,17.251.86,17.251.83,17.251.80,17.251.77,17.251.74,17.251.71,17.251.68,17.251.65,17.251.62,17.251.59,17.251.56,17.251.53,17.251.50,17.251.47,17.251.44,17.251.41,17.251.38,17.251.35,17.251.32,17.251.29,17.251.26,17.251.23,17.25
1.20,17.251.17,17.251.14,17.251.11,17.251.08,17.251.05,17.25
1.02,17.250.99,17.25
0.96,17.250.93,17.250.90,17.250.87,17.250.84,17.250.81,17.250.78,17.250.75,17.250.72,17.250.69,17.250.66,17.250.63,17.250.60,17.250.57,17.25
0.54,17.250.51,17.250.48,17.250.45,17.250.42,17.250.39,17.250.36,17.25
0.33,17.250.30,17.250.27,17.252.49,16.752.46,16.752.43,16.752.40,16.752.37,16.752.34,16.752.31,16.752.28,16.752.25,16.752.22,16.752.19,16.752.16,16.75
2.13,16.752.10,16.752.07,16.752.04,16.752.01,16.751.98,16.751.95,16.751.92,16.751.89,16.751.86,16.751.83,16.751.80,16.751.77,16.751.74,16.751.71,16.751.68,16.751.65,16.751.62,16.751.59,16.751.56,16.751.53,16.751.50,16.751.47,16.751.44,16.751.41,16.751.38,16.751.35,16.751.32,16.751.29,16.751.26,16.751.23,16.751.20,16.751.17,16.751.14,16.751.11,16.751.08,16.751.05,16.751.02,16.750.99,16.750.96,16.750.93,16.750.90,16.750.87,16.750.84,16.750.81,16.750.78,16.750.75,16.750.72,16.750.69,16.750.66,16.750.63,16.750.60,16.750.57,16.750.54,16.750.51,16.750.48,16.750.45,16.750.42,16.750.39,16.750.36,16.750.33,16.750.30,16.750.27,16.752.49,16.252.46,16.252.43,16.25
2.40,16.252.37,16.252.34,16.252.31,16.252.28,16.252.25,16.25
2.22,16.252.19,16.252.16,16.252.13,16.252.10,16.252.07,16.25
2.04,16.252.01,16.251.98,16.251.95,16.251.92,16.251.89,16.251.86,16.251.83,16.251.80,16.251.77,16.251.74,16.251.71,16.251.68,16.251.65,16.251.62,16.251.59,16.25
1.56,16.251.53,16.251.50,16.251.47,16.251.44,16.251.41,16.251.38,16.251.35,16.251.32,16.251.29,16.251.26,16.251.23,16.251.20,16.251.17,16.251.14,16.251.11,16.251.08,16.251.05,16.251.02,16.250.99,16.250.96,16.250.93,16.25
0.90,16.250.87,16.250.84,16.250.81,16.250.78,16.250.75,16.250.72,16.25
0.69,16.250.66,16.250.63,16.250.60,16.250.57,16.250.54,16.250.51,16.250.48,16.250.45,16.250.42,16.250.39,16.250.36,16.250.33,16.250.30,16.250.27,16.252.49,15.752.46,15.752.43,15.752.40,15.752.37,15.752.34,15.752.31,15.752.28,15.752.25,15.752.22,15.752.19,15.752.16,15.752.13,15.752.10,15.752.07,15.752.04,15.752.01,15.751.98,15.751.95,15.751.92,15.751.89,15.751.86,15.751.83,15.751.80,15.751.77,15.751.74,15.751.71,15.751.68,15.751.65,15.751.62,15.751.59,15.751.56,15.751.53,15.751.50,15.751.47,15.751.44,15.751.41,15.751.38,15.751.35,15.751.32,15.751.29,15.751.26,15.751.23,15.751.20,15.751.17,15.751.14,15.75
1.11,15.75
1.08,15.751.05,15.751.02,15.750.99,15.750.96,15.750.93,15.750.90,15.750.87,15.750.84,15.750.81,15.750.78,15.75
0.75,15.750.72,15.750.69,15.750.66,15.750.63,15.750.60,15.750.57,15.750.54,15.75
0.51,15.750.48,15.750.45,15.750.42,15.750.39,15.750.36,15.750.33,15.750.30,15.750.27,15.75
2.49,15.252.46,15.252.43,15.252.40,15.252.37,15.252.34,15.252.31,15.252.28,15.252.25,15.252.22,15.25
2.19,15.252.16,15.252.13,15.252.10,15.252.07,15.252.04,15.252.01,15.251.98,15.251.95,15.251.92,15.251.89,15.251.86,15.251.83,15.251.80,15.251.77,15.251.74,15.251.71,15.25
1.68,15.251.65,15.251.62,15.251.59,15.251.56,15.251.53,15.251.50,15.251.47,15.25
1.44,15.251.41,15.251.38,15.251.35,15.251.32,15.251.29,15.251.26,15.25
1.23,15.251.20,15.251.17,15.251.14,15.251.11,15.251.08,15.251.05,15.251.02,15.250.99,15.250.96,15.250.93,15.25
0.90,15.250.87,15.250.84,15.250.81,15.250.78,15.250.75,15.250.72,15.250.69,15.250.66,15.250.63,15.250.60,15.250.57,15.250.54,15.250.51,15.250.48,15.250.45,15.250.42,15.250.39,15.250.36,15.250.33,15.250.30,15.25
0.27,15.252.49,14.752.46,14.752.43,14.752.40,14.752.37,14.752.34,14.752.31,14.752.28,14.752.25,14.752.22,14.752.19,14.752.16,14.752.13,14.752.10,14.752.07,14.752.04,14.752.01,14.751.98,14.751.95,14.751.92,14.751.89,14.751.86,14.751.83,14.751.80,14.751.77,14.751.74,14.751.71,14.751.68,14.751.65,14.751.62,14.751.59,14.751.56,14.751.53,14.751.50,14.751.47,14.751.44,14.751.41,14.751.38,14.751.35,14.751.32,14.751.29,14.751.26,14.751.23,14.751.20,14.751.17,14.751.14,14.751.11,14.751.08,14.751.05,14.751.02,14.750.99,14.750.96,14.750.93,14.750.90,14.750.87,14.750.84,14.750.81,14.750.78,14.750.75,14.750.72,14.750.69,14.750.66,14.750.63,14.750.60,14.750.57,14.750.54,14.750.51,14.750.48,14.750.45,14.750.42,14.750.39,14.750.36,14.750.33,14.750.30,14.750.27,14.752.49,14.252.46,14.25
2.43,14.252.40,14.252.37,14.252.34,14.252.31,14.252.28,14.252.25,14.252.22,14.252.19,14.252.16,14.252.13,14.25
2.10,14.252.07,14.252.04,14.252.01,14.251.98,14.251.95,14.25
1.92,14.251.89,14.251.86,14.251.83,14.251.80,14.251.77,14.251.74,14.251.71,14.251.68,14.251.65,14.251.62,14.251.59,14.251.56,14.251.53,14.251.50,14.251.47,14.251.44,14.251.41,14.251.38,14.251.35,14.251.32,14.251.29,14.251.26,14.251.23,14.25
1.20,14.251.17,14.251.14,14.251.11,14.251.08,14.251.05,14.25
1.02,14.250.99,14.250.96,14.250.93,14.250.90,14.250.87,14.250.84,14.25
0.81,14.250.78,14.250.75,14.250.72,14.250.69,14.250.66,14.250.63,14.250.60,14.250.57,14.250.54,14.250.51,14.250.48,14.250.45,14.250.42,14.25
0.39,14.250.36,14.250.33,14.250.30,14.250.27,14.252.49,13.752.46,13.752.43,13.752.40,13.752.37,13.752.34,13.752.31,13.752.28,13.752.25,13.752.22,13.752.19,13.752.16,13.752.13,13.75
2.10,13.752.07,13.752.04,13.752.01,13.751.98,13.751.95,13.751.92,13.751.89,13.751.86,13.751.83,13.751.80,13.751.77,13.751.74,13.751.71,13.75
1.68,13.751.65,13.751.62,13.751.59,13.751.56,13.751.53,13.751.50,13.751.47,13.751.44,13.751.41,13.751.38,13.75
1.35,13.751.32,13.751.29,13.751.26,13.751.23,13.751.20,13.751.17,13.751.14,13.751.11,13.751.08,13.751.05,13.75
1.02,13.750.99,13.750.96,13.75
0.93,13.750.90,13.75
0.87,13.750.84,13.750.81,13.750.78,13.750.75,13.750.72,13.75
0.69,13.750.66,13.750.63,13.750.60,13.750.57,13.750.54,13.750.51,13.750.48,13.750.45,13.750.42,13.75
0.39,13.750.36,13.750.33,13.750.30,13.750.27,13.75
2.49,13.252.46,13.252.43,13.252.40,13.252.37,13.252.34,13.252.31,13.252.28,13.252.25,13.252.22,13.252.19,13.252.16,13.252.13,13.252.10,13.252.07,13.252.04,13.252.01,13.251.98,13.251.95,13.251.92,13.251.89,13.251.86,13.251.83,13.251.80,13.251.77,13.251.74,13.251.71,13.25
1.68,13.251.65,13.251.62,13.251.59,13.251.56,13.251.53,13.251.50,13.251.47,13.251.44,13.251.41,13.251.38,13.251.35,13.251.32,13.251.29,13.251.26,13.251.23,13.251.20,13.25
1.17,13.251.14,13.251.11,13.251.08,13.251.05,13.251.02,13.250.99,13.25
0.96,13.250.93,13.25
0.90,13.250.87,13.25
0.84,13.250.81,13.250.78,13.250.75,13.250.72,13.250.69,13.250.66,13.250.63,13.25
0.60,13.250.57,13.250.54,13.250.51,13.250.48,13.250.45,13.250.42,13.250.39,13.250.36,13.250.33,13.250.30,13.250.27,13.252.49,12.752.46,12.752.43,12.752.40,12.752.37,12.752.34,12.752.31,12.752.28,12.752.25,12.752.22,12.752.19,12.752.16,12.752.13,12.752.10,12.752.07,12.752.04,12.752.01,12.751.98,12.751.95,12.751.92,12.751.89,12.751.86,12.751.83,12.751.80,12.751.77,12.751.74,12.751.71,12.751.68,12.751.65,12.751.62,12.751.59,12.751.56,12.751.53,12.751.50,12.751.47,12.751.44,12.751.41,12.751.38,12.751.35,12.751.32,12.751.29,12.751.26,12.751.23,12.751.20,12.751.17,12.751.14,12.751.11,12.751.08,12.751.05,12.751.02,12.750.99,12.750.96,12.750.93,12.75
0.90,12.750.87,12.750.84,12.750.81,12.750.78,12.750.75,12.750.72,12.750.69,12.750.66,12.750.63,12.750.60,12.750.57,12.750.54,12.750.51,12.750.48,12.750.45,12.750.42,12.750.39,12.750.36,12.750.33,12.750.30,12.750.27,12.752.49,12.252.46,12.252.43,12.252.40,12.252.37,12.252.34,12.25
2.31,12.252.28,12.252.25,12.252.22,12.25
2.19,12.252.16,12.252.13,12.252.10,12.252.07,12.252.04,12.252.01,12.251.98,12.251.95,12.251.92,12.251.89,12.25
1.86,12.251.83,12.251.80,12.251.77,12.251.74,12.251.71,12.251.68,12.251.65,12.251.62,12.251.59,12.25
1.56,12.251.53,12.251.50,12.25
1.47,12.251.44,12.251.41,12.251.38,12.251.35,12.251.32,12.25
1.29,12.251.26,12.251.23,12.251.20,12.251.17,12.251.14,12.251.11,12.251.08,12.251.05,12.251.02,12.250.99,12.250.96,12.250.93,12.25
0.90,12.250.87,12.250.84,12.250.81,12.250.78,12.250.75,12.250.72,12.250.69,12.250.66,12.250.63,12.250.60,12.250.57,12.250.54,12.250.51,12.250.48,12.250.45,12.250.42,12.250.39,12.250.36,12.250.33,12.250.30,12.250.27,12.252.49,11.752.46,11.75
2.43,11.752.40,11.75
2.37,11.752.34,11.752.31,11.752.28,11.752.25,11.752.22,11.752.19,11.752.16,11.75
2.13,11.752.10,11.752.07,11.752.04,11.752.01,11.751.98,11.751.95,11.751.92,11.751.89,11.751.86,11.751.83,11.751.80,11.75
1.77,11.751.74,11.751.71,11.751.68,11.751.65,11.751.62,11.751.59,11.751.56,11.751.53,11.751.50,11.751.47,11.751.44,11.751.41,11.75
1.38,11.751.35,11.751.32,11.751.29,11.751.26,11.751.23,11.751.20,11.751.17,11.751.14,11.751.11,11.751.08,11.751.05,11.751.02,11.750.99,11.750.96,11.750.93,11.75
0.90,11.750.87,11.750.84,11.750.81,11.750.78,11.750.75,11.750.72,11.750.69,11.750.66,11.750.63,11.750.60,11.750.57,11.750.54,11.750.51,11.750.48,11.750.45,11.750.42,11.750.39,11.750.36,11.750.33,11.750.30,11.750.27,11.752.49,11.252.46,11.252.43,11.252.40,11.252.37,11.25
2.34,11.252.31,11.252.28,11.252.25,11.252.22,11.252.19,11.252.16,11.252.13,11.25
2.10,11.252.07,11.252.04,11.252.01,11.251.98,11.25
1.95,11.251.92,11.251.89,11.251.86,11.251.83,11.251.80,11.251.77,11.251.74,11.251.71,11.251.68,11.251.65,11.251.62,11.251.59,11.251.56,11.251.53,11.251.50,11.251.47,11.251.44,11.251.41,11.251.38,11.251.35,11.251.32,11.251.29,11.251.26,11.251.23,11.251.20,11.251.17,11.251.14,11.251.11,11.251.08,11.251.05,11.251.02,11.250.99,11.25
0.96,11.250.93,11.250.90,11.250.87,11.250.84,11.250.81,11.250.78,11.250.75,11.250.72,11.250.69,11.250.66,11.250.63,11.250.60,11.250.57,11.250.54,11.250.51,11.250.48,11.250.45,11.250.42,11.250.39,11.250.36,11.250.33,11.250.30,11.250.27,11.25
2.49,10.752.46,10.752.43,10.752.40,10.752.37,10.752.34,10.752.31,10.752.28,10.752.25,10.752.22,10.752.19,10.752.16,10.752.13,10.752.10,10.752.07,10.752.04,10.752.01,10.751.98,10.751.95,10.751.92,10.751.89,10.751.86,10.751.83,10.751.80,10.751.77,10.751.74,10.751.71,10.751.68,10.751.65,10.751.62,10.751.59,10.751.56,10.751.53,10.751.50,10.751.47,10.751.44,10.751.41,10.751.38,10.751.35,10.751.32,10.751.29,10.751.26,10.751.23,10.751.20,10.751.17,10.751.14,10.751.11,10.751.08,10.751.05,10.751.02,10.750.99,10.750.96,10.750.93,10.750.90,10.750.87,10.750.84,10.750.81,10.750.78,10.750.75,10.750.72,10.750.69,10.750.66,10.750.63,10.750.60,10.750.57,10.750.54,10.750.51,10.750.48,10.750.45,10.750.42,10.750.39,10.750.36,10.750.33,10.750.30,10.750.27,10.752.49,10.252.46,10.252.43,10.252.40,10.252.37,10.25
2.34,10.252.31,10.252.28,10.252.25,10.252.22,10.252.19,10.252.16,10.252.13,10.252.10,10.252.07,10.252.04,10.252.01,10.251.98,10.251.95,10.251.92,10.251.89,10.251.86,10.251.83,10.251.80,10.251.77,10.251.74,10.251.71,10.251.68,10.251.65,10.251.62,10.251.59,10.251.56,10.251.53,10.251.50,10.25
1.47,10.251.44,10.251.41,10.25
1.38,10.251.35,10.251.32,10.251.29,10.251.26,10.251.23,10.251.20,10.251.17,10.251.14,10.251.11,10.251.08,10.251.05,10.251.02,10.250.99,10.25
0.96,10.250.93,10.250.90,10.250.87,10.250.84,10.250.81,10.250.78,10.250.75,10.250.72,10.250.69,10.250.66,10.250.63,10.250.60,10.250.57,10.250.54,10.250.51,10.25
0.48,10.250.45,10.250.42,10.250.39,10.250.36,10.250.33,10.250.30,10.250.27,10.25
P[1]
P[2
]
MaltoseGlucose
Maltose
Lysine
Alanine
Leucine
Glutamate
A) B)
P[1]
P[2
]
74
acid side chains, a second HSQC PLS-DA was conducted to determine if any other significant
metabolites could be detected in the aliphatic region (1H 0.25-2.5 ppm, 13C 10.0-50.0 ppm). The
analysis of the aliphatic region was also performed using COSY and J-RES data but this did not
show any improvement in scores discrimination and thus is not shown or discussed. Figure 2.6b
shows the scores plot for the HSQC data for the aliphatic region between the control and exposed
earthworms with a MANOVA value of P=1.16 x 10-11. The separation is more pronounced than
the overall HSQC scores plot (Figure 2.6a) likely due to presence of numerous signals from amino
acids which exhibit flux during exposure. This is confirmed in the 2-D loadings plot where four
non-concentric clusters were detected (Figure 2.8b) as major contributors for the separation of the
two groups. Alanine (1.46, 19.25 ppm) and leucine (~0.92-0.95, ~23.75pm) were detected
respectively as metabolites of importance. At the bottom clusters, the detection of glutamate at
(~2.09, 29.75 ppm; and 2.33-2.37, 36.25 ppm) was also observed. Glutamate is known to be an
important amino acid in the nervous system as an excitatory neurotransmitter [37]. It has been
found that during stress conditions, the transport of glutamate in neurons can be affected due to
receptor-mediated depolarization and calcium influx causing cell death [38]. In the 1-D NMR
techniques, this region was difficult to elucidate from the PLS-DA model due to the signal overlap
in the PURGE and CPMG experiments and the large reduction in signal in this region from the J-
RES projections.
2.4.5. Merits of 1-D and 2-D NMR analysis as biomarker screening tools
The 1-D NMR techniques used in this study (PURGE, J-RES projections and CPMG) were
specifically chosen as they are used commonly in metabolomic based studies. One clear advantage
with the usage of 1-D NMR methods for metabolomics studies is their high through-put potential.
As shown in Table 2.1, all 1-D NMR experiments required a maximum of 13 minutes or less. The
75
processing steps (phasing and statistical data analysis) are relatively simple for all the 1-D NMR
spectra. PLS-DA of the 1-D NMR spectra of the control and endosulfan-exposed earthworm tissue
extracts enabled a quick method to detect significant metabolites of response due to contaminant
exposure. However, from the previous discussion above, many overlapping regions especially in
the sugar-rich regions (1H NMR δ=3.0-5.0 ppm) are detected and the potential of multiple
resonances at specific areas can cause difficulties in deducing the identification of specific
metabolites. In 2-D NMR, spectral overlap is often reduced through the introduction of the second
dimension and additional information can be obtained for metabolite identification due to the
additional “connectivity” information. The use of 2-D NMR data in multivariate analysis is
relatively new in the field of metabolomics and has increased in the last few years [18]. Recently,
new studies have emerged from the usage of 1H-1H COSY [39], 1H-1H total correlation
spectroscopy (TOCSY) [18] and more recently 1H-13C HSQC [19, 40]. However, none have
analyzed their potential together in an environmental setting such as contaminant exposure.
76
Table 2: Summary of metabolites identified with significant chemical shifts from partial
least-squares discriminant analysis (PLS-DA) loadings and acquisition times for the 1-D and
2-D NMR techniques. Note the 2-D experiments were optimised for fast acquisition (see Experimental section)
In this study, 1H-J-RES, 1H-1H COSY and 1H-13C HSQC were used to further assist the 1-
D NMR results to fully maximize biomarker screening potential. The results from the 2-D
loadings plots show the major advantages in utilizing 2-D NMR in metabolomics studies. The
additional connectivity information (J-coupling for J-RES, 1H-1H for COSY and 1H-13C for
HSQC) permitted easier identification of metabolites which was not often possible from the 1-D
1H NMR alone. With 1H-J-RES spectroscopy, most of the significant metabolites from the 1-D
NMR were confirmed but with the additional J-coupling information, the identification of glucose
Name of
Technique
1D/2D NMR
technique
Metabolites Identified using Significant
Chemical Shifts from PLS-DA Loadings Plot
(δ)
Total Time
Presaturation
UtilizaingRelaxation
Gradients and
Echos (PURGE)
1D Alanine: 1H:1.46-1.48 ppm
Leucine: 1H:0.94-0.96 ppmMaltose: 1H:5.39-5.40 ppm
9 mins and 12 s
Carr-Purcell-
Meiboom-Gill (CPMG)
1D Alanine: 1H:1.46-1.48 ppm
Leucine: 1H:0.94-0.96 ppmMaltose: 1H:5.39-5.40 ppm
8 mins and 54 s
J-Resolved (J-
RES) projections
1D Alanine: 1H:1.46-1.47 ppm
Maltose: 1H:5.39-5.40 ppm
12 mins and 58 s
1H- J-RES
Spectroscopy
2D Alanine: 1H:1.47 ppm. 3.5 Hz
Maltose: 1H:5.41 ppm, 0.5 HzGlucose:1H:3.25 ppm. -0.5 Hz
1H:4.63 ppm. 3.5-4.5 Hz
12 mins and 58 s
1H-1H
COrrelation Spectroscopy
(COSY)
2D Alanine: 1H:3.77 ppm, 1H:1.46 ppm1H:3.80 ppm, 1H:1.46 ppm
Maltose: 1H:5.42 ppm, 1H:3.59 ppmGlucose: 1H:4.64-4.67 ppm, 1H:3.23-3.29 ppm
Lysine: 1H:3.02 ppm,1H:1.73 ppm
27 mins and 7 s
1H-13C Single
Quantum Coherence
spectroscopy
(HSQC)
2D Alanine: 1H:1.46 ppm, 13C:19.25 ppm
Leucine: 1H:0.92-0.95 ppm, 13C:23.75 ppmMaltose: 1H:3.80-3.89 ppm, 13C:62.75-64.25 ppmGlucose : 1H:3.47-3.50 ppm,13C:78.75 ppm
Lysine: 1H:2.99 ppm, 13C:41.75-42.25 ppmGlutamate: 1H:2.09 ppm, 13C:29.75 ppm
1H:2.33-2.37 ppm,13C:36.25 ppm
47 mins and 26 s
77
as a metabolite of response was deduced. The result was similar with 1H-1H COSY but the
additional proton connectivity information permitted the identification of lysine as a significant
metabolite of response. However, the advantages of 2-D NMR are most clearly seen in the HSQC
data. 1H-13C HSQC showed the highest discrimination between the control and exposed groups,
and the additional dispersion afforded by the carbon axis permitted the identification of glutamate
as an additional significant metabolite that was not identified by any of the other 1-D or 2-D NMR
methods (see Table 1). Furthermore HSQC identified all of the metabolites identified from all the
other 1-D and 2-D NMR techniques combined, highlighting its considerable potential as a
biomarker screening tool. McKelvie et al. [23] also detected alanine, leucine and maltose as
potential biomarkers in E. fetida exposed to endosulfan using PURGE NMR. GC-MS was used in
their study but was unable to derivitize leucine and maltose proved highly variable and could not
be confirmed as a significant metabolite from GC-MS. The application of 2-D NMR in our study
complimented McKelvie’s findings by confirming these metabolites and detecting additional
biomarkers due to exposure to endosulfan.
The increased information provided by 2-D NMR unfortunately comes at a cost of
increased computing processing/data resources and spectrometer time. The processing steps and
statistical data analysis for all 2-D spectra require more time and computing resources due to the
large increase in data. For example, the PLS-DA results for 1-D NMR needed only ~861 buckets
for each NMR spectrum. However, the PLS-DA results for HSQC required a total of 37,000
buckets for each NMR dataset. This increase in data size substantially required higher system
storage space and processing power but was within the limit of a well-equipped PC. As for
spectrometer time, after optimization, ~27 minutes and ~48 minutes were required to collect
COSY and HSQC datasets respectively (see Table 1). While this is more than any of the 1-D NMR
78
techniques which required ~9mins, given the labor and time required to prepare metabolomic
samples, the additional information may be well worth the additional spectrometer time, especially
considering the 2-D NMR acquisition (as with the 1-D acquisition) can be fully automated.
However, 2-D is only feasible as long as metabolites are present at a concentration where they can
be easily observed by 2-D NMR. This is important to stress especially for HSQC as 2-D NMR
techniques are less sensitive than 1-D 1H NMR techniques. While it was adequate in this study to
provide a wealth of novel information on earthworm extracts, this may not be the case for all
biological fluids/extracts where metabolites are at lower concentrations. Therefore it is imperative
to test and optimize HSQC data acquisition on a select number of samples prior to undertaking a
large metabolomic screening program. Given the short acquisition time of 1-D NMR, it may be
prudent to combine a 1-D NMR approach along with HSQC to take advantage of both increased
sensitivity of 1-D NMR along with the increased dispersion of 2-D NMR.
The identification of biomarkers for ecotoxicology studies is crucial in environmental risk
assessment of contaminated areas. NMR is potentially a useful tool for this application as it is able
to detect changes in a wide range of metabolites in a non-targeted method. Bundy et al. [7]
utilized 1-D NMR to examine the metabolic profile of earthworm, Lumbricus rubellus, at various
metal contaminated field sites. PCA showed contaminant-specific effects on the metabolic profile
in the earthworms and detected zinc as the main contaminant causing a metabolic response in all
the sites. However, weak correlations to zinc within each site were seen due to the chemical and
biological variability from the earthworm metabolic profile. The usage of 2-D NMR techniques
such as HSQC can potentially alleviate this problem as the greater dispersion from the additional
dimension can enable a more detailed metabolic profile of each earthworm and help better
discriminate contaminant exposure. Jones et al. [35] assessed the exposure of earthworm,
79
Lumbricus rubellus to different concentrations of pyrene, a well known PAH using 1H NMR and
GC-MS for tissue analysis. The 1H NMR results identified different metabolites due to exposure
but the author commented on the large number of overlap between peaks with the same chemical
shifts which could potentially miss smaller signals due to the larger resonances. The application of
COSY and HSQC can potentially improve the congestion of these peaks and provide additional
screening potential of metabolites hidden due to the overlap in the 1H NMR. From the results in
these studies [7, 35], the application of NMR in environmental metabolomics to discriminate
changes in the metabolic profile of organisms depends on a large part, the technique used to
discriminate the exposed and control groups. Therefore, this study highlights the application of a
simple presaturation 1-D NMR technique in combination of a 2-D NMR technique such as HSQC
can extend the screening potential of metabolites due to exposure in environmental metabolomic
samples.
