biotechnology examination committee...i towards implementation of an itaconic acid biosensor for...
TRANSCRIPT
i
Towards implementation of an itaconic acid biosensor for
prospecting eukaryotic host producers
Inês Oliveira da Fonseca
Thesis to obtain the Master in Science Degree in
Biotechnology
Supervisor: Prof. Doctor Nuno Gonçalo Pereira Mira
Examination Committee
Chairperson: Prof. Doctor Arsénio do Carmo Sales Mendes Fialho
Supervisor: Prof. Doctor Nuno Gonçalo Pereira Mira
Member of the committee: Prof. Doctor Miguel Nobre Parreira Cacho Teixeira
December 2019
ii
Preface
The work presented in this thesis was performed at the Institute for Bioengineering and Biosciences of
Instituto superior Técnico (Lisbon, Portugal), during the period September 2018 – September 2019,
under the supervision of Prof. Doctor Nuno Gonçalo Pereira Mira.
iii
Declaration
I declare that this document is an original work of my own authorship and that it fulfills all the
requirements of the Code of Conduct and Good Practices of the Universidade de Lisboa.
iv
Acknowledgments
I would like to express my gratitude towards my supervisor Professor Doctor Nuno Mira, for sharing
his scientific knowledge and enthusiasm and for his guidance throughout this learning process, which
made me grow at a professional level and as a person. I would also like to acknowledge my co-
supervisor PhD student Ana Vila-Santa for walking me up along the course of my work and for
passing me her experience. To all the BSRG group and particularly to my lab partners Fernão
Castanheira Mendes, Maria João Tavares, Nuno Pedro, Sara Salazar, Maria Joana Pinheiro, João
Lucas, Joana Correia, Tiago Brito, Catarina Mendonça, Catarina Lima, Mafalda Cavalheiro, Mónica
Galocha, Pedro Pais, Diana Pereira, Romeu Viana and Elsa Oliveira, I would like to thank for the daily
help and companionship. My regards go also to Naglis Malys Lab from the University of Nottingham,
who kindly ceded the plasmids, which are the basis of my work. I also acknowledge the funding
received through the iBB-Institute for Bioengineering and Biosciences, Instituto Superior Técnico from
the Portuguese Foundation for Science and Technology through contracts (UID/BIO/04565/2019)
and TTRAFFIC - Toxicity and transport for Fungal Production of Industrial Compounds (ERA-IB2-
6/0003/2014) and Programa Operacional Regional de Lisboa 2020 (Project N. 007317). Last but not
least, I am truly grateful to my family and friends for their unconditional support, motivation and bright
advices about life, also, for being the best role-models I could ask for. In particular, I would like to
thank to my grandparents who never doubt my skills and resilience.
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Abstract
Itaconic acid is an added-value compound, with a very high economic addressable market. Recently
several strategies have been designed to implement microbe-based production of itaconic acid,
involving various host strains that show different degrees of success. Further optimization of this
process is severely hampered by itaconic acid being an inconspicuous molecule whose detection is
exclusively based on analytical methods, such HPLC. Biosensors emerge here as useful molecular
tools to screen and guide the design of over-producer strains. An Itaconic acid biosensor, based on
the exploration of the interaction between an itaconic acid-responsive regulator (itcR) from Yersinia
pseudotuberculosis and Pseudomonas aeruginosa and their target DNA sequence, is already
developed for bacterial hosts. As such, in this work is described the possibility of improving the activity
Yersinia-based biosensor by fine-tuning the recognition of ItcR to its target DNA sequence through
site-directed and random mutagenesis and also the primary steps for the further transfer of this
system to eukaryotic hosts. It was also envisaged a further exploration of ItcR from Pseudomonas
aeruginosa to serve as an alternative to the ItcR from Yersinia pseudotuberculosis. Finally, in order to
identify other ItcR-like regulators from other bacterial species that could also be interesting candidates
for new biosensors, a thorough bioinformatics analysis was performed aiming to identify orthologues
of the genes required for itaconic acid degradation across bacterial and fungal species.
Keywords: Transcription factor based-biosensor; itaconic acid-responsive biosensor; itcR; Itaconic
acid; itaconic-acid catabolism;
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Resumo
O ácido itacónico é um composto de valor acrescido, com uma procura de mercado em expansão.
Recentemente, diversas estratégias têm sido desenvolvidas para a implementação da produção de
ácido itacónico em diferentes espécies hospedeiros microbianos com diferentes taxas de sucesso.
Contudo, a otimização deste processo é limitada pelo facto do ácido itacónico ser uma molécula
inconspícua, sendo a sua deteção restrita a métodos analíticos, como HPLC. Neste contexto, os
biosensores surgem como ferramentas úteis no screening e no design de estirpes super-produtoras.
Já foi desenvolvido um biosensor bacteriano para ácido itacónico, o qual se baseia num fator de
transcrição (itcR) de Yersinia pseudotuberculosis e de Pseudomonas aeruginosa e a sequência de
DNA correspondente. Como tal, neste documento descreve-se a otimização do sistema de Yersinia
através da modulação interação entre o ItcR e a sua sequência-alvo no promotor por estratégias de
mutagénese dirigida e aleatória, e ainda os primeiros passos para a subsequente transferência deste
sistema para hospedeiros eucariotas. Igualmente, é explorado o ItcR de Pseudomonas aeruginosa
como potencial alternativa ao ItcR de Yersinia pseudotuberculosis. Finalmente, com vista a identificar
homólogos do ItcR potencialmente interessantes para aplicações de biosensing noutras espécies
bacterianas, é feita uma análise in sillico alargada para identificar ortólogos dos genes envolvidos na
degradação do ácido itacónico em bactérias e fungos.
Palavras-Chave: Biosensor com fator de transcrição; biossensor para o ácido itacónico; itcR; ácido
itacónico; catabolismo de ácido itacónico;
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Table of Contents
Preface ..................................................................................................................................................... ii
Acknowledgments ................................................................................................................................... iv
Declaration ............................................................................................... Erro! Marcador não definido.
Abstract..................................................................................................................................................... v
Resumo ................................................................................................................................................... vi
Table of Contents ................................................................................................................................... vii
List of Figures .......................................................................................................................................... ix
List of Tables .......................................................................................................................................... xii
Abbreviations ......................................................................................................................................... xiii
Introduction ...............................................................................................................................................1
Biosensors as screening tools ......................................................................................................... 3
Transcription factor-base biosensors .............................................................................................. 6
Introduction to the theme of the thesis: IA-responsive biosensors.................................................. 8
Materials and Methods .......................................................................................................................... 10
Strains and growth media .............................................................................................................. 10
Assessment of the Yp-ItcR biosensor performance in E. coli cells ............................................... 11
Growth curves of P. aeruginosa strains in the presence of itaconic acid ...................................... 12
Monitorization of Pa-ccl gene expression along P. aeruginosa growth in the presence of itaconic
acid ................................................................................................................................................ 12
Random mutagenesis of the Yp-ItcR biosensor promoter ............................................................ 13
Site-directed mutagenesis on the Yp-ItcR promoter ..................................................................... 14
In Sillico analysis of the itaconic acid degradation pathway across Gram negative bacteria ....... 15
In Sillico analysis of the intergenic region of the ItcR-operons ...................................................... 16
In Sillico analysis of the itaconic acid degradation pathway across Fungi .................................... 16
Optimization of the use of Yp-ItcR biosensor to profile supernatants produced by IA-producing
yeast strains................................................................................................................................... 17
Results and Discussion ......................................................................................................................... 18
Profiling of the use of Yersinia pseudotuberculosis itaconic acid biosensor for itaconic acid
detection ........................................................................................................................................ 18
Functionality of ItcR in itaconic acid-degradation in Pseudomonas aeruginosa ........................... 30
Bioinformatic analysis of itaconic acid degradation operons among bacteria ............................... 35
viii
Bioinformatic analysis of itaconic acid degradation pathways in Fungi ........................................ 44
Could there be other substrates triggering the itaconic acid-degradation operons?..................... 48
Concluding Remarks ............................................................................................................................. 51
References ............................................................................................................................................ 52
Appendixes ............................................................................................................................................ 56
ix
List of Figures
Figure 1. IA biosynthetic pathway from A. terreus (in blue) and U. maydis (in yellow). Common parts to
both organisms are highlighted in black (in Vicente, 2018). .................................................................... 2
Figure 2. Components of a biosensor. .................................................................................................... 3
Figure 3. Biosensor ideal response curve. Operational and dynamic range. (adapted from Rogers et
al., 2016). ................................................................................................................................................. 4
Figure 4. Schematic mode of action of metabolite-responsive TFs. Repressors mode of action (A)(B);
Activators mode of action (C)(D). (In Wan et al., 2019) .......................................................................... 6
Figure 5. Itaconate degradation pathway in bacteria (A) and itaconate degradation operons from Y.
pestis/ Y. pseudotuberculosis and P. aeruginosa (B) (from Hanko et al., 2018). ................................... 8
Figure 6. E. coli MG1655 (A) and C. necator H16 (B) with the Y. pseudotuberculosis (Yp) and P.
aeruginosa (Pa) itaconate-inducible systems composed of promoter and transcriptional regulator
(ItcR/P), and promoter-only (P) in the absence and presence of 5 mM itaconate. (in Hanko et al.,
2018). ....................................................................................................................................................... 9
Figure 7. Itaconate degradation pathway in fungi (in Hossain et al., 2019) ............................................ 9
Figure 8. Kinetics and dynamics of the Yp-ItcR biosensor along cultivation of E. coli cells in MM
medium (at pH 7) transformed with plasmid pEH086. Dose response-curve between the inducer (IA)
concentration and the fluorescence output 4, 8, 12 and 24 hours post-induction (hpi) (A); Absolute
normalized fluorescence of E. coli DH5 exposed to increasing IA concentration (B). .......................... 18
Figure 9. Experimental set-up created to test the IA YP-ItcR biosensor. .............................................. 19
Figure 10. Biosensor output for the One-pot two-strain assay with a 3-hour recovery stage. Absolute
normalized fluorescence of E. coli DH5 harbouring pEH086 in response to different IA concentration
(on the left) and respective DRC (on the right). First line corresponds to an induction stage with 25 mM
of IA (A), second line corresponds to an induction stage with 50 mM of IA (B). ................................... 20
Figure 11. Experimental set-up created to estimate the IA titers of eukaryotic IA-producer using the
YP-ItcR biosensor. ................................................................................................................................. 21
Figure 12. Experimental set-up created to produce mutated versions of the Yp-itcR biosensor. The
promotor of the system, Pccl, was targeted for error-prone PCR, being posteriorly cloned into a
linearized backbone (pEH086) by Gibson assembly. ........................................................................... 22
Figure 13. PCR products from the Pccl amplification with different annealing temperatures. First well
corresponds to the DNA ladder and the following four to the PCR reactions with an annealing
temperature of 49, 51, 53 and 55 ºC, respectively (A). PCR products from the pEH086 linearization.
First well corresponds to the original vector non-linearized and the second and third well to the
linearized plasmid (B). ........................................................................................................................... 23
Figure 14. DNA extracted from the random mutagenesis candidates. First well corresponds to the
original vector non-linearized and the following wells correspond to the plasmids containing the
mutated versions of Pccl. ...................................................................................................................... 23
Figure 15. Populational analysis of the absolute normalized fluorescence for 8 hpi (A). Mutants
exhibiting an improved response (B) or a poor response to IA (C) to IA at 8 hpi. ................................. 24
x
Figure 16. Dose-response curve for the wildtype and mutants 31. - wildtype strain, 8 hpi; -
mutant strain, 8 hpi; ............................................................................................................................... 25
Figure 17. Alignment of the ItcR from Y. pseudotuberculosis and the hits from the Protein BLAST. Blue
shades represent amino acid conservation. Quality, occupancy and the consensus sequence can be
found in the inferior part of the figure. ................................................................................................... 27
Figure 18. Alignment of the itcR intergenic region from Y. pseudotuberculosis YPIII (highlighted in
bold) and the intergenic regions from the Protein BLAST hits (after filtering the results). Blue shades
represent nucleotide conservation. In red are highlighted the presumable -10 box and -35 box from the
first gene of the IA degradation operon. ................................................................................................ 28
Figure 19. Candidate binding sites for Yp-ItcR based on the in silico comparative analysis (A) In grey
are highlighted the -10 box and -35 box from the first gene of the operon (Ccl). Nucleotides targeted
for a site-directed mutagenesis approach to further confirm their function as binding sites – described
as Mut1, Mut2, Mut3 and Mut4 (B). (adapted from Hanko et al., 2018). ............................................... 28
Figure 20. Site-directed mutagenesis experimental set-up: created to produce point mutations on,
Pccl, of the Yp-itcR biosensor. .............................................................................................................. 29
Figure 21. Schematic representation of the IA degradation operon found in Pseudomonas aeruginosa
PAO. Gene names are highlighted in bold and gene products can be found bellow the gene name.
The three enzymes common to Y. pestis/Y. pseudotuberculosis operon are itaconyl-CoA hydratase
(Ich), itaconate-CoA transferase (ict) and (S)-citramalyl CoA-lyase (ccl). ............................................ 30
Figure 22. P. aeruginosa growth represented by OD640nm on 0 and 48 hours of culture growing in either
40 mM of glucose or 40 mM of IA. One-way ANOVA was performed between the groups growing for
48 hours in IA, being the stars (**) representative of a significant difference observed between the
mutants and the wildtype. ...................................................................................................................... 31
Figure 23. P. aeruginosa growth in 40 mM of IA represented by OD640nm (A) and IA concentration in
the culture supernatant over 48 hours and 7 day of culture (B). - wildtype; - ΔitcR; - Δccl.
............................................................................................................................................................... 31
Figure 24. Relative expression of P. aeruginosa PA0883 gene using the wildtype (WT) strain grown for
2 hours in glucose as basal condition. Values were normalized using rpoS gene. .............................. 32
Figure 25. P. aeruginosa mutants represented by IA concentration in the culture supernatant over 24
hours. P. aeruginosa mutants for the core enzymes (A) and for the ‘’additional’’ enzymes (B). .......... 33
Figure 26. Schematic representation of the IA degradation operons in Y. pseudotuberculosis (A) and
P. aeruginosa (B). Gene names are highlighted in bold and gene products can be found bellow the
gene name. ............................................................................................................................................ 35
Figure 27. Phylogenetic Tree (Average Distance model) containing all the Yp-ItcR homologues
(highlighted in blue) and the Pa-ItcR homologues (highlighted in orange) that resulted from the Protein
BLAST analysis. .................................................................................................................................... 36
Figure 28. Schematic representation of the IA degradation operons in Y. pseudotuberculosis. Gene
names are highlighted in bold and gene products can be found bellow the gene name. ..................... 37
Figure 29. The identities and similarities of the enzymes belonging to the Yersinia-like IA operon are
illustrated in the Heat Map following the colour scheme above. The first line concerns the reference
xi
strain. Enzymes are ordered according to their role in the IA degradation pathway rather than its
position in the genome. Genes that are not connected to the operon are marked in grey. The red dots
indicate species which are known to be pathogenic to human. ............................................................ 38
Figure 30. Schematic representation of the IA degradation operons found within the Pseudomonas
genus: (A) P. aeruginosa PA0; (B) P. tolaasii PMS117; (C) P. fluorescens AHK-1; (D) P. stutzeri
DNSP21, P. balearica DSM6083, P. kunmingensis DSM25974, P. xanthomarina S11, P. kuykendallii
NRRLB-59562 and P. citronellolis S2_009_000_R2; (E) P. jinjuensis JCM21621, P. benzenivorans
DSM8628, P. marincola JCM14761, P. lutea DSM17257, P. graminis DSM11363, P. abietaniphila
ATCC700689, P. simiae 2-36, P. synxantha NBRC3913, P. poae MYb114, P. paralactis DSM29164,
P. fuscovaginae IRRI6609, P. syringae GR12-2, P. gessardii BS2982, P. batumici UCMB-321, P.
mendocina NEB698, P. agarici NCPPB2472, P. bohemica IA19, P. taetrolens NCTC10697, P.
protegens CHA0, P. veronii 1YB2 and P. lurida LMG21995; ................................................................ 40
Figure 31. The identities and similarities of the enzymes belonging to the Pseudomonas-like IA operon
(from Pseudomonas genus) are illustrated in the Heat Map following the colour scheme above. The
first line concerns the reference strain. Enzymes are ordered according to their role in the IA
degradation pathway rather than its position in the genome. The position of the last three enzymes in
the heat map is random since its role in the IA catabolism is unknown. Genes that are not connected
to the operon are marked in grey. The red dots indicate species which are known to be pathogenic to
human. ................................................................................................................................................... 41
Figure 32. The identities and similarities of the enzymes belonging to the Pseudomonas-like IA operon
(from gram-negative bacteria) are illustrated in the Heat Map following the colour scheme above. The
first line concerns the reference strain. Enzymes are ordered according to their role in the IA
degradation pathway rather than its position in the genome. The position of the last three enzymes in
the heat map is random since its role in the IA catabolism is unknown. Genes that are not connected
to the operon are marked in grey. The red dots indicate species which are known to be pathogenic to
human. ................................................................................................................................................... 42
Figure 33. Itaconate degradation pathway in fungi. Gene names are highlighted in bold below gene
products (adapted from Hossain et al., 2019) ....................................................................................... 44
Figure 34. The identities and similarities of the A. niger homologue genes (Aspergillus genus) are
illustrated in the Heat Map following the colour scheme above. ........................................................... 45
Figure 35. The identities and similarities of the A. niger homologue genes (non-Aspergillus genus) are
illustrated in the Heat Map following the colour scheme above. ........................................................... 46
Figure 36. Absolute normalized fluorescence of E. coli DH5 harbouring pEH086 in response to IA (50
mM), isoleucine (50 mM), leucine (50 mM), valine (50 mM) and alanine (50 mM) in mineral media with
5 mM of yeast extract. Left YY-axis (in red) concerns the absolute normalized fluorescence of IA, while
the right YY-axis (in black) concerns the absolute normalized fluorescence of the aminoacids .......... 49
xii
List of Tables
Table 1. Optical (fluorescent, bioluminescent, and colorimetric) biosensors. (adapted from Rogers et
al., 2016). ................................................................................................................................................. 5
Table 2. List of plasmids used in this study. .......................................................................................... 10
Table 3. P. aeruginosa strains used in this study. ................................................................................. 10
Table 4. Saline solution composition. .................................................................................................... 11
Table 5. Primers used in the q-PCR. ..................................................................................................... 13
Table 6. List of primers used to amplify the promotor region (Pccl). Highlighted in bold are the 5-end
recombination tails for latter cloning by Gibson assembly. ................................................................... 13
Table 7. List of primers used to amplify the vector backbone pEH086. ................................................ 14
Table 8. List of primers used in the PCR reaction of the site-directed mutagenesis. Mutated
nucleotides are highlighted in bold. ....................................................................................................... 15
Table 9. List of the new set of primers used in the PCR reaction of the site-directed mutagenesis.