2.5 Conclusion
This initial study demonstrates the biomarker screening potential of various 1-D and 2-D
NMR techniques on the toxicity of endosulfan to E. fetida. We have demonstrated that 1-D NMR
showed discrimination between the control and exposed earthworms with alanine, leucine and
maltose identified as potential biomarkers. However, specific regions such as sugars and lipids
can be problematic since their immense signals can overlap other peaks in that area. This study
was the first to compare different 2-D NMR techniques (J-RES, COSY and HSQC) in an
environmental context to highlight the potential of utilizing 2-D NMR in identifying metabolites of
response due to a contaminant. For J-RES, the additional J-coupling dimension provided further
information that allowed the deduction of glucose while the additional proton dimension from
COSY allowed higher dispersion to deduce lysine as well. However, HSQC provided a global
80
metabolic profile and was valuable in identifying all the significant metabolites detected in the
other 1-D and 2-D NMR techniques combined with an additional key metabolite glutamate which
was not detected by the other methods. The HSQC data showed excellent discrimination in the
PLS-DA scores plot between the exposed and control groups with a MANOVA value three orders
lower than all the other 1-D and 2-D NMR techniques. However, given the lower acquisition time
and higher sensitivity in 1-D 1H NMR, the application of HSQC with a simple 1-D presaturation
technique has potential for statistical discrimination and significant metabolite identification in
environmental metabolomic studies.
2.6 Acknowledgment
Funding was provided by the Natural Sciences and Engineering Research Council Strategic
Grants Program (NSERC). André Simpson would like to thank the government of Ontario for an
Early Researcher Award. Fellowships from NSERC and the Ontario Postdoctoral Program
(Ministry of Research and Innovation) to JRM are also acknowledged.
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CHAPTER THREE
1-D and 2-D NMR metabolomics of earthworm responses to sub-lethal trifluralin and endosulfan exposure
Published as: Yuk, J., Simpson, M.J., and Simpson, A.J., 1-D and 2-D NMR metabolomics of earthworm responses to sub-lethal trifluralin and endosulfan exposure. Environ.
Chem., 2011. 8(3): 281-294.
Reproduced with permission from Environmental Chemistry, 2011,(3): 281-294 (http://www.publish.csiro.au/paper/EN11033.htm). © Copyright CSIRO Publishing
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3.1 Abstract
1-D and 2-D Nuclear Magnetic Resonance (NMR) spectroscopy is used to examine the
metabolic response of the earthworm (Eisenia fetida) after contact test exposure to an
organofluorine pesticide, trifluralin and an organochlorine pesticide, endosulfan. Three sub-lethal
concentrations were used for each pesticide (0.1 mg cm-2, 0.5 mg cm-2 and 1.0 mg cm-2 for
trifluralin and 0.5 µg cm-2, 1.0 µg cm-2 and 2.0 µg cm-2 for endosulfan). Principal Component
Analysis (PCA) of the trifluralin and endosulfan NMR datasets showed separation between the
unexposed and the exposed earthworm groups. Alanine, glycine, maltose and ATP were
significant in the highest concentration (1.0 mg cm-2) for trifluralin-exposed earthworms and may
result from a non-polar narcosis toxic mode of action (MOA). Leucine, phenylalanine, tryptophan,
lysine, glutamate, valine, glycine, isoleucine, methionine, glutamine, alanine, maltose, glucose,
meibiose, malate, fumarate and ATP were detected as significant in the 2 highest concentrations
(1.0 µg cm-2 and 2.0 µg cm-2) for endosulfan-exposed earthworms and a neurotoxic MOA is
postulated. This study highlights the usage of 1-D and 2-D metabolomics for understanding the
biochemical response of environmental contaminants to model organisms such as earthworms.
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3.2 Introduction
Since 1940, the use of halogenated agrochemicals has increased due to their optimal
biological efficacy and economic viability [1]. However, in the past decade, an increasing number
of studies have identified negative health effects such as birth defects, neurotoxicity and
genotoxicity [2-12] associated with many of these chemicals. Organochlorine pesticides is one
class of chemicals that has received widespread attention due to their ecological persistence and
potential carcinogenic and estrogenic properties [13]. In particular, endosulfan is widely used
throughout the world especially within the Indian sub-continent to control vector borne diseases
on a large variety of crop types such as fruit trees, plantation crops and cotton [14]. However,
toxicology studies have shown that endosulfan may have both tetragenic and mutagenic properties
and adversely affect the central nervous system, kidney and liver [14]. In the past 30 years, there
has been a shift to fluorine-substituted agrochemicals due to their significant biological activity.
However, due to the inertness of fluorinated compounds, the potential for persistence and
accumulation in the environment is of concern, especially for perfluoroalkyl substituents such as
trifluoromethyl groups [15]. Trifluralin is an organofluorinated herbicide used throughout the
world for grass control for a large variety of crops [16]. However, the benefits of trifluralin have
recently been offset by questions over its long-term environmental impact. Recent toxicity studies
have shown that trifluralin has genotoxic and carcinogenic properties in mammalian organisms
[17, 18]. Furthermore, very few studies have characterized the degradation products of the
trifluoromethyl group and to date only a small number of studies have focused on the biological
response of these products in living organisms [15]. Trifluralin binds strongly to soil organic
matter [19], promoting its persistence in soil for long periods of time [20]. Due to these
environmental concerns, further investigation into both endosulfan and trifluralin are necessary.
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The assessment of the long-term environmental impact from agrochemical use requires an
improved understanding of these chemicals and their interaction with soil organisms. However,
ecotoxicological studies have frequently focused on easily measurable endpoints such as mortality,
reproduction rates and growth [21, 22]. These tests have merit in understanding their overall
toxicity but do not provide an understanding of the toxic mechanisms at a molecular level and how
they affect the functional changes in an organism. An alternative approach would be to investigate
the changes in endogenous metabolites resulting from exposure at sub-lethal concentrations.
Metabolite characterization has conventionally been achieved through a series of specific
biochemical assays [23]. However, these assays can be expensive, time consuming and labor
intensive due to the high number of biological samples analyzed [23]. Environmental
metabolomics is a new alternative approach where the metabolite profiles of organisms in their
environment are analyzed from their tissues, cells and biofluids. The changes in their metabolites
can provide crucial information into an organism’s response to disease, toxicity and potential
environmental stressors present [24].
In the soil ecosystem, earthworms are vital species for their decomposing activities,
nutrient mineralization and soil formation abilities [25]. Due to their ubiquity in the soil
environments, they make ideal biological indicators for the risk assessment of soil health [26].
Nuclear Magnetic Resonance (NMR) spectroscopy has been used extensively in earthworm
metabolomics as studies have examined their exposure to polyaromatic hydrocarbons (PAHS) [27,
28], metal contaminants [21, 29, 30] and halogenated anilines [31, 32]. 1-D NMR spectroscopy is
most frequently used in earthworm metabolomic studies but the identification of metabolites can
be difficult due to spectral overlap in complex biological samples [33, 34]. In our recent study
[34], various 1-D and 2-D NMR techniques were compared to alleviate the spectra overlap and 1-
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D Presaturation Utilizing Relaxation Gradients and Echos (PURGE) and 2-D 1H-13C Heteronuclear
Single Quantum Coherence (HSQC) NMR spectroscopy were found to be the most informative
experiments for the discrimination between the exposed and control earthworms and identification
of the response metabolites. In this study, 1-D PURGE is combined with 2-D HSQC NMR
spectroscopy to examine the response of the earthworm, Eisenia fetida (E. fetida) to two
pesticides, trifluralin and endosulfan across three sub-lethal exposure concentrations in contact
tests. The goal is to investigate the sub-lethal responses for these chemicals and if the toxic mode
of action (MOA) can be elucidated when data from both 1-D and 2-D NMR datasets are combined.
E. fetida has been used as standard test organisms for acute and chronic ecotoxicological studies
and is a recommended test organism from the Organization for Economic Co-operation and
Development (OECD) [35, 36]. Contact tests will be used as they are advantageous in
understanding the metabolic response of the earthworms under direct exposure to the contaminant
on a filter paper. Even though contact tests may not fully reflect the soil environment since it does
not consider pesticide exposure through ingestion [37], it is widely accepted for understanding
chemical risk or screening before studying more complex matrices such as soil [38].
Trajectory metabolomics analysis will also be performed in this study which compares
different contaminants together against the control to understand their differences or similarities on
the mode of action (MOA). Trajectory metabolomics analyses have been frequently used in
environmental studies such as developmental changes in an organism and time dependant changes
in an organism due to exposure by an environmental contaminant [33, 39, 40]. For example,
trajectory metabolomics analyses were used to study the exposure of a common groundwater
pollutant, trichloroethylene (TCE) to Japanese medaka at different development stages [33] and the
exposure of bacterial infections and chemicals such as Vibrio campbellii and 2,4 dinitrophenol
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(DNP) respectively on Atlantic blue crabs in a time dependent study [40]. Trajectory
metabolomics analysis will be used on the trifluralin- and endosulfan-exposed earthworms to
explore similarities or differences between the MOA’s of these two chemicals.
To the author’s knowledge, this will be the first study on analyzing the metabolic response
of E.fetida to trifluralin and endosulfan using 1-D and 2-D NMR metabolomic techniques. The
overall goal of this study is to investigate the potential of 1-D and 2-D NMR metabolomics as a
tool to monitor earthworm responses to endosulfan and trifluralin exposure.
3.3 Experimental methods
3.3.1 Earthworm contact test preparation and exposure
E. fetida specimens were purchased from The Worm Factory (Perth, ON, Canada) and
were maintained according to Brown et al. [41]. Mature earthworms with a visible clitellum were
depurated in groups of 5 in the dark for 96 hours on Whatman 4 Qualitative filter paper with a
diameter of 9 cm (Fisher Scientific, Waltham, MA, USA) in 500 mL jars to remove any residues
from their intestinal tracts [41]. The earthworms had a mean weight of 0.76 ± 0.03 g and there
were no significant differences in the average mass of the earthworms between the controls and
any of the concentrations for all the pesticides (ANOVA, F6,61=0.839, P=0.545) before exposure.
Earthworms were then transferred to individual 120 mL amber glass jars containing pre-treated
Whatman GF/A 4.25 cm diameter glass filter paper (Fisher Scientific). In the literature, the LC50
values for endosulfan and trifluralin were 5.7 µg cm-2 by Heimbach [42] and >1.0 mg cm-2 by
Roberts and Dorough [43] respectively. To ensure the exposure concentrations chosen for
endosulfan and trifluralin were sub-lethal; a preliminary experiment was conducted with 5
earthworms exposed to endosulfan and trifluralin concentrations near the LC50 values over 48
92
hours. For endosulfan, the exposure concentrations used were 2.0 µg cm-2 and 5.0 µg cm-2 and for
trifluralin, the exposure concentrations used were 1.0 mg cm-2 and 2.0 mg cm-2. Earthworms
exposed to endosulfan survived at a concentration of 2.0 µg cm-2 but died after exposure at 5.0 µg
cm-2. For trifluralin, the earthworms all survived at 1.0 mg cm-2 but more than half died at 2.0 mg
cm-2. Based on these results, endosulfan (99 % purity; Sigma Aldrich, St Louis, MO, USA) was
applied to the filter paper at three sub-lethal concentrations (0.5 µg cm-2, 1.0 µg cm-2 and 2.0 µg
cm-2) using 1 mL of acetone (HPLC grade; Caldeon, Georgetown, ON, Canada) as the carrier
solvent. Trifluralin (98% purity; Borche Scientific) was applied to the filter paper at three
concentrations (0.1 mg cm-2, 0.5 mg cm-2 and 1.0 mg cm-2) using 1 mL of acetone as the carrier
solvent. Acetone (1 mL) was applied to filter papers to serve as control treatments. In all cases,
the acetone was allowed to evaporate and 1 mL of distilled water was added prior to the addition
of earthworms. Earthworms were kept in the dark for 48 hours, as recommended by the OECD
LC50 contact test guideline [35]. Earthworms were then flash frozen in liquid nitrogen, lyophilized
and stored frozen until extraction. The physical conditions and weight of each earthworm was
recorded after exposure before being flash frozen. There were 10 replicates for control and 10 for
each of the three concentrations of the contaminant for the exposed specimen. All earthworms
survived the exposures tests at the sub-lethal concentrations used.
3.3.2. Earthworm tissue extraction and preparation for NMR
The lyophilized earthworms were homogenized in a 1.5 mL centrifuge tube using a 5 mm
wide stainless steel spatula. Samples were then extracted using 1 mL of a 0.2 M monobasic
sodium phosphate buffer solution (NaH2PO4·2H2O; 99.3%; Fisher Chemicals) containing 0.1%
(w/v) sodium azide (99.5% purity; Sigma Aldrich) as a preservative [41]. Buffer solution was
made with D2O (99.9% purity, Cambridge Isotope Laboratories Inc, Andover, MA, USA) and
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adjusted to a pD of 7.4 using NaOD (30% w/w in 99.5% D2O, Cambridge Isotope Laboratories
Inc). The buffer solution for all NMR samples also contained 10 mg/L of 2,2-dimethyl-2-
silapentane-5-sulfonate sodium salt (DSS; 97%, Sigma Aldrich) as an internal standard. Samples
were vortexed for 30 seconds using a VX 100 vortexer (Labnet, NJ, USA) and then sonicated for
15 minutes using a FS60 sonicator (Fisher Scientific) to aid with the extraction. Samples were
then centrifuged at 14,000 rpm using an International Equipment Company 21000 Centrifuge
(Fischer Scientific, Ottawa, ON, Canada) for 20 minutes and the supernatant was transferred into a
new 1.5 mL centrifuge tube. The centrifuge process was then repeated two more times to ensure
all additional precipitates were removed and then transferred into 5 mm High Throughputplus NMR
tubes (Norell Inc., Landisville, NJ, USA). All samples were frozen immediately after preparation
and each sample was thawed prior to NMR analysis.
3.3.3. 1-D NMR Spectroscopy
All NMR spectra were acquired using a Bruker Avance 500 MHz spectrometer with a 1H-
19F-15N-13C 5 mm broadband Quadruple Inverse (QXI) probe fitted with an actively shielded Z
gradient (Bruker BioSpin, Rheinstetten, Germany). The 1H 90o pulse was calibrated for each
sample in the study. 1H NMR experiments were performed using Presaturation Utilizing Gradients
and Echoes (PURGE) water suppression [44] and 128 scans, a recycle delay of 3 s, and 16 K time
domain points. All 1-D NMR spectra were manually phased and calibrated to the DSS internal
reference methyl singlet, set to a chemical shift (δ) of 0.00 ppm.
3.3.4 2-D NMR Spectroscopy
1H-13C HSQC NMR experiments were optimized experimentally in terms of the relaxation
delay (d1) and the number of increments in the indirect dimension (F1) as described in Yuk et al.
94
[34]. All HSQC NMR spectra were collected in phase-sensitive mode using echo/anti-echo
gradient selection, a 1J 1H-13C (145 Hz) and a relaxation delay of 0.5 s. Twenty scans and 2048
data points were collected for each of the 196 increments in the F1 dimension. The F2 dimension
was processed using an exponential function corresponding to a line broadening of 15 Hz while the
F1 dimension was processed using a sine-squared function with a π/2 phase shift. Both
dimensions were zero-filled by a factor of two while forward linear prediction using 32
coefficients was applied in the F1 dimension. All 2-D NMR spectra were manually phased and
calibrated to the DSS internal reference methyl singlet, set to a chemical shift (δ) of 0.00 ppm.
3.3.5. Data and Statistical Analysis
Principal Component Analysis (PCA) was performed on the 1-D and 2-D NMR spectra
using Analysis of Mixtures (AMIX) statistics package (version 3.9.7; Bruker BioSpin). The 1H
NMR spectra were divided into width bins of 0.01 ppm from the region 0.25-9.0 ppm and the
region from 4.70-4.85 ppm was not analyzed due to residual H2O/HOD signals present in this
region. In the 1H-13C HSQC experiments, the carbon spectrum (F2) was evaluated from regions
10.0-140.0 ppm which was divided into 0.50 ppm bins. For the 1H spectrum (F1), the region 0.25-
9.0 ppm was investigated and was divided into 0.05 ppm. The region 4.70-4.85 ppm for the 1H
spectrum with their associated carbon area from 10.0-140.0 ppm was also excluded due to the
residual H2O/HOD signals present. The “sum of intensities” was used as the integration mode and
the scaling was set to “total intensity”. An initial PCA was performed with the entire dataset to
identify any outliers. Two earthworms at the highest endosulfan concentration (2.0 µg cm-2) were
identified to be outside the Hotelling’s T2 ellipse at the 95% confidence interval and thus were
removed from the dataset prior to subsequent analysis [39, 45]. The initial PCA of the highest
endosulfan concentration with outliers before removal is provided in the Accessory publication
95
(Figures A3.1a and A3.1b). PCA was performed at the 95% confidence level for each pesticide
(trifluralin and endosulfan) with their respective concentrations and any variances that represented
less than 1% or 2.5% in the bins for 1-D and 2-D NMR respectively were excluded [27, 39]. Mean
PCA scores and their associated standard errors for control and exposure concentrations for each
pesticide (endosulfan/trifluralin) were calculated and graphed to understand the differences
between the unexposed and exposed earthworm groups. In addition, an overall mean PCA scores
and their associated standard errors for controls and both pesticides with their respective
concentrations were calculated to understand the trajectories between the two pesticides. Analysis
of variance (ANOVA) with Dunnett’s multiple comparsion tests were conducted on the PC scores
to indicate which treatment groups were significantly different from the control group (p<0.05).
ANOVA, t-test and Dunnett’s multiple comparison tests were performed using SPSS 17.0 (IBM,
Somers, NY USA).
Multiple t-test filtered difference 1H NMR and 1H-13C HSQC NMR spectra were
constructed to identify increases or decreases in peaks between the control and each exposure
concentration set for each pesticide [46]. Each difference NMR spectrum (1-D and 2-D) was
generated by subtracting the averaged bucket intensities of the control group from each of the
exposed group concentrations. In addition, a t-test was conducted on each bin to determine if the
intensity difference was significantly different to the control (p<0.05). Any intensity values that
were significantly different were kept in the spectrum but if not, were replaced with a zero. The
final t-test filtered difference NMR spectrum allows the identification of potential metabolites
from the significant peaks that were increasing/decreasing after exposure. Influential peak signals
identified in the 1-D and 2-D NMR difference spectra were then matched with metabolite signals
from a previous study which identified the major metabolites in E. fetida [41] and were also
96
compared to the Bruker Biofluid Reference Compound Database version 2-0-0 (Bruker BioSpin).
Percent changes for the identified metabolites in the difference spectrum (1-D and 2-D NMR) of
exposed earthworms relative to control were calculated by dividing the bucket intensity
corresponding to the identified metabolite from the exposed earthworm group by the control group
for each concentration.
3.4 Results and Discussions
3.4.1 Earthworm weight change during exposure
The mean percent change in weight between the control and the exposed earthworms to
various concentrations of each contaminant were calculated to examine changes in body weight
with pesticide exposure (Accessory Publication, Figure A3.2). Each earthworm was weighed
before and after exposure to examine their changes in body weight due to pesticide exposure. A
decrease in weight was seen with increasing trifluralin concentration with a loss of -16.7% ±3.3 at
the highest concentration (1.0 mg cm-2) after exposure. However, none of the trifluralin exposed
groups had a significant decrease in weight after exposure (ANOVA, F3,36=2.50, P=0.075)
compared to the control. Significant weight decrease was seen with the endosulfan exposed
groups (ANOVA, F3,34=17.5, p=4.80x 10-7) and through a dunnett t-test, which compares all the
exposure groups to each other and the control, identified 1.0µg cm-2 and 2.0 µg cm-2 endosulfan
groups were significantly decreased (-24.9% ±2.2 and -34.3% ±2.4 respectively after exposure)
compared to the controls (P <0.05). Analysis of weight change after exposure is a good indicator
of chemical stress and can provide a direct link to the energy dynamics in the earthworms [47].
Even at the two highest sub-lethal endosulfan concentrations (which were 1/5 and 1/3 respectively
of the LC50) significant perturbation on the earthworm’s weight was observed.
97
3.4.2 Multivariate statistical analysis of trifluralin exposure
Mean PCA scores plots (Figures 3.1A and 3.1B) were calculated using the 1H NMR spectra
and 1H-13C HSQC (an example 1H and 1H-13C HSQC spectrum is provided in the Accessory
publication, Figures A3.3a and A3.3b respectively) of earthworm tissue extracts between the
control and trifluralin- exposed groups (The individual PCA scores plot using the 1H NMR spectra
and 1H-13C HSQC were also calculated for each trifluralin exposure concentration (Accessory
publications Figures A3.4a and A3.4b). The mean PCA scores plot consists of data points that
represent the average scores value with their associated error for each concentration class (i.e.
average over all the specimens in the class). This method allows for an overview of each exposure
concentration relative to each other and accounts for the variations seen in each class. The
variations in each group are related to: a) natural inter-differences between each earthworm, as
some organisms can have a more prominent response than others [48]; and b) potential
experimental errors from the extraction or analytical method [49]. The 1H NMR mean scores
PCA plot (Figure 3.1A) had the highest separation on PC1 versus PC2 axes which explained
49.6% and 27.8% respectively for a total of 77.4% of the total variance. Separation between the
trifluralin exposure groups and control increased on both the PC1 and PC2 axes but higher
separation was detected on the PC1 axis by the lower p-value in the ANOVA analysis (PC1:
ANOVA, F3,36=1.158, P=0.452 compared to PC 2: ANOVA, F3,36=0.285, P=0.834).
98
Figure 3.1: Mean principal component analysis (PCA) score plots of PC1 v. PC2 of trifluralin-exposed Eisenia fetida aqueous tissue extracts using: (a) 1-D presaturation utilising relaxation gradients and echos (PURGE) and (b) 2-D 1H-13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance spectra. Each point represents the mean PCA score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the trifluralin exposure concentration for each point.
The highest trifluralin concentration at 1.0 mg cm-2 had the largest separation compared to control
with the lowest p-value of P=0.435. The 1H-13C HSQC mean PCA plot (Figure 3.1B) had similar
results as the 1-D NMR PCA scores plot (Figure 3.1A) with the highest separation on the PC1
versus PC2 axes which explained 48.4% and 28.3% respectively for a total variance of 76.7%.
Separation was more prominent on the PC1 axis compared to the PC2 axis as separation was not
seen on the PC2 axis for the 2 highest concentrations (0.5 mg cm-2 and 1.0 mg cm-2). The 1.0 mg
cm-2 trifluralin concentration had the largest separation compared to the control with the lowest p-
value of P=0.538 which was consistent with the 1-D NMR results. A concentration dependant
relationship could be seen in the mean 1-D and 2-D NMR PCA scores plots as the separation
increased from the lowest to the highest trifluralin concentration. Neither the 1-D or 2-D NMR
PCA scores plot displayed significance at the P<0.05 level from the ANOVA analysis but
A) 1D PURGE B) 2D HSQC
-0.2 0.0 0.2
-0.2
0.0
0.2 Control
0.1 mg cm-2
0.5 mg cm-2
1.0 mg cm-2
PC
2 (
27
.8%
Va
ria
nc
e)
PC1 (49.6% Variance)
-0.2 0.0 0.2-0.2
0.0
0.2 Control
0.1 mg cm-2
0.5 mg cm-2
1.0 mg cm-2
PC
2 (
28.3
% V
ari
an
ce)
PC1 (48.4% Variance)
99
increasing separation could be seen in the exposed earthworm groups relative to the control group
indicated by the lower p value with exposure concentration. Therefore, the relative metabolite
change in the trifluralin-exposed earthworms was investigated further to understand if there were
statistically significant changes in specific metabolites not reflected in the overall PCA scores plot.
3.4.3. Relative metabolite changes in trifluralin-exposed earthworms
A t-test filtered NMR difference spectrum was constructed for the 1H NMR spectrum
(Figure 3.2) and 2-D HSQC NMR spectrum (Figure 3.3) for each exposure concentration (0.1 mg
cm-2, 0.5 mg cm-2 and 1.0 mg cm-2). The t-test filtered NMR difference spectra contain only
signals from metabolites that are statistically significant (p<0.05) and are the ones that contribute
to the separation in the mean PCA scores plot. The 1-D method has been utilized in other NMR
metabolomic studies to examine the significant changes in the NMR spectrum between different
treatment groups [45, 46]. Chemical shifts from the t-test filtered NMR difference spectra can be
matched against an NMR database to identify the responsible metabolites. The t-test filtered
difference spectra had similar results as the loading plots but allowed the visualization of which
metabolites are significant and thus are effective in understanding the metabolite flux in complex
mixtures. This is the first study, to our knowledge, that utilizes the application of 2-D t-test
filtered NMR difference spectra to identify metabolites resulting from contaminant exposure.
In the t-test filtered 1-D difference NMR spectrum (Figure 3.2), alanine (δ 1.46 ppm), maltose (δ
5.40 ppm) and adenosine triphosphate (ATP) (δ 8.22 ppm) were identified as the metabolites that
were significantly different from the unexposed earthworms (p < 0.05).
100
Figure 3.2: t-test filtered 1H nuclear magnetic resonance (NMR) difference spectra of Eisenia
fetida tissue extracts are obtained by subtracting the mean buckets of each trifluralin-exposed earthworm concentration: (a) 0.1, (b) 0.5 and (c) 1.0 mg cm-2 with the mean buckets of the control earthworms. Signals that are significantly different from the control (P<0.05) are retained whereas others are excluded.
8 7 6 5 4 3 2 1 0
Chemical Shift (ppm)
1.4
6 A
lan
ine
4.7
-4.8
5 R
esid
ua
l Wa
ter
5.4
0 M
alto
se
8.2
2 A
TP
Ove
rlap
pin
g s
ug
ars
an
d a
min
o a
cid
s (3
.2-4
.5)
A) 0.1 mg cm-2
B) 0.5 mg cm-2
C) 1.0 mg cm-2
101
Figure 3.3: t-test filtered 1H–13C heteronuclear single quantum coherence (HSQC) difference nuclear magnetic resonance (NMR) spectra of Eisenia fetida tissue extracts are obtained by subtracting the mean buckets of each trifluralin-exposed earthworm concentration: (a) 0.1, (b) 0.5 and (c) 1.0 mg cm-2 with the mean buckets of the control earthworms. Signals that are significantly different from the control (P<0.05) are retained whereas everything else is excluded. Only metabolites that are detected in the 2-D NMR spectra and not in the 1-D NMR spectra are shown.
In the t-test filtered 2-D difference NMR spectrum (Figure 3.3), each contour represents a spectral
signal that is significantly different from the unexposed control (p< 0.05) and the increase or
decrease in intensity is indicated by the colour legend. At the 0.1 mg cm-2 trifluralin concentration
(Figure 3.3A), negligible signals were detected with no significant metabolites identified which
was consistent with the minor separation between the control and lowest exposure concentration
group in the 2-D NMR PCA scores plot. At the 0.5 mg cm-2 (Figure 3.3B) and 1.0 mg cm-2 (Figure
9 8 7 6 5 4 3 2 1
140
120
100
80
60
40
20
-1.200E-04
-7.200E-05
-2.400E-05
2.400E-05
7.200E-05
1.200E-04
13C
Ch
em
ical
Sh
ift
(pp
m)
1H Chemical Shift (ppm)
9 8 7 6 5 4 3 2 1140
120
100
80
60
40
20
-7.500E-04
-4.500E-04
-1.500E-04
1.500E-04
4.500E-04
7.500E-04
1H Chemical Shift (ppm)
13C
Ch
em
ica
l S
hif
t (p
pm
)
9 8 7 6 5 4 3 2 1
140
120
100
80
60
40
20
-1.900E-04
-1.140E-04
-3.800E-05
3.800E-05
1.140E-04
1.900E-04
1H Chemical Shift (ppm)
13C
Ch
em
ical
Sh
ift
(pp
m)
B) 0.5 mg cm-2A) 0.1 mg cm-2
C) 1.0 mg cm-2
Glycine
Glycine
102
3.3C) exposure levels, glycine (1H δ:3.525 ppm, 13C δ: 44.25 ppm) was identified as a metabolite
of exposure in the t-test filtered 2-D difference NMR and was not seen in the 1-D difference NMR
spectrum due to the spectral overlap in that region. Only metabolites not identified in the t-test
filtered 1-D difference NMR spectrum are shown in the t-test filtered 2-D difference NMR
spectrum (Figure 3.3) but the assignments of all significant metabolites in the 2-D difference NMR
spectrum are shown (for the highest trifluralin exposure concentration (1.0 mg cm-2)) in the
Accessory Publication Figure A3.5a.
To examine the metabolic flux from each metabolite, relative percent changes were
calculated from the 1-D and 2-D difference NMR spectra (Figure 3.4). Alanine concentrations
increased significantly as the concentration of trifluralin increased to 1.0 mg cm-2. The increase in
alanine has been known to be a primary stress signal in a variety of organisms [50, 51]. Studies
have shown that an increase of free alanine in organisms results in the production of heat shock
proteins or activity in protein kinase to handle various stress conditions [52-54]. Through in-vivo
and in-vitro experiments, it has been shown that the stimulation of heat-shock proteins allows a
strong cellular defense to infer tolerance against nutrient deprivation, chemical and metal toxicity
in cells [55-57]. Past earthworm studies with E. fetida have also observed increases in alanine
after exposure to organochlorine pesticides, DDT and endosulfan, [58] and polycyclic aromatic
hydrocarbons (PAHs) naphthalene, phenanthrene and pyrene [28]. Glycine concentrations also
increased as the trifluralin-exposure concentration increased and had a significant change at
concentrations 0.5 mg cm-2 and 1.0 mg cm-2. The increase in alanine and glycine are known to
stimulate the expression of stress proteins which offers a cytoprotective action against toxic injury
which is not shared by the increase in other amino acids such as glutamate, aspartate, arginine and
leucine [59].
103
Figure 3.4: Percentage change (%) of all identified metabolites from the t-test-filtered 1-D and 2-D nuclear magnetic resonance (NMR) difference spectra of trifluralin-exposed Eisenia fetida tissue extracts. Percentage changes that are significantly different from the control (P<0.05) are labelled with an asterisk. Each percentage change is shown with their associated standard error.
In addition, both alanine and glycine offer protection near the plasma membrane at the acceptor-
ligand interaction sites due to their small, neutral structure [60]. Their presence near membranes
can balance lipid fluidity as they are known osmolytes, but their exact protective mechanisms are
unknown [59]. Trifluralin is a highly hydrophobic contaminant and the accumulation of trifluralin
in the earthworm’s plasma membranes can cause possible non-polar narcosis. Many proteins are
found near cellular membranes and the accumulation of narcotic chemicals can cause the
5
10
15
Concentrations (mg cm-2)
Rela
tiv
e P
erc
en
t C
han
ge (
%)
*Alanine
0.1 0.5 1.0
0
20
40
60
80
*
Concentrations (mg cm-2)
Rela
tive P
erc
en
t C
han
ge (
%)
*
ATP
0.1 0.5 1.0
-25
-20
-15
-10
-5
0
Concentrations (mg cm-2)
Re
lati
ve
Perc
en
t C
han
ge
(%
)
*
Maltose0.1 0.5 1.0
10
20
30
40
50
*
Concentrations (mg cm-2)
Rela
tive P
erc
en
t C
han
ge (
%)
*
Glycine
0.1 0.5 1.0
104
disturbance of membrane lipids [61, 62]. This can alter the function of membrane proteins since
many proteins are needed to fuse in order for activation. Therefore, the presence of trifluralin near
the protein-lipid interface can potentially affect the fluidity of the surrounding membrane lipids
and affect their operation.
Maltose decreased after exposure to trifluralin and was significantly different (p<0.05) at
the highest exposure concentration (1.0 mg cm-2) compared to control. Decreases in sugars can
indicate an up regulation of glycogenolysis and glycolysis which increases the production of ATP.
This was confirmed as ATP increased significantly at the higher trifluralin exposure concentrations
(0.5 mg cm-2 and 1.0 mg cm-2). This extra energy production by the earthworm due to trifluralin
exposure could be stress-induced and required for removal of trifluralin by the action of
cytochrome (Cyt) P450. Cytochrome (Cyt) P450 is found in many living organisms where their
crucial role is for the detoxification and inactivation of xenobiotics [63]. Hydrophobic xenobiotics
are converted to more polar compounds through a two phase process of functionalization and
conjugation [64] which makes the compound more water soluble to facilitate excretion [65]. Past
earthworm studies with E. fetida have detected the increase of Cyt P450 activity after exposure to
hydrophobic compounds such as pyrene [66] and benzo[a]pyrene [66, 67] using bioassays.
Therefore, the MOA of trifluralin in E. fetida may be due to its hydrophobic nature which results
in a non-specific perturbation of the metabolic profile. Bierkens et al. [68], conducted a battery of
bioassays using varying biological endpoints on E.fetida after exposure to trifluralin and found
their immunopathology and weight loss were most affected. In our study, the decrease in the
earthworm’s weight was detected in all concentrations but was not significant relative to the
control. However, metabolomics was able to explain a non-polar narcosis MOA by trifluralin
through the cytoprotective action by alanine and glycine near the cell membranes with the action
105
of Cyt P450 to remove the xenobiotic from the earthworm’s system. Even though the 1-D and 2-D
PCA scores plot were not significant at the P<0.05 level, certain individual metabolites were
significantly different compared to the control after exposure to trifluralin. This result explains the
concentration dependent separation in the PCA scores plot of the control and trifluralin-exposed
groups, and demonstrates the importance of conducting a closer analysis using the 1-D and 2-D t-
test NMR difference spectra than just the overall PCA scores plot analysis.
3.4.4 Multivariate statistical analysis of endosulfan exposure
Mean PCA scores plots (Figures 3.5A and 3.5B) were performed on the 1H NMR and 1H-
13C HSQC NMR spectra of earthworm tissue extracts to detect concentration dependant
differences between the control and endosulfan-exposed groups (The individual PCA scores plot
using the 1H NMR spectra and 1H-13C HSQC were also calculated for each endosulfan exposure
concentration (Accessory publications Figures A3.6a and A3.6b). The 1H NMR PCA plot showed
the highest separation on the PC1 versus PC2 axes with an explained variance of 71.2% and 13.0%
respectively for a total variance of 84.2%. There was significant separation only on the PC1 axis
(PC1: ANOVA, F3,34=4.549,p=0.009, PC2: ANOVA, F3,36=0.363,p=0.780) and through a
dunnett’s t-test, the two highest concentrations (1.0 µg cm-2 and 2.0 µg cm-2) were found to be
significantly different than the control (P<0.05). The 2-D HSQC NMR PCA scores plot (Figure
3.5B) showed similar results with the largest separation on the PC1 versus PC2 axes of the control
and endosulfan-exposed groups. The variance for the PC1 and PC2 axes were 74.1% and 11.4%
respectively with a total variance of 85.5%.
106
Figure 3.5: Mean principal component analysis (PCA) score plots of PC1 v. PC2 of endosulfan-exposed Eisenia fetida aqueous tissue extracts using: (a) 1-D presaturation utilising relaxation gradients and echos (PURGE) and (b) and 2-D 1H-13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance spectra. Each point represents the mean PCA score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the endosulfan exposure concentration for each point. The asterisk represents the mean concentrations that are significantly different from the control (P<0.05) using Dunnett’s multiple comparison test.
There was significant separation on the PC1 axis but not on the PC2 axis (PC1: ANOVA,
F3,34=4.712,p=0.007 compared to PC2: ANOVA, F3,36=0.017,P=0.997) with the two highest
concentrations (1.0 µg cm-2 and 2.0 µg cm-2) being significantly different (p<0.05) than the control
from the dunnett’s t-test. From the 1-D and 2-D NMR PCA scores plots, the earthworms may
have exhibited a concentration dependent response due to endosulfan exposure with a large
fluctuation in their metabolic response at the two highest concentrations. Further experiments with
a wider range of sub-lethal concentrations will be required to confirm this result.
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6-0.2
0.0
0.2 Control
0.5 µg cm-2
1.0 µg cm-2
2.0 µg cm-2
PC
2 (
11
.4%
Vari
an
ce
)
PC1 (74.1% Variance)
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6-0.2
0.0
0.2 Control
0.5 µg cm-2
1.0 µg cm-2
2.0 µg cm-2
PC
2 (
13
.0%
Va
ria
nc
e)
PC1 (71.2% Variance)
A) 1D PURGE B) 2D HSQC
*
*
**
107
3.4.5. Relative metabolite changes in endosulfan-exposed earthworms
A t-test filtered difference NMR spectrum was made for each exposure concentration (0.5
µg cm-2, 1.0 µg cm-2 and 2.0 µg cm-2) from the 1H NMR spectra (Figure 3.6) and 2-D HSQC NMR
spectra (Figure 3.7) to determine the metabolic differences from the control and the endosulfan-
exposed groups. In Figure 3.6, amino acids such as leucine (δ0.95 ppm), valine (δ 1.02), alanine
(δ1.46 ppm), lysine (δ 1.72 ppm), glutamate (δ 2.32 ppm), glutamine (δ 2.44 ppm), tryptophan (δ
7.19 ppm) and phenylalanine (δ 7.40 ppm) were found to be significantly different than the control
(P <0.05) in the 1-D NMR spectra. Other metabolites such as the sugar maltose (δ 5.40 ppm),
citric acid intermediate fumarate (δ 6.52 ppm) and energy metabolite ATP (δ 8.22 ppm) were also
found to be significantly different than the control (p <0.05). Due to the many overlapping peaks
seen in the 1-D NMR spectra, the t-test filtered difference 2-D NMR spectra (Figure 3.7) assisted
in the identification of other important metabolites as well due to the extended dispersion provided
by the carbon axis. Additional amino acids such as glycine (1H δ:3.525 ppm, 13Cδ: 44.25 ppm),
isoleucine (1H δ:0.925 ppm, 13Cδ: 13.25 ppm), methionine (1H δ:2.125 ppm, 13Cδ: 33.75 ppm),
sugars such as glucose (1H δ:3.775 ppm, 13Cδ: 74.25 ppm) and melibiose (1H δ:0.925 ppm, 13Cδ:
13.25 ppm) and citric acid cycle intermediate malate (1H δ: 4.975 ppm,13Cδ: 100.25 ppm) were
found to be significantly different relative to the control P <0.05). Only metabolites not identified
in the t-test filtered 1-D difference NMR spectrum are shown in the t-test filtered 2-D difference
NMR spectrum (Figure 3.7) but the assignments of all significant metabolites in the 2-D difference
NMR spectrum are shown (for the highest endosulfan exposure concentration (2.0 µg cm-2)) in the
Accessory Publication Figure A3.5b.
108
Figure 3.6: t-test filtered 1H nuclear magnetic resonance difference spectra of Eisenia fetida tissue extracts are obtained by subtracting the mean buckets of each endosulfan-exposed earthworm: (a) 0.5, (b) 1.0 and (c) 2.0 mg cm-2 with the mean buckets of the control earthworms. Signals that are significantly different from the control (P<0.05) are retained whereas everything else is excluded.
8 7 6 5 4 3 2 1 0
Chemical Shift (ppm)
Ove
rlap
pin
g s
ug
ars
an
d a
min
o a
cid
s (3
.2-4
.5)
0.9
5 L
eu
cin
e1
.02
Va
line
1.4
6 A
lan
ine
2.3
2 G
luta
ma
te2
.44
Glu
tam
ine
4.7
-4.8
5 R
esid
ua
l wa
ter
5.4
0 M
alto
se
7.1
9 T
ryp
top
ha
n7
.40
Ph
en
yla
lan
ine
8.2
2 A
TP
6.5
2 F
um
ara
te
1.7
2 L
ysin
e
A) 0.5 µg cm-2
B) 1.0 µg cm-2
C) 2.0 µg cm-2
109
Figure 3.7: t-test filtered 1H–13C heteronuclear single quantum coherence (HSQC) difference nuclear magnetic resonance (NMR) spectra of Eisenia fetida tissue extracts are obtained by subtracting the mean buckets of each endosulfan exposed earthworm concentration: (a) 0.5, (b) 1.0 and (c) 2.0 µg cm-2 with the mean buckets of the control earthworms. Signals that are significantly different from the control (P<0.05) are retained whereas everything else is excluded. Only metabolites that are detected in the 2-D NMR spectra and not the 1-D NMR spectra are identified.
The relative percent changes for each significant metabolite were calculated to understand
their flux through 3 different exposure concentrations from the 1H NMR spectra (Figure 3.8) and
2-D HSQC NMR spectra (Figure 3.9). Many amino acids (leucine, phenylalanine, tryptophan,
lysine, glutamate, valine, glycine, isoleucine, methionine) increased at the two highest
concentrations (1.0 µg cm-2 and 2.0 µg cm-2) with alanine and glutamine observed to be significant
9 8 7 6 5 4 3 2 1140
120
100
80
60
40
20
-3.400E-04
-2.040E-04
-6.800E-05
6.800E-05
2.040E-04
3.400E-04
1H Chemical Shift (ppm)
13C
Ch
em
ica
l S
hif
t (p
pm
)
9 8 7 6 5 4 3 2 1
140
120
100
80
60
40
20
-8.000E-05
-4.800E-05
-1.600E-05
1.600E-05
4.800E-05
8.000E-05
1H Chemical Shift (ppm)
13C
Ch
em
ica
l S
hif
t (p
pm
)
A) 0.5 µg cm-2
C) 2.0 µg cm-2
B) 1.0 µg cm-2
9 8 7 6 5 4 3 2 1
140
120
100
80
60
40
20
-3.500E-04
-2.100E-04
-7.000E-05
7.000E-05
2.100E-04
3.500E-04
13C
Ch
em
ica
l S
hif
t (p
pm
)
1H Chemical Shift (ppm)
MalateGlucose
Melibiose
Methionine
MalateGlucose
Melibiose
Methionine
Glycine
Isoleucine
Glycine
110
in all three concentrations. Glutamine is an important metabolite in the central nervous system and
has a major role in the synthesis of the neurotransmitters γ-aminobutyric acid (GABA) [69].
Figure 3.8: Percentage change (%) of identified metabolites from the t-test-filtered 1-D nuclear magnetic resonance difference spectra of endosulfan exposed Eisenia fetida tissue extracts.
10
20
30
40
50
Concentrations (µµµµg cm-2)
*
**
Alanine
Rela
tive P
erc
en
t C
han
ge
(%
)
0.5 1.0 2.00
10
20
30
40
50
Re
lati
ve
Pe
rce
nt
Ch
an
ge
(%
)
**
Leucine
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
10
20
30
*
Rela
tive P
erc
en
t C
han
ge
(%
)
*
*
Glutamine
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
0
10
20
30
40
50
60
Re
lati
ve P
erc
en
t C
han
ge (
%)
*
*
Phenylalanine
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
10
20
30
40
50
60
70
Re
lati
ve P
erc
en
t C
han
ge (
%)
*
*
Tryptophan
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
0
10
20
30
40
50
Lysine
*
*
Re
lati
ve P
erc
en
t C
han
ge (
%)
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
-40
-30
-20
-10
0
Rela
tive P
erc
en
t C
han
ge
(%
)
**
Maltose0.5 1.0 2.0
Concentrations (µµµµg cm-2)
0
10
20
30
40
50
60
Rela
tive P
erc
en
t C
han
ge (
%)
*ATP
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
-70
-60
-50
-40
-30
-20
-10
0
10
Fumarate
**R
ela
tive P
erc
en
t C
han
ge
(%
)
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
0
10
20
30
40
Glutamate
Rela
tive P
erc
en
t C
han
ge
(%
)
*
*
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
10
20
30
40
50
Valine
Re
lati
ve P
erc
en
t C
han
ge (
%)
**
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
111
Percentage changes that are significantly different from the control (P<0.05) are labelled with an asterisk. Each percentage change is shown with their associated standard error.
Figure 3.9: Percentage change (%) of identified metabolites from the t-test-filtered 2-D nuclear magnetic resonance difference spectra of endosulfan exposed Eisenia fetida tissue extracts. The percentage changes that were significantly different from the control (P<0.05) are labelled with an asterisk. Each percentage change is shown with their associated standard error.
This inhibitory neurotransmitter has been detected in many different tissues including the nervous
and gut tissue in earthworms and is found to be responsible for the regulation of gut motility [70].
Endosulfan is an organochlorine cyclodiene pesticide which targets the central nervous system.
Endosulfan is known to induce hyperexcitability in living organisms by the binding of the GABA
and glycine receptors to reduce the GABA-induced Cl- flux [71]. These receptors are critical in
the nervous system as they mediate inhibitory synaptic transmissions [71]. Physiological studies
using endosulfan on living organisms have identified mostly neurological problems such as
convulsions, irritability, muscular twitching and restlessness [72]. All the earthworms exposed to
-40
-30
-20
-10
0
Re
lati
ve P
erc
en
t C
han
ge
(%
)
**
Malate0.5 1.0 2.0
Concentrations (µµµµg cm-2)
-20
-15
-10
-5
0
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%)
**
Glucose0.5 1.0 2.0
Concentrations (µµµµg cm-2)
10
20
30
40
50
60
Re
lati
ve
Pe
rce
nt
Ch
an
ge
(%
)
**
Glycine
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
-20
0
20
40
60
80
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%)
*Isoleucine
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
-50
-40
-30
-20
-10
0
10
R
ela
tive
Pe
rce
nt
Ch
an
ge (
%)
Melibiose
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
-10
0
10
20
30
40
50
60
R
ela
tive
Perc
en
t C
han
ge
(%
)
*
*
Methionine
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
112
endosulfan had similar behavioral symptoms of convulsions at lower concentrations (0.5 µg cm-2
and 1.0 µg cm-2) and body stiffness at the highest concentration (2.0 µg cm-2) before being flash
frozen. This increased physical excursion can explain the significant decreases in all the sugars
(maltose, glucose and melibiose) because glycolysis and glycogenolysis would take place to fuel
the production of ATP which was increased in all exposure concentrations. In addition, the citric
acid cycle metabolites fumarate and malate significantly decreased correlating to their usage for
ATP production. The increase in the other amino acids with increasing exposure concentration of
endosulfan suggests muscle and tissue breakdown to handle the neurotoxic stress induced on the
earthworms. Decreases in tissue weight were seen especially at the two highest concentration (1.0
µg cm-2 and 2.0 µg cm-2) as they were significantly different compared to the unexposed controls
(P<0.05). Past exposure studies with different concentrations of pyrene had similar results as
amino acids were increased and significant weight loss was detected especially at the highest
concentration [47, 73]. Drewes and Vining [74] reported similar physiological observations in E.
fetida after exposure to another organochlorine neurotoxic pesticide, dieldrin, using noninvasive
electrophysiological recordings of their escape reflex activity. From their results, spontaneous
bursts of muscle fibers were detected with tonic spasms and body stiffening at higher exposure
concentrations. The study also showed significant reduction in body weight and skin secretions as
body fluid discharge were detected from the disruption in the neurohormonally regulated ions after
exposure [74]. Other toxicity studies have detected DNA damage in E. fetida with comet assays
after sub-lethal exposure to endosulfan [75] and used the growth rate and total protein content as
suitable biomarkers for endosulfan exposure [76]. In this study, our results confirm the
physiological behaviour in E. fetida after exposure to endosulfan and using metabolomics,
113
identified key metabolites to connect the biochemical understanding to the neurotoxic response by
the earthworm.
Endosulfan is considered a hydrophobic contaminant with a log n-octanol/water partition
coefficient (Kow) of 4.94 [77] and log organic carbon adsorption coefficient (Koc) of 3.6 [77]. A
non-polar narcosis MOA similar to trifluralin can potentially be exhibited in the earthworms but in
comparison to trifluralin (log Kow=5.34 [78] and log Koc=3.94 [79]), it is lower in hydrophobicity.
It is possible that both neurotoxic and non-polar narcosis MOAs are exhibited in the earthworms
but the neurotoxic response by endosulfan had a greater toxic MOA response than a non-specific
narcosis response seen with trifluralin.
3.4.6. Trajectory multivariate statistical analysis of trifluralin and endosulfan
An overall mean PCA scores plot was constructed from the 1H NMR and 1H-13C HSQC
NMR spectra of earthworm tissue extracts to compare trifluralin- and endosulfan-exposed
earthworms together with their respective exposure concentrations. Both 1-D and 2-D NMR
scores plots have similar results with the highest separation on the PC1 and PC2 axes with a total
variance of ~80% (Figures 3.10A and 3.10B). The results indicate that endosulfan- and trifluralin-
exposed earthworms display different trajectory pathways as the endosulfan-exposed earthworms
were more dominant in the PC1 axis and the trifluralin-exposed earthworm were more dominant in
the PC2 axis. Significant difference was observed in the PC1 axis in both datasets by the
endosulfan-exposed groups (PC1: ANOVA, F6,61=4.608, p=0.01) and through a dunnett’s t-test,
the higher two endosulfan concentrations (1.0 µg cm-2 and 2.0 µg cm-2) were significantly different
compared to the control (p <0.05). The PC2 axis was not significant from the ANOVA analysis
but a lower p value was seen with increasing trifluralin concentration. A concentration-dependant
relationship was detected for both agrochemicals as each exposure concentration for trifluralin and
114
endosulfan had projected further away in the scores plots compared to the unexposed control group
but this trend should be further tested over a wider range of sub-lethal concentrations.
Figure 3.10: Mean principal component analysis (PCA) score plots of PC1 v. PC2 of trifluralin- and endosulfan-exposed Eisenia fetida aqueous tissue extracts using: (a) 1-D presaturation utilising relaxation gradients and echos (PURGE) and (b) 2-D 1H–13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance spectra. Each point represents the mean PCA score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the exposure concentrations for each point. The asterisk represents mean concentrations that are significantly different from that of the control (P<0.05) using Dunnett’s multiple comparison test. The arrows indicate the trajectory of exposure by trifluralin or endosulfan.
The difference in trajectories by endosulfan and trifluralin provide a clear illustration in how
different their MOAs are in the earthworm (non-polar narcosis by trifluralin and neurotoxic
response by endosulfan) and were clearly seen by their significant differences in their metabolite
changes discussed previously. Endosulfan-exposed earthworms had a greater metabolic response
compared to trifluralin as it had the highest separation compared to the trifluralin-exposed
earthworms. Even though a non-polar narcosis MOA could have been elicited in the endosulfan-
-0.4 -0.2 0.0 0.2 0.4 0.6-0.2
0.0
0.2
Control
Trifluralin-0.1 mg cm-2
Trifluralin-0.5 mg cm-2
Trifluralin-1.0 mg cm-2
Endosulfan-0.5 µg cm-2
Endosulfan-1.0 µg cm-2
Endosulfan-2.0 µg cm-2
PC
2 (
17
.3%
Va
rian
ce)
PC1 (63.4% Variance)
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6-0.2
0.0
0.2
Control
Trifluralin-0.1 mg cm-2
Trifluralin-0.5 mg cm-2
Trifluralin-1.0 mg cm-2
Endosulfan-0.5 µg cm-2
Endosulfan-1.0 µg cm-2
Endosulfan-2.0 µg cm-2
PC
2 (
15.0
% V
ari
an
ce
)
PC1 (65.1% Variance)
A) 1D PURGE B) 2D HSQC
**
**
115
exposed earthworms, the neurotoxic response had a higher influence on their metabolic profile as it
had a different trajectory on PC1 compared to trifluralin-exposed earthworms on PC2.
3.5 Conclusion
This study demonstrates the potential of using 1-D and 2-D NMR metabolomics to
determine the MOA of two widely used agrochemicals, trifluralin and endosulfan. This is the first
study, to our knowledge, that utilizes 1H-13C HSQC NMR spectroscopy with the application of 2-
D t-test filtered difference spectrum in an environmental context to identify significant metabolites
of exposure. The 1-D 1H NMR and 2-D 1H-13C HSQC NMR PCA analysis showed increasing
discrimination between trifluralin- and endosulfan-exposed earthworms from the unexposed
controls as the exposure concentrations increased. Trifluralin-exposed earthworms had a
significant increase in amino acids, alanine and glycine and energy metabolite ATP with decreases
in the sugar maltose. The results suggest a non-polar narcosis MOA on E.fetida due to the high
non-polar nature of the contaminant with the energy expended for its removal by the action of Cyt
P450. Endosulfan-exposed earthworms had significant weight loss at the two highest
concentrations with physiological symptoms of convulsions and body stiffness after exposure.
Endosulfan-exposed earthworms had significant decreases in sugars (maltose, glucose and
melibiose) and citric acid intermediates, malate and fumarate, which correlate to the increase in
ATP production. The increases in neurological metabolite, glutamine, suggest a neurological
MOA by endosulfan on the earthworms through the inhibition of the GABA transmitters.
Trajectory metabolomic analysis was also performed on both pesticides with their respective
concentrations and showed completely different pathways for trifluralin- and endosulfan-exposed
earthworms. These different pathways correlate to a difference in MOAs and allow an
116
understanding of which MOA had a greater response on the metabolic profile. The 2-D NMR
results compliment the 1-D NMR results in our study and assisted in the identification of
additional metabolites of response due to trifluralin- or endosulfan-exposure. In addition, the
multivariate PCA analysis from the 1-D and 2-D NMR showed consistent results with similar
increases and decreases in the metabolite changes which negate potential differences in the NMR
spectra. This study further demonstrates the great potential of NMR-based metabolomics for
understanding toxicity of problematic environmental pesticides on soil earthworms.
3.6 Acknowledgment
Funding was provided by the Natural Sciences and Engineering Research Council Strategic
Grants Program (NSERC). André Simpson would like to thank the government of Ontario for an
Early Researcher Award. We would also like to extend to Dr. Melissa Whitfield Åslund and Brian
Lankadurai for technical assistance and valuable discussions.