Mutated nucleotides are highlighted in bold. ......................................................................................... 15
Table 10. General parameters of the elements belonging to the analysed Yersinia-like operons.
Retention rate, Identity and similarity of the gene products belonging to the operon and length of the
intergenic region. * - Exceptionally, the Pseudomonas species possessed ict homologues with lower
identities and similarities than the one presented in the table, of 27 and 56 respectively. ................... 37
Table 11. General parameters of the elements belonging to the analysed Pseudomonas-like operons.
Retention rate, Identity and similarity of the gene products belonging to the operon and length of the
intergenic region. * - The lowest retention rated for acyl-CoA dehydrogenase were found when the
enzyme suffered duplication events in the genomes. ........................................................................... 40
Table 12. General parameters of the structural analysis of fungal species harbouring a itcA
homologue. Retention rate, Identity and similarity of the gene products belonging to the IA catabolism.
............................................................................................................................................................... 44
Table 13. Top-10 compounds exhibiting the highest structural similarity with IA.................................. 48
xiii
Abbreviations
ABS Activation Binding Site
Adi Aconitate-Δ-isomerase
A.U. Arbitrary Units
β-Gal Beta-galactosidase
BiFC Bimolecular Fluorescence Complementation
BLAST Basic Local Alignment Search Tool
bp Base pairs
C-terminal Carboxyl-terminal
CAD Cis-Aconitate Decarboxylase
Ccl (S)-citramalyl-CoA lyase
CclA Citramalate-CoA lyase
cDNA Complementary deoxyribonucleic acid
CFP Cyan Fluorescent Protein
DNA Deoxyribonucleic acid
dNTP Deoxyribonucleotide triphosphate
DRC Dose-Response Curve
EC Enzyme Commission
FACS Fluorescence Activated Cell Sorting
FRET Forster Resonance Energy Transfer
FC Flow Cytometry
GFP Green Fluorescence Protein
hpi Hours post-induction
HPLC High-Performance Liquid Chromatography
IA Itaconic Acid
Ich Itaconyl-CoA hydratase
IchA Itaconyl-CoA hydratase A
xiv
Ict Itaconate CoA transferase
IctA Itaconyl-CoA transferase A
LB Luria-Bertani broth
LTTR Lys-R Type Transcriptional Regulators
N-terminal Amino-terminal
OD640nm Optical density at the wavelength of 640 nanometres
PBS Phosphate-Buffered Saline
PCR Polymerase Chain Reaction
RBS Recognition Binding Site
RFP Red Fluorescent Protein
RNA Ribonucleic acid
rpm Rotations per minute
RSAT Regulatory Sequence Analysis Tool
Ta Annealing temperature
TAD Trans-Aconitate Decarboxylase
Tam Trans-aconitate 2-methyltransferase (from E. coli)
TCA Tricarboxylic Acid Cycle
TF Transcription Factor
Tm Melting temperature
Tmt1 Trans-aconitate 3-methyltransferase (from S. cerevisiae)
TmtA Trans-aconitate 2-methyltransferase (from A. niger)
UV Ultraviolet radiation
YFP Yellow Fluorescent Protein
1
Introduction
The world is experiencing a significant increase in population and urbanization, widening the demand
for resources and greatly enhancing generation of wastes (Mohan et al., 2016). This growth is mainly
based in chemical manufacturing techniques and highly dependent on fossil fuels, causing a strong
negative impact over the limited natural resources and in the environment (Clomburg et al., 2017;
Mohan et al., 2016). In specific, it is observed that the chemical processes dominating nowadays the
fuel and chemical industries employ large amounts of energy and materials and are composed of
multiple unit operations and heat integration schemes, resulting in the need of having large-scale
infrastructures (Clomburg et al., 2017). To circumvent this, it is emerging the concept of industrial
biotechnology or biotechnological manufacturing as an alternative to conventional chemistry, these
green processes being more streamlined, less technologically complex, requiring lower energy inputs
and attaining higher carbon- and energy-conversion efficiencies in single-unit operations (compared to
chemical processes) (Clomburg et al., 2017; Mohan et al., 2016).
Itaconic acid (IA) (C5H4O4, 128.08 Da) is a C5 dicarboxylic acid (Werpy et al., 2004) used as a co-
monomer in resins and in the manufacture of synthetic fibres, coatings, adhesives, thickeners and
binders (Willke and Vorlop, 2001). IA is considered a highly add-value “green” molecule because it
can be used as a substitute for petrochemical-based acrylic or methacrylic acids and it can be
obtained via microbial fermentation. The interest in IA as a platform molecule for multiple synthetic
routes is based on its several functional groups (including two carboxyl groups and a methylidene
group) that can be transformed into new families of useful molecules, such as 2-Methyl-1,4-butanediol,
3-methyltetrahydrofuran, 3- and 4-gamma-butyrolactone, 2-Methyl-1,4-butanediamine and
pyrrolidones (Werpy et al., 2004; Verma and Iqbal, 2016). Market studies predict a growing demand of
IA: from 2015 (which market size was over USD 75 million), to 2024 are expected gains at over 16.8%
for the IA market (Verma and Iqbal, 2016). China is currently the main IA producer and consumer,
holding 50% of the global demand, being the main application of the molecule the production of
synthetic latex. By 2024 the main application of IA is expected to be in the synthesis of methacrylate
and polymethyl methacrylate (Verma and Iqbal, 2016).
IA is naturally produced by Aspergillus terreus via cis-aconitate decarboxylase (CAD) enzyme
(Kanamasa et al., 2008). CAD converts cis-aconitic (an intermediate of the Tricarboxylic Acid Cycle
(TCA)) into IA and carbon dioxide (Fig. 1) (Cordes et al.,2015; Kanamasa et al., 2008). Ustilago
maydis is another natural producer of IA by the sequential action of aconitate-Δ-isomerase (Adi) and
trans-aconitate decarboxylase (TAD). In this alternative pathway, the cis-aconitic is secreted out of the
mitochondria to the cytosol where it is converted in trans-aconitate by Adi. After this isomerization,
TAD catalyses the decarboxylation of trans-aconitate, generating IA and carbon dioxide (Fig. 1)
(Geiser et al., 2016). IA production also occurs in macrophages during inflammation as an
antibacterial response. IA inhibits the activity of the bacterial isocitrate lyase, consequently blocking
2
the glyoxylate cycle, which is key in the production of acetyl-CoA during fatty acid degradation (the
major carbon source available for intracellular pathogens) (Sasikaran et al., 2014).
Integrating several process and genetic engineering strategies, A. terreus has been engineered to
produce up to 160 g/L itaconic acid, with a yield of 0.69 mol/mol glucose (Young et al., 2018). The use
of filamentous fungi, like A. terreus, in fermentations is appreciated to their efficiency in producing
organic acids as well as ability to break down complex (and usually cheaper) carbohydrate structures,
such as lignocellulose (Hossain et al., 2019). However, difficulties in obtaining reproducible
fermentations, sensitivity of some species to impurities in the cultivation medium, difficulties caused by
the fungal morphology that result in complex broth rheology and maintenance of aeration are
considered significant hurdles (Wang et al., 2005; Hossain et al., 2019). Furthermore, A. terreus is
considered pathogenic and therefore its utilization in countries with more limitative legislations, as it is
the case of the United States or the European Union, require industrial facilities with a higher safety
level thereby compromising the economy of the process. As an alternative, other filamentous fungi
non-pathogenic, such as A. niger or U.maydis have been engineered to produce itaconic acid, the
maximum titers reported in these species reaching 86 g/L and 53 g/L, respectively (Dwiarti et al.,
2002; Zhao et al., 2018).
Yeast species are also being considered a possible option for the industrial production of IA including
Saccharomyces cerevisiae, Pichia kudriavzevii (formerly Issatchenkia orientalis) or Yarrowia
lypolytica. In all these systems, itaconic production has been enabled through the over-expression of
the CAD1 gene from A. terreus, but the titers obtained are far from those obtained with the filamentous
fungi (S. cerevisiae 0.168 g/L (1.3 mM); P. kudriavzevii 1.31 g/L (10.1 mM); Y. lypolytica 22.03 g/L
(169.3 mM) (Blazeck et al., 2014; Costa, 2017; Zhao et al., 2019). Besides the non-filamentous
Figure 1. IA biosynthetic pathway from A. terreus (in blue) and U. maydis (in yellow). Common parts to both organisms are highlighted in black (in Vicente, 2018).
3
morphology, yeast cells also offer a more genetically accessible platform for subsequent engineering
of the producing cells. By the employment of the high-throughput genetic engineering tools (e.g
through error-prone PCR or the degenerate oligonucleotides-Pfu system) (Chusacultanachai and
Yuthavong 2004), it is possible to generate large libraries of strains with multiple deletions in their
genome or expressing variants of the CAD1 gene, for example. However, being an inconspicuous
molecule, the screening of these libraries can only be performed via high-performance liquid
chromatography (HPLC), which is limited to the testing of fewer than 102 variants per day (Hanko et
al., 2018; Rogers et al., 2016). Besides this temporal issue, the design of tailored assay methods for a
given target molecule is a laborious and time-consuming process (Dietrich et al., 2010). In this context,
the development of tools that allow a rapid high-throughput screening of producing strains is of great
advantage, biosensors emerging in this context as highly interesting alternatives since they can
convert a biological response into a measurable signal (even in real-time, depending on the type of
biosensor) (Dietrich et al., 2010; Keasling, 2010; Rogers et al., 2016;). By coupling the presence of a
molecule to a reporter gene or to a fitness advantage, biosensors allow a rapid automated multiplex
phenotype evaluation, increasing the throughput of the screening/selection process for all molecules
to 105 to 109 variants per day, maintaining a high degree of sensitivity and selectivity regardless of the
number of variants to analyse (Dietrich et al., 2010; Keasling, 2010). The following section will be
dedicated to explaining what biosensors are, what are their basis and how they can be improved and
developed with emphasis on biosensors based on transcription factors which are those explored in
this thesis.
Biosensors as screening tools
In a formal manner, a biosensor can be designated as an analytical device composed of a biological
recognition element that responds to a target analyte and an actuator. The actuator consists of a
physical transducer that converts the biological response into a measurable signal proportional to the
analyte concentration (Fig. 2) (Su et al., 2011).
Analyte Biological Element Actuator Signal Output
Optical transducer
Electrochemical
transducer
Acoustical transducer
Mechanical transducer
Calorimetric transducer
Figure 2. Components of a biosensor.
4
Despite its individual components all biosensors must exhibit basic features such as: 1) orthogonality,
which means that the biosensor is a closed system and does not interfere with the endogenous
metabolism and vice-versa; 2) tunability, which means that a change in the biosensors individual parts
will proportionally modify the output; 3) universality, which means that the system has a standardized
operating mode; 4) and they must allow real-time monitoring (Mahr and Frunzke, 2016). Apart from
the previously stated features, an ideal biosensor would also exhibit wide operational range (that is, a
large concentration spectrum in which the biosensor exhibits a graded concentration-dependent
change in response), high dynamic rage, low false positive rate and high molecular specificity (Fig. 3)
(Rogers et al., 2016).
The biological recognition element of a biosensor can include cofactors, enzymes, antibodies,
organelles, microorganisms (whole-cells) and even tissues (Lei et al., 2006) and the sensing
techniques can be electrochemical, optical, acoustical, mechanical, calorimetric or electronic, being
the first two the most used (Lei et al., 2006; Su et al., 2011). From the most employed sensing
techniques, the electrochemical has an increased sensitivity and specificity, however, their application
often requires one or more electrodes, which hammers the miniaturization of the biosensor (Su et al.,
2011). In contrast, in optical biosensors miniaturization is not a limitation since all the components of
the biosensor can be genetically-encoded, resulting in a compact and flexible system (Lei et al., 2006).
In optical biosensors, a measurable optical signal (such as luminescence, fluorescence or colour) is
correlated with the analyte concentration, as a consequence of the interaction of the biological
recognition element with the target compound (Su et al., 2011). When the compound interacts with the
recognition element, the transducer responds by undergoing a dynamic change in its optical properties
(absorption, reflectance, emission) which is recorded by a photodetector and converted in an electrical
signal (Borisov et al., 2008). Optical biosensors are widely applied in genetically-engineered microbes
and are a promising tool in high throughput screenings, since they enable the detection and
monitoring of multiple analytes simultaneously (Su et al., 2011).
Below are presented some examples of optical biosensors whose output is based on fluorescence,
bioluminescence or colour (Table 1).
Figure 3. Biosensor ideal response curve. Operational and dynamic range. (adapted from Rogers et al., 2016).
5
Table 1. Optical (fluorescent, bioluminescent, and colorimetric) biosensors. (adapted from Rogers et al., 2016).
Among optical biosensors, those based on fluorescence are, by far, the more commonly used
because of their improved stability, sensitivity and for its non-invasiveness (emitted fluorescence can
be easily detected by optical equipments with little or no damage to the host system) (Su et al., 2011).
Usually fluorescence sensing is based on the measurement of intensity or hue, excitation profile or
emission profile in a proportional response to the stimulus of interest (Su et al., 2011). In this optical
detection technique presence of fluorescent protein is the quantified variable (Belkin, 2003). The
fluorescent molecules more used in this context are intrinsically fluorescent proteins derived from
marine animals such as jellyfishes, corals or anemones (such as the Aequorea victoria Green
Fluorescent Protein (GFP)) (Lalonde et al., 2005). The interest of these intrinsically fluorescent
proteins resides in the fact that they are self-sufficient to perform the necessary reactions for the
formation of the chromophore, meaning that the protein formation doesn’t require additional gene
products from their original host, therefore allowing their heterologous expression (Frommer et al.,
Molecule(s) Molecule
type
Biological Recognition
Element
Biological Recognition
Element type
Output Host Reference
3,4-Dihydroxybenzoate
Aromatic PobR Allosteric TF Fluorescence
(GFP) E. coli
Jha et al., 2015
Biphenyl, nitrotoluenes
Aromatics XylR Allosteric TF Colour (β-Gal) P. putida
SF05 Garmendia et al., 2001
Mevalonate Isoprenoid precursor
AraC Allosteric TF Fluorescence
(GFP) E. coli
Tang and Cirino, 2011
Pyruvate Alpha-
keto acid De novo FRET
Fluorescence (YFP and Venus)
Mammalian cells
San Martín et al., 2014
Theophylline Alkaloid De novo Riboswitch Colour (β-Gal) E.
coli TOP10 F’
Lynch and Gallivan,
2008
Thiamine-pp Vitamin De novo Riboswitch Fluorescence
(GFP) E.
coli TOP10 Muranaka et al., 2009
Trehalose-6-p Sugar De novo FRET Fluorescence
(CFP and Venus)
E. coli BL21 Peroza et al., 2015
Tetracycline Antibiotic TetA Allosteric TF Luminescence
(Luciferase) E. coli K12
Virolainen et al., 2008
Triacetic acid lactones
Feedstock AraC Allosteric TF Colour (β-Gal) E. coli HF22 Tang et al.,
2013
Vanillin Aromatic, flavoring
QacR Allosteric TF Fluorescence
(GFP) E. coli
DH5αZ1
de los Santos et al., 2015
Zn2+ Ion De novo FRET Fluorescence
(CFP and YFP)
β cells Vinkenborg et al., 2009
6
2009). Fluorescence-based biosensors are highly attractive because they can be easily implemented
resorting to molecular biology techniques that may allow modification of the host system to express
the above mentioned naturally fluorescent proteins. However, to have a complete genetically-encoded
biosensor, the recognition element must also be encoded from one or more gene(s). These
genetically-encoded biosensors can be classified in several categories depending on the biosensor
structure: post translational reporters, which include Forster Resonance Energy Transfer (FRET)-
based and Bimolecular Fluorescence Complementation (BiFC)-based biosensors, aptamer-based
biosensors and transcription factor (TF)-target DNA biosensors (Ibraheem and Campbel, 2010).