117
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76. Mosleh, Y.Y., et al., Acute and sublethal effects of two insecticides on earthworms
(Lumbricus terrestris L.) under laboratory conditions. Environ. Toxicol., 2003. 18(1): p. 1-
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77. Weber, J., et al., Endosulfan, a global pesticide: A review of its fate in the environment and
occurrence in the Arctic. Sci. Total Environ., 2010. 408(15): p. 2966-2984.
78. Li, H., et al., Uptake of trifluralin and lindane from water by ryegrass. Chemosphere, 2002.
48(3): p. 335-341.
79. Doucette, W.J., Quantitative structure-activity relationships for predicting soil-sediment
sorption coefficients for organic chemicals. Environ. Toxicol. Chem., 2003. 22(8): p. 1771-
1788.
126
CHAPTER FOUR
Coelomic fluid: A complimentary biological medium to assess sub-lethal endosulfan exposure using 1H NMR-based earthworm metabolomics
Published as: Yuk, J., Simpson, M.J., and Simpson, A.J., Coelomic fluid: A complimentary
biological medium to assess sub-lethal endosulfan exposure using 1H NMR-based earthworm metabolomics. Ecotoxicology, 2012: (In Press).
Reproduced with permission from Ecotoxicology, 2012, In Press. (http://www.springerlink.com/content/f44h11206g4150wh/). © Copyright Springer Publishing
127
4.1 Abstract
Endosulfan is an environmentally persistent pesticide and has been shown to be genotoxic,
neurotoxic and carcinogenic to surrounding organisms. Earthworms are widely used in
environmental metabolomic studies to assess soil ecotoxicity. Previous NMR-based metabolomic
studies have analyzed earthworm tissue extracts after exposure to endosulfan and identified some
key metabolic indicators that can be used as biomarkers of stress. However, some metabolites may
have been masked due to overlap with other metabolites in the tissue extract. Therefore, in this
study, the coelomic fluid (CF) and the tissue extract of the earthworm, Eisenia fetida, were both
investigated using 1H NMR-based metabolomics to analyze their metabolic profile in response to
endosulfan exposure at three sub-lethal (below LC50) concentrations. Principal component analysis
(PCA) determined the earthworm CF and earthworm tissue extract to both have significant
separation between the exposed and control at the two highest sub-lethal endosulfan exposures (1.0
µg cm-2 and 2.0 µg cm-2). Alanine, glycine, malate, alpha-ketoglutarate, succinate, betaine, myo-
inositol, lactate and spermidine in the earthworm CF and alanine, glutamine, fumarate, glutamate,
maltose, melibiose, ATP and lactate in earthworm tissue extract were all detected as having
significant fluctuations after endosulfan exposure. An increase in ATP production was detected by
the increase activity in the citric acid cycle and by anaerobic metabolism. A significant decrease in
the polyamine, spermidine after endosulfan exposure describes an apoptotic mode of protection
which correlates to a previous endosulfan exposure study where DNA damage has been reported.
This study highlights that earthworm CF is a complementary biological medium to tissue extracts
and can be helpful to better understand the toxic mode of action of contaminants at sub-lethal
levels in the environment.
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4.2 Introduction
Endosulfan is an organochlorine pesticide that emerged in the 1960’s as one of the leading
chemicals used against a wide spectrum of agriculturally important insect pests [1]. It is currently
produced in at least six countries around the world with India being one of the largest producers.
Endosulfan has been ubiquitously detected in air, soil and water, and at long distances from the
direct source of application [2]. In recent years, endosulfan has been recognized as a persistent
toxic substance by many environmental agencies such as the United Nations Environment
Programme, World Health Organization and Environmental Justice Foundation [3]. Endosulfan
has been shown to have genotoxic, neurotoxic and carcinogenic properties to mammals, birds, fish
and bees [3, 4]. Endosulfan has a half-life in water of around 180 days but in soil, it has much
higher persistence with a half-life close to 60-900 days [1]. Due to its persistence in the
environment, it is important to understand its impact on organisms in soil ecosystems at sub-lethal
levels.
Past studies have demonstrated that the total contaminant concentrations in soil do not
necessarily relate to the bioavailable fraction or to soil toxicity [5]. Several ecotoxicological tests
are frequently utilized that focuses on the endpoint of toxicity such as the organism’s mortality,
reproduction rates and overall growth [6]. However, these results do not allow the understanding
of the toxic mode of action (MOA) of the contaminant after sub-lethal exposure at a molecular
level and how it affects the organism at a function level [7]. Environmental metabolomics is an
emerging field of research that examines the changes in the metabolic profile of native living
organisms in their environment to potential environmental stressors [8]. Metabolomics offers the
advantage of understanding the dynamic state of the organism (cell, tissue or biofluid) which can
mechanistically relate to the organism’s phenotype [9]. The endogenous metabolites identified
129
after exposure by metabolomics can uncover unforeseen relationships to further understand the
organism’s biochemical response to the contaminant and is finding an increasing number of
applications in ecotoxicology [10].
Earthworms play a major role in the decomposition activity of the soil environment and
contribute to the maintenance of the soil physical structure [11]. Their feeding and burrowing
activities in the soil increase soil fertility, water infiltration and soil aeration [12]. Earthworms
represent close to 60-80% of the soil total biomass, are ubiquitous in a vast range of soils [13] and
their absence or well-being in the soil is directly connected to microbial community health and
biodiversity [14]. Due to their importance in the soil, earthworms are common biological
indicators in ecotoxicological studies to assess the health of the soil environment. Metabolomic
studies using earthworms have been frequently used to detect subtle biomarkers of exposure to
environmental contaminants such as polyaromatic hydrocarbons (PAHS) [15, 16] and metal
contaminants [17-19]. Earthworm metabolomic studies have shown potential to be important
monitoring tools to detect stress in the soil system [16, 20]. Nuclear Magnetic Resonance (NMR)
spectroscopy has been commonly used in earthworm metabolomics to identify key metabolites as
it is non-destructive and a rapid technique for high-throughput of samples [15, 21]. Previous
metabolomics studies have analyzed the exposure of the earthworm, Eisenia fetida to endosulfan
using one-dimensional (1-D) and two-dimensional (2-D) NMR spectroscopy [22-24] on their
aqueous tissue extract. From their results, leucine, phenylalanine, tryptophan, lysine, glutamate,
valine, glycine, isoleucine, methionine, glutamine, alanine, maltose, glucose, meibiose, malate,
fumerate and ATP were detected as significant in two sub-lethal concentrations (1.0 µg cm-2 and
2.0 µg cm-2) using the earthworm tissue extract and a neurotoxic MOA was postulated [24].
However, the analysis of the earthworm tissue extract may mask the metabolic change of
130
endogenous metabolites because of its chemical heterogeneity [25]. In addition, certain
metabolites can be higher in concentration in the sample, dominate the spectrum to increase signal
overlap and decrease sensitivity to other metabolites in the same NMR region. Sugars are a major
component in earthworm tissue extracts and tend to exhibit complex NMR profiles which in turn
mask metabolites at lower concentration over a large spectral region in both -1-D and 2-D NMR
[23]. Past earthworm metabolomic studies [26, 27], have experimented using a three solvent
extraction system (chloroform, methanol and water) to separate the polar and non-polar
metabolites for NMR analysis. However, in both studies, the polar fraction still exhibited
significant overlap in the sugar region. Therefore, from both an analytical and ecotoxicity
perspective, it may be advantageous to investigate another biological medium especially one where
the concentration of sugars is reduced which in turn can help to detect obscured metabolites.
The earthworm coelomic fluid (CF) plays an important role in homeostasis and in immune
defenses against external stimuli [28]. CF has many haemolytic, proteolytic and cytotoxic
enzymes that are active against foreign cells and peptides [25]. Studies have shown alterations in
the components of the CF to be early indicators of immunotoxicity or biomarkers [29, 30]. A past
study analyzed the exposure of an environmental contaminant, 3-fluoro-4 nitrophenol, to the
earthworm CF using 1-D NMR and detected acetate and malonate to be significantly decreased in
the exposed earthworms [25]. Another study analyzed the earthworm CF on their exposure to 3-
trifluoromethylaniline using NMR spectroscopy and identified lactate as a potential biomarker of
acute toxic stress compared to the control earthworms [31]. Past metabolomic studies have shown
promise using either the earthworm tissue extracts or CF, however, to our knowledge, both have
not been compared simultaneously to assess their ability to discriminate metabolites of exposure to
a contaminant. In this study, the CF from one set of earthworms and the tissue extract from
131
another set of earthworms (both with control and endosulfan-exposed treatments) will be analyzed
using NMR. Readers should note that the tissue extract set will also contain the CF but as
discussed previously, are masked due to the higher abundant metabolites such as carbohydrates.
As such, the isolation of the CF can potentially reduce this background and permit a more targeted
and in-depth analysis of the CF itself. The comparison of the CF to the remaining tissue after
extraction was not investigated based on the principle that earthworm metabolomic studies do not
remove the CF prior to analysis. Instead, this study compares the isolated CF to the whole tissue
analysis to investigate what additional information the CF can provide when compared to the
commonly employed technique in the field after contaminant exposure. Contacts tests are used to
examine the specific changes in both the metabolic profile of the CF and tissue extracts after
endosulfan exposure. Contacts tests are widely used for understanding chemical risk or screening
before studying more complex matrices such as soil [9]. The results from this study will further
our understanding of the MOA by an environmentally relevant contaminant such as endosulfan
and provide further insight on the potential of utilizing the earthworm CF for environmental
metabolomic studies.
4.3 Experimental methods
4.3.1. Earthworm contact test preparation and exposure
Eisenia fetida earthworms were purchased from The Worm Factory (Perth, ON, Canada)
and were maintained according to Brown et al. [21]. Mature earthworms with a visible clitellum
were depurated in groups of 5 in the dark for 96 hours on Whatman 4 Qualitative filter paper with
a diameter of 9 cm (Fisher Scientific, Waltham, MA, USA) in 500 mL jars to remove any residues
from their intestinal tracts [21]. To ensure there were no significant differences in the weights of
the earthworms before exposure for the control and endosulfan treatment groups, an analysis of
132
variance (ANOVA) was conducted. The earthworms had a mean weight of 0.60 ± 0.13 g and from
the ANOVA analysis (ANOVA, F7,71=0.892,p=0.517), the p-value was higher than the α=0.05
level and therefore, no significant difference was seen between the controls (0.58 ± 0.08 g) and any
of the endosulfan concentrations groups (0.61± 0.14 g) before exposure. Earthworms were then
transferred to individual 120 mL amber glass jars containing pre-treated Whatman GF/A 4.25cm
diameter glass filter paper (Fisher Scientific). The half-lethal concentration (LC50) value for
endosulfan is reported to be 5.7 µg cm-2 [32]. To ensure the exposure concentrations chosen for
endosulfan were sub-lethal, endosulfan (99 % purity; Sigma Aldrich, St. Louis, MO, USA) was
applied to the filter paper at three sub-lethal concentrations: 1/9th (0.5 µg cm-2), 1/6th (1.0 µg cm-2)
and 1/3rd (2.0 µg cm-2) of the literature LC50 using 1 mL of acetone (HPLC grade; Caldeon,
Georgetown, ON, Canada) as the carrier solvent. One mL of acetone was applied to control
treatments. In all cases, the acetone was allowed to evaporate and 1 mL of distilled water was
added prior to the addition of earthworms. All earthworms survived the exposure tests.
Earthworms were kept in the dark for 48 hours, as recommended by the OECD LC50 contact test
guideline [33]. The experiment was kept at 24oC which is an optimal temperature for Eisenia
fetida [34]. Earthworms used for the CF extraction, were separated from the earthworms used for
the tissue extraction (detailed procedures are outlined below). There were 10 replicates for control
and 10 for each of the three concentrations of the contaminant for the exposed specimen.
4.3.2. Earthworm coelomic fluid extraction and preparation for NMR
Each earthworm after exposure (control or exposed) were placed in individual 25 mL glass
vials with 365 µL of 0.2 M monobasic sodium phosphate buffer solution (NaH2PO4·2H2O; 99.3%;
Fisher Scientific) containing 0.1% (w/v) sodium azide (99.5% purity; Sigma Aldrich) as a
preservative. Buffer solution was made with D2O (99.9% purity, Cambridge Isotope Laboratories
133
Inc., Andover, MA, USA) and adjusted to a pD of 7.4 using NaOD (30% w/w in 99.5% D2O,
Cambridge Isotope Laboratories Inc). The buffer solution for all NMR samples also contained 10
mg/L of 2,2-dimethyl-2-silapentane-5-sulfonate sodium salt (DSS; 97%, Sigma Aldrich) as an
internal standard. Earthworm CF was extracted by electrical extrusion using a 9 V battery and
using short exposure (<1s) repeated 10 times [30, 35]. The earthworm was then removed and the
extracted fluid was placed in a 1.5 mL centrifuge tube and centrifuged for 20 minutes at 15,000
rpm (~17,000 x g) using an International Equipment Company 21000 Centrifuge (Fisher
Scientific, Canada) to remove any biosolids or coelomocytes. The supernatant was then
transferred into a 5 mm High Throughputplus NMR tubes (Norell Inc., Landisville, NJ, USA). All
samples were frozen immediately after preparation and each sample was thawed prior to NMR
analysis.
4.3.3. Earthworm tissue extraction and preparation for NMR
After exposure, earthworms were immediately flash frozen in liquid nitrogen and
lyophilized [21]. The lyophilized earthworms were homogenized in a 1.5 mL centrifuge tube
using a 5 mm wide stainless steel spatula. Samples were then extracted using 1 mL of a 0.2 M
monobasic sodium phosphate buffer solution (NaH2PO4·2H2O; 99.3%; Fisher Scientific)
containing 0.1% (w/v) sodium azide (99.5% purity; Sigma Aldrich) as a preservative [21]. Buffer
solution was made with D2O (99.9% purity, Cambridge Isotope Laboratories Inc) and adjusted to a
pD of 7.4 using NaOD (30% w/w in 99.5% D2O, Cambridge Isotope Laboratories Inc). The buffer
solution for all NMR samples also contained 10 mg/L of 2,2-dimethyl-2-silapentane-5-sulfonate
sodium salt (DSS; 97%, Sigma Aldrich) as an internal standard. Samples were vortexed for 30
seconds using a VX 100 vortexer (Labnet, Edison, NJ, USA) and then sonicated for 15 minutes
134
using a FS60 sonicator (Fisher Scientific) to aid with the extraction. Samples were then
centrifuged at 14,000 rpm (~15,000 x g) using an International Equipment Company 21000
Centrifuge (Fisher Scientific) for 20 minutes and the supernatant was transferred into a new 1.5
mL centrifuge tube. The centrifuge process was then repeated two more times to ensure all
additional particulates were removed and then samples were transferred into a 5 mm High
Throughputplus NMR tubes (Norell Inc). All samples were frozen immediately after preparation
and each sample was thawed prior to NMR analysis.
4.3.4. NMR Spectroscopy
All NMR spectra were acquired using a Bruker Avance 500-MHz spectrometer with a 1H-
19F-15N-13C 5mm broadband Quadruple Inverse (QXI) probe fitted with an actively shielded Z
gradient (Bruker BioSpin, Rheinstetten, Germany). The 1H 90o pulse was calibrated for each
sample in the study. 1H NMR experiments were performed using Presaturation Utilizing
Relaxation Gradients and Echoes (PURGE) water suppression [36] and 512 scans, a recycle delay
of 3 s, and 65 K time domain points. All 1-D NMR spectra were manually phased and calibrated
to the DSS internal reference methyl singlet, set to a chemical shift (δ) of 0.00 ppm.
A 2-D 1H-13C HSQC NMR experiment was performed on one control earthworm for both
the CF and aqueous tissue homogenate samples to aid in spectral interpretation. HSQC was
collected in phase-sensitive mode using echo/anti-echo gradient selection, a 1J 1H-13C (145 Hz)
and a relaxation delay of 0.5 s. Eight hundred and eighty scans and 2048 data points were
collected for each of the 196 increments in the F1 dimension. The F2 dimension was processed
using an exponential function corresponding to a line broadening of 15 Hz while the F1 dimension
was processed using a sine-squared function with a π/2 phase shift. Both dimensions were zero-
135
filled by a factor of two while forward linear prediction using 32 coefficients was applied in the F1
dimension. The 2-D NMR spectra were manually phased and calibrated to the DSS internal
reference methyl singlet, set to a chemical shift (δ) of 0.00 ppm.
4.3.5 Data and Statistical Analysis
Principal Component Analysis (PCA) was performed on the 1-D NMR spectra using an
Analysis of Mixtures (AMIX) statistics package (version 3.9.8, Bruker BioSpin). The earthworm
CF 1H NMR spectra were divided into width bins of 0.02 ppm from the region 0.25 to 9.0 ppm and
the region from 4.35 to 5.21 ppm was not analyzed due to residual H2O/HOD signals present in
this region. The earthworm tissue extract 1H NMR spectra were divided into width bins of 0.02
ppm from the region 0.25-9.0 ppm and the region from 4.75-4.90 ppm was not analyzed due to
residual H2O/HOD signals present in this region. The “sum of intensities” was used as the
integration mode and the scaling was set to “total intensity” for all the NMR spectra. PCA was
performed at the 95% confidence level and any variances that represented less than 1% were
excluded [16]. In the earthworm tissue extract dataset, one earthworm at the lowest endosulfan
concentration (0.5 µg cm-2) was identified as an outlier through the Hotelling’s T2 ellipse at the
95% confidence interval and was removed prior to analysis [20, 37]. Therefore in this study, each
treatment group and unexposed control had 10 earthworms for both CF and tissue extract
experiments with one excluded from the lowest concentration of endosulfan (0.5 µg cm-2), which
had 9 earthworms. Mean PCA scores and their associated standard errors for control and exposure
concentrations were calculated and graphed to understand the differences between the unexposed
and exposed earthworm groups. Individual PCA score plots for the earthworm CF and tissue
extract 1H NMR spectra were also calculated to understand the differences between each
136
endosulfan exposure concentration and the control earthworm group. PCA is an exploratory data
analysis tool to identify general similarities and differences through a multivariate approach.
However, PCA scores plots themselves do not determine if the difference between various groups
is significant. To achieve this, Analysis of Variance (ANOVA) with a dunnett’s multiple
comparison test was used to determine if the separation between each treatment group compared to
the control group is significant (α=0.1 or α=0.05). [24, 38]. ANOVA in this study will be reported
as: ANOVA, Fdf=F value, p-value, where df is the degrees of freedom. A p-value of <0.1
generally indicates significance, whereas a p-value >0.1 indicates less statistical significance.
ANOVA, t-test and Dunnett’s multiple comparison tests were performed using SPSS 19.0 (IBM,
Somers, NY, USA).
Multiple t-test filtered difference 1H NMR spectra were constructed to identify increases or
decreases in the peaks between the control and each exposure concentration set [10]. Each
difference NMR spectrum was generated by subtracting the averaged bucket intensities of the
control group from each of the exposed group concentrations. In addition, a t-test was conducted
on each bin to determine if the intensity difference was significantly different to the control
(p<0.05). Any intensity values that were significantly different were kept in the spectrum but if
not, were replaced with a zero. The final t-test filtered difference NMR spectrum allows the
identification of potential metabolites from the significant peaks that were increasing/decreasing
after exposure. Influential peak signals identified in the 1-D difference spectra were then matched
with metabolite signals from a previous study which identified the major metabolites in E. fetida
[21] and were also compared to the Bruker Biofluid Reference Compound Database version 2-0-3
(Bruker BioSpin). The 1H-13C HSQC NMR spectra for the CF and tissue extract also assisted in
the identification of the major metabolites through the comparison with the Bruker Biofluid
137
Reference Compound Database version 2-0-3 (Bruker BioSpin). Percent changes for the identified
metabolites in the difference spectrum of exposed earthworms relative to control were calculated
by the equation: (IE - IC)/IC x 100 where IE is the mean bucket intensity for the exposed earthworm
group and IC is the mean bucket intensity for the control earthworm group.
4.4 Results and Discussions
4.4.1 Comparison of 1H and
1H-
13C HSQC NMR spectra of earthworm extracts
A 1-D 1H NMR and 2-D 1H-13C HSQC NMR spectrum was acquired for both the
earthworm CF (Figure 4.1A and Figure 4.1B) and tissue extracts (Figure 4.2A and Figure 4.2B) to
identify the various metabolites in the samples and to understand their metabolic profile
differences. From the 1-D NMR spectrum of the earthworm CF (Figure 4.1A), carbohydrates are
present at trace levels (for example, glucose anomeric peak, detected at δ 5.22 ppm), which is in
sharp contrast to the tissue extract, where various sugars including maltose, glucose and melibiose
dominate the spectral profile between δ 3.0-5.4 ppm. The lower concentration of sugars in the
earthworm CF significantly reduces overlap in δ 3.0-5.4 ppm region and permits many obscured
metabolites such as myo-inositol (δ 3.53 and δ4.05 ppm), scyllo-inositol (δ3.34 ppm), citric acid
intermediates (malate (δ 2.33-2.36, δ 2.65-2.68 and δ 4.30 ppm), α-ketoglutarate (δ 2.43 and δ 2.99
ppm) and succinate (δ 2.39 ppm) and polyamines (putrescine (δ 3.04 ppm) and spermidine (δ 3.13
ppm)) to be identified with certainty in the NMR spectrum.
138
Figure 4.1: 1-D and 2-D NMR spectra of control earthworm CF acquired using A) 1-D PURGE and B) 2-D 1H–13C HSQC NMR spectroscopy
Figure 4.2: 1-D and 2-D NMR spectra of control worm tissue extracts acquired using A) 1-D PURGE and B) 2-D 1H–13C HSQC NMR spectroscopy
8 7 6 5 4 3 2 1 0
Chemical Shift (ppm)
8.4
48.2
6 A
TP
7.9
8 7.7
7 7.5
8
7.2
47.1
67.0
56.9
16.8
0
6.5
1 F
um
ara
te
5.2
2 G
luco
se
4.7
8 R
esid
ual
Wate
r
4.3
0 M
ala
te
3.7
93.7
7
3.7
53.5
53.5
33.3
4 S
cyllo
-In
osit
ol
3.2
5 B
eta
ine
3.1
3 S
perm
idin
e
2.9
9 α
-keto
glu
tara
te2.6
82.6
52.4
3 α
-keto
glu
tara
te2.3
9 S
uccin
ate
2.3
62.3
32.1
2 G
luta
min
e
1.9
1 L
ysin
e
1.4
6 A
lan
ine
1.3
1 L
acta
te
0.0
0 D
SS
In
tern
al S
tan
dard
Aro
mati
c M
eta
bo
lite
s
My
o-i
no
sit
ol
Ma
late
Ma
late
4.0
5 M
yo
-in
osit
ol
2.0
5 G
luta
mate
3.0
4 P
utr
escin
e
1.7
2 L
ysin
e1.7
6 P
utr
escin
eGly
cin
e
8 7 6 5 4 3 2 1ppm
20
40
60
80
100
120
140
pp
m
Alanine
AlanineLysine
Putrescine
Glutamate/Glutamine/Lysine Glutamate
Glutamate
Malate
Malate
Succinate
α-ketoglutarateGlutamine
Myo-inositol
Scyllo-inositol
Betaine
Fumarate
Spermidine
Glycine
Aromatic
metabolites
Glucose
(1H)
(13C
)
A) B)
8 7 6 5 4 3 2 1ppm
20
40
60
80
100
120
140
pp
m
Tyrosine
Histidine
Phenylalanine
Maltose
Glucose/Maltose
Melibiose
Overlapping
Sugar/Amino Acid
resonances
Serine
Betaine
Glycine
Phenylalanine
Histidine
Methionine
Iso-leucine
Alanine
Glutamine
Glutamate
Leucine
Lactate
Asparagine
Aspartate
Valine
Lysine
Arginine
Threonine
Arginine
Lysine/Putrescine
(1H)
(13C
)
Malate
Malate
8 7 6 5 4 3 2 1 0
Chemical Shift (ppm)
8.34
Ad
en
ine
8.2
3A
TP
7.4
2
7.3
2
7.1
7 T
ryp
top
han
6.9
0 T
yro
sin
e
6.5
1 F
um
ara
te
5.4
0 M
alto
se
5.2
2 M
alto
se/G
luco
se
4.9
6 M
eli
bio
se
4.6
5 M
alto
se/G
luco
se
4.2
8
4.1
73.
96 3.9
23
.90
3.88
3.8
43
.77 3.7
13.
70
3.4
93
.39
3.2
43
.11
3.0
1 Ly
sin
e
2.34
Glu
tam
ate
2.1
0 G
luta
min
e/G
luta
ma
te
1.9
1 A
rgn
ine
/Lys
ine
1.7
2 L
ysi
ne
1.4
6 A
lan
ine
1.3
2 L
act
ate
1.0
4 V
alin
e0
.95
Le
uci
ne
0.0
0 D
SS I
nte
rnal
Sta
nd
ard
Ph
en
yla
lan
ine
Overlapping
resonances
from
Sugar & amino
acids
2.4
4 G
luta
min
e
A) B)
139
In the tissue extract, the intense sugar resonances completely mask these metabolites and are not
seen. A larger water signal was present (δ 4.78 ppm) in the earthworm CF because of the natural
water content in the sample but did not disrupt the nearby peak resonances and was excluded prior
to the multivariate analysis. Many unique metabolite signals were detected in the aromatic region
(δ 6.9-8.0 ppm) of the earthworm CF but are as yet unidentified. None of these aromatic signals
matched any entries in our standard metabolite database (Bruker Biofluid Reference Compound
Database) and will require further investigation in future studies. In the 1-D NMR spectrum of the
earthworm tissue extract (Figure 4.2A), tyrosine (δ 6.90 ppm), tryptophan (δ 7.17 ppm) and
phenylalanine (δ7.32-7.42 ppm) were detected in the aromatic region but was not seen in the
earthworm CF.
Most of the metabolites identified in the 1-D NMR spectrum of the earthworm CF were
confirmed by the cross-peaks in the 2-D HSQC NMR spectrum (Figure 4.1B) and corresponds to
the chemical shifts of the proton and carbon connections. The tissue extract 2-D HSQC NMR
spectrum (Figure 4.2B) confirmed many of the metabolites identified in the 1-D NMR spectrum
and shows the complexity of this biological medium as many cross-peaks are detected compared to
the earthworm CF. However, with the decrease in sugars in the earthworm CF, myo-inositol,
scyllo-inositol and malate could be clearly discerned in that region with spermidine, α-
ketoglutarate, succinate and fumarate appeared as additional metabolites in other spectral regions
of the CF. The analysis of the earthworm CF provides a unique metabolic profile that cannot be
completely discerned from the whole tissue extract alone. This difference in the metabolic profile
may potentially enable an alternate window into understanding the earthworm’s response to
environmental contaminants.
140
4.4.2. Multivariate statistical analysis of endosulfan exposure on earthworms
Mean PCA plots (Figures 4.3A and 4.3B) for the earthworm CF and tissue extract 1H NMR
datasets respectively were performed between the endosulfan-exposed earthworms and the control.
Individual PCA score plots for the earthworm CF and tissue extract 1H NMR datasets respectively
were also performed for each endosulfan exposure concentration and the control (Accessory
publication, Fig. A4.1A and A4.1B). For the earthworm CF (Figures 4.3A), the PC 1 vs PC 2
scores plot showed a trend for separation with the highest separation on the PC 2 axis while high
variance was detected on the PC 1 axis. The high variance in PC 1 could be biological variation
which is not related to the endosulfan treatment as this is occasionally seen with unsupervised PCA
analysis [39]. PC 2 had an explained variance of 19.7% while PC 1 had an explained variance of
48.2% for a total of 67.9% for total variance. There was significant separation on the PC 2 axis
(ANOVA, F3,36=21.1,p=4.69E-8) and through a dunnett’s t-test, which compares all the individual
concentrations with the control, the 2 highest concentrations (1.0 µg cm-2 and 2.0 µg cm-2) were
found to be significantly different than the control (p<0.05). For the earthworm tissue extract
(Figures 4.3B), separation was detected in the PC 1 versus PC 3 scores plot with an explained
variance of 67.6% and 8.4% respectively for a total variance of 76.0%. Higher PCs are commonly
analyzed in metabolomic studies using PCA analysis [16, 17, 27, 40, 41] as PCs explain the
highest amount of total data variation without discrimination in regard to the source of the
variation. Some PCs might not be related to the experimental treatment since other factors such as
metabolic variations (sampling error and natural biological variation) are incorporated [42].
Therefore, it is advantageous to explore higher order PCs to properly represent the metabolomic
datasets [43].
141
Figure 4.3: Mean PCA scores plot of 1H NMR spectra of endosulfan exposed E. fetida using their A) CF (PC 1 vs PC 2) and B) aqueous tissue extract (PC 1 vs PC 3). Each point represents the mean PC score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the endosulfan exposure concentration for each point. The ‘‘*’’ represents the mean concentrations that were significantly different from the control (p<0.05) using Dunnett’s multiple comparison test.
From ANOVA, separation was found on the PC 1 axis at α=0.1 (ANOVA, F3,35=2.248,p=0.100)
and PC 3 axis at α=0.05 (ANOVA, F3,35=4.277, p=0.011). To further evaluate the PCA separation,
a dunnett’s multiple comparison t-test determined that the 2 highest concentrations (1.0 µg cm-2
and 2.0 µg cm-2) were significantly different than the control (p<0.05). The earthworm CF
contains many immunocompetent cells which act as a protective hydrostatic skeleton around the
organism [44] and is the main communicator between the inner and outer environment [28].
Therefore, the earthworm CF was found to be as responsive to the subtle concentrations of
contaminants in an environment as the earthworm tissue extract. A concentration dependant
exposure was seen in both the earthworm CF and tissue extract from the PCA scores plot as
significance of the separation was increased according to the concentration. However, further
confirmation will be required using a wider range of sub-lethal concentrations.
A) Coelomic Fluid B) Tissue Extract
*
*
*
*
-0.2 0.0 0.2
-0.2
0.0
0.2
PC
2 (
19
.7%
Va
ria
nc
e)
PC1 (48.2% Variance)
Control
0.5 µg cm-2
1.0 µg cm-2
2.0 µg cm-2
-0.4 -0.2 0.0 0.2 0.4-0.2
0.0
0.2
Control
0.5µg cm-2
1.0µg cm-2
2.0µg cm-2
PC
3 (
8.4
% V
ari
an
ce
)
PC1 (67.6% Variance)
142
4.4.3. Relative metabolite changes in endosulfan-exposed earthworms
To determine the metabolic response differences that contribute to the separation in the
earthworm CF and tissue extract of the endosulfan-exposed earthworms PCA scores plots, a t-test
filtered difference NMR spectrum was made for each exposure concentration from the 1H NMR
spectra (Figures 4.4 and Figures 4.5 respectively). This method allows the identification of
significant metabolites that were specifically perturbed in the earthworm after endosulfan exposure
and has been frequently used in other metabolomic studies [10, 24, 37, 45]. In the earthworm CF
(Figure 4.4), lactate (δ 1.31 ppm), alanine (δ 1.45 ppm), succinate (δ 2.39 ppm), malate (δ 2.69
ppm and δ 4.29-4.31 ppm), α-ketoglutarate (δ 2.41 and 2.99 ppm), spermidine (δ 3.15 ppm),
betaine (δ 3.25 ppm), myo-inositol (δ 3.53 ppm) and glycine (δ 3.55 ppm) were detected as
significant (p<0.05) in the endosulfan-exposed earthworms compared to the control. None of the
unidentified aromatic signals in the earthworm CF were determined to be statistically significant in
relation to endosulfan exposure. In the earthworm tissue extract (Figure 4.5), similar metabolites
were detected as significant such as alanine and lactate, but many other metabolites such as
glutamate (δ 2.35 ppm), glutamine (δ 2.41 ppm), melibiose (δ 4.95 ppm), maltose (δ 5.40 ppm),
fumarate (δ 6.51 ppm) and ATP (δ 8.23 ppm) were identified as well.
To understand the metabolite level changes from each exposure concentration, the relative
percent change was calculated for each significant metabolite in the earthworm CF (Figure 4.6)
and tissue extract (Figure 4.7). In the earthworm CF, spermidine decreased to significant levels at
the lowest and highest endosulfan concentrations (0.5 µg cm-2 and 2.0 µg cm-2). Spermidine is part
of a class of aliphatic nitrogenous bases called polyamines which are found in all organisms [46].
143
Figure. 4.4: t-test filtered 1H NMR difference spectra of E. fetida CF were obtained by subtracting the mean buckets of each endosulfan-exposed earthworm concentration: (a) 0.5 µg cm-2, (b) 1.0 µg cm-2 and (c) 2.0 µg cm-2 with the mean buckets of the control earthworms. Signals that were significantly different from the control (p<0.05) were retained while others are excluded. Only the major metabolites are labeled for clarity.
8 7 6 5 4 3 2 1
Chemical Shift (ppm)
0.5 µg cm-2
1.0 µg cm-2
2.0 µg cm-2
1.4
5 A
lan
ine
1.3
1 L
ac
tate
2.3
9 S
uc
cin
ate
2.6
9 M
ala
te2
.99
α-k
eto
glu
tara
te3
.15
Sp
erm
idin
e3
.25
Be
tain
e3
.53
My
o-in
osito
l
4.2
9-4
.31
Mala
te
2.4
1 α
-ke
tog
luta
rate
3.5
5 G
lyc
ine
144
Figure 4.5: t-test filtered 1H NMR difference spectra of E. fetida tissue extracts were obtained by subtracting the mean buckets of each endosulfan-exposed earthworm: (a) 0.5 µg cm-2, (b) 1.0 µg cm-2 and (c) 2.0 µg cm-2 with the mean buckets of the control earthworms. Signals that were significantly different from the control (p<0.05) were retained while everything else were excluded. Only the major metabolites are labeled for clarity.
8 7 6 5 4 3 2 1
Chemical Shift (ppm)
0.5 µg cm-2
1.0 µg cm-2
2.0 µg cm-2
1.4
5 A
lan
ine
1.3
1 L
ac
tate
2.4
1 G
luta
min
e2
.35
Glu
tam
ate
4.9
5 M
elib
iose
5.4
0 M
alto
se
6.5
1 F
um
ara
te
8.2
3 A
TP
Ov
erla
pp
ing
Su
ga
rs an
d
am
ino
ac
ids
145
Figure 4.6: Percent change (%) of identified metabolites from the t test filtered 1-D NMR difference spectra of endosulfan-exposed E. fetida CF. Percent changes that were significantly different from the control (p<0.05) were labelled with ‘‘*’’. Each percent change is shown with their associated standard error. Polyamines have gained popularity in the last 15 years due to their role in gene regulation [47]. In
addition, polyamines are involved in cell growth and differentiation processes which mediate cell
apoptosis [48, 49]. Apoptosis is a gene-controlled process of cellular destruction where the
apoptotic cell is digested by phagocytes [48]. As DNA or other critical components are damaged
in organisms, death programs in affected cells are activated in order to prevent further insult [49].
-5
0
5
10
15
20
25
Concentrations (µµµµg cm-2)
Re
lati
ve
Pe
rce
nt
Ch
an
ge
(%
)
*
Glycine
0.5 1.0 2.0
-60
-50
-40
-30
-20
-10
0
Concentrations (µµµµg cm-2)
*
*
R
ela
tive
Perc
en
t C
han
ge
(%
)
*
Alpha-Ketoglutarate0.5 1.0 2.0
-80
-60
-40
-20
0
Concentrations (µµµµg cm-2)
*
**
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%)
Malate0.5 1.0 2.0
-40
-30
-20
-10
0
Concentrations (µµµµg cm-2)
*
R
ela
tiv
e P
erc
en
t C
han
ge
(%
)
Succinate0.5 1.0 2.0
0
10
20
30
40
50
60
Concentrations (µµµµg cm-2)
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%)
**
Betaine
0.5 1.0 2.0 0
5
10
15
20
25
30
Concentrations (µµµµg cm-2)
Re
lati
ve
Pe
rce
nt
Ch
an
ge (
%)
*
*
Myo-inositol
0.5 1.0 2.0
-40
-30
-20
-10
0
Concentrations (µµµµg cm-2)
**
Re
lati
ve
Perc
en
t C
ha
ng
e (
%)
Spermidine2.01.00.5
0
5
10
15
20
25
30
35
40
Re
lati
ve
Pe
rce
nt
Ch
an
ge (
%)
*
Alanine
0.5 1.0 2.0
Concentrations (µµµµg cm-2)
0
10
20
30
40
50
60 * *
R
ela
tive
Perc
en
t C
ha
ng
e (
%)
*
Lactate
0.5 1.0 2.0
Concentrations µµµµg cm-2
146
Figure 4.7: Percent change (%) of identified metabolites from the t test filtered 1-D NMR difference spectra of endosulfan-exposed E. fetida tissue extracts. Percent changes that were significantly different from the control (p<0.05) were labelled with ‘‘*’’.
Studies have shown that the decrease in polyamines can cause apoptosis activation in organisms
especially spermidine [46, 48]. Putrescine, a precursor metabolite to spermidine and another
polyamine in the earthworm CF, decreased consistently in all three endosulfan concentrations
(~14%) but was not significant compared to the control (data not shown). Polyamines are known
to influence processes in carcinogenesis [46]. Cells with elevated levels of polyamines express
higher cell proliferation, decreased apoptosis and expressions of genes that allow tumor invasion
-5
0
5
10
15
20
25
30
Concentrations (µµµµg cm-2)
*
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%)
*
ATP
2.01.00.5
-40
-20
0
20
40
Concentrations (µµµµg cm-2)
*
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%) Fumarate
0.5 1.0 2.0
0
5
10
15
20
25
30
Concentrations (µµµµg cm-2)
*
*
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%) Glutamate
0.5 1.0 2.0
-50
-40
-30
-20
-10
0
Concentrations (µµµµg cm-2)
*
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%)
Maltose2.01.00.5
-60
-40
-20
0
20
Concentrations (µµµµg cm-2)
*
*
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%) Melibiose
2.01.00.5
0
5
10
15
20
25
30
Concentrations (µµµµg cm-2)
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%)
*
Alanine
0.5 1.0 2.0
-5
0
5
10
15
20
Concentrations (µµµµg cm-2)
Glutamine
Re
lati
ve
Pe
rce
nt
Ch
an
ge
(%
)
*
*
0.5 1.0 2.00
5
10
15
20
25
30
Concentrations (µµµµg cm-2)
*
*
Lactate
0.5 1.0 2.0
Re
lati
ve
Pe
rce
nt
Ch
an
ge
(%
)
147
and metastasis [46]. On the contrary, with decreased polyamine levels, higher apoptosis will be
exhibited with less gene expression for tumor formation [50]. In a recent study [3], comet assays
were conducted on E.fetida after sub-lethal exposure to endosulfan in soil and detected significant
DNA damage (p <0.01) from both concentration and length of exposure. This study concluded
that the early detection of DNA damage by comet assays could be an early detection for
endosulfan exposure in earthworms. In our study, the decrease of spermidine in the earthworm CF
could be an important biological indicator of a protective response from the earthworm to increase
apoptosis due to the genotoxic potential by endosulfan.
Amino acids, alanine and glycine, were increased to significant levels at the highest
endosulfan concentration (2.0 µg cm-2) compared to the controls in the earthworm CF. Alanine
was also identified in the earthworm tissue extract and similar to the earthworm CF, had the same
relative percent change. Alanine and glycine are both universal stress indicators and have been
known to provide a cytoprotective action against stress damage [51]. The increase in alanine and
glycine in separate studies have been found to increase the gene expression for stress protein
synthesis in organisms [52, 53]. This significant increase in both of these amino acids could elicit
a defensive mechanism from the exposure to endosulfan. Other amino acids, glutamine and
glutamate were detected in the earthworm tissue extract at 2 sub-lethal concentrations (0.5 µg cm-2
and 1.0 µg cm-2) and confirm our previous endosulfan-exposure results [24]. Both of these amino
acids are crucial in the excitatory and inhibitory synapses. Glutamine is the precursor for the
synthesis of γ-aminobutyric acid (GABA), a inhibitory neurotransmitter and also for glutamate
which is the excitatory neurotransmitter [54]. Endosulfan is a neurotoxin and is known to inhibit
the GABA-gated chloride channels which antagonize the action of GABA [55]. This inhibition by
endosulfan causes a partial repolarization of the neuron which leads to involuntary muscle
148
contractions and convulsions [56]. The end result can potentially cause the earthworms to be
unable to regulate the flux of neurotransmitters, causing a large increase of these metabolites in
their system.
The increased muscle activity due to the neurotoxic response from endosulfan will cause a
large increase in the earthworm’s energy expenditure. The disaccharides, maltose and melibiose
detected in the earthworm tissue extract, were decreased after endosulfan exposure with maltose
decreasing significantly at the highest concentration (2.0 µg cm-2) and melibiose decreasing
significantly at the 2 highest concentrations (1.0 µg cm-2 and 2.0 µg cm-2). The large decrease in
disaccharides will increase the production of glucose by glucogenolysis to increase glycolysis.
The breakdown of glucose to pyruvate by glycolysis would then be linked to the citric acid cycle
(CAC), which is the main energy transfer cycle for energy production in living organisms [57].
The CAC intermediate, fumarate identified in the earthworm tissue extract, had a significant
decrease at the highest endosulfan concentration (2.0 µg cm-2). The increase in CAC activity is
further confirmed in the earthworm CF as other major (CAC) intermediates, malate, alpha-
ketoglutarate and succinate were all decreasing to significant levels after endosulfan exposure.
The heightened activity in glycogenolysis, glycolysis and CAC would be used to increase ATP
production which was confirmed in the earthworm tissue extract with significant increases at the
two highest concentrations (1.0 µg cm-2 and 2.0 µg cm-2). Lactate detected in both the earthworm
CF and tissue extract had similar increases in all endosulfan exposure concentrations. Lactate
which is the byproduct from anaerobic metabolism from pyruvate, is an additional energy pathway
when the muscle activity in an organism is beyond steady state and O2 levels are not enough for
oxidation phosphorylation after CAC [58]. The increase of lactate in the earthworm is an
indication for a greater dependency on ATP production by the toxic response to endosulfan where
149
the overactivity in the aerobic energy cycles such as CAC and anaerobic pathways such as
glycolysis [58] are insufficient.
Betaine and myo-inositol, identified in the earthworm CF, were both significantly increased
after endosulfan exposure. Betaine and myo-inositol are well known osmolytes to regulate cell
volume due to the loss of inorganic ions and organic solutes [59]. Exposure to high salinity or
hydrophobic contaminants could cause an imbalance in the intracellular solute content or
extracellular osmolality [60, 61]. Past studies have reported the binding of hydrophobic
contaminants such as polyaromatic hydrocarbons to biological membranes which can decrease the
stability and alter the fluidity of molecules across the membrane [60, 62]. Even though the
increase or decrease of any organic solute can bring a cell to osmotic balance, only a set of organic
osmolytes such as betaine and myo-inositol are utilized in biological systems [59, 61]. In addition,
studies have shown that the increase in osmolytes in the cell during external stress does not affect
the cellular architecture and function [61, 63]. Endosulfan is considered a hydrophobic compound
due to its high log n-octanol/water partition coefficient (Kow) of 4.94 [2] and log organic carbon
adsorption coefficient (Koc) of 3.6 [2]. The exposure of endosulfan could potentially disrupt the
fluidity of the cell membrane and the increase in betaine and myo-inositol help maintain osmotic
balance in the earthworm.
4.5. Conclusions
Our study demonstrates the potential of using the earthworm’s CF and tissue extract in
combination to further our understanding of the sub-lethal exposure to endosulfan. The 1H and 1H-
13C HSQC NMR spectrum of the earthworm CF displayed a unique metabolic profile compared to
the tissue extract. The absence of sugars (maltose and melibiose) in the earthworm CF decreased
the signal overlap in the chemical shift region (3.0-4.5 ppm) and along with the different chemical
150
composition of CF compared to tissue extracts, allowed a range of unique endogeneous
metabolites to be identified. The different metabolic profile of the earthworm CF allowed a new
perspective on the earthworm’s response to endosulfan. The earthworm CF was as sensitive to
endosulfan exposure as the earthworm tissue extract as significant separation between the exposed
and control was seen using PCA multivariate analysis in the highest two sub-lethal endosulfan
exposures. The t-test filtered difference NMR spectrum of the earthworm CF and tissue extract
detected a plethora of metabolites that had significant fluctuations compared to the controls. The
decrease of spermidine in the earthworm CF explains an apoptotic response after endosulfan
exposure due to its potential genotoxicity and can be an important biological indicator for DNA
damage. The decrease in CAC intermediates, alpha-ketoglutarate, malate and succinate in the
earthworm’s CF and fumarate in the tissue extract enabled a clearer depiction of the high activity
of the CAC for the increase production of ATP. The binding of cell membranes by endosulfan
could potentially occur as increases in osmolytes, betaine and myo-inositol in the earthworm CF
were detected. Alanine and lactate were both identified in the earthworm CF and tissue extract and
their similar increases in both biological mediums deter any differences in their biochemical
response. Even though the earthworm tissue extract is commonly used as the main biological
medium for earthworm metabolomic studies [16, 20, 22, 64], this study demonstrates the potential
of using both the earthworm CF and tissue extract to describe a more complete picture of the subtle
responses to environmental contaminants. Contact tests were used in this study as an initial basis
for method development and direct analysis of pesticide exposure. Future earthworm metabolomic
experiments using both biological mediums will transition to soil environments to understand other
important factors such as ingestion and pesticide-soil interactions. In addition, to validate the
apopotic response after endosulfan exposure, it would be interesting to conduct a fluorescence
151
staining method of cells isolated from CF which would allow another method in understanding the
extent of apoptosis after endosulfan exposure [65].
4.6 Acknowledgment
Funding was provided by the Natural Sciences and Engineering Research Council Strategic
Grants Program (NSERC). André Simpson would like to thank the government of Ontario for an
Early Researcher Award. We would also like to extend to Dr. Melissa Whitfield Åslund and Brian
Lankadurai for technical assistance and valuable discussions.
152
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CHAPTER FIVE
1-D and 2-D NMR-based metabolomics of earthworms exposed to endosulfan and endosulfan sulfate in soil
Content in this chapter has been submitted as:
Yuk, J., Simpson, M.J., and Simpson, A.J., 1-D and 2-D NMR metabolomics of earthworm responses to sub-lethal endosulfan and endosulfan sulfate exposure in soil. Environ. Pollut., 2012 (Submitted).
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5.1 Abstract
One-dimensional (1-D) and two-dimensional (2-D) nuclear magnetic resonance (NMR)-
based metabolomics was used to investigate the toxic mode of action (MOA) of endosulfan, an
organochlorine pesticide, and its degradation product, endosulfan sulfate, to Eisenia fetida
earthworms in soil. Three soil concentrations (0.1, 1.0 and 10.0mg/kg) were used for both
endosulfan and endosulfan sulfate. Both earthworm coelomic fluid (CF) and tissues were
extracted and then analyzed using 1H and 1H-13C NMR techniques. A similar separation trajectory
was observed for endosulfan and endosulfan sulfate- exposed earthworms in the mean principal
component analysis (PCA) scores plot for both the earthworm CF and tissue extracts. A
neurotoxic and apoptotic MOA was postulated for both endosulfan and endosulfan sulfate exposed
earthworms as significant fluctuations in glutamine/GABA-glutamate cycle metabolites and
spermidine were detected respectively. This study highlights the potential of NMR-based
metabolomics to understand molecular-level toxicity of persistent organochlorine pesticides and
their degradation products directly in soil environments.
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5.2 Introduction
Organochlorine pesticides are a group of persistent environmental pollutants that have
caused major worldwide concern as many are semi-volatile, bioaccumulative and toxic [1]. Not
only are they environmentally persistent, they have potential to spread widely both regionally and
globally [2]. Endosulfan is an organochlorine pesticide that has been commonly used in
agriculture for the control of various pests [3]. Due to its semi-volatility and persistent
physicochemical properties, endosulfan has been observed in many environmental compartments
and often in areas far away from their location of direct application [4]. It is considered
carcinogenic and an endocrine disruptor [1]. Endosulfan is also believed to cause central nervous
system and neurodegenerative disorders in many mammals including humans [5]. Its breakdown
products include endosulfan sulfate, diol, ether, -hydroxy ether and –lactone: all of which are
considered to be less toxic [3] with the exception of endosulfan sulfate.
Endosulfan sulfate is the main metabolite of endosulfan degradation in soil and sediments
[6]. The half-life for endosulfan is around one to three months while endosulfan sulfate can persist
close to two to six years depending on the soil conditions [7]. Understanding the environmental
impact of contaminants and their degradation products in the soil has become a major priority for
the Organization for Economic Co-operation and Development (OECD) and its member countries
[8]. Currently, most studies have examined the toxicity of endosulfan sulfate in aquatic
environments and results have shown similar toxicity levels to the parent compound, endosulfan [3,
9, 10]. However, little focus has been placed on the toxicity of endosulfan and endosulfan sulfate
in soil environments, especially to soil-dwelling organisms such as earthworms. Earthworms are
important soil organisms because they play a critical role in soil development such as aeration,
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drainage and transportation of lower soil to the surface [11]. With these major functions,
earthworms are useful biological indicators for soil fertility and soil ecosystem health [12].
Nuclear magnetic resonance (NMR)-based metabolomics is a powerful diagnostic tool in
understanding the metabolic response of organisms to genetic modifications, external stimulus,
and/or stressors [13-15]. Environmental metabolomics is an emerging sub-discipline that
investigates the metabolic profile of native organisms in their environment to potential stressors
that they might encounter [16]. In addition, environmental metabolomics has the capability to
study biochemical fluxes in endogenous metabolites after exposure to sub-lethal concentrations of
contaminants [17]. This in turn allows the understanding of a contaminant’s toxic mode of action
(MOA), and the identification of the biomarkers that pertain to the exposure [18, 19]. In the soil
environment, earthworms have been commonly used in NMR-based metabolomic studies to
understand their response to various contaminants such as polyaromatic hydrocarbons [20, 21],
polychlorinated biphenyls [22] and metals [23].
In the present study, one-dimensional (1-D) and two-dimensional (2-D)-based
metabolomics will be used to understand the response of the earthworm, Eisenia fetida (E. fetida),
to various concentrations of endosulfan and endosulfan sulfate in soil. Both tissue extracts and
coelomic fluid (CF) are used in tandem to investigate the toxicity and MOA of endosulfan and
endosulfan sulfate. Our past metabolomic study [24] used various 1-D and 2-D NMR techniques
to examine endosulfan exposure using OECD acute toxicity tests on contact test filter paper [25].
The results found that 1H NMR (Presaturation Utilizing Relaxation Gradients and Echoes, PURGE)
and 1H-13C Heteronuclear Single Quantum Coherence (HSQC) techniques to be most effective in
identifying significant metabolites of exposure between the unexposed and exposed earthworms.
In a recent earthworm metabolomics study [26], the earthworm’s CF was tested as a
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complementary biological medium to the earthworm tissue extract after endosulfan exposure. The
CF is part of a hydrostatic skeleton around the earthworm and acts as the communicator between
the inner and outer environment [27]. The CF is responsible for cellular regulation of nutrition and
excretion, which could be potential biomarkers of stress from the exposure to contaminants [28].
The CF’s metabolic profile helped alleviate spectral overlap from certain metabolites, such as
sugars seen in the earthworm tissue extract, to allow the detection of other metabolites in the same
spectral region. Previous studies focused on contact tests and method development. Thus, this
study represents an important transition from traditional contact filter paper tests to endosulfan soil
exposure which will provide insight into contaminant-soil interactions and soil ingestion as another
mode of contact for earthworms. In addition, this study will investigate and compare the toxicity
and MOA of endosulfan sulfate in soil as well.
5.3 Experimental Methods
5.3.1. Soil Spiking
An artificial soil was prepared according to the OECD Earthworm Acute Toxicity test
protocol [25]. It was prepared using 10% sphagnum peat (Ward’s Natural Science, ON, Canada),
20% kaolin clay (Ward’s Natural Science) and 70% sand (Ward’s Natural Science).
Approximately 125g (dry weight) of the artificial soil was placed in each of seven 1 L clear glass
jars. Six jars were then spiked with 10ml of endosulfan (99.9 % purity; Sigma Aldrich, St. Louis,
MO, USA) or endosulfan sulfate (98.8 % purity; Sigma Aldrich, St. Louis, MO, USA) using three
different spiking concentrations (5mg/L, 50mg/L and 500mg/L) in acetone (HPLC grade, Fisher
Scientific). The exposure concentrations for endosulfan and endosulfan sulfate mimicked a comet
assay endosulfan study done on earthworms by Liu et al. 2009 using the OECD soil. Since no
study has analyzed endosulfan sulfate to earthworms in soil environments, the exposure
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concentrations were kept the same as endosulfan for a direct comparison. The last glass jar was
the unexposed control treatment and was spiked with 10ml of acetone (Fisher Scientific) only. All
the spiked jars were left in the fume hood to vent for 16 hours to allow all the acetone to evaporate
[22]. Soil (at 375 g) was then added to each of the seven spiked soil glass jars and mixed
thoroughly for a total endosulfan or endosulfan sulfate concentration of 0.1, 1.0 and 10.0mg/kg
(dry weight) for the pesticide-exposed treatments. To ensure proper moisture content [25], all the
soils were adjusted using deionized water to 35% moisture content of the soil dry weight. All
spiked endosulfan or endosulfan sulfate soil concentrations were confirmed after earthworm
exposure using soxhlet extraction and quantification via gas chromatography/mass spectrometry
(discussed in detail in Supplementary Material Section S5.1 and Table S5.1).