Because this thesis is focused on the last type of biosensors, these will be further detailed below.
Transcription factor-base biosensors
Transcription factors (TF) are DNA-binding proteins that bind to specific sequences in the regulatory
regions of the gene/s and regulate gene expression at the transcription level in a targeted way
(Browning and Busby, 2004). Adjacent to the promotor there is a cis-regulatory DNA sequence,
generally called operator or enhancer, that is specifically recognized by the TF, restricting or
enhancing the access of RNA polymerase to the promoter (Wan et al., 2019). Consequently, TFs may
function as activators and/or repressors of transcription and, consequently, increasing or decreasing
the transcription levels (Browning and Busby, 2004; Wan et al., 2019). The particular interest in TFs as
sensing elements for biosensors is related with their ability to bind (via their C-terminal or N-terminal
region) and respond to small molecules or to environmental stresses (such as, salt, osmosis, pH,
oxygen, redox, light or radiation) (Wan et al., 2019). Metabolite-responsive TFs can be used as
biological recognition elements in microbial biosensors. In these, a reporter gene is fused with the
regulatory sequences (operator and promotor) that are regulated by that metabolite-responsive TF. In
the presence of the analyte the promotor is activated by the TF and consequently lifts (or blocks, if the
TF is a repressor) the expression of the reporter gene (Fig. 4) (Su et al., 2011). Furthermore, bacterial
transcriptional repressors can be converted into a transcriptional activators by inverting the output,
namely by setting the expression of a second transcriptional repressor under the control of the target
repressor (Wan et al., 2019; Mahr and Frunzke, 2016).
Figure 4. Schematic mode of action of metabolite-responsive TFs. Repressors mode of action (A)(B); Activators mode of action (C)(D). (In Wan et al., 2019)
7
Due to the complexity of eukaryotic transcription (that involves the recruitment and association of
several DNA-binding proteins and bending of the DNA) and due to compartmentalization (the nucleus
is a physical barrier between transcription and translation), this type of biosensors is often developed
in bacterial systems and afterwards transferred to eukaryotic hosts (Wan et al., 2019). The
employment of bacterial TFs in eukaryotic hosts can often be performed directly, without the need of
sigma factors or other auxiliary transcriptional enzymes and avoids the common cross-talk between
endogenous TFs and native promoters. The transfer of the system only requires a codon-optimized TF
for the host and the operator sequence (in particular the DNA-binding motifs). The final step of
optimizing the bacterial system to the eukaryotic host can be accomplished by tuning the repressor
expression, operator position and operator sequence in a well-characterized native promotor (Wan et
al., 2019; Skjoedt et al. 2016).
Besides contributing to increase the throughput of the screening process, genetically encoded
biosensors can also be used for single-cell analysis and real-time observation of cellular metabolism.
These two features allow the analysis of the cell-to-cell variability that is highly relevant to explain
phenotypic variations observed within the population. In this context, the use of genetically-encoded
biosensors, coupled with microfluidic chips for microbial immobilization and Flow Cytometry (FC) to
evaluate the phenotypes - eventually followed by Fluorescence Activated Cell Sorting (FACS) to
isolate the potentially interesting cells (cells with a higher fluorescence output) - allow the identification
of interesting microbial production strains (Keasling, 2010; Rogers et al., 2016).
In addition to the role of genetically encoded biosensors in the screening stage, they can also be
valuable tools in the design and build steps from the design-build-test cycle. Genetically encoded
biosensors can be used in the development of strains with the desired phenotypes resourcing to
dynamic pathway control and biosensor-driven adaptive evolution. This is also valid for the
development of strains with enhance small-molecule biosynthesis, contributing for the improvement of
product yields or pathway efficiencies (Keasling, 2010; Rogers et al., 2016; Dietrich et al., 2010).
Dynamic pathway control is important in this context since the introduction of heterologous
biosynthetic pathways can lead to unbalanced metabolite concentrations and accumulation of toxic
products that might interfere with the host metabolism or lead to complex cellular stress. TF-based
biosensors can be integrated as regulatory switches to balance metabolic fluxes by positive or
negative feedback loops (Mahr and Frunzke, 2016). Evolution approaches driven by mutation and
(positive and negative) selection are effective tools to adapt microorganisms to stress conditions or to
improve product formation, yet they fail when it comes to the analysis of phenotypes that are not
directly linked to the microbial growth or phenotype. In biosensor-driven adaptive evolution, mutation
and selection techniques are coupled with genetically-encoded biosensors, expanding the laboratorial
evolution process to inconspicuous phenotypes (Mahr and Frunzke, 2016; Dietrich et al., 2010).
Furthermore, the quantitative framework of genetically-encoded biosensors (dynamic range and linear
range of detection) allows a precise comparison between different variants within a library (Dietrich et
al., 2010).
8
Introduction to the theme of the thesis: IA-responsive biosensors
This thesis was focused on the development and implementation of biosensors that could be used to
screen the production of itaconic acid in eukaryotic hosts and specifically in the collection of mutants
that compose the S. cerevisiae “disruptome”. This idea of screening all available S. cerevisiae mutants
aims not only at identifying non-obvious gene deletions that could result in improved production,
productivity or yield of itaconic acid, but it also serves as a proof-of-concept of the utilization of
biosensors for the production of this organic acid, a knowledge that can be later transferred for other
producing species including P. kudriavzevii. Not only it was envisaged the optimization of the
biosensor to be used, but also the optimization of what could be the experimental setup that would be
more compatible with the aimed high-throughput screening. The biosensor that was explored was
based on the recent work of Hanko et al., 2018 that described the utilization of itaconic acid-
responsive transcription factor, itcR, found in pathogenic bacteria like Yersinia sp. or Pseudomonas
aeruginosa. During bacterial infection of mammal hosts, it was found that macrophages trigger the
synthesis of itaconic acid (via a specific cis-aconitic decarboxylase) to inhibit the bacterial isocitrate
lyase. During infection of the mammalian host, the activation of ItcR would help the bacteria to
counteract the deleterious effects of itaconic acid by promoting its catabolism (Strelko et al., 2011;
Sasikaran et al., 2014). This catabolic pathway, schematically represented in Figure 5 (A), involves the
activation of itaconate by an itaconate CoA transferase (Ict), generating itaconyl-CoA, which is then
hydrated by itaconyl-CoA hydratase (Ich) to yield (S)-citramalyl-CoA (Fig. 5 (A)). (S)-citramalyl-CoA is
finally cleaved by (S)-citramalyl-CoA lyase (Ccl) into acetyl-CoA and pyruvate (Sasikaran et al., 2014;
Hanko et al., 2018). These enzymes responsible for IA catabolism in Y. pseudotuberculosis and in P.
aeruginosa were found to be organized in three- or six-genes operons, respectively, presumably under
the control of ItcR (Fig. 5 (B)).
The IA biosensor developed by Hanko et al., 2018 is composed of an analyte (IA), a sensing element
(ItcR), and a physical transducer (Red Fluorescent Protein (RFP)) responsible for converting the
itaconic acid concentration present in the environment (which can be the inside of a bacterial cell) into
a fluorescent signal proportional to the analyte concentration (Hanko et al., 2018). In this biosensor
developed by Hanko et al., 2018, a plasmid encoding itcR and also having RFP under the control of
the intergenic region of the Y. pseudotuberculosis ItcR operon was used. This biosensor making use
of the itcR from Yersinia pseudotuberculosis was already implemented with success in Escherichia
Figure 5. Itaconate degradation pathway in bacteria (A) and itaconate degradation operons from Y. pestis/ Y.
pseudotuberculosis and P. aeruginosa (B) (from Hanko et al., 2018).
9
coli and in the less conventional species Cupriavidus necator, as it is clear from the results shown in
Figure 6.
Starting from the Yp-ItcR biosensor, in this work we have explored the possibility of improving its
activity by fine-tuning the recognition of ItcR to its target DNA sequence, something that is limited by
the lack of knowledge concerning what are the precise nucleotide sequence that is recognized by ItcR
in the promoter region of the ItcR-regulon. It was also envisaged a further exploration Pa-ItcR would
serve as an alternative to the ItcR from Y. pseudotuberculosis, this coming from the observation that
this biosensor was not working in E. coli but only in C. necator (as seen in Fig.6). An in silico analysis
revealed that C. necator harbors a putative ItcR regulator for which the response attributable the
response of the Pa-derived biosensor in this host could be attributable to increased activity of this
endogenous protein and not to PaItcR. Altogether the results suggested that the PaItcR biosensor was
not functional in both systems and a closer analysis revealed that this could result from difficulties in
heterologous expression of this proteins since the authors have not codon-optimized the protein
during the cloning sequence resorting to the original nucleotide sequence from P. aeruginosa.
Finally, in order to identify other ItcR-like regulators from other bacterial species that could also be
interesting candidates for new biosensors, a thorough bioinformatics analysis was performed aiming to
identify orthologues of the genes required for itaconic acid degradation across bacterial species.
Interestingly, an unexpected link between itaconic acid synthesis and degradation was recently
uncovered in A. niger, being found that the degradation pathway is composed of the same
intermediates and of functionally related enzymes with the bacterial IA catabolism (Fig.7) (Hossain et
al., 2019). The main difference regarding the IA catabolism between bacteria and fungi, is the
additional putative bioconversion pathway in A. niger that makes use of trans-aconitate 2-
methyltransferase (TmtA), which converts IA into 1-methyl itaconate (Hossain et al., 2019). The
biological function proposed for IA catabolism in fungi was to act as a defence mechanism in biological
warfare over the acquisition of scarce resources (Hossain et al., 2019).
Figure 6. E. coli MG1655 (A) and C. necator H16 (B) with the Y. pseudotuberculosis (Yp) and P. aeruginosa (Pa) itaconate-inducible systems composed of promoter and transcriptional regulator (ItcR/P), and promoter-only (P) in
the absence and presence of 5 mM itaconate. (in Hanko et al., 2018).
Figure 7. Itaconate degradation pathway in fungi (in Hossain et al., 2019)
B A
10
Materials and Methods
Strains and growth media
Escherichia coli DH5 was used for molecular biology procedures and also for testing the Yp-ItcR
biosensor. The strain was propagated in solid LB medium at 37ºC with orbital agitation (250 rpm). The
LB solid medium was prepared by adding 25 g/L of LB broth and 20 g/L of agar, prior to sterilization in
autoclave. For E. coli DH5 strains harbouring plasmids pEH086 and pEH172 (Table 2) (Hanko et al.,
2018), 1mL of chloramphenicol was added to the medium at a final concentration of 25 µg/L.
Table 2. List of plasmids used in this study.
Plasmid ID Plasmid Description Source
pEH086 Plasmid encoding the Ip-itcR biosensor. Contains the IA-
responsive promotor, Pccl, regulating both the TF itcR and a RFP. Chloramphenicol resistance gene (CmR)
Hanko et al., 2019
pEh172 Plasmid encoding the negative control of the Ip-itcR biosensor. Contains the IA-responsive promotor, Pccl, regulating a RFP.
Chloramphenicol resistance gene (CmR).
Hanko et al., 2019
A set of eight Pseudomonas aeruginosa PA01 strains were obtained from the transposon mutant
library of Manoil Lab (Held et al., 2012) and are described in Table 3. P. aeruginosa strains were
cultivated in LB medium at 37ºC with orbital agitation (250 rpm).
Table 3. P. aeruginosa strains used in this study.
Strain Description Source
P. aeruginosa PA01 Reference strain (wildtype) Manoil Lab Collection
P. aeruginosa ΔPA0877 (ΔitcR)
Reference strain (single transposon mutant for the Pa0877 gene)
Manoil Lab Collection
P. aeruginosa ΔPA0878 (Δich)
Reference strain (single transposon mutant for the Pa0878 gene)
Manoil Lab Collection
P. aeruginosa ΔPA0879 Reference strain (single transposon
mutant for the Pa0879 gene) Manoil Lab Collection
P. aeruginosa ΔPA0880 Reference strain (single transposon
mutant for the Pa0880 gene) Manoil Lab Collection
P. aeruginosa ΔPA0881 Reference strain (single transposon
mutant for the Pa0881 gene) Manoil Lab Collection
P. aeruginosa ΔPA0882 (Δict)
Reference strain (single transposon mutant for the Pa0882 gene)
Manoil Lab Collection
P. aeruginosa ΔPA0883 (Δccl)
Reference strain (single transposon mutant for the Pa0883 gene)
Manoil Lab Collection
11
To assess consumption of IA the cells were cultivated in mineral medium (pH 7), containing 12 mM of
ammonium chloride (NH4Cl), 300 mg/L of yeast extract, 40 mM of IA (or glucose), 10 mM of PBS and
a saline solution (Table 3). Media was prepared by dissolving 640 mg/L of ammonium chloride (NH4Cl)
and 300 mg/L of yeast extract in 900 mL of deionized water and sterilized in autoclave. After
sterilization, a previously filtered solution containing 40 mL of the carbon source stock, 60 mL of PBS
and 2.15 mL of salts solution was added. Carbon source stock were either glucose solution (1 M) or IA
solution (1 M). Mineral medium (pH 7) supplemented with 40 mM of glucose was also used for testing
of biosensors Yp-ItcR. For this the medium was supplemented with chloramphenicol at a final
concentration of 25 µg/L and different IA concentrations (1. 2,5, 5, 10, 25, 50 and 100 mM). To test the
effect of specific amino acids in the response of biosensor Yp-ItcR, the mineral media was further
supplemented with leucine (50 mM), isoleucine (50 mM), valine (50 mM), alanine (50 mM) and
tryptophan (50 mM).
Table 4. Saline solution composition.
Assessment of the Yp-ItcR biosensor performance in E. coli cells
E. coli DH5 cells transformed with the pEH086 plasmid were cultivated in glass tubes overnight in
mineral media (at pH 7) supplemented with 40 mM of glucose, at 37ºC with orbital agitation (250 rpm).
E. coli DH5 strains containing pEH172 were grown in the same conditions and used as control. After
that, the cells were re-inoculated at an OD640nm of 0.2 in the 96-well microplates containing 100 µL of
mineral media (pH 7) 2 times concentrated supplemented with IA (0, 1, 2,5, 5, 10, 25, 50 and 100
mM). These microplates were incubated at 37ºC for a minimum period of 24 hours and a maximum of
120 hours. In every hour absorbance and fluorescence readings were performed. Absorbance was
measured at the wavelength of 640 nm (OD640nm), while fluorescence was measured using the
excitation filter F485/20 and the emission filter A595/8. The fluorescence data was normalized for
biomass.
Component Concentration
(mg/mL)
Calcium chloride dihydrate (CaCl2.2H2O) 15,7
Magnesium Sulphate Heptahydrate (MgSO4.7H2O) 46,7
Magnesium Sulphate Monohydrate (MgSO4.H2O) 27,8
Iron II Sulphate Heptahydrate (FeSO4.7H2O) 23,3
Copper II Sulphate Pentahydrate (CuSO4.5H2O) 3,3
Cobalt II Sulphate Heptahydrate (CoSO4.7H2O) 6,7
Zinc Sulphate Heptahydrate (ZnSO4.7H2O) 6,7
12
Growth curves of P. aeruginosa strains in the presence of itaconic acid
P. aeruginosa PAO1 wild type and mutant strains deleted for the ItcR (ΔitcR) and for the enzyme (S)-
cytramalyl-coA transferase (Δccl) were grown, overnight, with orbital agitation (250 rpm), in mineral
media (at pH 7) supplemented with 40 mM of glucose in 100 mL Erlenmeyer flasks. After this, cells
were re-inoculated at an OD640nm of 0.2 in 96-well plates containing 100 µL of mineral media (pH 7)
2 times concentrated supplemented with sterile water (negative control), 40 mM of glucose (control) or
40 mM of IA. Microplates were incubated at 37ºC for 48 hours. In every 2 hours absorbance reading
were performed at the wavelength of 640 nm (OD640nm).