5.3.2. Earthworm exposure
E. fetida earthworms were purchased from The Worm Factory (Perth, ON, Canada) and
were maintained according to Brown et al. (2008). Twenty mature earthworms with a visible
clitellum were added to each of the endosulfan, endosulfan sulfate (3 each) and control soil (7 total
soil treatments). All the earthworms had a mean weight of 0.48 ± 0.015g and there were no
significant differences in the average mass of the earthworms between the controls and any of the
endosulfan and endosulfan sulfate treatment groups (P=0.953) before exposure (data not shown).
According to the OECD soil exposure tests [25], earthworms were kept in lightly closed
jars for 7 days at 21oC in natural light [29], then removed and depurated for 96 h on damp filter
paper to remove any excess soil in their gut [30]. Earthworms used for the CF extraction were
separated from the earthworms used for the tissue extraction (detailed procedures are outlined
below). For the highest endosulfan and endosulfan sulfate exposure concentration (10.0 mg/kg),
six and two earthworms died respectively after depuration and were removed from the study.
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5.3.3. Earthworm coelomic fluid and and tissue extraction and preparation for NMR
Each earthworm’s CF after exposure (control or exposed) were extracted non-invasively
using electrical stimulation described in Yuk et al. (2012) (Section S5.2). The earthworms that
were separated for tissue extraction were immediately flash frozen in liquid nitrogen, lyophilized
and prepared according to Yuk et al. (2011) (Section S5.2). All samples were frozen immediately
after preparation and each was thawed prior to NMR analysis.
5.3.4. 1-D and 2-D NMR Spectroscopy
All NMR spectra were acquired using a Bruker Avance III 500MHz spectrometer with a
1H-19F-15N-13C 5mm broadband Quadruple Inverse (QXI) probe fitted with an actively shielded Z
gradient (Bruker BioSpin, Rheinstetten, Germany). The 1H 90o pulse was calibrated for each
sample in the study. 1H NMR experiments were performed using Presaturation Utilizing Gradients
and Echoes (PURGE) water suppression [31] and 512 scans, a recycle delay of 3s, and 65K time
domain points. All 1-D NMR spectra were manually phased and calibrated to the DSS internal
reference methyl singlet, set to a chemical shift (δ) of 0.00 ppm.
1H-13C HSQC NMR experiments were optimized experimentally in terms of the relaxation
delay (d1) and the number of increments in the indirect dimension (F1) as described in Yuk et al.
(2010). All HSQC NMR spectra were collected in phase-sensitive mode using echo/anti-echo
gradient selection, a 1J 1H-13C (145 Hz), and a relaxation delay of 0.5s. Twenty scans and 2048
data points were collected for each of the 196 increments in the F1 dimension. The F2 dimension
was processed using an exponential function corresponding to a line broadening of 15Hz, while the
F1 dimension was processed using a sine-squared function with a π/2 phase shift. Both
dimensions were zero-filled by a factor of two while forward linear prediction using 32
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coefficients was applied in the F1 dimension. All 2-D NMR spectra were manually phased and
calibrated to the DSS internal reference methyl singlet, set to a chemical shift (δ) of 0.00 ppm.
5.3.5 Data and Statistical Analysis
Principal Component Analysis (PCA) was performed on the 1-D and 2-D NMR spectra of
the earthworm’s CF and tissue extracts using an Analysis of Mixtures (AMIX) statistics package
(version 3.9.8, Bruker BioSpin). The specific details about the binning procedures for the 1-D and
2-D NMR spectra of the CF and tissue extract are described in the supplementary material (Section
S5.3). Individual and mean PCA scores with their associated standard errors for control and
exposure concentrations were calculated and graphed to understand the differences between the
unexposed and exposed earthworm groups. Dunnett’s multiple comparison tests were conducted
on the PC scores to indicate which treatment groups were significantly different from the control
group (P<0.05). T-test and Dunnett’s multiple comparison tests were performed using SPSS 19.0
(IBM, Somers, NY, USA).
Multiple t-test filtered difference 1H NMR and 1H-13C HSQC NMR spectra were
constructed to identify increases or decreases in the peaks between the control and each exposure
concentration set for the identification of metabolites [32, 33]. T-test filtered difference NMR
spectra were calculated for each exposure concentration (0.1, 1.0 and 10.0mg/kg) from the 1H-
NMR spectra for the earthworm CF (Figures S5.3 and S5.4, respectively) and 1H NMR spectra for
the tissue extract, (Figures S5.5 and S5.6, respectively) and 1H-13C HSQC NMR spectra for the
tissue extract (Figures S5.7 and S5.8, respectively). More detail about the t-test filtered difference
NMR spectra and identification of metabolites are described in the supplementary material
(Section S5.4). Percent changes for the identified metabolites in the difference spectrum of
exposed earthworms relative to control were calculated by the equation: (IE - IC)/IC x 100. IE is the
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mean bucket intensity for the exposed earthworm group and IC is the mean bucket intensity for the
control earthworm group.
5.4 Results and Discussions
5.4.1. Multivariate analysis on earthworm CF and tissue extracts
Mean and individual PCA scores plots were calculated for the 1H NMR and 1H-13C HSQC
NMR spectra for the earthworm’s CF and tissue extract after endosulfan (Figure 5.1 and Figure
S5.1) and endosulfan sulfate exposure (Figure 5.2 and Figure S5.2). For mean PCA scores plot of
the endosulfan exposed earthworm CF (Figure 5.1A), the highest separation was seen in the first
two principal components (PC1 and PC2) and both accounted for 64.9% of the total variance.
Separation from the control group increased with endosulfan exposure concentration.
Figure 5.1 Mean PCA scores plots of PC1 versus PC2 of endosulfan-exposed E. fetida: A) coelomic fluid using 1-D NMR, B) tissue extracts using 1-D NMR, and C) tissue extracts using 2-D HSQC NMR spectra. Each point represents the mean PC score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the exposure concentrations for each point. The “*” represents mean concentrations that were significantly different from the control (p<0.05) using Dunnett’s multiple comparison test.
The highest concentration (10 mg/kg), was found to be significantly different from the control
(P<0.05). The separation between the lowest and middle exposure concentration (0.1 and
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1.0mg/kg) were clustered close together and separation compared to the control group was seen.
This suggests similar response to endosulfan in that range of concentrations studied. For the mean
PCA scores plot of the endosulfan sulfate-exposed earthworm CF (Figure 5.2A), PC1 and PC2 had
the highest separation in PCA scores plot with both axes accounting for 54.2% of the total variance.
The endosulfan sulfate PCA scores separation was analogous to the endosulfan PCA scores and
there was comparable separation at the low and middle concentrations (0.1 and 1.0mg/kg), and at
the highest concentration (10mg/kg), there was significant separation in comparison compared to
the control group (P<0.05).
For the endosulfan-exposed earthworm tissue extracts, 1H NMR and 1H-13C HSQC NMR
PCA scores plot (Figures 5.1B and 5.1C) showed the greatest separation in the PC1 and PC2 axes
and both accounted for 90.6% and 90.9% of the total variance respectively. The highest
endosulfan concentration (10 mg/kg) was significant at α=0.05 level for both 1-D and 2-D NMR
PCA scores plots.
Figure 5.2: Mean PCA scores plots of PC1 versus PC2 of endosulfan sulfate-exposed E. fetida: A) coelomic fluid using 1-D NMR, B) tissue extracts using 1-D NMR, and C) tissue extracts using 2-D HSQC NMR spectra. Each point represents the mean PC score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the exposure concentrations for each point. The “*” represents mean concentrations that were significantly different from the control (p<0.05) using Dunnett’s multiple comparison test.
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The endosulfan sulfate-exposed earthworm tissue extracts 1H NMR and 1H-13C HSQC
NMR PCA scores plots (Figures 5.2B and 5.2C) had similar separation trends along PC1 and PC2
axes and accounted for 89.1% and 93% of the total variance respectively. The low and middle
concentrations (0.1 and 1.0mg/kg) were close together in both 1-D and 2-D NMR mean PCA
scores plots. However, the highest endosulfan sulfate concentration (10 mg/kg) was significantly
different at the P<0.05 level.
Earthworm CF was more significant to endosulfan and endosulfan sulfate soil exposure
compared to the tissue extracts, especially at the highest exposure concentration (10mg/kg) from
comparison of their significance value (P=4.0x10-4 vs. P=0.04 for endosulfan and P=5.1x10-7 vs.
P=0.04 for endosulfan sulfate, respectively). The earthworm’s CF has a primary role in regulating
homeostasis and is the first immune defense against external stimuli [28]. With various
haemolytic, proteolytic and cytotoxic enzymes in the CF to constantly and actively protecting the
organism from any foreign substances [34], it is plausible that the CF may be more sensitive to
immediate changes in the soil environment by the presence of contaminants.
5.4.2. Metabolic response after endosulfan and endosulfan sulfate exposure
From Table 1, the advantage of analyzing both the earthworm’s CF and tissue extract using
1-D and 2-D NMR can be seen as a number of response metabolites can be identified to be
significant due to endosulfan and endosulfan sulfate exposure. From the relative percent change
for each identified metabolite in the CF (1H NMR) and tissue extracts (1H and 1H-13C NMR) for
each endosulfan exposure concentration (Figure 5.3 and Figure 5.4A and 5.4B, respectively) and
endosulfan sulfate exposure concentration (Figures 5.5 and Figure 5.6A and 5.6B, respectively),
the same metabolites of response were identified in both endosulfan and endosulfan sulfate
exposed earthworms with similar increases or decreases at the various exposure concentrations.
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The similar metabolic trends observed for both contaminants suggest a similar toxic MOA for
earthworms. Duplicate metabolites identified in the tissue extract by 1H-13C HSQC that were
already detected in the 1H NMR were added to the supplementary material (Figure S5.9 for
endosulfan and Figure S5.10 for endosulfan sulfate).
Table 5.1: Summary of metabolites identified after endosulfan or endosulfan sulfate exposure in E.fetida: A) coelomic fluid using 1-D NMR, B) tissue extract using 1-D NMR and C) tissue extract using 2-D NMR techniques.
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Figure 5.3: Percent change (%) of all identified metabolites from the t-test filtered 1-D NMR difference spectra of endosulfan-exposed E. fetida coelomic fluid. Percent changes that were significantly different from the control (p<0.05) are labelled with “*”. Each percent change is shown with their associated standard error.
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Figure 5.4: Percent change (%) of all identified metabolites from the t-test filtered A) 1-D NMR and B) 2-D NMR difference spectra of endosulfan-exposed E. fetida tissue extracts. Percent changes that were significantly different from the control (p<0.05) are labelled with “*”. Each percent change is shown with their associated standard error.
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Figure 5.5: Percent change (%) of all identified metabolites from the t-test filtered 1-D NMR difference spectra of endosulfan sulfate-exposed E. fetida coelomic fluid. Percent changes that were significantly different from the control (p<0.05) are labelled with “*”. Each percent change is shown with their associated standard error.
Endosulfan is known to inhibit the action of the neurotransmitter gamma aminobutyric acid
(GABA), which is responsible for the uptake of chloride ions by neurons [35]. This is a critical
biological process because GABA assists in returning the neuron into its homeostatic state after
depolarization.
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Figure 5.6: Percent change (%) of all identified metabolites from the t-test filtered A) 1-D NMR and B) 2-D NMR difference spectra of endosulfan sulfate-exposed E. fetida tissue extracts. Percent changes that were significantly different from the control (p<0.05) are labelled with “*”. Each percent change is shown with their associated standard error.
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However, the inhibitory action by endosulfan results in only a partial repolarization of the neuron
and leads to uncontrolled muscle contractions, convulsions and hyperactivity in organisms [5]. A
number of studies, using lethality experiments analyzed the toxicity of endosulfan sulfate and
suggested a similar toxicity to the parent compound in aquatic organisms [7, 9, 10]. In the present
study, the same response metabolites were detected with similar fluctuations in both endosulfan
and endosulfan sulfate exposed earthworms. This finding further suggests a similar neurotoxic
MOA in soil.
Glutamine, a precursor to GABA, and glutamate, an exhibitory neurotransmitter,
significantly increased compared to the control (P<0.05) in all exposure concentrations for both
endosulfan- and endosulfan sulfate-exposed earthworms. Neurons are considered to be
metabolically challenged as they do not have the mechanisms for producing glutamate and GABA
from simpler molecules such as glucose [36]. Therefore, an essential metabolic transport system
called the glutamine/GABA-glutamate cycle is used to regulate their concentrations from nearby
astrocytes for effective inhibitory and exhibitory transmissions [37]. Extracellular glutamate levels
has been reported in neurological stress conditions especially linked to environmental toxins [38]
and can cause an excitotoxic response in earthworms. The production of glutamate in astrocytes
comes from the citric acid cycle (CAC) intermediate alpha-ketoglutarate [39] and was detected to
be significantly decreased compared to the control at the highest concentration (10.0mg/kg) in the
endosulfan and endosulfan sulfate exposed earthworm’s CF. Due to the competitive inhibition of
the GABA chloride channels by endosulfan or endosulfan sulfate exposure, an influx of GABA
will be present in the neurons. The removal of excess GABA in the neurons is caused by
catabolism to the CAC intermediate succinate [39], and this increased in the CF of both the
endosulfan and endosulfan sulfate exposed earthworm. No other CAC intermediates in the
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exposed earthworms were significantly different compared to the control group and this illustrates
the sufficient regulation of the CAC for energy production during exposure. However in our past
research study [26], the CAC intermediates alpha-ketoglutarate, succinate, malate and fumarate
were significantly decreased compared to the control after sub-lethal endosulfan exposure using
contact test filter paper experiments [26]. In this study, the soil environments which contain
organic matter were used and can potentially provide a source of energy for the earthworms
compared to their exposure on contact test filter paper. Therefore, the transition into soil
environments can potentially provide the earthworms with the essential nutrients needed to sustain
important aerobic energy cycles such as the CAC even in the presence of contaminants.
An increased energy expenditure, due to the stress caused by endosulfan and endosulfan
sulfate exposure in the earthworms, was detected as sugar concentrations (maltose, melibiose and
glucose) decreased at all exposure concentrations, while ATP production increased to significant
levels (P<0.05) at the highest exposure concentration (10.0 mg/kg). Lactate, a key metabolite in
anaerobic metabolism for energy production and commonly detected in organisms after extraneous
muscle activity [10], increased as well in all exposure concentrations.
The polyamine spermidine decreased to significant levels in the earthworm’s CF for
endosulfan- and endosulfan sulfate-exposed earthworms. Polyamines are essential for cellular
proliferation and gene regulation. A past study has shown that their abnormal levels can impair
cellular function [40]. Significant decreases in spermidine concentrations are known to cause
apoptosis in cells due to external stressors [41]. A past study [42] detected DNA damage using
comet assays in E. fetida after endosulfan exposure in OECD soil. Significant DNA damage was
detected at soil concentrations (0.1, 1.0 and 10.0mg/kg) after 7, 14, 21 and 28 days exposure in
their study (P<0.01), and it was concluded that the usage of comet assays is a reliable indicator for
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endosulfan exposure. In this study, the results provided further biochemical insight for endosulfan
and gave a first look into endosulfan sulfate exposure using the same soil exposure concentrations
as the decrease in spermidine was also detected. An apoptotic MOA resulting from endosulfan
and endosulfan sulfate exposure could be due to a potential defensive mechanism by a significant
DNA damage in the earthworms. The rise in apoptosis will increase muscle and protein
degradation in organisms [43] and this was detected as free amino acids, lysine and methionine,
significantly increased in the endosulfan- and endosulfan sulfate-exposed earthworms compared to
the control.
Alanine and glycine also increased in the endosulfan- and endosulfan-exposed earthworm’s
CF and tissue extract compared to the unexposed earthworms. Alanine and glycine are both
known for their cytoprotective action in cells against stress damage by contaminants [44] and are
known to increase in cells to induce gene expression for stress protein synthesis [45, 46].
Osmolytes, betaine and myo-inositol, also increased in endosulfan- and endosulfan sulfate-exposed
earthworms compared to the unexposed earthworms. Hydrophobic contaminants such as
endosulfan and endosulfan sulfate can cause fluctuations in the intracellular solute content and
stability in biological membranes [47]. Betaine and myo-inositol are both common organic
osmolytes in biological systems that assist in maintaining osmotic balance in cells [48, 49].
Increases in betaine and myo-inositol were also identified in E. fetida from exposure to other
hydrophobic contaminants such as polyaromatic hydrocarbons [19].
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5.4.3. Comparison of endosulfan- and endosulfan sulfate- exposed earthworm CF and tissue
extracts using multivariate analysis
To investigate the relative toxicity between endosulfan and endosulfan sulfate, an overall
mean PCA scores plot was constructed using all exposure concentrations from the 1-D and 2-D
NMR spectra of the earthworm CF and tissue extract (Figure 5.7).
Figure 5.7: Mean PCA scores plots of PC1 versus PC2 of endosulfan- and endosulfan sulfate-exposed E. fetida: A) coelomic fluid using 1-D NMR, B) tissue extracts using 1-D NMR, and C) tissue extracts using 2-D HSQC NMR spectra. Each point represents the mean PC score for each exposure concentration and the error bar represents the standard error of the mean. The legend indicates the exposure concentrations for each point. The “*” represents mean concentrations that were significantly different from the control (p<0.05) using Dunnett’s multiple comparison test. The arrows indicate the trajectory of exposure of endosulfan and endosulfan sulfate.
Examining the separation trajectory using various contaminant exposure groups provides insight
into differences in their toxicity or MOAs. For example, in our previous earthworm NMR-based
metabolomics study [32], an overall PCA scores plot showed separate trajectories for two different
pesticides (trifluralin and endosulfan) using three sub-lethal exposure concentrations. Different
metabolites of response were detected for both trifluralin- and endosulfan- exposed earthworms
and explained two different toxic MOAs. In another study [50], two pesticides (carbaryl and
chlorpyrifos), three pharmaceuticals (carbamazephine, estrone and caffeine), two persistent
organohalogens (Aroclor 1254 and PBDE 209) and two industrial compounds (nonylphenol and
180
dimethyl phthalate) were investigated on their sub-lethal exposure to E. fetida using an overall
PCA scores plot. Their results were able to identify contaminant specific biomarkers from the
various MOAs using 1-D NMR-based metabolomics.
In the overall mean PCA scores plot of the 1-D NMR spectra of the earthworm’s CF
(Figure 5.7A), the endosulfan- and endosulfan sulfate-exposed earthworms had similar separation
trajectories on the PC1 and PC2 axis as both axis represented a total variance of 57.5%. The low
and middle concentrations (0.1 and 1.0mg/kg) were closely grouped for both contaminants and
were similar in their separation to the control group on the PC1 axis. The two highest
concentrations of endosulfan and endosulfan sulfate (10mg/kg) were significantly different
compared to the control group at P<0.05. For the overall mean PCA scores plot of the 1-D and 2-
D NMR spectra of the earthworm tissue extract (Figure 5.7B and 5.7C, respectively), a similar
separation trajectory from the exposed earthworms to the control was seen on the PC1 and PC2
axes with a total variance of 89.2% and 92% respectively. Higher separation by the exposed
groups were seen for both 1-D and 2-D NMR PCA scores plots to the control group as the
concentration increased for both contaminants. The highest exposure concentrations of both the
endosulfan and endosulfan sulfate exposure groups were significantly different than the control
(P<0.05).
A similar separation trajectory, by the endosulfan and endosulfan sulfate exposure groups
to the control group in the PCA scores plot, shows the similarity of their toxicity. In addition, this
result explains the identical response metabolites detected in the t-test filtered difference NMR
spectra and confirms their MOA. There were no significant differences between each of the
endosulfan and endosulfan sulfate exposure groups even at the highest exposure concentration (10
mg/kg) in the earthworm CF and tissue extract. The results in this study confirm past endosulfan
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and endosulfan sulfate toxicity studies where only aquatic species were examined using
growth/survival bioassays [7] or half maximal effective concentrations (EC50) [10]. The use of
NMR-based metabolomics provided a rapid and in-depth approach in probing soil environments
using earthworms as biological indicators in comparing the toxic MOA of these two contaminants.
Generally, the degradation of pesticides in soil has a lower toxicity to native organisms compared
to the parent compound [51]. However, in this study, the main degradation product, endosulfan
sulfate, was detected to be just as toxic as the parent compound, endosulfan, in soil. Since
endosulfan sulfate is more persistent in the soil environment than endosulfan [52], higher priority
should be given to the identification of endosulfan sulfate in contaminated soils during
bioremediation efforts because the results from this study show that toxicity does not change as the
parent compound, endosulfan, degrade.
5.5 Conclusion
Understanding the environmental consequences by persistent organohalogen pesticides on
ecosystem health is a major priority for many environmental agencies such as the OECD and
United Nations Environment Program [42]. Our study demonstrates the potential of 1-D and 2-D
NMR-based metabolomics in delineating the toxic MOA of a persistent environmental
contaminant, endosulfan, and its main degradation product, endosulfan sulfate, at various exposure
concentrations in soil for seven days. The results displayed a similar toxicity for both
contaminants by the same increase in separation from the control group as the exposure
concentration increased in the PCA scores plot. A similar neurotoxic and apopotic MOA was
observed in both endosulfan and endosulfan sulfate exposed earthworms as identical metabolites
of response were detected compared to the unexposed earthworms. However, in this study, unaged,
spiked soil with only seven days of exposure were used as an initial experiment to understand the
182
toxicity of contaminants and their degradation product to native soil organisms. Future studies
will use aged spiked soils and longer exposure times (> seven days) to assess contaminant toxicity
as past studies have shown the decrease in the bioavailability of contaminants over time in soil [21,
53] and a change in the earthworms metabolic response to contaminants over various times of
exposure [19]. In addition, a high earthworm mortality rate in the highest endosulfan and
endosulfan sulfate exposure concentrations occurred and a future study will be conducted to focus
on expanding the exposure concentrations between the middle (1.0mg/kg) and the highest
(10.0mg/kg) concentration. This will allow a better understanding of the sub-lethal range before
mortality occurs and assists in confirming the fluctuating metabolites detected in this study.
Nevertheless, this is the first metabolomics study that utilizes 1-D and 2-D NMR techniques and
tests both earthworm CF and tissue extracts to compare their biochemical response to a concerning
environmental contaminant and its main degradation product in soil. NMR-based metabolomics
can be a powerful ecotoxicology tool to provide vital toxicity information for assessing
contaminated soil sites using earthworms as biological indicators.
5.6. Acknowledgment
Funding was provided by the Natural Sciences and Engineering Research Council Strategic
Grants Program (NSERC). André Simpson would like to thank the government of Ontario for an
Early Researcher Award. Jimmy Yuk would like to thank the government of Ontario for an
Ontario Graduate Scholarship (OGS). We would also like to extend to Dr. Melissa Whitfield
Åslund, Dr. Ronald Soong, Diana Tseng, Jasmine Wang and Brian Lankadurai for technical
assistance and valuable discussions.
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CHAPTER SIX
Conclusions and future research
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6.1 Conclusions
The application of organohalogenated pesticides has been a milestone in today’s society for
crop protection. Agrochemical companies, academic institutions and government agencies have
made immense efforts in the past two decades to properly assess newly developed pesticides and to
create adequate regulating frameworks on existing ones for a safer environment. With the goals of
international environmental organisations such as REACH to register all new and existing
pesticides with reliable experimental data, efficient methods need to be explored that are high
through-put and inexpensive. Environmental NMR-based metabolomics is an emerging field that
examines the metabolic profile of native organisms in their environments and in the presence of
environment stressors [1, 2]. The application of this powerful technique, however, has not been
utilized for organohalogentated pesticides, especially in soil environments to native organisms
such as earthworms. The aim of this dissertation was to provide more insight into the sub-lethal
exposure to widely-used organohalogenated pesticides to an OECD recommended earthworm,
Eisenia fetida, using NMR-based metabolomics.
The results in this thesis determined that 1-D and 2-D NMR techniques, such as PURGE
and 1H-13C HSQC NMR, create an effective combination to compare exposed and control Eisenia
fetida earthworms, and to identify metabolites of response after sub-lethal exposure to a commonly
used organochlorine pesticide, endosulfan (Chapter 2). This finding was further investigated as
both PURGE and 1H-13C HSQC NMR techniques were applied to various sub-lethal
concentrations of endosulfan and a widely used organofluorinated pesticide, trifluralin. The PCA
results from this study (Chapter 3) revealed distinct separation between the exposed and control
earthworms at various sub-lethal concentrations for both contaminants. In addition, many
significant metabolites, arising after exposure, were identified by the 1-D and 2-D NMR
191
techniques. Furthermore, a neurotoxic MOA for endosulfan and a non-polar narcotic MOA for
trifluralin were delineated. This dissertation also explored the potential of utilizing the
earthworm’s coelomic fluid (CF) as a complementary biological medium for NMR analysis to the
earthworm tissue extract in an endosulfan exposure metabolomic study (Chapter 4). The results
detected a number of significant metabolites due to endosulfan exposure that were masked in the
high number of overlapping resonances in the tissue extract NMR spectrum. An apoptotic MOA
was identified in the earthworm due to endosulfan exposure. This was not detected in past tissue
extract metabolomic studies. Since the experiments above were all conducted through contact test
filter experiments, the transition into soil environments was done to understand the toxicity of
endosulfan and its main degradation product, endosulfan sulfate (Chapter 5). Using the results
from previous experiments, PURGE and 1H-13C HSQC NMR techniques were used to analyze the
earthworm’s CF and tissue extract after exposure to the two contaminants. To the author’s
knowledge, this is the first comprehensive 1-D and 2-D NMR metabolomic study to understand the
toxic response of organohalogenated pesticides and their degradation products in soil to native soil
earthworms. The results clearly show similar toxicity between the parent and the degradation
product through similar patterns of PCA separation in the various exposure concentrations.
Similar neurotoxic and apopotic MOAs were observed in both endosulfan- and endosulfan sulfate-
exposed earthworms as similar metabolites of response were identified. This thesis showed the
potential of NMR-based metabolomics to probe soil environments using biological indicators such
as earthworms to various persistent organohalogenated pesticides.
6.2 Future Research
6.2.1 Assessment of organohalogenated field soils using NMR-based metabolomics
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Due to the long-term application of organohalogenated pesticides for agricultural use and
their high persistence in the environment, many soil sites will ultimately contain their residue [3].
With the large costs associated with remediating and assessing contaminated soils for their safety,
better methods need to be developed that are high-throughput and cost-effective. One future
application of this dissertation is to utilize 1-D and 2-D NMR-based metabolomics in the field to
assess organo-halogenated-contaminated soils using earthworms as biological indicators. A past
study has successfully applied NMR-based metabolomics using the earthworm, Lumbricus
rubellus, in seven different metal contaminated soil sites with various soil conditions [4]. Even
with the large confounding differences between each site, such as soil pH and different varying
concentrations of metals, the results were still able to pinpoint zinc as the main contaminant
causing a difference in their metabolic profile and detected both site- and contaminant-specific
responses. This dissertation has shown similar toxicity levels and toxic MOAs of endosulfan and
endosulfan sulfate to E. fetida earthworms in laboratory OECD soil at varying exposure
concentrations. The next step would be to apply similar techniques in contaminated fields.