P. aeruginosa PAO1 wild type and all the single mutants from Table 3 were grown, overnight, with
orbital agitation (250 rpm), in mineral media (at pH 7) supplemented with 40 mM of glucose in 100 mL
Erlenmeyer flasks. After this, cells were re-inoculated at an OD640nm of 0.1 in 250 mL Erlenmeyer
flasks containing mineral media (pH 7) supplemented either with 40 mM of glucose and 40 mM of IA.
Flasks were incubated for 7 days at 37ºC and under orbital agitation (250 rpms). In the first 48 hours
of culture OD640nm readings and supernatant collection were performed every two or three hours, being
the same tasks performed daily in the remaining time of the experiment. The collected supernatants
were stored at -20 ºC for further High Performance Liquid Chromatography (HPLC) analysis. To
accompany the consumption of IA by P. aeruginosa PAO1 wild type and the single mutants, the
supernatants of the cultures were centrifuged at 8000 rpm in Scanspeed Mini (Labogene) for 5
minutes to pellet cells. 10 µL of these supernatants were separated by HPLC in an Aminex HPX87H
column (Biorad) eluted with a solution of 0.005M sulphuric acid at a flow rate of 0.6 mL/min. A UV-VIS
(Ultra-Violet - Visible) detector was used for detection of IA. Appropriate calibration curves using
standard solutions were made to estimate the IA concentration.
Monitorization of Pa-ccl gene expression along P. aeruginosa growth in the presence of
itaconic acid
P. aeruginosa PAO1 wild type and the single mutants ΔitcR and Δccl were cultivated, in triplicate, in
mineral media (pH 7) supplemented with 40 mM of glucose overnight in 100 mL Erlenmeyer flasks
with orbital agitation (250 rpm). After this, the cells were re-inoculated at an OD of 0.1 in 250 mL of the
same growth media supplemented either with 40 mM of glucose or with 40 mM of IA. The cultures
were incubated at 37ºC and under orbital agitation (250 rpms) and after 2 and 4 hours appropriate
culture volumes (calculated to obtain 50 mL of a cellular suspension with an OD640nm of 0.1) were
centrifuged (8000 rpm in Scanspeed Mini (Labogene)) for 5 minutes. The pellet obtained was
resuspended in PBS 0.1M (pH 7), pelleted again and stored at -80ºC until further use. Total RNA
extraction was performed using Hot-Phenol method (Köhrer and Domdey, 1991) and RNA
concentration and purity were quantified using Nanodrop ND-1000 spectrophotometer. The
concentration of the RNA samples was adjusted to 500 ng/µL. RNA was preserved at -80ºC. For the
RT step, for each RNA sample, 10 µL PCR mixtures were prepared using the kit TaqMan Reverse
Transcription from Applied Biosystems by Life Technologies. Each mixture contained 2.2 µL of MgCl2
25 mM; 1 µL of buffer 10x, 2 µL dNTPs 2.5 mM, 0.5 µL of random hexamers, 0.2 µL Rnase inhibitor, 2
13
µL of RNA (500 ng/µL), 0.25 µL of enzyme and 1.85 µL of dionized water. PCR program was
composed of a 10 minute stage at 25ºC, 30 minute stage at 48ºC and a 5 minute stage at 95ºC. For
the qPCR step, a 25 µL mixture was prepared for each cDNA sample. Each mixture contained 12.5 µL
of Mix SYBERGREEN from Applied Biosystems by Life Technologies, 2.5 µL of each primer (forward
and reverse) at the concentration of 4 pmol/µL (Table 5), 2.5 µL of cDNA (previously diluted 1:4) and 5
µL of dionized water. The PCR was runned in the mode Quantification comparative (ΔΔCt)
SYBR@Green Reagent program of the 7500 software version 2.0.6. The rpoD and rpoS
housekeeping genes were used separatly to normalize the values and the results were using the delta
delta Ct (ΔΔCt) method. The qPCR primers were designed using Primer Express Software version 3.0
(Thermo Fisher Scientific), using the default features and a melting temperature (Tm) of 54ºC to 56ºC.
Table 5. Primers used in the q-PCR.
Primer ID Primer Sequence
PA0883-Fw 5'- GGG TTG CTG TGC ATC CAT -3'
PA0883-Rv 5'- GGG ACT CGG CAT CAA TGT -3'
rpoD-Fw 5'- AGA GCC GAT CTC CAT GGA A -3'
rpoD-Rv 5'- CTC GCT GGT CGC CAT CT -3'
rpoS-Fw 5'- GGA TGA CGA TCT CAG CGA AA -3'
rpoS-Rv 5'- CCA CCT CAC GCT GCT TGT -3'
Random mutagenesis of the Yp-ItcR biosensor promoter
Error-prone was used to create a library of Yp-ItcR mutants. For this, the GeneMorph II Random
Mutagenesis Kit (Agilent Technologies) were used aiming for a low mutation frequency. For that, it
was prepared a 50 µL PCR mixture contained 1 µL of dNTPs mix 40 mM, 5 µL of Mutazyme II reaction
buffer 10x, 1 µL of each primer 250 ng/µL (Table 6), 1 µL of Mutazyme II DNA polymerase (2,5 U),
100 ng of the DNA fragment to amplify (Pccl) and 41.5 µL of deionized water. The PCR program was
composed of a 2 minutes stage at 95ºC, 35 cycles composed of 30 seconds at 95ºC, 30 seconds at
55ºC and 1 minute at 72ºC and a final stage of 10 minutes at 72ºC.
Table 6. List of primers used to amplify the promotor region (Pccl). Highlighted in bold are the 5-end
recombination tails for latter cloning by Gibson assembly.
The pEH086 vector was linearized by PCR reaction using the kit Phusion (New England BioLabs).
PCR mixture was prepared with 10 µL of HF buffer 10x, 1 µL of dNTPs mix 40 mM, 1 µL of each
primer 10 ng/µL (Table 7), 0,5 µL of Phusion DNA polymerase, 0,5 ng of plasmid DNA (pEH086) and
Primer ID Primer Sequence
RMut_itcR_Fw 5'- GACGTCTCAAGGAAACACGGTCAGG -3'
RMut_itcR_Rv 5'- CATATGGTTCCTCCTCCAACTTCGC -3'
14
41.5 µL of deionized water. The PCR program was composed of a 30 seconds stage at 98ºC, 35
cycles composed of 10 seconds at 98ºC, 30 seconds at 61ºC and 6 minutes at 72ºC and a final stage
of 7 minutes at 72ºC.
Table 7. List of primers used to amplify the vector backbone pEH086.
From the PCRs reaction tubes, 10 µL were sampled in agarose gel (0,8%) for 1 hour at 95 mV. After
confirming the correct length of the fragments, DNA concentration and purity were quantified using
Nanodrop ND-1000 spectrophotometer. The remaining products were treated with DPNI (Fisher
BioReagents) overnight at 37ºC. DPNI inactivation was performed at 80ºC for 20 min. Gibson
Assembly for the two PCR products was performed using the kit Gibson Assembly Master Mix (New
England BioLabs). Plasmid (pEH086) and the mutated region (Pccl) were mixed in 1:5 proportion (for
a total volume of 3 µL), together with 10 µL of Gibson Assembly Master Mix (2X) and 7 µL of
deionized water. The mixture was incubated at 50ºC for 15 minutes. Immediately after, NEB 5-alpha
Competent E. coli were transformed with the 2 μl of the assembled product by heat shock, according
to the manufacturer instructions, and plated in selective media (LB with chloramphenicol). The
candidates were frozen at -80ºC in 96 well plates for further biosensor testing. The plasmid DNA of 5
of the candidate colonies was extracted using the NZYMiniprep Kit (NZYTech), being the DNA ran in
agarose gel in the same conditions to confirm the construction size.
Site-directed mutagenesis on the Yp-ItcR promoter
Point mutations targeting the promotor Pccl in the Y. tuberculosis species were performed using
mutagenic PCR reactions making use of PfuTurbo DNA polymerase and using pEH086 as a template.
Several protocols were attempted: i) A 50 µL PCR mixture for each site to mutate was prepared. The
PCR mixture contained 1 µL of dNTPs 10 mM, 5 µL of Pfu reaction buffer 10x, 1 µL of forward primer
10 pM/µL (Table 8), 1 µL of reverse primer 10 pM/µL (Table 8), 1 µL of PfuTurbo DNA polymerase, 50
ng of plasmid DNA (pEH086) and 40 µL of deionized water. PCR program was composed of a 30
seconds stage at 95ºC, 16 cycles composed of 30 seconds at 95ºC, 1 minute at 60ºC and 6 minutes
at 68ºC and a final stage of 7 minutes at 68ºC. The PCR products were treated with DPNI (Fisher
BioReagents) for 1 hour at 37ºC. E. coli DH5 competent cells were transformed by heat shock (Froger
and Hall, 2007) with the mutated versions of pEH086. The DNA of the candidate colonies were
extracted using the NZYMiniprep Kit (NZYTech), being DNA concentration and purity quantified using
Nanodrop ND-1000 spectrophotometer. The DNA samples were sent for sequencing at STAB VIDA
using the primer MutD_confirm (5’ GGAGGCTGCCCTATTCCTAG -3’); ii) The original protocol was
repeated with the exception of the annealing temperature that was lowered to 55ºC; iii) The original
Primer ID Primer Sequence
pEH086_Lin_Fw 5'- CATATGGCGAGTAGCGAAG -3'
pEH086_Lin_Rv 5'- GATGGAGTTACGTCATATACGC -3'
15
protocol was repeated with an DPNI incubation time of 12 hours; iv) The original protocol was
repeating changing the set of primers, being the new ones on Table 9; v) The initial protocol was
repeated with the new set of primers (Table 9) and an annealing temperature of 55ºC; vi) The initial
protocol was repeated with the new set of primers (Table 9), an annealing temperature of 55ºC and a
DPNI incubation time of 12 hours;
Table 8. List of primers used in the PCR reaction of the site-directed mutagenesis. Mutated nucleotides are highlighted in bold.
Table 9. List of the new set of primers used in the PCR reaction of the site-directed mutagenesis. Mutated nucleotides are highlighted in bold.
In Sillico analysis of the itaconic acid degradation pathway across Gram negative bacteria
An in sillico analysis was carried out to study the distribution of the itcR-regulon across Gram negative
bacteria. For this, the itcR amino acid sequence was retrieved from the National Center for
Biotechnology Information (NCBI) website from the genome database (protein IDs: ACA68544.1 (Y.
Primer ID Primer Sequence
MutD R1 - Fw 5'- CTCCATCTTCATATCCAAAAGCAAGGAAACACACCGGTATCATATATTGG -3'
MutD R1 - Rv 5' -CCAATATATGATACCGGTGTGTTTCCTTGCTTTTGGATATGAAGATGGAG -3'
MutD R2 - Fw 5'- CCAAAAGCAAGGAAACACACCGGGGTCATATATTGGATAAATGATAACG -3'
MutD R2 - Rv 5'- CGTTATCATTTATCCAATATATGACCCCGGTGTGTTTCCTTGCTTTTGG -3'
MutD Y - Fw 5'- AAGCAATTAAACACACCGGTATCATATATTGGATGGGTGATAACGGCGACCATAG -3'
MutD Y - Rv 5'- CTATGGTCGCCGTTATCACCCATCCAATATATGATACCGGTGTGTTTAATTGCTT -3'
MutD B - Fw 5'- GTATCATATATTGGATAAATGATAACGAAAACCATAGACTAAGCGAAGTTGGAGGAG -3'
MutD B - Rv 5'- CTCCTCCAACTTCGCTTAGTCTATGGTTTTCGTTATCATTTATCCAATATATGATAC -3'
Primer ID Primer Sequence
MutD R1 - 1N - Fw 5'- CTCCATCTTCATATCCAAAAGCAAGTAAACACACCGGTATCATATATTGG -3'
MutD R1 - 1N - Rv 5' -CCAATATATGATACCGGTGTGTTTACTTGCTTTTGGATATGAAGATGGAG -3'
MutD R2 - 1N - Fw 5'- CCAAAAGCAAGGAAACACACCGGGATCATATATTGGATAAATGATAACG -3'
MutD R2 - 1N - Rv 5'- CGTTATCATTTATCCAATATATGATCCCGGTGTGTTTCCTTGCTTTTGG -3'
MutD Y - 1N -Fw 5'- AAGCAATTAAACACACCGGTATCATATATTGGATGAATGATAACGGCGACCATAG -3'
MutD Y - 1N - Rv 5'- CTATGGTCGCCGTTATCATTCATCCAATATATGATACCGGTGTGTTTAATTGCTT -3'
MutD B - 1N - Fw 5'- GTATCATATATTGGATAAATGATAACGACGACCATAGACTAAGCGAAGTTGGAGGAG -3'
MutD B - 1N - Rv 5'- CTCCTCCAACTTCGCTTAGTCTATGGTCGTCGTTATCATTTATCCAATATATGATAC -3'
16
Pseudotuberculosis YPIII) and NP_249568.1 (P. aeruginosa PAO1)). Protein Basic Local Alignment
Search Tool (BLAST) from NCBI, NIH, was used to find homologues for these proteins within the
Pseudomonas and Yersinia genus and in other gram-negative bacteria. The amino acid conservation
of the retrieved sequences was analysed using Protein Multiple Sequence Alignment Tool from Clustal
Omega (European Bioinformatic Institute). For the Protein BLAST hits phylogenetic trees (average
distance method) were constructed resorting to Blosum62 score model from Jalview 2.10.5. To
identify the remaining IA degradation genes the gene products of the Y. pseudotuberculosis or P.
aeruginosa operons (composed by 3 or 6 genes, respectively) were used: Y. Pseudotuberculosis YPIII
(protein IDs: WP_012105185.1 – CoA-ester lyase; WP_010981405.1 – MaoC family dehydratase;
WP_012304150.1 – acetyl-CoA hydrolase/transferase familyprotein) and of P. aeruginosa PAO1
(NP_249573.1 – CoA transferase; NP_249570.1 – acyl-CoA dehydrogenase; NP_249571.1 – ring-
cleaving dioxygenase; NP_249572.1 – MmgE/PrpD family protein; NP_249573.1 – CoA transferase;
NP_249574.1 – CoA-ester lyase). Based on the results obtained a graphical representation describing
the presence or absence of the operon in the different bacterial species examined was made in the
form of heat map using the MORPHEUS software (https://software.broadinstitute.org/morpheus). The
information regarding the isolation of the species was based on the information available at The
Bacterial Diversity Metadatabase (BacDive) (Reimer et al., 2018).
In Sillico analysis of the intergenic region of the ItcR-operons
Using the above described identification of genes composing the ItcR-operon it was defined whether
or not the different bacterial species examined harboured or not the operon. In those cases where the
presence of the operon was confirmed, the corresponding intergenic region found upstream of the first
enzyme of the operon was retrieved and analysed using Nucleotide Multiple Sequence Alignment Tool
from Clustal Omega (European Bioinformatic Institute). To identify a putative binding site for ItcR the
more divergent regions (e.g. intergenic regions with less than 60% identity; intergenic regions with
extensive size; intergenic regions interrupted by a transposase) were discarded.
In Sillico analysis of the itaconic acid degradation pathway across Fungi
To study the distribution across Fungi of the IA catabolic pathway, the pathway described in A. niger
was used as an anchor using strain CBS 513.88 as a reference. For this analysis only the A. niger
genes associated with IA degradation by Hussein et al., 2019 and belonging to IA degradation
pathways similar to those described in bacteria were considered. For this, the amino acid sequence of
the following proteins were retrieved from NCBI, NIH: XP_001391168.1 (itaconyl-CoA transferase A),
XP_001391996.1 (itaconyl-CoA transferase A), XP_001389283.1 (citramalate-CoA lyase),
XP_001398966.1 (CoA transferase superfamily enzyme), XP_001389413.1 (2-methylcitrate
dehydratase), XP_001400602.1 (MmgE_PrpD superfamily protein OahA class family) and
XP_001397447.1 (Glyoxalase domain containing protein 5)). Only fungal species harbouring
homologues of the itaconyl-CoA transferase A, a Protein Basic Local Alignment Search Tool (BLAST)
17
were considered as candidates to harbour an IA-degradation pathway and selected for a more
detailed analysis in which it was searched for homologues of the remaining enzymes.
Optimization of the use of Yp-ItcR biosensor to profile supernatants produced by IA-producing
yeast strains.
E. coli DH5 strains transformed with plasmid pEH086 were inoculated in mineral media (pH 7)
supplemented with 40 mM of glucose for 12 hours in Erlenmeyer flasks at 37ºC with orbital agitation
(250 rpm). After that, cells were re-inoculated at an OD640 of 0.5 in mineral media (pH 7)
supplemented with 40 mM of glucose and 10, 25 or 50 mM of IA and were cultivated for another 12
hours at 37ºC with orbital agitation (250 rpm). Afterwards, cells were centrifuged at 8000 rpm Sigma
2K15 for 5 minutes in order to pellet cells and then resuspended in mineral media (pH 7)
supplemented with 40 mM of glucose and left to recover for 3 or 6 hours at 37ºC under orbital agitation
(250 rpm). After that, 100 µL of these “IA sensing E. coli cells” prepared under the different conditions
were inoculated in 96-well microplates containing 100 µL of mineral media (pH 7) supplemented 40
mM of glucose and different concentrations of IA (0, 10, 25 and 50 mM). Microplates were incubated
at 37ºC for a period of 24 hours. In every two hours absorbance and fluorescence readings were
performed. Absorbance was measured at the wavelength of 640 nm (OD640nm), while fluorescence was
measured using the excitation filter F485/20 and the emission filter A595/8. The fluorescence data
was normalized for biomass.
18
Results and Discussion
Profiling of the use of Yersinia pseudotuberculosis itaconic acid biosensor for itaconic acid
detection
The work of Hanko et al. (2018) reported the use of the Yp-ItcR biosensor as a detection tool for IA,
however, the fact that the linear response was only observed for IA concentrations ranging from 0,07
and 0,7 mM and the saturation observed for concentrations above 2,5 mM, are limitations that could
be improved. As such, this work started by assessing the performance of the Yp-ItcR biosensor in E.
coli cells cultivated in a mineral media (different from the work of Hanko et al., 2018 that used the M9
minimal medium), the results obtained after exposing the “sensing” cells to increasing concentrations
of IA being shown in Figure 8
In the tested conditions, it is visible a linear increase in fluorescence in IA concentrations ranging from
0 to 2,5 mM. As described by Hanko et al., 2018, after 2.5 mM of IA the signal is saturated and the
DRC assumes almost an hyperbolic configuration. (Fig. 8 (A)). With this, the minimum detection
threshold can be set as the minimum IA concentration tested, 1 mM. It is important to notice that
these parameters (Yp-itcR biosensor operational range and minimum detection threshold) can only be
perceived when the biosensor is incubated for a minimum of 8 hours in the different IA concentrations
(being valid for 8 hpi, 12 hpi and 24 hpi) (Fig. 8 (A)), for biosensor activation took approximately 4 to 5
Figure 8. Kinetics and dynamics of the Yp-ItcR biosensor along cultivation of E. coli cells in MM medium (at pH 7) transformed with plasmid pEH086. Dose response-curve between the inducer (IA) concentration and the fluorescence output 4, 8, 12 and 24 hours post-induction (hpi) (A); Absolute normalized fluorescence of E. coli DH5 exposed to increasing IA concentration (B).
A
B
19
hours after the addition of IA (Fig 8 (B)). The Yp-itcR biosensor activation was faster for higher IA
concentrations, what is in line with the presumed function of itcR as IA-responsive transcriptional
activator. These results show that the Yp-itcR biosensor can be used to screen the S. cerevisiae
culture supernatants in a quantitative framework, since the IA titers produced (below 1,3 mM) are
within the range of concentrations in which the biosensor exhibits a linear response. The sensor could
also be used for preliminary screenings in a qualitative manner (that is, it can be used for comparative
analysis but not for concentration estimation based on the fluorescence values) of strains capable of
producing higher IA titers, such as P. kudriavzevii and Y. lypolytica, in this way reducing the number of
strains to interrogate through analytical methods. A noticeable difference between these results and
those of work Hanko et al. (2018) was that the response of the biosensor herein observed was slower
which can be attributtled to the different strains (we have used E. coli DH5 and the authors of the other
study used E. coli MG1655) as well as to the growth media used (we have used mineral media
supplemented with glucose while M9 minimal media was used in the study of Hanko et al.(2018)).
Having established the functionality of the Yp-ItcR biosensor for IA detection, a second step was to try
to utilize this biosensor in fermentations undertaken by different yeast strains. For that we have aimed
at optimizing a protocol that could be used with that aim. The envisaged approach was based on the
work of Zheng et al. (2018), which describes the “one-pot two-strains” system. This one-pot two-strain
system was composed of one producing strain for the target compound and a sensing strain, which
was equipped with a TF-based biosensor responsive to the same target compound. The experimental
setup that was tested is briefly represented in Figure 9 and it contains three stages: i) a growth stage
where the IA-sensing E. coli cells (those transformed with the pEH086 plasmid) are cultivated in
mineral medium; ii) an induction stage where the cultivated E. coli cells are induced by being exposed
to IA; iii) a recovery step in mineral media which has the goal of bringing the fluorescence back to its
basal levels . Three different concentrations of IA were tested in the induction step and two time points
of recovery. After these steps, the cells were finally transferred for 96-microwell plates where they
were exposed to different concentrations of IA (mimicking culture supernatants).
Figure 9. Experimental set-up created to test the IA YP-ItcR biosensor.
OD640 = 0.5
25 mM ITA
50 mM ITA
10 mM ITA
0, 10, 25, 50 mM ITA
Induction (12 h) Recovery (3 or 6 h) Growing (12 h)
20
In Figure 10 can be found the results concerning the results obtained with those experimental
conditions found to be more favourable which were: 12-hours growing stage followed by a 12-hour
induction stage in media supplemented with 25 mM of IA and a recovery stage of 3 hours and 12-
hours growing stage followed by a 12-hour induction stage in media supplemented with 50 mM of IA
and a recovery stage of 3 hours. The results of the conditions with an induction with 10 mM and a 3-
hour recovery stage and a 6-hour recovery stage can be found on Appendixes 1 and 2, respectively.
Comparing with the initial biosensor testing (Fig. 8), biosensor activation using this experimental set-
up was reduce by half, taking approximately 2 hours. Concerning the DRCs on Figure 10, as IA
concentration increases, it is observed a rapid increase in fluorescence, which gradually slows down
until the point in which the biosensor becomes saturated. The linear range of the biosensor seems to
expand as higher IA concentrations are used in the induction stage, what validates the ability of Yp-
ItcR to sense IA. Nonetheless, to confirmed if the linear range as in fact expanded, the experiment
must be repeated using a wider range of IA concentrations, in particular concentrations in which the
biosensor exhibits a linear range (0,07 to 0,7).
Based on these preliminary results the best set-up to employ a one-pot two strain methodology in
these conditions is to have a 12-hours growing stage followed by a 12-hour induction stage in media
supplemented with 50 mM of IA and a recovery stage of 3 hours. After the recovery stage, cells must
be inoculated in a microplate and left to grow for 8 hours, being at that time performed fluorescence
and OD640nm readings performed. This set-up would allow a qualitative analysis of enzymatic assays
A
B
Figure 10. Biosensor output for the One-pot two-strain assay with a 3-hour recovery stage. Absolute normalized fluorescence of E. coli DH5 harbouring pEH086 in response to different IA concentration (on the left) and respective DRC (on the right). First line corresponds to an induction stage with 25 mM of IA (A), second line corresponds to an induction stage with 50 mM of IA (B).
- 0 mM IA; - 10 mM IA; - 25 mM IA; - 50 mM IA;
- 4 hpi; - 8 hpi; - 12 hpi; - 24 hpi.
21
to fermentative processes (for titters below 2,5 mM) and a preliminary quantitative analysis of titers
above that value. The screening of the S. cerevisiae disruptome, of the mutant libraries (with variants
of Cad, Tad, IA transporters, among others) and the prospecting of new eukaryotic IA producers. The
experimental set-up envisaged for above mentioned processes is illustrated on Figure 11.
12 h
OD640 = 0.5
50 mM ITA
12 h
OD640nm and fluorescence measurements performed
after 8 h
3 h
Preparation of the sensing cells
E. coli DH5 cells harbouring the Yp-ItcR
Inoculation of the sensing cells (in mineral media) and of the
collected supernatants
Fermentative process
Supernatant collection
Preparation of the IA-producing cells
Eukaryotic IA-producing cells
Estimation of IA concentration
Figure 11. Experimental set-up created to estimate the IA titers of eukaryotic IA-producer using the YP-ItcR biosensor.
22
Towards modified Yp-ItcR-based biosensors using rational or random engineering strategies
focused on the ItcR-PccI promoter association
In the second part of this work it was attempted to obtain modified versions of the Yp-ItcR biosensor
focusing specifically the modulation of the ItcR-target promoter association. The first approach
involved the random mutagenesis of the promoter sequence aiming to identify mutants that could
show improved properties as compared to the wildtype version. Indeed, it has been shown that the
tunability of a biosensor can be improved by employing error-prone PCR strategies in the promoter
region (Mannan et al., 2017). With this in mind, the promotor Pccl from the Yp-ItcR system (see Fig. 5
(B)) was targeted for random mutagenesis using an error-prone PCR approach. Since there were no
cutting sites for restriction enzymes on both sides of the promotor, the strategy designed (briefly
represented in Figure 12) involved separate PCR amplifications of the promoter and of the vector
backbone which were subsequently assembled by Gibson assembly.
For that, an error prone PCR targeting the PccI promoter and aiming a low mutation rate was
performed using primers with 5’-end overhangs that were complementary to the vector backbone
(pEH086). The expected size of the DNA fragment amplified by PCR is 121 bp (91 base pairs of Pccl
plus 15 bp from each of the recombination tails). In Figure 13 (A) in each well (corresponding to the
different annealing temperatures tested) it is visible one band slightly below the 200 bp band of the
marker, being the bands more specific as the annealing temperature increases. The visible bands
correspond to the expected mutated versions of Pccl promoter. Seeing that the annealing temperature
of 55ºC was the better one, the products corresponding to this reaction were the ones used for further
cloning.
Pccl
Pccl
XXX
Pccl
Pccl
XXX
Error-prone PCR
PCR for Backbone linearization
Gibson Assembly
Figure 12. Experimental set-up created to produce mutated versions of the Yp-itcR biosensor. The promotor of the system, Pccl, was targeted for error-prone PCR, being posteriorly cloned into a linearized backbone (pEH086)
by Gibson assembly.
23
To linearize the vector backbone, primers that anneal on the flanking regions of Pccl were designed, in
order to obtain a pEH086 version without the original promotor. The expected size of the PCR product
was 5444 bp (that corresponds to the size of the original vector, 5535 bp, minus the 91 bp of Pccl).
The obtained bands are in between the third and fourth bands of the ladder (counting from the top)
that correspond to the 6000 bp and 5000 bp, respectively (Fig. 13 (B)), being safe to assume that the
PCR products have the correct size. In the first well there is the original pEH086, being the two bands
the coiled and supercoiled form of the plasmid. In vivo assembly of the mutated PccI promoters and of
the vector backbone was performed in E. coli cells. From two separate transformations, 112 colonies
were obtained. From these, the plasmid DNA of 6 random colonies was extracted and sampled in a
gel, to confirm the size of the constructs (Fig. 14).
Figure 14. DNA extracted from the random mutagenesis candidates. First well corresponds to the original vector non-linearized and the following wells correspond to the plasmids containing the mutated versions of Pccl.
Figure 13. PCR products from the Pccl amplification with different annealing temperatures. First well corresponds to the DNA ladder and the following four to the PCR reactions with an annealing temperature of 49, 51, 53 and 55 ºC, respectively (A). PCR products from the pEH086 linearization. First well corresponds to the original vector non-linearized and the second and third well to the linearized plasmid (B).
A B
24
The 112 E. coli colonies were profiled for their response to the presence of IA using a similar
experimental setup as the one described above. For this, E. coli cells harbouring the wildtype PccI
promoter (meaning cells harbouring the pEH086 plasmid) and the 112 colonies that are presumed to
contain mutated versions of PccI, were incubated in mineral media with glucose containing different
concentrations of IA (0, 25, 50 and 100 mM) for 24 hours, during which OD640nm and fluorescence
readings were performed hourly. In Figure 15 (A), it is shown the normalized fluorescence of the
mutant strains at 8 hours post-induction, in comparison with the one attained by the wildtype promoter.
The first aspect that emerges from this analysis is a significant heterogeneity in the fluorescence
values even when cells are not exposed to IA. Whether this variation result from alterations in the
promoter sequence that turn it more active or if it results from a non-specific effect is something that
requires further studies and namely a more exhaustive phenotyping of the collection with more
technical replicates to check for consistency. Nonetheless, to find the Pccl mutants with an improved
responsiveness to IA, their fluorescence was normalized to its basal levels (i.e. to the fluorescence
levels the mutants exhibit in medium without IA addition at 8 hpi), being selected those whose a 5-fold
increase in their fluorescence levels. From the 42 mutants obtained from this analysis, one candidate,
mutant 31, consistently showed increased fluorescence in the different assays that were performed
with the increasing concentrations of IA (this being highlighted in Figure 15 (B) as blue circles).
Similarly mutants exhibiting a poor responsiveness to IA (those with less than 1-fold increase in their
fluorescence levels) were regarded as interesting for they might contain mutations in the promoter
region that reduce either the affinity of the TF or of the transcriptional machinery to the promoter (Fig.
15 (C)), being selected a total of 16 mutants.
Figure 15. Populational analysis of the absolute normalized fluorescence for 8 hpi (A). Mutants exhibiting an improved response (B) or a poor response to IA (C) to IA at 8 hpi.
- wildtype; - mutants; - mutants with a consistent increase in their fluorescence levels as IA concentration
increases.
B C
A
25
As stated before, in those mutants with a 5-fold increase in their fluorescence, we additionally look for
those with increasing fluorescence measurements as the IA concentration increases. In Figure 16 is
shown the Dose-Response Curves (DRC) of mutant 31, the one candidate that matched both the
above mentioned criteria, in comparison to the wildtype strain. Mutant 31 may correspond to Yp-itcR
version with improved responsiveness to IA and an expanded dynamic range. The DRC of the
remaining mutants can be found on Appendix 3.
To understand the rationale behind the observed output change, further studies regarding the
molecular alterations the promoter sequence and the kinetics of the Yp-ItcR are required to profile the
biosensor response to IA. As such, biosensor phenotyping must be repeated with more biological
replicates to check for consistency and with a wider range of IA concentrations (namely in the range in
which biosensor exhibits linearity – 0,07 and 0,7 mM of IA) to access additional biosensor parameters,
such as operational range. The biosensor response to other compounds must also be tested to check
the biosensor specificity. Also, the sequencing of the promotor region will help pin-point the mutations
which contribute to an improved responsiveness to IA.
Figure 16. Dose-response curve for the wildtype and mutants 31. - wildtype strain, 8 hpi; - mutant strain, 8 hpi;
26
The second approach that was used to improve the performance of the Yp-ItcR biosensor was
focused on improving the association between the Yp-ItcR promoter and the PccI promoter in the wild-
type construct by targeting directly the nucleotides involved in this binding (e.g. replacing them by
better suited ones). This approach has been used before with success by Lale et al., 2011. However,
this approach is difficulted by the fact that the precise DNA binding sequence recognized by Yp-itcR in
the PccI promoter is not known and it is essential to perform a more efficient optimization. To help
mapping this Yp-itcR binding site an in sillico analysis was carried out aiming to identify conserved
regions in the promoter region present in other species also carrying the Yp-itcR regulator homologue.
This principle is linked to the idea that promoter regions evolve rapidly, however, binding sites are
frequently conserved and can therefore be identified based on their conservation in the promoter
region of homologous gene pairs (McGuire et al., 2000). ItcR proteins belong to the family of LysR-
type transcriptional regulators (LTTR) which is a group of structurally and functionally-related global
transcriptional regulators of single or operonic genes found in bacteria (Maddocks and Oyston, 2008).
LTTRs usually have 300 amino-acid residues, being the DNA-binding domain located in the N-terminal
region of the protein (which is highly conserved among bacteria), while the co-inducer-binding domain
locates in the C-terminal region, being responsible for the binding of small effector molecules
(Maddocks and Oyston, 2008; Alanazi et al., 2013). Although the DNA target sequence of LTTRs has
not been mapped at the nucleotide level, it is known that is composed by two portions: a Recognition
Binding Site (RBS) and an Activation Binding Site (ABS). While the ABS is located near the -35 RNA
polymerase binding site of the target gene and is not specific (thus rendering its identification more
difficult), the RBS is known to often correspond to a dyadic sequence (having around 15-bp and
exhibiting a general conserved T-N11-A motif) centred near the -65 region of the regulated gene
(Schell, 1993). However, this motif can vary in both base pair composition and length (Maddocks and
Oyston, 2008). A BLAST analysis using Yp-ItcR was performed in order to find homologues of this
regulator within the Yersinia genus and in Gram Negative bacteria. A high degree of conservation was
found along the entire amino acid sequence, with stronger emphasis on the C-terminal region of the
protein which includes the presumed co-inducer domain (Fig. 17).