Ecological surveys and laboratory tests of field soil are two approaches that can be used to
assess the contaminated field soils using NMR-based metabolomics. For the ecological survey
method, it will be similar to the metal-contaminated soil study done by Bundy et al. [4] as the
indigenous earthworm populations are sampled. A baseline of the earthworm metabolic profile
will need to be conducted from areas without contaminants (natural soil) and the perturbation of
the earthworm’s metabolic profile in the contaminated areas will be measured. The similarity of
the earthworm profile in the contaminated soils and the natural soils will determine the
effectiveness of remediation measures and the safety of contaminated sites. The advantage of this
method is a relatively easy and lower cost approach with all the biomarker responses indicative of
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all the potential abiotic factors (temperature and moisture) present. However, this method has its
limitations – the complexity of the environment can cause many confounding variables such as
unknown exposure history and potential for multiple species of earthworms present in the
contaminated and non-contaminated soils. This can be mitigated with laboratory tests of the field
soil. The contaminated soils can be removed from the site and the metabolic response of
earthworms to the soil can be done in a controlled laboratory environment. Laboratory tests on
contaminated soils provide many advantages as model earthworms (E.fetida) grown in laboratories
can be used with standardized test protocols (OECD) and abiotic factors controlled (moisture and
temperature). A key disadvantage of this method is the lack of ecological meaning because of
potential changes from the realistic environment. This could also lead to criticism about the
conclusions made on risk assessment using non-native soil species. Nevertheless, the ability of
NMR-based metabolomics to detect subtle responses to sub-lethal contaminant exposure can be a
powerful risk assessment and monitoring tool for contaminated and remediated soil sites.
6.2.2 Application of NMR-based metabolomics to pesticide mixtures
Environmental metabolomics has the potential to understand the toxic MOA of
contaminants in the environment to native organisms at sub-lethal concentrations. This
dissertation was able to apply NMR-based metabolomics to delineate the toxic MOA of an
organochlorine pesticide, endosulfan, and its degradation product, endosulfan sulfate, and also an
organofluorine pesticide, trifluralin, to a native soil earthworm, E. fetida. However, in common
agricultural practices, a mixture of pesticides is often applied simultaneously for crop protection,
and this can lead to a combination of pesticide residues in the soil environment [5]. Furthermore,
mixed pesticides can cause significant synergistic toxic responses on non-target organisms, such as
earthworms, when individual pesticides would have originally been considered harmless. A past
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study reported that an organochlorine pesticide, atrazine, in combination of three organophosphate
insecticides (chlorpyrifos, methyl parathion, and diazinon) caused a significant increase in toxicity
to a common amphipod crustacean, Hyalella azteca; on its own, however, atrazine induces a much
lower toxic response [6]. Another study analyzed the enhancement in the impact of atrazine with
another organochlorine pesticide, cyanazine, to an aquatic midge, Chironomus tentans [7]. In soil
environments, a recent study conducted a 14-day OECD soil exposure toxicity test on E. fetida
using two organochlorine pesticides, cypermethrin and chlorpyrifos. Both of these pesticides are
widely used for crop protection in China and are often used in combination to give a wide-
spectrum insecticide potential [8]. From their results, the toxicity was significantly higher,
especially for the mortality tests, compared to exposure to the individual pesticides. At the lowest
exposure concentrations, the pesticide mixture caused significant reduction on the growth and
reproduction rates of the earthworms. However, on their own, they induced no adverse responses.
The results from the OECD study suggest that the toxicity data currently used in regulatory
agencies for single-pesticide exposures might underestimate the ecological risk of pesticides that
are actually in the field. The application of NMR-based metabolomics can provide insight into
metabolic profile fluctuations from exposure to mixed pesticides. Researchers can compare this to
the earthworm’s exposure to one pesticide on its own. Such a comparison can determine which
toxic MOA became more dominant or whether a new toxic MOA emerged due to the pesticide
mixture. However, many factors will need to be carefully assessed, for example: the different
combinations of pesticides to use, and the adequate levels of exposure concentrations. In addition,
the interactions of different pesticides can change their interactions in the soil and their physical
properties, such as degradation rates. For example, a past study found that a pesticide mixture of
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isoproturon and chlorothanoil had much higher degradation rates compared to its individual
components in soil [9].
6.2.3 Application of NMR-based metabolomics to genetic modified plants
With the advancement of genetic engineering, genes can now be introduced into the plant
genome to produce metabolites that will enhance their tolerance to biotic or abiotic stress [10].
Currently, this is a new approach for crop protection and represents a significant part of pesticide
research and development in agrochemical industries [11]. There have been genetically modified
(GM) plants that express insecticidal metabolites to minimize yield losses caused by insects.
There are, for example, plants that have been modified to express insecticidal properties from
Bacillus thuringiensis (Bt plants) and have been used in many agricultural practices [12]. Bacillus
thuringiensis is a gram positive, soil dwelling bacterium that produces δ-endotoxins, which cause
cell lysis in the cell membranes of various insects [13]. The usage of GM crops has presented a
new safety concern. Due to the alterations in the GM plant’s genome and the expression of
potentially harmful metabolites, there has been no extensive study that analyzes their response to
non-target organisms in the soil environments or animals. This dissertation has shown the
application of NMR-based metabolomics as a potential environmental monitoring or screening tool
in soil environments for organohalogenated pesticides. This presents a difficult challenge for
applying metabolomics to understand the safety of GM crops as there are complicating factors
which include the plants’ unique metabolome and their interaction with the surrounding
environment. Some initial NMR-based metabolomic studies have been applied to examine the
difference between wild and transgenic maize carrying the B. Thuringiensis Cry1Ab gene [14].
Their analyses revealed substantial differences in the metabolic profiles of the wild and transgenic
maize. The transgenic maize had higher concentrations of ethanol, citric acid, glycine, betaine,
196
trehalose, as well as other compounds that could not be identified. A similar study was also
applied to understand the metabolic differences between non-transgenic and transgenic tomatoes
carrying two maize transcription factors leaf color (LC) and colourless-1 (C1) [15]. These
transcription factors in the tomato allow them to up-regulate their flavonoid biosynthesis and
enhance their antioxidant capacity. From the PCA and PLS results, the study identified six main
flavonoid glycosides and at least 15 different metabolites – such as citric acid, sucrose,
phenylalanine, and trigonelline – to be different between the two types of tomatoes. It would be
interesting to apply NMR-based metabolomics using earthworms developed in this thesis to probe
their metabolic profile in environments where transgenic plants are grown. This will enable
monitoring of significant perturbations in their metabolic profile compared to wild plant
environments especially if mortality occurs. Although currently, no direct conclusions can be
made on the safety of GM crops, it can be expected that the usage of NMR-based metabolomics
will play a major role in the future to provide an in-depth analysis on this concern.
6.2.4 Validation of biomarkers using a systems biology approach
The validation of biomarkers before using it as an early indicator for a contaminant in the
environment is crucial and essential for their effectivity in risk assessment. To properly assess the
usage of a biomarker detected from a contaminant, the sensitivity, variability, availability and
robustness must also be understood. This challenge can potentially be solved by a systems biology
approach where the combination of all “omic” technologies such as genomics, transcriptomics,
proteomics and metabolomics are utilized [16]. This approach allows a thorough understanding of
the toxic response of a contaminant from the gene/transcript level through to the protein expression
to the final metabolite level production. The synergistic combination of these cellular and
197
molecular techniques captures the overall biological response of a contaminant from the initial
toxic insult to the clear toxic physical response. In this thesis, various biomarkers of response
were detected in the earthworm, Eisenia fetida, after organohalogenated pesticide exposure using
NMR-based metabolomics. To properly validate the significant metabolites that were detected
from the various studies in this thesis, it would be important to conduct future experiments
utilizing another “omic” technology such as genomics or proteomics to understand the biological
connection to the metabolomic results. For example, in chapter 3, a non-polar narcosis MOA for
trifluralin was postulated through the detection of alanine, glycine, maltose and ATP that were
significant at the highest exposure concentration (1.0 mg cm-2). Future validation experiments
should be conducted by studying the action of cytochrome p450, which is one of the main enzymes
used for detoxifying hydrophobic xenobiotics in living organisms [17]. This can be done through
a fluorescence assay of cytochrome P450 using 7-benzoyloxy-4-trifluoromethyl coumarin as the
probe substrate which undergoes O-dealkylation to give the fluorescent product 7-hydroxy-4-
trifluoromethyl coumarin (λexcitation 405 nm/λemission 510–545 nm) [18]. The decrease in
fluorescence will indicate the inhibition or activity by the xenobiotic such as trifluralin on the
cytochrome P450 binding sites and allow the understanding from a proteomic level if a non-polar
narcosis is occurring [18]. In addition to this experiment, a genomic approach can also be used
with the expression of the aryl hydrocarbon receptor. The aryl hydrocarbon receptor acts as a
ligand-dependent transcription factor [19]. When xenobiotics are present, specific regulatory DNA
motifs are bound which activate the receptor to regulate gene expression for various enzymes such
as cytochrome P450 for detoxification [20]. The overexpression of the aryl hydrocarbon
transcriptional factors will be another indicator for non-polar narcosis by trifluralin exposure. The
application of systems biology allows an integral approach in validating the biomarkers detected
198
from NMR-based metabolomics presented in this thesis and assists in confirming the
contaminant’s MOA in the living organism.
6.2.5 Application of various analytical platforms for metabolomics studies
This thesis focused mainly on the application of NMR-based metabolomics in assessing
sub-lethal organo-halogenated pesticide exposures to earthworms using contact filter paper and
artificial soil environments. The utilization of NMR in this thesis allowed a high throughput and
reproducible method of analysis that required minimal sample preparation to visualize the
metabolic profile of the earthworm under stress. However, discussed in chapter one, a
disadvantage of NMR as a comprehensive metabolite profiling tool is its lower sensitivity which
could detect at most at the low micro molar range [2]. This potentially can overlook the low-
abundance metabolites that are below the limit of detection. Therefore to fully capture the broad
range of metabolites that could have been perturbed in the earthworm due to pesticide exposure, it
is prudent for future studies to apply a more sensitive analytical platform such as mass-
spectrometry (MS), liquid chromatography-mass spectrometry (LC-MS), gas chromatography–
mass spectrometry (GC-MS) and capillary electrophoresis-mass spectrometry (CE-MS) to increase
verify the metabolites detected and potentially identify additional biomarkers in this these past
exposure studies. This section will introduce a brief overview of other commonly used analytical
techniques used in metabolomics and readers are encouraged to read the following references for
more specific information [21-23].
MS has become a widely used analytical technique in metabolomic studies due to its higher
sensitivity (low nano molar range) and detection of a wide range of metabolites [2]. Direct
injection MS can provide a rapid technique to analyze a plethora of metabolites which is ideal for
199
metabolite profiling. However, co-suppresion could present a problem as compounds with low
ionization efficiencies would have their signals lost. Therefore, it is common for MS to be
hyphenated with a separation technique first by either chromatography such as GC, LC or
electrophoresis such as CE. This decreases the complexity of compounds and offers the capability
of being a targeted approach. GC-MS is an ideal analytical technique for volatile and thermally
stable compounds. For compounds that do not fit these criteria, they must first be chemically
derivatized to provide volatility before analysis can be done. GC columns allow the separation of
compounds with different degrees of polarity and provide high chromatographic resolution of
compounds with the high sensitivity from the MS. Identification of metabolites can be done
through correlating the retention times or retention indexes and mass spectrum of the sample peak
with the run of various standards using the same conditions [21]. One major advantage is the vast
libraries of compounds readily available which allow easy identification of unknown compounds.
GC-MS has been successfully been utilized in various metabolomics studies such as urine
screening, intra-cellular and volatile metabolite analysis of plants and other biofluids [21, 22, 24].
LC-MS has been shown to have higher sensitivity and allows a wider range of analyte polarity and
molecular mass for identification that GC-MS [25]. This is due to the usage of capillary columns
which have greater resolution capability. LC usually uses electrospray ionization (ESI) which is
considered a soft ionization method and samples do not need to be derivatized in order for it to be
analyzed [25]. However, one disadvantage is the lack of ESI libraries compared to GC-MS but
metabolite identification can be done using accurate mass measurements and/or tandem MS
(MS/MS) [25]. Ultra Performance Liquid Chromatography is a new improvement in LC-MS
technology as it can provide 10-fold increase in speed and a 5 fold increase in sensitivity vs.
conventional HPLC [26] . Currently LC-MS has been widely used in clinical applications for the
200
discovery of disease biomarkers [27]. CE-MS is an emerging technique and has gained notice in
the field of metabolomics [26]. CE-MS has very high resolving power due to high plate numbers
with rapid analysis time and only requires low µl of sample [25]. Many classes of metabolites can
be separated due to its ability to separate cations, anions and uncharged molecules in a single
analytical run. Current studies have utilized CE-MS for human urine profiling and plant
metabolite studies [22, 28].
The utilization of any of these techniques in tandem with NMR can provide a powerful
combination to verify the metabolites of response that are fluctuating in the earthworm due to
pesticide exposure. With an untargeted approach from NMR-based metabolomics, the knowledge
obtained from these studies can then be focused on targeted approaches using one of the separation
techniques (GC, LC or CE) coupled to a MS. However readers should be aware that there is no
single analytical technique that is ideal for detecting all the metabolites within a cell and will
require a combination of techniques in detecting the various metabolites of different polarity and
range of molecular weights [25]. To summarize the advantages and disadvantages of each
technique including NMR, table 6.1 shows each analytical technique used in metabolomics.
201
Table: 6.1: Analytical techniques used in metabolomics [23,26-27] Analytical method Advantage Disadvantage
NMR • Rapid analysis • High resolution • No derivation required • Non-destructive
• Low sensitivity • Convoluted spectra • More than one peak per
component
LC-MS
• No derivation required (most of the time)
• Many modes of separation possible
• Large sample capacity
• Slow • Limited commercial libraries
GC-MS
• Sensitive • Robust • Large linear range • Large commercial and public
libraries
• Slow • Often requires derivation • Many analytes thermally
unstable or too large for analysis
CE-MS
• High separation power • Small sample requirementsd • Rapid analysis • Can separate neutrals, anions
and cations in a single run • No derivation required (most of
the time)
• Limited commercial libraries • Poor retention time
reproducibility
6.3 References
1. Bundy, J.G., et al., Environmental Metabonomics: Applying combination biomarker
analysis in earthworms at a metal contaminated site. Ecotoxicology, 2004. 13: p. 797-806.
2. Simpson, M.J. and J.R. McKelvie, Environmental metabolomics: New insights into
earthworm ecotoxicity and contaminant bioavailability in soil. Anal. Bioanal. Chem., 2009.
394(1): p. 137-149.
3. Zhang, Y., et al., Microemulsion-enhanced remediation of soils contaminated with
organochlorine pesticides. Environ. Technol., 2011. 32(16): p. 1915-1922.
202
4. Bundy, J.G., et al., Metabolic profile biomarkers of metal contamination in a sentinel
terrestrial species are applicable across multiple sites. Environ. Sci. Technol., 2007.
41(12): p. 4458-4464.
5. Swarcewicz, M. and A. Gregorczyk, The effects of pesticide mixtures on degradation of
pendimethalin in soils. Environ. Monit. Assess., 2011: p. 1-8.
6. Anderson, T.D. and M.J. Lydy, Increased toxicity to invertebrates associated with a
mixture of atrazine and organophosphate insecticides. Environ. Toxicol. Chem., 2002.
21(7): p. 1507-1514.
7. Jin-Clark, Y., M.J. Lydy, and K.Y. Zhu, Effects of atrazine and cyanazine on chlorpyrifos
toxicity in Chironomus tentans (Diptera : Chironomidae). Environ. Toxicol. Chem., 2002.
21(3): p. 598-603.
8. Zhou, S.P., et al., Individual and combined toxic effects of cypermethrin and chlorpyrifos
on earthworm. J. Environ. Sci., 2011. 23(4): p. 676-680.
9. Fogg, P., A.B.A. Boxall, and A. Walker, Degradation of Pesticides in Biobeds: The Effect
of Concentration and Pesticide Mixtures. J. Agr. Food. Chem., 2003. 51(18): p. 5344-5349.
10. Kos, M., et al., Transgenic plants as vital components of integrated pest management.
Trends Biotechnol., 2009. 27: p. 621-627.
11. Aliferis, K. and M. Chrysayi-Tokousbalides, Metabolomics in pesticide research and
development: review and future perspectives. Metabolomics, 2011. 7(1): p. 35-53.
12. Betz, F.S., B.G. Hammond, and R.L. Fuchs, Safety and advantages of Bacillus
thuringiensis-protected plants to control insect pests. Regul. Toxicol. Pharmacol. , 2000.
32(2): p. 156-173.
203
13. Roh, J.Y., et al., Bacillus thuringiensis as a specific, safe, and effective tool for insect pest
control. J. Microbiol. Biotechnol. , 2007. 17(4): p. 547-559.
14. Piccioni, F., et al., NMR Metabolic Profiling of Transgenic Maize with the Cry1A(b) Gene.
J. Agr. Food. Chem., 2009. 57(14): p. 6041-6049.
15. Le Gall, G., et al., Metabolite profiling of tomato (Lycopersicon esculentum) using H-1
NMR spectroscopy as a tool to detect potential unintended effects following a genetic
modification. J. Agr. Food. Chem., 2003. 51(9): p. 2447-2456.
16. Robertson, D.G., et al., Metabonomics in Toxicity Assessment. Vol. 1st. 2005, New York:
Taylor and Francis Group. 536.
17. Rocha-e-Silva, T.A.A., et al., Spectral characteristics of a compound altering cytochrome
P450 spectra from vertebrate microsomes suggest that it is a functional protein. Comp.
Biochem. Physiol. C: Toxicol. Pharmacol., 2001. 130(1): p. 53-66.
18. Cheng, Q., C.D. Sohl, and F.P. Guengerich, High-throughput fluorescence assay of
cytochrome P450 3A4. Nat. Protoc., 2009. 4(9): p. 1258-1261.
19. McAlister, D.R., et al., Application of an aryl hydrocarbon receptor based screening assay
for assessing U.S. EPA draft remediation goals for dioxin in soil and sediment samples. Int.
J. Environ. Anal. Chem., 2011: p. 1-13.
20. Akahoshi, E., et al., Tyrosine hydroxylase assay: a bioassay for aryl hydrocarbon receptor-
active compounds based on tyrosine hydroxylase promoter activation. Toxicol. Mech.
Methods., 2012. 22(6): p. 458-460.
21. Dunn, W.B. and D.I. Ellis, Metabolomics: Current analytical platforms and methodologies.
Trends in Analytical Chemistry, 2005. 24(4): p. 285-294.
204
22. Hollywood, K., D.R. Brison, and R. Goodacre, Metabolomics: Current technologies and
future trends. Proteomics, 2006. 6: p. 4716-4723.
23. Shulaev, V., Metabolomics technology and bioinformatics. Briefings in Bioinformatics,
2006. 7(2): p. 128-139.
24. Alvarez, I., et al., Simultaneous determination of methadone, heroin, cocaine and their
metabolites in urine by GC-MS. Analyt. Lett., 2006. 39(7): p. 1393-1399.
25. Shulaev, V., Metabolomics technology and bioinformatics. Brief Bioinform 2006. 7(2): p.
128-139.
26. Zhang, A.H., et al., Modern analytical techniques in metabolomics analysis. Analyst, 2012.
137(2): p. 293-300.
27. Lenz, E.M. and I.D. Wilson, Analytical strategies in metabonomics. J. Proteome Res., 2007.
6(2): p. 443-458.
28. Sato, S., et al., Simultaneous determination of the main metabolites in rice leaves using
capillary electrophoresis mass spectrometry and capillary electrophoresis diode array
detection. Plant J. 2004. 40(1): p. 151-163.
205
Appendix A
Supplementary Material for Chapter Three
Published as: Yuk, J., Simpson, M.J., and Simpson, A.J., 1-D and 2-D NMR metabolomics of
earthworm responses to sub-lethal trifluralin and endosulfan exposure. Environ.
Chem., 2011. 8(3): 281-294.
Reproduced with permission from Environmental Chemistry, 2011,(3): 281-294 (http://www.publish.csiro.au/paper/EN11033.htm). © Copyright CSIRO Publishing
206
Figure A3.1: PCA scores plot of PC1 versus PC2 of endosulfan-exposed E. fetida (●) at 2.0 µg cm-2 and control E. fetida (■) (n = 10) using: A) 1-D PURGE and B) 2-D 1H-13C HSQC NMR spectra. The P value for control and endosulfan exposed earthworms for the PCA components are reported using a two-sample t-test. Two earthworms at the highest endosulfan concentration (2.0 µg cm-2) were identified to be outside the Hotelling’s T2 ellipse at the 95% confidence interval and thus were removed from the dataset prior to subsequent analysis.
A) 1D PURGE B) 2D HSQC
-1.6 -0.8 0.0 0.8 1.6 2.4 3.2
-0.8
-0.4
0.0
0.4
0.8
PC
2 (1
0.3
9%
Vari
an
ce
; p
=0
.180
)
PC1 (81.85% Variance; p =0.004)
-1.2 -0.6 0.0 0.6 1.2 1.8
-0.4
0.0
0.4
PC
2 (7
.76
% V
ari
an
ce
; p
=0
.650
)
PC1 (83.65% Variance; p =0.001)
Outliers Outliers
207
Figure A3.2: Average percent weight change of E.fetida after exposure to trifluralin and endosulfan. The exposure concentrations are given below each bar and each “*” represents significant weight change compared to control. Significance was determined by a two-sample t-test with a confidence interval of 95% (p<0.05). The chemical structures of trifluralin and endosulfan are shown below their respective bar graphs.
-35
-30
-25
-20
-15
-10
-5
0
*
*
Pe
rce
nt
ch
an
ge in
we
igh
t aft
er
ex
po
su
re (
%) Control
Treatments
Trifluralin Endosulfan
0.5
µµ µµg
cm
-2
1.0
µµ µµg
cm
-2
2.0
µµ µµg
cm
-2
0.1
mg
cm
-2
0.5
mg
cm
-2
1.0
mg
cm
-2
S
O
O
O
Cl
Cl
Cl Cl
Cl
Cl
N+
N+
N
O-
O-
O
O
F
F
F
CH3
CH3
208
Figure A3.3: 1-D and 2-D NMR spectra of control worm tissue extracts acquired using A) 1-D PURGE and B) 2-D 1H-13C HSQC NMR spectroscopy.
8 7 6 5 4 3 2 1Chemical Shift (ppm)
8 7 6 5 4 3 2 1ppm
20
40
60
80
100
120
ppm
8 7 6 5 4 3 2 1ppm
20
40
60
80
100
120
ppm
A) 1D PURGE B) 2D HSQC
(1H)
(13C
)
209
Figure A3.4: PCA scores plot of PC1 versus PC2 of trifluralin-exposed E. fetida (●) and control E. fetida (■) (n = 10) using A) 1-D PURGE and B) 2-D 1H-13C HSQC NMR spectra at: i) 0.1 mg cm-2, ii) 0.5 mg cm-2 and iii) 1.0 mg cm-2. The P value for control and endosulfan exposed earthworms for the PCA components are reported using a two-sample t-test.
-0.8 -0.4 0.0 0.4 0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
PC
2 (27
.89
% V
ari
an
ce
; p
=0
.196
)
PC1 (51.02% Variance; p =0.277)
-0.8 -0.4 0.0 0.4 0.8
-0.4
-0.2
0.0
0.2
0.4
PC
2 (2
1.4
7%
Vari
an
ce; p
=0.1
36
)PC1 (63.60% Variance; p =0.403)
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6-0.4
-0.2
0.0
0.2
0.4
PC
2 (
22
.37
% V
ari
an
ce; p
=0
.44
2)
PC1 (59.91% Variance; p =0.885)
-0.4 0.0 0.4 0.8
-0.4
-0.2
0.0
0.2
0.4
PC
2 (2
5.7
2%
Va
ria
nc
e; p
=0.1
35
)
PC1 (50.96% Variance; p =0.403)
-0.4 0.0 0.4 0.8-0.4
-0.2
0.0
0.2
0.4
0.6
PC
2 (2
6.7
1%
Va
ria
nc
e; p
=0
.46
2)
PC1 (56.48% Variance; p =0.306)
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6-0.6
-0.4
-0.2
0.0
0.2
0.4
PC
2 (2
7.3
3%
Vari
an
ce
; p
=0
.68
3)
PC1 (47.47% Variance; p =0.524)
A) 1D PURGE
B) 2D HSQC
i) 0.1 mg cm-2 ii) 0.5 mg cm-2 iii) 1.0 mg cm-2
i) 0.1 mg cm-2 ii) 0.5 mg cm-2 iii) 1.0 mg cm-2
210
Figure A3.5: T-test filtered 1H-13C HSQC difference spectra of E. fetida tissue extracts were obtained by subtracting the mean buckets of: A) trifluralin-exposed earthworms at 1.0mg cm-2 and B) endosulfan-exposed earthworms at 2.0 µg cm-2 concentrations, with the mean buckets of the control earthworms. Signals that were significantly different from the control (p<0.05) were retained while everything else were excluded.
9 8 7 6 5 4 3 2 1140
120
100
80
60
40
20
-3.400E-04
-2.040E-04
-6.800E-05
6.800E-05
2.040E-04
3.400E-04
1H Chemical Shift (ppm)
13C
Ch
em
ica
l S
hif
t (p
pm
)
B) Endosulfan (2.0 µg cm-2)
Malate
Glucose
Melibiose
MethionineGlycine
Isoleucine
Phenylalanine
Maltose
Glutamine
Glutamate
9 8 7 6 5 4 3 2 1140
120
100
80
60
40
20
-7.500E-04
-4.500E-04
-1.500E-04
1.500E-04
4.500E-04
7.500E-04
1H Chemical Shift (ppm)
13C
Ch
em
ica
l S
hif
t (p
pm
)
A) Trifluralin (1.0 mg cm-2)
Glycine
Maltose
Alanine
Leucine
Lysine
211
Figure A3.6: PCA scores plot of PC1 versus PC2 of endosulfan-exposed E. fetida (●) and control E. fetida (■) (n = 10) using A) 1-D PURGE and B) 2-D 1H-13C HSQC NMR spectra at: i) 0.5 µg cm-2, ii) 1.0 µg cm-2 and iii) 2.0 µg cm-2. The P value for control and endosulfan exposed earthworms for the PCA components are reported using a two.
-0.8 -0.4 0.0 0.4 0.8 1.2
-0.4
-0.2
0.0
0.2
PC
2 (9
.36
% V
ari
an
ce
p =
0.5
13
)
PC1 (80.07% Variance p =0.001)
-0.8 -0.4 0.0 0.4 0.8 1.2
-0.6
-0.4
-0.2
0.0
0.2
0.4
PC
2 (15
.23
% V
ari
an
ce
p =
0.9
32
)PC1 (70.31% Variance p =0.004)
-0.8 -0.4 0.0 0.4 0.8
-0.4
-0.2
0.0
0.2
PC
2 (11
.41
% V
ari
an
ce
p =
0.8
40
)
PC1 (74.18% Variance p=0.094)
-0.8 -0.4 0.0 0.4 0.8 1.2-0.4
-0.2
0.0
0.2
0.4
0.6
PC
2 (
13
.42
% V
ari
an
ce
p =
0.9
82
)
PC1 (75.70% Variance p =0.0003)
-0.8 -0.4 0.0 0.4 0.8 1.2-0.6
-0.4
-0.2
0.0
0.2
0.4
PC1 (72.84% Variance p =0.002)
PC
2 (12
.65
% V
ari
an
ce
p =
0.7
24
)
-0.8 -0.4 0.0 0.4 0.8 1.2-0.6
-0.4
-0.2
0.0
0.2
0.4
PC
2 (1
2.4
5%
Va
ria
nce
p =
0.9
26
)
PC1 (75.84% Variance p =0.066)
A) 1D PURGE
B) 2D HSQC
i) 0.5 µg cm-2 ii) 1.0 µg cm-2 iii) 2.0 µg cm-2
i) 0.5 µg cm-2 ii) 1.0 µg cm-2 iii) 2.0 µg cm-2
212
Appendix B
Supplementary Material for Chapter Four
Published as: Yuk, J., Simpson, M.J., and Simpson, A.J., Coelomic fluid: A complimentary
biological medium to assess sub-lethal endosulfan exposure using 1H NMR-based earthworm metabolomics. Ecotoxicology, 2012: (In Press).
Reproduced with permission from Ecotoxicology, 2012, In Press. (http://www.springerlink.com/content/f44h11206g4150wh/). © Copyright Springer Publishing
213
Figure A4.1: PCA scores plot of endosulfan-exposed Eisenia fetida (●) and control (■) of the earthworms (A) coelomic fluid and (B) tissue extract 1H-NMR spectra at: (i) 0.5 µg cm-2, (ii) 1.0 µg cm-2 and (iii) 2.0 µg cm-2. The p-value for control and endosulfan exposed earthworms for the PCA components are reported using a two-sample t-test.
A) Coelomic Fluid
B) Tissue Extract
i) 0.5 µg cm-2 ii) 1.0 µg cm-2 iii) 2.0 µg cm-2
i) 0.5 µg cm-2 ii) 1.0 µg cm-2 iii) 2.0 µg cm-2
-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
PC1 (76.3% Variance p=0.037)
PC
3 (
7.6
% V
ari
an
ce
p=
0.0
04
)
-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
PC
3 (
7.9
% V
ari
an
ce
p=
0.0
10
)
PC1 (72.2% Variance p=0.070)
-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
PC
3 (
8.3
% V
ari
an
ce
p=
0.0
85)
PC1 (62.9% Variance p=0.076)
-0.8 -0.4 0.0 0.4 0.8
-0.8
-0.4
0.0
0.4
0.8
PC
2 (
32
.7%
Va
ria
nc
e p
=2
.94
E-0
6)
PC1 (39.3% Variance p=0.784)-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
PC
2 (
15
.3%
Va
ria
nc
e p
=0
.00
28
)PC1 (47.9% Variance p=0.432)
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
PC
2 (
13
.8%
Va
rian
ce
p=
0.0
34
)
PC1 (62.4% Variance p=0.689)
214
Appendix C
Supplementary Material for Chapter Five
Content in this chapter has been submitted as:
Yuk, J., Simpson, M.J., and Simpson, A.J., 1-D and 2-D NMR metabolomics of earthworm responses to sub-lethal endosulfan and endosulfan sulfate exposure in soil. Environ. Pollut., 2012 (Submitted).
215
S5.1 Analysis of soil endosulfan or endosulfan sulfate concentrations
Endosulfan and endosulfan sulfate spiked soils used in the earthworm exposure
experiments were extracted and quantified using Environmental Protection Agency (EPA)
methods 3540C and 8081B [1, 2]. The determination of percent dry weight was determined before
analysis with approximately 10g (wet weight) of each soil used for the earthworm soil
experiments. A separate 10 g (wet weight) of each soil was collected , mixed with 10 g sodium
sulfate (Na2SO4, Fisher Scientific), and spiked with 100 µl of 10 µg /ml tetrachloro-m-xylene (min
99.9% purity, Supelco) as a surrogate standard. The samples were then soxhlet extracted for 24
hours using 200 mL hexane: acetone (1:1) (Optima grade, Fisher Scientific). Extracts were then
concentrated by rotary evaporation and an N2 gas blower. Triplicates for each exposure
concentration for endosulfan and endosulfan sulfate were performed.
Endosulfan and endosulfan sulfate concentrations were analyzed using an Agilent 6890 N
Gas chromatograph coupled to an Agilent 5973 quadrupole mass selective detector equipped with
an HP-5MS column (30 m x 0.25 mm i.d., 0.25 µm film thickness) and an Agilent 7683
autosampler. Using the recommended settings from EPA methods 8081B [2], the injection volume
used was 1.0 µL (splitless), inlet temperature was set at 225oC and total flow was 1.0 mL/min.
The starting temperature was set at 100oC which was held for 2 min and then ramped at 15oC/min
to 160oC and finally 5oC/min to 270oC. The mass spectrometer was operated in electron impact
mode (EI) at 70 eV ionization energy and in selective ion monitoring mode (m/z 207 and 244 for
ml tetrachloro-m-xylene, 195 and 236.9 for endosulfan and 228.9 and 271.8 for endsulfan sulfate).
The data was analyzed using Agilent Chemstation G 1701 DA software and endosulfan and
endosulfan sulfate were identified and quantified using external standards. The endosulfan and
endosulfan sulfate concentration in the soil were corrected for extraction efficiency using the
216
surrogate standard percent recovery and calculated on a dry weight basis by comparison to the
measured soil percent dry weight [1]. The relative standard deviation between all the triplicate
samples was 6% and the average surrogate recovery was 80%.
S5.2 Earthworm coelomic fluid and tissue extraction and preparation for NMR
Each earthworm after exposure (control or exposed) were placed in individual 25 mL glass
vials with 415 µL of 0.2 M monobasic sodium phosphate buffer solution (NaH2PO4·2H2O; 99.3%;
Fisher Scientific) containing 0.1% (w/v) sodium azide (99.5% purity; Sigma Aldrich) as a
preservative. Buffer solution was made with D2O (99.9% purity, Cambridge Isotope Laboratories
Inc., Andover, MA, USA) and adjusted to a pD of 7.4 using NaOD (30% w/w in 99.5% D2O,
Cambridge Isotope Laboratories Inc). The buffer solution for all NMR samples also contained 10
mg/L of 2,2-dimethyl-2-silapentane-5-sulfonate sodium salt (DSS; 97%, Sigma Aldrich) as an
internal standard. A non-invasive electrical extrusion technique was done to extract the earthworm
CF using a 9 V battery and short exposure (<1s) repeated 10 times [3-5]. The earthworm was then
removed and the extracted fluid was placed in a 1.5 mL centrifuge tube and centrifuged for 20
minutes at 15,000 rpm (~17,000 x g) using an International Equipment Company 21000
Centrifuge (Fisher Scientific, Canada) to remove any biosolids or coelomocytes. The supernatant
was then transferred into a 5 mm High Throughputplus NMR tubes (Norell Inc., Landisville, NJ,
USA).
The lyophilized earthworms were homogenized in a 1.5 mL centrifuge tube using a 5 mm
wide stainless steel spatula. The homogenized samples were then extracted using 1 mL of a 0.2 M
monobasic sodium phosphate buffer solution (NaH2PO4·2H2O; 99.3%; Fisher Scientific)
containing 0.1% (w/v) sodium azide (99.5% purity; Sigma Aldrich) as a preservative [6]. Buffer
solution was made with D2O (99.9% purity, Cambridge Isotope Laboratories Inc) and adjusted to a
217
pD of 7.4 using NaOD (30% w/w in 99.5% D2O, Cambridge Isotope Laboratories Inc). The buffer
solution for all NMR samples also contained 10 mg/L of 2,2-dimethyl-2-silapentane-5-sulfonate
sodium salt (DSS; 97%, Sigma Aldrich) as an internal standard. Samples were vortexed for 30
seconds using a VX 100 vortexer (Labnet, Edison, NJ, USA) and then sonicated for 15 minutes
using a FS60 sonicator (Fisher Scientific) to aid with the extraction. Samples were then
centrifuged at 14,000 rpm using an International Equipment Company 21000 Centrifuge (Fisher
Scientific) for 20 minutes and the supernatant was transferred into a new 1.5 mL centrifuge tube.
The centrifuge process was then repeated two more times to ensure all additional particulates were
removed and then samples were transferred into a 5 mm High Throughputplus NMR tubes (Norell
Inc).
S5.3 Preparation of NMR spectral data for Principal Component Analysis (PCA)
The earthworm CF 1H NMR spectra were divided into width bins of 0.02 ppm from the
region 0.25-9.0 ppm using Analysis of Mixtures (AMIX) statistics package (version 3.9.8, Bruker
BioSpin) and the region from 4.35-5.21 ppm was not analyzed due to residual H2O/HOD signals
present in this region. The earthworm tissue extract 1H NMR spectra were divided into width bins
of 0.02 ppm from the region 0.25-9.0 ppm and the region from 4.75-4.90 ppm was not analyzed
due to residual H2O/HOD signals present in this region. For the earthworm tissue extract 1H-13C
HSQC spectra, the carbon spectrum (F2) was divided into width bins of 0.50 ppm from the region
of 10.0-140.0 ppm and the 1H spectrum (F1) was divided into width bins of 0.05 ppm from the
region 0.25-9.0 ppm. The region of 4.75-4.90 ppm on the 1H spectrum with their associated
carbon spectrum from the region 10.0-140.0 ppm was excluded due to the residual H2O/HOD
signals present in this region. The “sum of intensities” was used as the integration mode and the
scaling was set to “total intensity” for all the NMR spectra. PCA was performed at the 95%
218
confidence level and any variances that represented less than 1% or 2.5% in the bins for 1-D and 2-
D NMR spectra were excluded [7, 8]. Therefore in this study, each treatment group and
unexposed control group had ten earthworms each for both CF and tissue extract experiments
except for the highest endosulfan concentration (10.0mg/kg) which had six earthworms in the
earthworm CF group and eight earthworms in the tissue extract group and for the high endosulfan
sulfate concentration (10.0mg/kg) which had eight earthworms in the tissue extract group.
S5.4. 1-D and 2-D t-test filtered NMR difference plots
Each difference NMR spectrum was generated by subtracting the averaged bucket
intensities of the control group from each of the exposed group concentrations. In addition, a t-test
was conducted on each bin to determine if the intensity difference was significantly different to the
control (p<0.05). Any intensity values that were significantly different were kept in the spectrum
but if not, were replaced with a zero. The final t-test filtered difference NMR spectrum allows the
identification of potential metabolites from the significant peaks that were increasing/decreasing
after exposure. Influential peak signals identified in the 1-D and 2-D difference spectra were then
matched with metabolite signals from a previous study which identified the major metabolites in E.
fetida [6] and were also compared to the Bruker Biofluid Reference Compound Database version
2-0-3(BrukerBioSpin).
219
S5.5 References
1. (EPA), U.S.E.P.A., Method 3540C Soxhlet Extraction, P.C.M. Test Methods for Evaluating
Solid Waste, Editor 1996, National Technical Information Service (NTIS): Springfield,
VA.
2. (EPA), U.S.E.P.A., Method 8081B Organochlorine Pesticides by Gas Chromatography. ,
D.o. Commerce, Editor 2007, National Technical Information Services (NTIS):
Springfield, VA.
3. Bundy, J.G., et al., Earthworm species of the genus Eisenia can be phenotypically
differentiated by metabolic profiling. FEBS Let, 2002. 521: p. 115-120.
4. Hendawi, M., et al., A new ultrasound protocol for extrusion of coelomocyte cells from the
earthworm Eisenia fetida. Ecotoxicol. Environ. Saf., 2004. 59(1): p. 17-22.
5. Yuk, J., M.J. Simpson, and A.J. Simpson, Coelomic fluid: A complimentary biological
medium to assess sub-lethal endosulfan exposure using 1H NMR-based earthworm
metabolomics. Ecotoxicology, 2012: p. In Press.
6. Brown, S.A.E., A.J. Simpson, and M.J. Simpson, Evaluation of sample preparation
methods for nuclear magnetic resonance metabolic profiling studies with Eisenia fetida.
Environ. Toxicol. Chem., 2008. 27(4): p. 828-836.
7. Brown, S.A.E., et al., 1H NMR metabolomics of earthworm exposure to sub-lethal
concentrations of phenanthrene in soil. Environ. Pollut., 2010.
8. Yuk, J., M.J. Simpson, and A.J. Simpson, 1-D and 2-D NMR metabolomics of earthworm
responses to sub-lethal trifluralin and endosulfan exposure. Environ. Chem., 2011. 8(3): p.
281-294.
220
Table S5.1. Total endosulfan and endosulfan sulfate concentrations extracted by soxhlet
extractions and analyzed using GC/MS
Spiked endosulfan and
endosulfan concentration
(mg/kg dry weight)
Measured endosulfan
concentration (mg/kg dry
weight)
Measured endosulfan sulfate
concentration (mg/kg dry
weight)
0 (Control) Below detection limits Below detection limits 0.1 0.098 0.086 1.0 0.92 0.88
10.0 12.25 8.85
221
Figure S5.1 PCA scores plot of PC1 versus PC2 of endosulfan-exposed E. fetida (●) and control E. fetida (■) using A) Coelomic fluid (1-D PURGE), B) Tissue extract (1-D PURGE ) and C) Tissue extract (2-D HSQC) NMR spectra at: i) 0.1 mg kg-1, ii) 1.0 mg kg-1 and iii) 10.0 mg kg-1.
A) Coelomic Fluid (1D PURGE)
i) 0.1 mg kg-1 ii) 1.0 mg kg-1 iii) 10.0 mg kg-1
B) Tissue Extract (1D PURGE)
C) Tissue Extract (2D HSQC)
i) 0.1 mg kg-1 ii) 1.0 mg kg-1 iii) 10.0 mg kg-1
i) 0.1 mg kg-1 ii) 1.0 mg kg-1 iii) 10.0 mg kg-1
-0.4 -0.2 0.0 0.2 0.4
-0.4
-0.2
0.0
0.2
0.4
PC
2 (
26
.0%
Va
ria
nc
e)
PC1 (33.1% Variance)-0.4 -0.2 0.0 0.2 0.4
-0.4
-0.2
0.0
0.2
0.4
PC1 (40.6% Variance)P
C2
(2
6.0
% V
ari
an
ce
)-1.2 -0.8 -0.4 0.0 0.4 0.8 1.2
-1.2
-0.8
-0.4
0.0
0.4
0.8
1.2
PC1 (56.2% Variance)
PC
2 (
17
.7%
Va
ria
nc
e)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
PC
2 (
8.0
% V
ari
an
ce
)
PC1 (83.8% Variance)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
PC
2 (
7.3
% V
ari
an
ce
)
PC1 (85.9% Variance)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
PC
2 (
6.9
% V
ari
an
ce
)
PC1 (85.3% Variance)
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
PC
2 (
5.9
% V
ari
an
ce
)
PC1 (85.6% Variance)
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
PC
2 (
6.4
% V
ari
an
ce
)
PC1 (87.6% Variance)
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
PC
2 (
4.7
% V
ari
an
ce
)
PC1 (84.5% Variance)
222
Figure S5.2 PCA scores plot of PC1 versus PC2 of endosulfan sulfate-exposed E. fetida (●) and control E. fetida (■) using A) Coelomic fluid (1-D PURGE), B) Tissue extract (1-D PURGE ) and C) Tissue extract (2-D HSQC) NMR spectra at: i) 0.1 mg kg-1, ii) 1.0 mg kg-1 and iii) 10.0 mg kg-1.
A) Coelomic Fluid (1D PURGE)
i) 0.1 mg kg-1 ii) 1.0 mg kg-1 iii) 10.0 mg kg-1
B) Tissue Extract (1D PURGE)
C) Tissue Extract (2D HSQC)
i) 0.1 mg kg-1 ii) 1.0 mg kg-1 iii) 10.0 mg kg-1
i) 0.1 mg kg-1 ii) 1.0 mg kg-1 iii) 10.0 mg kg-1
-0.4 -0.2 0.0 0.2 0.4
-0.4
-0.2
0.0
0.2
0.4
PC
2 (
22
.4%
Va
rian
ce
)
PC1 (33.9% Variance)
-0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
PC
2 (
27
.8%
Va
rian
ce
)PC1 (29.4% Variance)
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
PC
2 (
16
.0%
Va
rian
ce
)
PC1 (46.4% Variance)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
PC
2 (
9.4
% V
ari
an
ce)
PC1 (80.0% Variance)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
PC
2 (
7.9
% V
ari
an
ce)
PC1 (83.9% Variance)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
PC
2 (
8.0
% V
ari
an
ce)
PC1 (84.8% Variance)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
PC
2 (
7.8
% V
ari
an
ce)
PC1 (83.2% Variance)
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
PC
2 (
8.6
% V
ari
an
ce)
PC1 (85.5% Variance)
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
PC
2 (
6.0
% V
ari
an
ce)
PC1 (87.2% Variance)
223
Figure S5.3: T-test filtered 1H NMR difference spectra of E. fetida coelomic fluid were obtained by subtracting the mean buckets of each endosulfan-exposed earthworm concentration: A) 0.1 mg kg-1, B) 1.0 mg kg-1 and C) 10.0 mg kg-1 with the mean buckets of the control earthworms. Spectral signals that were significantly different from the control (p<0.05) were retained while others are excluded.
8 7 6 5 4 3 2 1
Chemical Shift (ppm)
1.3
1 L
ac
tate
1.4
5 A
lan
ine
2.3
9 S
ucc
ina
te
2.9
9 α
-ke
tog
luta
rate
3.1
1 S
pe
rmid
ine
3.2
5 B
eta
ine
3.5
5 G
lycin
e3
.59
My
o-i
no
sito
l
A)
B)
C)
224
Figure S5.4: T-test filtered 1H NMR difference spectra of E. fetida coelomic fluid were obtained by subtracting the mean buckets of each endosulfan sulfate-exposed earthworm concentration: A) 0.1 mg kg-1, B) 1.0 mg kg-1 and C) 10.0 mg kg-1 with the mean buckets of the control earthworms. Spectral signals that were significantly different from the control (p<0.05) were retained while others are excluded.
8 7 6 5 4 3 2 1
Chemical Shift (ppm)
1.3
1 L
ac
tate
1.4
5 A
lan
ine
2.3
9 S
uc
cin
ate
2.9
9 α
-ke
tog
luta
rate
3.1
1 S
pe
rmid
ine
3.2
5 B
eta
ine
3.5
5 G
lyc
ine
3.5
9 M
yo
-in
osi
tol
A)
B)
C)
225
Figure S5.5: T-test filtered 1H NMR difference spectra of E. fetida tissue extract were obtained by subtracting the mean buckets of each endosulfan-exposed earthworm concentration: A) 0.1 mg kg-
1, B) 1.0 mg kg-1 and C) 10.0 mg kg-1 with the mean buckets of the control earthworms. Spectral signals that were significantly different from the control (p<0.05) were retained while others are excluded.
8 7 6 5 4 3 2 1
Chemical Shift (ppm)
1.3
1 L
ac
tate
1.4
5 A
lan
ine
2.4
3 G
luta
min
e
Ov
erl
ap
pin
g
Su
ga
rs a
nd
am
ino
ac
ids
4.9
6 M
eli
bio
se
5.4
1 M
alt
ose
8.2
3 A
TP
A)
B)
C)
226
Figure S5.6: T-test filtered 1H NMR difference spectra of E. fetida tissue extract were obtained by subtracting the mean buckets of each endosulfan-exposed earthworm concentration: A) 0.1 mg kg-
1, B) 1.0 mg kg-1 and C) 10.0 mg kg-1 with the mean buckets of the control earthworms. Spectral signals that were significantly different from the control (p<0.05) were retained while others are excluded.
8 7 6 5 4 3 2 1
Chemical Shift (ppm)
1.3
1 L
ac
tate
1.4
5 A
lan
ine
2.4
3 G
luta
min
e
Ov
erl
ap
pin
g
Su
ga
rs a
nd
am
ino
ac
ids
4.9
6 M
eli
bio
se
5.4
1 M
alt
ose
8.2
3 A
TP
A)
B)
C)
227
Figure S5.7: T-test filtered 1H-13C HSQC difference spectra of E. fetida tissue extract were obtained by subtracting the mean buckets of each endosulfan-exposed earthworm concentration: A) 0.1 mg kg-1, B) 1.0 mg kg-1 and C) 10.0 mg kg-1 with the mean buckets of the control earthworms. Spectral signals that were significantly different from the control (p<0.05) were retained while others are excluded.
8 7 6 5 4 3 2 1140
120
100
80
60
40
20
-0.003500
-0.002938
-0.002375
-0.001812
-0.001250
-6.875E-04
-1.250E-04
4.375E-04
1.000E-03
1H Chemical Shift (ppm)
13C
Ch
em
ical
Sh
ift
(pp
m)
Glutamine
A)
8 7 6 5 4 3 2 1140
120
100
80
60
40
20
-0.003500
-0.002938
-0.002375
-0.001812
-0.001250
-6.875E-04
-1.250E-04
4.375E-04
1.000E-03
1H Chemical Shift (ppm)
13C
Ch
em
ical S
hif
t (p
pm
)
B)
C)
8 7 6 5 4 3 2 1140
120
100
80
60
40
20
-0.003500
-0.002938
-0.002375
-0.001812
-0.001250
-6.875E-04
-1.250E-04
4.375E-04
1.000E-03
1H Chemical Shift (ppm)
13C
Ch
em
ical
Sh
ift
(pp
m)
Alanine
Lactate
Methionine
Glutamate
Lysine
Glycine
Betaine
Maltose
Glucose
Melibiose
Glutamine
LactateGlutamate
Glycine
Betaine
Glucose
Melibiose
Glutamine
Lactate
Methionine
Glutamate
Lysine
Betaine
Glucose
Melibiose
228
Figure S5.8: T-test filtered 1H-13C HSQC difference spectra of E. fetida tissue extract were obtained by subtracting the mean buckets of each endosulfan sulfate-exposed earthworm concentration: A) 0.1 mg kg-1, B) 1.0 mg kg-1 and C) 10.0 mg kg-1 with the mean buckets of the control earthworms. Spectral signals that were significantly different from the control (p<0.05) were retained while others are excluded.
8 7 6 5 4 3 2 1
140
120
100
80
60
40
20
-0.003500
-0.002938
-0.002375
-0.001812
-0.001250
-6.875E-04
-1.250E-04
4.375E-04
1.000E-03
1H Chemical Shift (ppm)
13C
Ch
em
ical
Sh
ift
(pp
m)
8 7 6 5 4 3 2 1140
120
100
80
60
40
20
-0.003500
-0.002938
-0.002375
-0.001812
-0.001250
-6.875E-04
-1.250E-04
4.375E-04
1.000E-03
1H Chemical Shift (ppm)
13C
Ch
em
ica
l S
hif
t (p
pm
)
8 7 6 5 4 3 2 1140
120
100
80
60
40
20
-0.003500
-0.002938
-0.002375
-0.001812
-0.001250
-6.875E-04
-1.250E-04
4.375E-04
1.000E-03
1H Chemical Shift (ppm)
13C
Ch
em
ica
l S
hif
t (p
pm
)
Glutamine
Alanine
Lactate
Methionine
Glutamate
Lysine
Glycine
Betaine
Maltose
Glucose
Melibiose
Glutamine
Lactate
Glucose
Glutamine
Melibiose
A) B)
C)
229
Figure S5.9: Percent change (%) of identified metabolites from the t-test filtered 1H-13C HSQC NMR difference spectra of endosulfan-exposed E. fetida tissue extracts. Percent changes that were significantly different from the control (p<0.05) are labelled with “*”. These metabolites have been already identified in the t-test filtered 1H NMR difference spectra of endosulfan-exposed E.
fetida tissue extracts and were not repeated again. Each percent change is shown with their associated standard error.
-20
0
20
40
60
80
p<0.1
*
*
Rela
tive P
erc
en
t C
han
ge (
%) Alanine
Concentrations (mg kg-1)
0.1 1.0 10.00
10
20
30
40
50
60* *
R
ela
tive P
erc
en
t C
han
ge (
%)
*
Glutamine
Concentrations (mg kg-1)
0.1 1.0 10.00
10
20
30
40
50
60
70
**
R
ela
tive P
erc
en
t C
han
ge (
%)
*
Lactate
Concentrations (mg kg-1)
0.1 1.0 10.0
-80
-70
-60
-50
-40
-30
-20
-10
0
*
*
R
ela
tive P
erc
en
t C
han
ge (
%)
*
Maltose
Concentrations (mg kg-1)
0.1 1.0 10.0
-90
-80
-70
-60
-50
-40
-30
-20
-10
0
*
*
R
ela
tive P
erc
en
t C
han
ge
(%
)
*
Melibiose
Concentrations (mg kg-1)
0.1 1.0 10.0
230
Figure S5.10: Percent change (%) of identified metabolites from the t-test filtered 1H-13C HSQC NMR difference spectra of endosulfan sulfate-exposed E. fetida tissue extracts. Percent changes that were significantly different from the control (p<0.05) are labelled with “*”. These metabolites have been already identified in the t-test filtered 1H NMR difference spectra of endosulfan sulfate-exposed E. fetida tissue extracts and were not repeated again. Each percent change is shown with their associated standard error.
0
10
20
30
40
50
60
70
80*
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%)
*
Lactate
Concentrations (mg kg-1)
0.1 1.0 10.0
-80
-70
-60
-50
-40
-30
-20
-10
0
*
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%)
Maltose
Concentrations (mg kg-1)
0.1 1.0 10.0
-60
-50
-40
-30
-20
-10
0
*
*
R
ela
tiv
e P
erc
en
t C
ha
ng
e (
%)
Melibiose
Concentrations (mg kg-1)
0.1 1.0 10.0
0
20
40
60
80
100
*
R
ela
tiv
e P
erc
en
t C
han
ge
(%
) Alanine
Concentrations (mg kg-1)
0.1 1.0 10.00
10
20
30
40
50
60
70
80
90
100
*
*
R
ela
tiv
e P
erc
en
t C
han
ge
(%
)
*
Glutamine
Concentrations (mg kg-1)
0.1 1.0 10.0