The structural conservation found on the N-terminal region of these itcR proteins, that harbours the
DNA-binding domain, makes it conceivable to hypothesize that they may recognize similar DNA
sequences and strongly supports the existence of a common DNA-binding motif in the promoter of the
target-genes. Further details on this comparative analysis of the Yp-ItcR proteins will be provided in
section ‘’Bioinformatic analysis of IA degradation operons among bacteria’’. A comparative analysis of
the promoter region of the PccI gene that was found in these species harbouring a Yp-ItcR-like
regulator was performed and the results are shown in Figure 18.
27
Fig
ure
17.
Alig
nm
ent
of
the
Itc
R f
rom
Y.
pseu
do
tub
erc
ulo
sis
an
d t
he h
its f
rom
th
e P
rote
in B
LA
ST
. B
lue
sh
ad
es r
ep
rese
nt
am
ino a
cid
con
se
rva
tio
n.
Qua
lity,
occu
pan
cy a
nd
th
e
co
nse
nsu
s s
eq
ue
nce
can
be
fo
un
d in
th
e in
ferio
r p
art
of th
e fig
ure
.
28
The comparative analysis of the PccI promoter in the different species revealed a high conservation
rendering difficult a clear identification of what can be the ItcR binding site. To help this identification a
motif search was performed considering the proposed structure for binding sites of these regulators
(TN{5,15}A) (Appendix 4) and also the need of it being a dyad motif (Appendix 5). From this analysis
three putative candidate to serve as binding sites for Yp-itcR were identified these being highlighted in
Figure 19.
Figure 19. Candidate binding sites for Yp-ItcR based on the in silico comparative analysis (A) In grey are highlighted the -10 box and -35 box from the first gene of the operon (Ccl). Nucleotides targeted for a site-directed mutagenesis approach to further confirm their function as binding sites – described as Mut1, Mut2, Mut3 and Mut4 (B). (adapted from Hanko et al., 2018).
Figure 18. Alignment of the itcR intergenic region from Y. pseudotuberculosis YPIII (highlighted in bold) and the intergenic regions from the Protein BLAST hits (after filtering the results). Blue shades represent nucleotide conservation. In red are highlighted the presumable -10 box and -35 box from the first gene of the IA degradation operon.
GG
Mut1
GG
Mut2
GGG
Mut3
AAA
Mut
B
A
29
The site-directed mutagenesis designed was based on the use of mutagenic PCR reactions using long
mutagenic primers (with approximately 45 bp) containing the mutated nucleotides in the middle.
Despite numerous attempts performed under different experimental conditions (different annealing
temperatures, different reaction times with the DpnI enzyme) no positive candidates harbouring the
envisaged mutations were obtained.
As such, a new strategy was conceived making use as Phusion and of mutagenic primers harbouring
the mutated nucleotides in a 5’-end tail of the primer (Fig. 20). The employment of this strategy
already lead to potential candidates, waiting to be confirmed (results not shown).
X
X Site-directed mutagenesis PCR
Figure 20. Site-directed mutagenesis experimental set-up: created to produce point mutations on, Pccl, of the Yp-itcR biosensor.
30
Functionality of ItcR in itaconic acid-degradation in Pseudomonas aeruginosa
As said in the “introduction to the theme of the thesis” section, the work of Hanko et al. (2018) has
explored the Yp-ItcR biosensor but also a similar system coming from P. aeruginosa. This follows the
work of Sasikaran et al., (2014) that demonstrated that P. aeruginosa cells are also able to use
itaconic acid as the sole carbon source using an enzymatic system similar to the one described in Y.
pestis or Y. pseudotuberculosis. Furthermore, the IA degradation operon from P. aeruginosa, shown in
Figure 21, is organized differently from what is described in Y. pseudotuberculosis, being composed of
three additional genes, an acyl-CoA dehydrogenase, a MmgE/PrpD family protein and a glyoxalase
family protein, whose function in IA catabolism is unknown.
Also as said above, it is possible that the results presented by Hanko et al., (2018) exploring the Pa-
ItcR biosensor in C. necator (because in E. coli the experiment failed) may be influenced by this
species having an endogenous ItcR regulator. In this sense, in this work we have started by examining
whether or not this regulator would be required for growth on itaconic acid of P. aeruginosa, since this
was not previously demonstrated. To do this, we resorted to the use of a strain deleted for the itcR
gene and compared growth of this strain with the one from the parental P. aeruginosa PAO strain
(wildtype) (Fig. 22). We have also profiled growth of a strain devoid of the PA0883 (ccl) gene, for
Sasikaran et al., 2014 had already reported the inability of this mutant to grow on IA. For this, the
strains were inoculated in 96-microwell plates containing mineral media (pH 7) supplemented with 40
mM of either glucose (as a control) or IA and grown for 48 hours at 37ºC.
0
PA0877
ItcR
PA0878
Ich
PA0883
Ccl
PA0882
Ict
PA0879
Acyl-CoA
dehydrogenase
PA0881
MmgE/PrpD
PA0880
Glyoxalase
Figure 21. Schematic representation of the IA degradation operon found in Pseudomonas aeruginosa PAO. Gene names are highlighted in bold and gene products can be found bellow the gene name. The three enzymes common to Y. pestis/Y. pseudotuberculosis operon are itaconyl-CoA hydratase (Ich), itaconate-CoA transferase
(ict) and (S)-citramalyl CoA-lyase (ccl).
31
The results obtained show that deletion of ItcR or of the CcI enzyme drastically abolished growth of P.
aeruginosa in media having IA as the sole carbon source. As said above, the growth defect of the Δccl
strain was expected since it had been reported before in the study of Sasikaran et al., 2014, but this is
the first demonstration that the itcR regulator is also essential for IA degradation process. Further
monitorization of the growth curve of the three strains (WT, ΔitcR and Δccl) in shake flasks confirmed
the growth defect of the ΔitcR and Δccl strains, however, it has to be pointed out that these strains
were able to resume exponential growth after a delay of around 24h (Fig. 23 (A)). The growth curves
obtained were consistent with the profiling of the concentration of itaconic acid in the culture broth,
with the wildtype cells rapidly exhausting the IA present, while in the two mutants no significant
differences in the IA concentration was observed along the entire time the experiment took place (Fig.
23 (B)). The fact that the ΔitcR and Δccl mutants are showing growth but no consumption of itaconic
acid leads to the hypothesis that other nutrients present in the medium could be mobilized for growth,
out of which the yeast extract emerges as the most likely candidate. This hypothesis was further
supported by the observation that three strains are able to grow in the same medium without
supplementation of glucose or itaconic acid (Appendix 7), something that was also observed in the
work of Sasikaran et al. (2014).
A B
Glucose IA
** **
Figure 22. P. aeruginosa growth represented by OD640nm on 0 and 48 hours of culture growing in either 40 mM of glucose or 40 mM of IA. One-way ANOVA was performed between the groups growing for 48 hours in IA, being the stars (**) representative of a significant difference observed between the mutants and the wildtype.
Figure 23. P. aeruginosa growth in 40 mM of IA represented by OD640nm (A) and IA concentration in the culture
supernatant over 48 hours and 7 day of culture (B). - wildtype; - ΔitcR; - Δccl.
32
To further confirm the role of itcR as a positive regulator of the IA degradation operon, a quantitative
RT-PCR was carried out. For this, wild-type and itcR mutant strains were cultivated in 250 mL
Erlenmeyer flasks in media containing IA as the sole carbon source. After 2 and 4 hours of cultivation,
total RNA of the cultures was extracted and quantitative RT-PCR was performed to measure the
transcript levels of the ccI gene (Fig. 24). The results obtained confirmed the anticipated up-regulation
of ccl during growth on itaconic acid in an itcR-dependent manner (Fig. 24).
These results support the utilization of ItcR from P. aeruginosa as an IA-sensing element. In the work
of Hanko et al., (2018) the authors tried to pursue this idea however, the Pa-ItcR system was only
functional in C. necator and failed in E. coli cells. Significantly, C. necator also harbours an ItcR-like
regulator (with up to 50% identity) thus leaving open whether the responses observed by Hanko et al.,
(2018) are attributable to this endogenous regulator and not to the Pa-ItcR. The lack of functionality in
E. coli cells could be attributable to the non-optimization of codons that might have resulted in
introduction of rare codons that are difficult to translate and lead to translation hampering. Indeed, a
closer analysis of the Pa-ItcR coding sequence used by Hanko et el., (2018) for heterologous
expression in E. coli, revealed that the codon usage in P. aeruginosa and E. coli (Appendix 8) differs
by 44.67%. Additionally, we verify that the Pa-ItcR contains 9 codons that are likely to hamper its
expression in E. coli (Appendix 9). Having proved that the Pa-ItcR is responsive to IA, we decided to
perform a codon optimization of the itcR from P. aeruginosa for E. coli expression. The redefinition of
this system is essential to have a functional itcR that could be used as IA-sensing element in a
biosensor. It is possible that the Pa-ItcR sensor might have different properties than the one described
for Yp-itcR, since the homology between these two regulators is limited (22% identity) and mostly
concentrated at the level of the DNA binding domain (Appendix 10). In fact, the limited similarity in the
C-terminal region of the proteins was surprising considering that this is the region that includes the
itaconic acid-sensing domain and that both Pa-ItcR and Yp-itcR appear to “sense” itaconic acid.
Figure 24. Relative expression of P. aeruginosa PA0883 gene using the wildtype (WT) strain grown for 2 hours in glucose as basal condition. Values were normalized using rpoS gene.
IA Glucose
(2h)
IA
(4h)
33
As said above, the organization of the itaconic acid degradation operon in P. aeruginosa (Fig. 21)
includes 6 enzymes, besides the three core enzymes that are common to Y. pseudotuberculosis (ich,
ict and ccI), it contains other three enzymes that presumably do not have a primary role in IA
degradation (acyl-CoA dehydrogenase, glyoxalase family protein and MmgE/PrpD family protein). We
have also monitored whether these enzymes would be required for itaconic acid degradation. So, P.
aeruginosa wild type strain and mutants deleted for the 6 enzymes composing the operon were grown
in mineral media (pH7) with IA as sole carbon source (Fig. 25).
Concerning the mutants deleted for the three core enzymes ich, ict and cci, it was expected that they
would not consume IA, for they anticipated sequential role in the degradation pathway in bacteria
(Sasikaran et al., 2014). Indeed, the IA concentration from the supernatants obtained from cultures
from the mutant strains suffered little variations in comparison to the wildtype strain (that reaches 0
mM at 8 hours of culture). For the Δich and Δccl after 6 hours of culture, an unknown peak (with a
retention time of 16 minutes) which was not visible in the supernatant obtained from wild-type cells,
was found to accumulate (Appendix 12). This unknown peak is likely to correspond to the first or
second intermediate species of the pathway, itaconyl-CoA or (S)-citramalyl-CoA, since Δich and Δccl
catalyse the second and third step of the degradation pathway, respectively. Both compounds are
visible in the UV-HPLC, as previously reported by Sasikaran et al. (2014), however without a proper
standard solution to confirm it is difficult to be sure which of the molecules might be. Surprisingly, the
Δict mutant (mutant devoid of the enzyme that catalyses de first reaction of the pathway) shows a
slight decrease in IA concentration over time and the accumulation of this metabolite discussed above
(corresponding to the unknown peak) also accumulated after 6 hours of culture. Since mutants devoid
of this enzyme are theoretically unable to activate IA and carry on with the pathway, this decrease
may be the result activity of another enzyme that could catabolise IA less specifically. In fact, it has
B A
Figure 25. P. aeruginosa mutants represented by IA concentration in the culture supernatant over 24 hours. P. aeruginosa mutants for the core enzymes (A) and for the ‘’additional’’ enzymes (B).
- wildtype; - Δich; - Δict; - Δccl. - ΔPA0879; - ΔPA0880; - ΔPA0881;
34
been described for some Pseudomonas species that succinyl-CoA synthetase can unspecifically
convert itaconate into itaconyl-CoA (Cooper and Kornberg, 1964).
In what concerns to the ΔPA0879 (devoid of the acyl-CoA dehydrogenase), ΔPA0880 (devoid of the
glyoxalase family protein) and ΔPA0881 (devoid of the MmgE/PrpD family protein) mutants, it was
expected that they would be able to fully consume IA since the enzymes lacking in these strains are
not reported to act directly in itaconic degradation. In fact, the role attributed to these three enzymes
was the channelling of additional substrates to the IA degradation pathway (Hanko et al., 2018).
However, this hypothesis was not confirmed since only the ΔPA0880 mutant displayed metabolization
of IA similar to the one observed in the wildtype strain. For unknown reasons both ΔPA0879 and
ΔPA0881 are unable to fully metabolize IA. The IA concentration in the culture supernatant of
ΔPA0881 strain suffered a subtle decrease, being reported the presence of the ‘’unknown’’ peak after
6 hours, meaning that IA can be metabolized by this strain to a certain extent. As for the ΔPA0879
strain, the IA concentration in the culture supernatants remained constant and no ‘’unknown peak’’
was observed in chromatograms of this strain, meaning that it is not able to degrade IA. The inability
of both ΔPA0879 and ΔPA0881 strains to completely degrade IA raises the hypothesis of an
alternative pathway to IA degradation in P. aeruginosa, different from the one found in Y.
pseudotuberculosis. Enzymatic assays are required to properly understand the role of these three
‘’additional’’ enzymes in IA degradation.
35
Bioinformatic analysis of itaconic acid degradation operons among bacteria
The bioinformatic analyses performed until so far, either using the Yp-itcR or the Pa-ItcR revealed that
this regulator is present in several bacterial species (Sasikaran et al., 2014) as well as the enzymes
required for degradation of IA This is a relevant information from the biosensor engineering point of
view because it provides new possibilities for other IA sensing elements. The limited similarity of the
Pa-ItcR and Yp-ItcR along with the different organization of the itaconic acid degradation operon in Y.
pseudotuberculosis and in P. aeruginosa (compare the schematic representation of the operons in
Figure 26) support the idea that these systems had a different evolutive path, something that was also
suggested in the work of Sasikaran et al., (2014).
To further understand the distribution of the IA degradation system across Gram negative bacteria, a
BLASTP analysis over bacterial genomes available at NCBI was performed using as targets Yp-itcR
and Pa-ItcR. 56 Yp-itcR homologues and 55 Pa-ItcR homologues were identified (using the criteria
defined in materials and methods and based on their ranking score). A phylogenetic analysis clearly
showed that the Yp-itcR- and Pa-ItcR- like proteins can be clustered in two different groups (Fig. 27).
Below it is provided a more detailed analysis of the results obtained over the analysis of the
distribution the Yp-itcR- and Pa-ItcR-like dependent systems.
0
PA0877
ItcR
PA0878
Ich
PA0883
Ccl
PA0882
Ict
PA0879
Acyl-CoA
dehydrogenase
PA0881
MmgE/PrpD
PA0880
Glyoxalase
YP_2265
ItcR
YP_2267
Ich
YP_226
Ccl
YP_2268
Ict
(A)
(B)
Figure 26. Schematic representation of the IA degradation operons in Y. pseudotuberculosis (A) and P. aeruginosa (B). Gene names are highlighted in bold and gene products can be found bellow the gene name.
36
Figure 27. Phylogenetic Tree (Average Distance model) containing all the Yp-ItcR homologues (highlighted in blue) and the Pa-ItcR homologues (highlighted in orange) that resulted from the Protein BLAST analysis.
Yp-ItcR homologues Pa-ItcR homologues
37
Yersinia-like itaconic acid degradation operon
In the search for the Yp-ItcR homologues, 56 gram-negative bacteria strains were found, 12 of which
belonging to the Yersinia genus. From all strains harbouring an Yp-ItcR homologue, 25 are known
pathogens, including the Yersinia sp. and Salmonella sp. (specially S. enterica) as previously reported
by Sasikaran et al., 2014. Among the 56 gram-negative bacteria species that were found to encode an
homologue of Yp-itcR, 36 were found to harbour in their genome an Yersinia-like IA degradation
operon, that is, a system in which the ItcR gene is followed by three-gene cluster containing the ccl,
ich and ict enzymes (Fig. 28).
In the vast majority of the cases, the species that harboured an Yp-ItcR homologue displayed an
organization of the IA degradation operon similar to the one described in Yersinia sp., only changing
the orientation of the operon in their genome. Slight variations were observed in the extension of the
intergenic region. Nonetheless, for the three Pseudomonas species that possess an YP-ItcR
homologue, the operon had a different composition, it was composed of four genes (instead of three)
a ccl, another transcriptional regulator (belonging to the LTTR family), an ict and an acyl-CoA
dehydrogenase. The gene encoding the LTTR was found to be in an opposite orientation of the
remaining genes. In Table 10 and in Figure 29 can be found the data regarding the structural analysis
of the genetic elements belonging to the studied operons.
Table 10. General parameters of the elements belonging to the analysed Yersinia-like operons. Retention rate, Identity and similarity of the gene products belonging to the operon and length of the intergenic region. * - Exceptionally, the Pseudomonas species possessed ict homologues with lower identities and similarities than the one presented in the table, of 27 and 56 respectively.
Operon elements General parameters of the operon elements
Conservation (%) Identity (%) Similarity (%) Length (bp)
ItcR 100 99 - 55 100 - 86 -
Intergenic Region - - - 74 - 213
ict 55 100 - 75 * 100 - 80 * -
ich 48 100 - 85 100 - 90 -
ccl 54 100 - 51 100 - 69 -
YP_2265
ItcR
YP_2267
Ich
YP_2266
Ccl
YP_2268
Ict
Figure 28. Schematic representation of the IA degradation operons in Y. pseudotuberculosis. Gene names are highlighted in bold and gene products can be found bellow the gene name.
38
Identities Similarities
Figure 29. The identities and similarities of the enzymes belonging to the Yersinia-like IA operon are illustrated in the Heat Map following the colour scheme above. The first line concerns the reference strain. Enzymes are ordered according to their role in the IA degradation pathway rather than its position in the genome. Genes that are not connected to the operon are marked in grey. The red dots indicate species which are known to be pathogenic to human.
39
The enzymes ich and ccl, responsible for the first and last step in the IA degradation pathway,
respectively, possess the highest conservation being found in 55 and 54 % of the species examined.
The ict enzyme shows a lower conservation only being present in 48% of the species examined,
although the conservation is observed to occur at higher identities/similarities than the one observed
for the other two enzymes. On the overall, out of the 56 analysed species, 27 were found to harbour a
complete three-genes operon with a strong similarity among the enzymes, being reasonable to
assume all these 27 species are able to metabolize IA into pyruvate and acetyl-CoA. Around 81% of
these 27 species, however, this operon was also found in five species are not human pathogens
including Bradyrhizobium sp., Rhizobiales sp., Pseudomonas sp., Paraburkholderia sp. and
Granulibacter sp., including two (Bradyrhizobium erythrophlei and Rhizobiales bacterium) that are
known to have an environmental origin. This observation puts in question the biological reasoning of
the idea that bacteria harbour the IA degradation operon only to counteract the deleterious effects of
macrophage’s activity, leaving open whether there are other niches where IA can be found and its
catabolism favourable to increase competitiveness.
Pseudomonas-like itaconic acid degradation operon
An IA-degradation operon similar to the one found in P. aeruginosa was found in 55 Gram-negative
species, 29 of which belong to the Pseudomonas genus. From the total of analysed species only 19
are known pathogens, being clearer here the above mentioned aspect that IA degradation may not be
an exclusive of human pathogenic species Among the environmental species that were found to
include an IA degradation operon similar to the one of P. aeruginosa are Azotobacter spp.,
Achromobacter spp. and Halomonas spp.
The operon configurations found in species having a Pa-ItcR-like operon were very diverse, showing
at least 5 different configurations as detailed in Figure 30. An interesting aspect that emerged from this
analysis was the observation that frequently the operon included a transposase in what should be the
place of ich, with a consequent increase of the intergenic region between the genes encoding ItcR and
the first gene of the operon (Fig. 30).
When moving away from the Pseudomonas genus more complex forms of the IA operon start to
appear, with no fixed order of the genes integrating the operon and with incomplete and interrupted
operons.
40
In Table 11 is presented the general parameters of the operon elements analysed in the species
containing a Pa-ItcR homologue. In the Figures 31 and 32 can be found the structural analysis of the
genetic elements belonging Pseudomonas genus and in other gram negative bacteria, respectively.
Table 11. General parameters of the elements belonging to the analysed Pseudomonas-like operons. Retention rate, Identity and similarity of the gene products belonging to the operon and length of the intergenic region. * - The lowest retention rated for acyl-CoA dehydrogenase were found when the enzyme suffered duplication events in the genomes.
Operon Elements General parameters of the operon elements
Conservation (%) Identity (%) Similarity (%) Length (bp)
ItcR 100 88 - 60 94 - 79 -
Intergenic Region - - - 22 - 429
ict 95 82 - 69 90 - 78 -
ich 16 71 - 59 80 - 70 -
ccl 93 73 - 31 81 - 44 -
acyl-CoA dehydrogenase 78 95 - 21 * 98 - 42 * -
glyoxalase 16 81 - 63 87 - 69 -
MmgE/PrpD 65 79 -71 87 - 78 -
ItcR Ich Ccl Ict Acyl-CoA
dehydrogenase
MmgE/PrpD
Glyoxalase
ItcR Transposase Ccl Ict Acyl-CoA
dehydrogenase
MmgE/PrpD
ItcR Ich Ccl Ict Acyl-CoA
dehydrogenase
MmgE/PrpD
ItcR Transposase Ccl Ict Acyl-CoA
dehydrogenase
MmgE/PrpD HP
A
B
C
D
E
ItcR Transposase Ccl Ict Acyl-CoA
dehydrogenase
MmgE/PrpD
Glyoxalase
Figure 30. Schematic representation of the IA degradation operons found within the Pseudomonas genus: (A) P. aeruginosa PA0; (B) P. tolaasii PMS117; (C) P. fluorescens AHK-1; (D) P. stutzeri DNSP21, P. balearica DSM6083, P. kunmingensis DSM25974, P. xanthomarina S11, P. kuykendallii NRRLB-59562 and P. citronellolis S2_009_000_R2; (E) P. jinjuensis JCM21621, P. benzenivorans DSM8628, P. marincola JCM14761, P. lutea DSM17257, P. graminis DSM11363, P. abietaniphila ATCC700689, P. simiae 2-36, P. synxantha NBRC3913, P. poae MYb114, P. paralactis DSM29164, P. fuscovaginae IRRI6609, P. syringae GR12-2, P. gessardii BS2982, P. batumici UCMB-321, P. mendocina NEB698, P. agarici NCPPB2472, P. bohemica IA19, P. taetrolens
NCTC10697, P. protegens CHA0, P. veronii 1YB2 and P. lurida LMG21995;
41
Identities Similarities
Figure 31. The identities and similarities of the enzymes belonging to the Pseudomonas-like IA operon (from Pseudomonas genus) are illustrated in the Heat Map following the colour scheme above. The first line concerns the reference strain. Enzymes are ordered according to their role in the IA degradation pathway rather than its position in the genome. The position of the last three enzymes in the heat map is random since its role in the IA catabolism is unknown. Genes that are not connected to the operon are marked in grey. The red dots indicate species which are known to be pathogenic to human.
42
Identities Similarities
Figure 32. The identities and similarities of the enzymes belonging to the Pseudomonas-like IA operon (from gram-negative bacteria) are illustrated in the Heat Map following the colour scheme above. The first line concerns the reference strain. Enzymes are ordered according to their role in the IA degradation pathway rather than its position in the genome. The position of the last three enzymes in the heat map is random since its role in the IA catabolism is unknown. Genes that are not connected to the operon are marked in grey. The red dots indicate species which are known to be pathogenic to human.
43
It was expected that the three core enzymes (ict, ich and ccl) would be conserved since they are
considered essential to degrade IA. The results obtained with the in silico analysis confirmed a higher
conservation of ict, however, Ich was not significantly conserved among the species examined (Table
11). The ‘’additional’’ enzyme acyl-CoA dehydrogenase has a relatively high conservation and
similarity (even higher than the ones found for the core enzymes). Inclusively, 7 hits harbour two acyl-
CoA dehydrogenases (Fig. 32), in which the first seems to have suffered an incomplete duplication
event, for it presents lower query cover and identitiy/similarity vallues than the second.
Only 8 species (out of 55) have an active IA degradation operon and are able to degrade IA. Even so,
the presence of the Pa-ItcR in the genomes without the presence of IA catabolism-related genes
raises the hypothesis of existing an alternative pathway to degrade the compound, or at least
suggests a different genomic organization for the IA degradation genes in gram negative bacteria
harbouring a PA-ItcR homologue.
An important aspect that emerged from the analysis was the lack of any correlation between species
that have a full IA-degradation operon and their pathogenic potential. From a total of 13 species
harbouring a Pa-ItcR operon, only 6 correspond to known human pathogens with Azoctobacter sp.,
Pseudomonales sp., Pigmentiphaga sp., Parabulkholderia sp., Pseudogulbenkiana sp., Cupriavids sp.
and Halomonas sp. having no ecological association to human colonization. Furthermore, the majority
of the species that possess a PA-ItcR homologue (and some homologues of the enzymes responsible
for IA degradation) are environmental isolates.
44
Bioinformatic analysis of itaconic acid degradation pathways in Fungi
An IA degradation pathway, with similarity to the one described in Y. pseudotuberculosis and P.
aeruginosa, was recently identified in A. niger (Fig. 33) (Hossain et al., 2019). Through a
transcriptomic analysis Hossain et al. (2019) identified 18 differently expressed genes linked with IA
biosynthesis and degradation in A. niger including an itaconyl-CoA transferase A (ictA), an itaconyl-
CoA hydratase A (ichA) and citramalate-CoA lyase A (cclA) (homologues to the three core bacterial
enzymes ict, ich and ccl, respectively). This was highly surprising since it was the first description that
biosynthesis of itaconic acid in A. niger led to the induction of an itaconate degradation pathway.
Notably, deletion of the IA degradation genes, ictA and ichA, led to the increase of the IA final titers in
engineered A. niger, therefore confirming that these genes are involved in IA catabolism (Hossain et
al., 2019).
In this context, an in sillico analysis was performed to understand whether this itaconic acid-
conservation pathway would be conserved across Fungi, starting from species belonging to the
Aspergillus genus, and afterwards considering other all-available fungal species outside of this genus.
Due to the extensive number of species involved in this analysis, a filtering criterion was applied only
selecting for further analysis started species harbouring an ictA homologue resulting in 85 species
confirmed to harbour an IctA homologue, 45 belonging to the Aspergillus genus. These species were
afterwards searched for homologues of ichA, cclA, CoA transferase superfamily enzyme (homologues
of the bacterial acyl-CoA dehydrogenase), 2-methylcitrate dehydratase and MmgE_PrpD superfamily
protein OahA class family (homologues of the bacterial MmgE/PrpD family protein) and a glyoxalase
domain-containing protein 5 (homologues of the bacterial glyoxalase family protein). In Table 12 are
presented the results of the analysis, being the particular analysis of the Aspergillus genus and to the
other fungal species represented in the Figures 34 and 35, respectively.
Table 12. General parameters of the structural analysis of fungal species harbouring a itcA homologue. Retention rate, Identity and similarity of the gene products belonging to the IA catabolism.
Genetic Elements General parameters of the genetic elements
Conservation (%) Identity (%) Similarity (%)
ict 100 100 - 70 100 - 79
ich 100 99 - 29 100 - 45
ccl 100 99 - 58 100 - 74
CoA transferase 100 100 - 35 100 - 52
2-Methyl citrate dehydratase 100 100 - 47 100 - 62
MmgE/PrpD 96 100 -21 100 - 41
glyoxalase 88 98 - 27 99 - 42
ANI_1_1432064 ANI_1_2118064 ANI_1_1156014
Figure 33. Itaconate degradation pathway in fungi. Gene names are highlighted in bold below gene products (adapted from Hossain et al., 2019)
45
Similarities Identities
Figure 34. The identities and similarities of the A. niger homologue genes (Aspergillus genus) are illustrated in the Heat Map following the colour scheme above.
46
Similarities Identities
Figure 35. The identities and similarities of the A. niger homologue genes (non-Aspergillus genus) are illustrated in the Heat Map following the colour scheme above.
47
Since all the analysed species (both from in and out of Aspergillus genus) retain the three-core
enzymes with high structural conservation, it is conceivable that their IA degradation pathway is
functional and that they are all able to degrade IA. The four ‘’additional’’ enzymes may also play a part
in an alternative IA degradation pathway, similarly to what occurs in P. aeruginosa, for in a general
manner the retention rates and structural conservation levels were very high. The high conservation of
the pathway raises the possibility of existing a transcriptional regulator homologous to ItcR found in
bacteria that controls the coordinated expression of all these genes and that could also serve as
interesting IA-biosensor.
The proposed biological function of this degradation pathway in fungi was to be a defense mechanism
during biological warfare, especially since A. niger and A. terreus share the same habitats and have to
fight over limited resources (Hossain et al., 2019). Data regarding the isolation source and natural
habitat of each species would be useful in this context to validate this hypothesis, however no
database was found with this information. Nonetheless, being A. terreus and U. maydis the only
known IA producers, it is highly unlikely that all of the species present in this analysis live in habitats
where IA is broadly produced. Therefore, the reason behind the maintenance of this system in fungi
must be more than being a defense mechanism during biological warfare
Similarly, to what is presumed for Y. pseudotuberculosis and P. aeruginosa by Sasikaran et al. (2014),
the biological function of this operon might be to be a pathogenicity determinant, yet there are no
available pathogenicity data for the species.
Despite the biological function attributed to the IA degradation mechanism, it is questionable that
distant species from bacteria and fungi have such similar mechanisms to respond to different stimulus
(for example persistence factor/pathogenicity determinant to respond to macrophages during infection
in the case of Y. pseudotuberculosis and P. aeruginosa and biological warfare in the case of A. niger).
A hypothesis that would explain this is the response of the system to another molecule structuraly
related to IA that plays a more basal function in metabolism (possibly central metabolism-related), as it
will be further discussed.
48
Could there be other substrates triggering the itaconic acid-degradation operons?
The fact that we could observe widespread distribution of itaconic acid-degradation pathways in many
bacterial species and in fungal species whose ecology is not consistent with the presence of itaconic
acid in the environment prompted us to raise the possibility that other molecules could actually be the
main substrates of these identified degradation pathways. With that in mind, an in sillico analysis was
performed to find compounds present in microbial metabolism that could have a high similarity with IA.
This search resulted in the identification of 159 metabolites exhibiting similarity (55% or above) with
IA. The top 10 compounds exhibiting the highest similarity with IA can be found in Table 13. Some of
these metabolites (like succinate. Fumarate and oxaloacetate) were found not to induce the Yp-itcR-
system by Hanko et al., 2018.
Table 13. Top-10 compounds exhibiting the highest structural similarity with IA.
Compound Similarity
Methylitaconate 0,87
Succinate 0,83
Citramalate 0,81
(R)-2-Methylmalate 0,81
(S)-2-Methylmalate 0,81
Fumarate 0,81
Maleic acid 0,81
Acetylenedicarboxylate 0,79
Iminoaspartate 0,78
Oxaloacetate 0,74
Since many of the Top-10 compounds were already proved not to induce the system, and the high
number of molecules identified hampers an individual analysis, the compounds were grouped in
categories. The two categories with more compounds were “amino acid biosynthesis/degradation
intermediates” that included 91 compounds showing similarity to IA and “Carbohydrates
biosynthesis/degradation intermediates” with 70 compounds (complete list of compounds belonging to
the amino acid category can be found in Appendix 14). Within the amino acid category, the compound
exhibiting highest similarity with IA was R-(2)-methylmalate, an intermediate of the valine, leucine and
isoleucine biosynthetic pathway, for what the 8 compounds belonging to this pathway were selected
for further analysis. Notably, it has been reported the existence of enzymes that respond to both
leucine intermediates and to IA: the natural substrate for Trans-aconitate 2-methyltransferase (Tmt1)
is 3-isopropylmalate (an intermediate of the leucine biosynthetic pathway in S. cerevisiae) but the
49
enzyme is also able to form IA methyl esters using IA. This enzyme is considered a ‘’moonlighting’’
enzyme due to its multiple functions in different pathways (Hossain et al., 2019). This indicatives
prompted us to test whether intermediates of valine, leucine and isoleucine metabolism could trigger
the Yp-ItcR biosensor, this being driven by the identification of several metabolites belonging to these
pathways as being similar to IA including (R)-2-Methylmalate (0.81), 2-Methylmaleate (0.74), D-
erythro-3-Methylmalate (0.63), alpha-Isopropylmalate (0.67) and 2-Isopropylmaleate (0.61) from the
biosynthetic route and Methylmalonate (0.60), 3-Hydroxyisovalerate (0.56), L-Valine (0.55) from the
degradation route. The designed approach consists on incubating E. coli DH5 harbouring the Yp-ItcR
biosensor for four days either with 50 mM of IA, valine, leucine, isoleucine or alanine (the last used as
negative control), to allow the amino acids to be converted into the respective intermediates, which
could then activate the sensor. Using this approach only alpha-Isopropylmalate (also called (2S)- 2-
Isopropylmaleate), 2-Isopropylmaleate and L-Valine will be tested for they are the only compounds the
host organism can metabolize by the conversion of L-Valine to L-Leucine, generating the previously
mentioned compounds (Appendixes 15 and 16). The results obtained with the Yp-ItcR biosensor are
shown in Figure 36.
As expected a rapid activation of the biosensor was observed when IA was used in the medium,
however, induction with isoleucine and, less significantly, with valine was also observed (Fig. 36). It is
important to highlight that when alanine (whose degradation does not lead to the formation of the IA-
similar molecules mentioned above) was used no significant fluorescence was observed (Fig. 36). The
delay in the increase of the biosensor activation observed with valine and isoleucine could be
attributable to the need of these cells to accumulate the metabolites that could serve as inducers.
Figure 36. Absolute normalized fluorescence of E. coli DH5 harbouring pEH086 in response to IA (50 mM), isoleucine (50 mM), leucine (50 mM), valine (50 mM) and alanine (50 mM) in mineral media with 5 mM of yeast extract. Left YY-axis (in red) concerns the absolute normalized fluorescence of IA, while the right YY-axis (in black) concerns the absolute normalized fluorescence of the aminoacids
- IA (50 mM); - Isoleucine (50 mM); - Leucine (50 mM); - Valine (50 mM); - Alanine (50 mM).
50
When comparing this activation to the sensor activation by IA, it seems to have no apparent meaning.
Further testing, supplementing the media directly with the respectives intermediates (namely (R)-2-
Methylmalate, the compound with the highest similarity with IA) is required to further confirm this
hypothesis. It would also be interesting to explore E. coli mutant strains in the different steps of the
pathways as these are expected to accumualte these products intracellularly which could trigger a
more potent and rapid induction of the biosensor. These results point to a link between amino acid
metabolism and the IA-degradation pathway, something that needs much more investigation but that
somehow fits the observed divergence between the widespread distribution of the IA-degradation
pathway and the small number of species that do produce IA.
51
Concluding Remarks
The already developed Yersinia itcR IA biosensor can be used to easily determine the intracellular
levels of IA in different producing host strains. The coupling of this biosensor to a FACS will even allow
an immediate separation of the more producing strains which will guide the design of IA overproducer
strains and will significantly increase the throughput of the screening stage. Also, the value of
biosensors as a screening tool doesn’t only reside in its ability to qualitative indicate the production of
a give compound, but rather in its power to quantify the compound. As such, the different correlations
between IA concentration and fluorescent output found in the mutated versions of the Yp-itcR
biosensor are quite promising, for they suggest that the mutated versions of the biosensor may have
an improved operational range and dynamic range.
Being eukaryotic organisms the main IA producers, the transfer of the Yersinia biosensor to eukaryotic
hosts is a priority. Therefore, a new site-directed mutagenesis strategy must be conceived. This
strategy can then accomplish by linearizing the plasmid in a PCR using Phusion, in which the used
primers contain the mutated nucleotides in the 5’-end tail. The successful implementation of this
strategy will lead to the confirmation of the itcR DNA-binding sequences. Once they are confirmed, the
itcR DNA-binding sequences can be shuffled in to a universal, strong and well-characterized
eukaryotic promotor to make it responsive to itcR and, consequently to IA. This synthetic eukaryotic
IA-responsive promoter will then be cloned in a plasmid controlling the expression of RFP protein. The
transfer of this system to eukaryotic organisms where IA production has been enabled will allow the
testing of their ability to produce IA and the improvement of their production titters.
Furthermore, the confirmation of the role of itcR from P. aeruginosa as activator of the IA degradation
operon in response to the compound, together with the structural differences found between it and the
Yp-ItcR, reinforces the interest in the use of this protein as an eventual. Since the implementation of
the Pa-ItcR biosensor in bacterial hosts was hampered by translational problems, the synthesis of a
codon optimized version of this gene (adapted to the organism hosting the biosensor) and the cloning
of the optimized sequence in the already existing plasmid would generate a functional version of the
biosensor, creating one additional tool to screen itaconic acid producers.
Finally, in what concerns the biological function of the IA catabolism, little conclusions were retrieved,
due to poor genome annotations and lack of information regarding pathogenicity and isolation sources
data. Even so, a possible link between IA metabolism and valine, leucine and isoleucine metabolism
was established and must be further explored using the Yersinia biosensor, namely to test the
response of itcR to (2S)- 2-Isopropylmaleate and 2-Isopropylmaleate. This can be accomplished by
adding these compounds to the media (preferably using as a host strain E. coli strains devoid of
enzymes that metabolize these two compounds). The testing of these compounds would either
validate or reject this proposed association.
52
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Appendixes
Appendix 1. Biosensor output for the One-pot two-strain assay with an induction stage with 10 mM of IA and a 3-hour recovery stage. Absolute normalized fluorescence of E. coli DH5 harbouring pEH086 in response to different IA concentration (on the left) and respective DRC (on the right).
- 0 mM IA; - 10 mM IA; - 25 mM IA; - 50 mM IA;
- 4 hpi; - 8 hpi; - 12 hpi; - 24 hpi.
57
C
B
A
Appendix 2. Biosensor output for the One-pot two-strain assay with a 6-hour recovery stage. Absolute normalized fluorescence of E. coli DH5 harbouring pEH086 in response to different IA concentration (on the left) and respective DRC (on the right). First line corresponds to an induction stage with 10 mM of IA (A), second line corresponds to an induction stage with 25 mM of IA (B) and the third line corresponds to an induction stage with 50 mM of IA (C).
- 0 mM IA; - 10 mM IA; - 25 mM IA; - 50 mM IA;
- 4 hpi; - 8 hpi; - 12 hpi; - 24 hpi.
58
Appendix 3. Library testing: dose-response curves for the wildtype and Pccl mutated versions (from mutant 1
to 23). - 4 hpi; - 8 hpi; - 12 hpi; - 24 hpi.
Wildtype Mutant 1 Mutant 2 Mutant 3
Mutant 4 Mutant 5 Mutant 6 Mutant 7
Mutant 8 Mutant 9 Mutant 10 Mutant 11
Mutant 12 Mutant 13 Mutant 14 Mutant 15
Mutant 16 Mutant 17 Mutant 18 Mutant 19
Mutant 20 Mutant 21 Mutant 22 Mutant 23
59
Appendix 3 (continuation). Library testing: dose-response curves for the wildtype and Pccl mutated versions
(from mutant 24 to 46). - 4 hpi; - 8 hpi; - 12 hpi; - 24 hpi.
Wildtype Mutant 24 Mutant 25 Mutant 26
Mutant 27 Mutant 28 Mutant 29 Mutant 30
Mutant 31 Mutant 32 Mutant 33 Mutant 34
Mutant 35 Mutant 36 Mutant 37 Mutant 38
Mutant 39 Mutant 40 Mutant 41 Mutant 42
Mutant 43 Mutant 44 Mutant 45 Mutant 46
60
Appendix 3 (continuation). Library testing: dose-response curves for the wildtype and Pccl mutated versions
(from mutant 47 to 69). - 4 hpi; - 8 hpi; - 12 hpi; - 24 hpi.
Wildtype Mutant 47 Mutant 48 Mutant 49
Mutant 50 Mutant 51 Mutant 52 Mutant 53
Mutant 54 Mutant 55 Mutant 56 Mutant 57
Mutant 58 Mutant 59 Mutant 60 Mutant 61
Mutant 62 Mutant 63 Mutant 64 Mutant 65
Mutant 66 Mutant 67 Mutant 68 Mutant 69
61
Appendix 3 (continuation). Library testing: dose-response curves for the wildtype and Pccl mutated versions
(from mutant 70 to 92). - 4 hpi; - 8 hpi; - 12 hpi; - 24 hpi.
Wildtype Mutant 70 Mutant 71 Mutant 72
Mutant 73 Mutant 74 Mutant 75 Mutant 76
Mutant 77 Mutant 78 Mutant 79 Mutant 80
Mutant 81 Mutant 82 Mutant 83 Mutant 84
Mutant 85 Mutant 86 Mutant 87 Mutant 88
Mutant 89 Mutant 90 Mutant 91 Mutant 92
62
Appendix 3 (continuation). Library testing: dose-response curves for the wildtype and Pccl mutated versions
(from mutant 93 to 112). - 4 hpi; - 8 hpi; - 12 hpi; - 24 hpi.
Wildtype Mutant 93 Mutant 94 Mutant 95
Mutant 96 Mutant 97 Mutant 98 Mutant 99
Mutant 100 Mutant 101 Mutant 102 Mutant 103
Mutant 104 Mutant 105 Mutant 106 Mutant 107
Mutant 108 Mutant 109 Mutant 110 Mutant 111
Mutant 112
63
Motif Start End Sequence of the Motif Notes
T-N8-A -93 -83 TTCATATCCA
T-N9-A -93 -82 TTCATATCCAA
T-N10-A -93 -81 TTCATATCCAAA
T-N11-A -93 -80 TTCATATCCAAAA
T-N7-A -92 -83 TCATATCCA
T-N8-A -92 -82 TCATATCCAA
T-N9-A -92 -81 TCATATCCAAA
T-N10-A -92 -80 TCATATCCAAAA
T-N13-A -92 -77 TCATATCCAAAAGCA
T-N8-A -89 -82 TATCCAA
T-N9-A -89 -81 TATCCAAA
T-N10-A -89 -80 TATCCAAAA
T-N11-A -89 -77 TATCCAAAAGCA
T-N12-A -89 -76 TATCCAAAAGCAA
T-N14-A -89 -73 TATCCAAAAGCAATTA
T-N15-A -89 -72 TATCCAAAAGCAATTAA
T-N5-A -87 -80 TCCAAAA
T-N7-A -87 -77 TCCAAAAGCA
T-N8-A -87 -76 TCCAAAAGCAA
T-N11-A -87 -73 TCCAAAAGCAATTA
T-N12-A -87 -72 TCCAAAAGCAATTAA
T-N13-A -87 -71 TCCAAAAGCAATTAAA
T-N5-A -76 -70 TTAAACA
T-N7-A -76 -68 TTAAACACA
T-N13-A -76 -62 TTAAACACACCGGTA
T-N6-A -75 -68 TAAACACA
T-N12-A -75 -62 TAAACACACCGGTA
T-N14-A -75 -59 TAAACACACCGGTATCA
T-N5-A -63 -57 TATCATA
T-N7-A -63 -55 TATCATATA
T-N12-A -63 -50 TATCATATATTGGA overlaps with -35 region of the 1st gene of the operon
T-N14-A -63 -48 TATCATATATTGGATA overlaps with -35 region of the 1st gene of the operon
T-N15-A -63 -47 TATCATATATTGGATAA overlaps with -35 region of the 1st gene of the operon
T-N5-A -61 -55 TCATATA
T-N10-A -61 -50 TCATATATTGGA overlaps with -35 region of the 1st gene of the operon
T-N12-A -61 -48 TCATATATTGGATA overlaps with -35 region of the 1st gene of the operon
T-N13-A -61 -47 TCATATATTGGATAA overlaps with -35 region of the 1st gene of the operon
Appendix 4 (continuation). TN{5,15}A motifs found in the itcR intergenic region from Y. pseudotuberculosis.
64
Motif Start End Sequence of the Motif Notes
T-N14-A -61 -46 TCATATATTGGATAAA overlaps with -35 region of the 1st gene of the operon
T-N7-A -58 -50 TATATTGGA overlaps with -35 region of the 1st gene of the operon
T-N9-A -58 -48 TATATTGGATA overlaps with -35 region of the 1st gene of the operon
T-N10-A -58 -47 TATATTGGATAA overlaps with -35 region of the 1st gene of the operon
T-N11-A -58 -46 TATATTGGATAAA overlaps with -35 region of the 1st gene of the operon
T-N14-A -58 -45 TATATTGGATAAATGA overlaps with -35 region of the 1st gene of the operon
T-N5-A -56 -50 TATTGGA overlaps with -35 region of the 1st gene of the operon
T-N7-A -56 -48 TATTGGATA overlaps with -35 region of the 1st gene of the operon
T-N8-A -56 -47 TATTGGATAA overlaps with -35 region of the 1st gene of the operon
T-N9-A -56 -46 TATTGGATAAA overlaps with -35 region of the 1st gene of the operon
T-N11-A -56 -43 TATTGGATAAATGA overlaps with -35 region of the 1st gene of the operon
T-N12-A -56 -41 TATTGGATAAATGATA overlaps with -35 region of the 1st gene of the operon
T-N13-A -56 -40 TATTGGATAAATGATAA overlaps with -35 region of the 1st gene of the operon
T-N5-A -54 -48 TTGGATA overlaps with -35 region of the 1st gene of the operon
T-N6-A -54 -47 TTGGATAA overlaps with -35 region of the 1st gene of the operon
T-N7-A -54 -46 TTGGATAAA overlaps with -35 region of the 1st gene of the operon
T-N9-A -54 -43 TTGGATAAATGA overlaps with -35 region of the 1st gene of the operon
T-N10-A -54 -41 TTGGATAAATGATA overlaps with -35 region of the 1st gene of the operon
T-N11-A -54 -40 TTGGATAAATGATAA overlaps with -35 region of the 1st gene of the operon
T-N5-A -53 -47 TGGATAA overlaps with -35 region of the 1st gene of the operon
T-N6-A -53 -46 TGGATAAA overlaps with -35 region of the 1st gene of the operon
T-N9-A -53 -43 TGGATAAATGA overlaps with -35 region of the 1st gene of the operon
T-N11-A -53 -41 TGGATAAATGATA overlaps with -35 region of the 1st gene of the operon
T-N12-A -53 -40 TGGATAAATGATAA overlaps with -35 region of the 1st gene of the operon
T-N5-A -49 -43 TAAATGA overlaps with -35 region of the 1st gene of the operon
T-N7-A -49 -41 TAAATGATA overlaps with -35 region of the 1st gene of the operon
T-N8-A -49 -40 TAAATGATAA overlaps with -35 region of the 1st gene of the operon
T-N14-A -49 -34 TAAATGATAACGGCGA overlaps with -35 region of the 1st gene of the operon
T-N10-A -45 -34 TGATAACGGCGA
T-N13-A -45 -31 TGATAACGGCGACCA
T-N15-A -45 -29 TGATAACGGCGACCATA overlaps with -10 region of the 1st gene of the operon
T-N7-A -42 -34 TAACGGCGA
T-N10-A -42 -31 TAACGGCGACCA
T-N12-A -42 -29 TAACGGCGACCATA overlaps with -10 region of the 1st gene of the operon
T-N14-A -42 -27 TAACGGCGACCATAGA overlaps with -10 region of the 1st gene of the operon
T-N5-A -30 -24 TAGACTA overlaps with -10 region of the 1st gene of the operon
T-N6-A -30 -23 TAGACTAA overlaps with -10 region of the 1st gene of the operon
Appendix 4 (continuation). TN{5,15}A motifs found in the itcR intergenic region from Y. pseudotuberculosis.
65
Motif Start End Sequence of the Motif Notes
T-N10-A -30 -19 TAGACTAAGCGA overlaps with -10 region of the 1st gene of the operon
T-N11-A -30 -18 TAGACTAAGCGAA overlaps with -10 region of the 1st gene of the operon
T-N5-A -25 -19 TAAGCGA overlaps with -10 region of the 1st gene of the operon
T-N6-A -25 -18 TAAGCGAA overlaps with -10 region of the 1st gene of the operon
T-N12-A -25 -12 TAAGCGAAGTTGGA overlaps with -10 region of the 1st gene of the operon
T-N15-A -25 -9 TAAGCGAAGTTGGAGGA overlaps with -10 region of the 1st gene of the operon
T-N6-A -16 -9 TTGGAGGA
T-N9-A -16 -6 TTGGAGGAGGA
T-N10-A -16 -5 TTGGAGGAGGAA
T-N12-A -16 -3 TTGGAGGAGGAACA
T-N5-A -15 -9 TGGAGGA
T-N8-A -15 -6 TGGAGGAGGA
T-N9-A -15 -5 TGGAGGAGGAA
T-N11-A -15 -3 TGGAGGAGGAACA
Dyad Sequence Start End Sequence of the Motif Notes
ACTNNGCNNAGT -27 -16 ACTAAGCGAAGT Overlaps with -10 region of the 1st gene
of the operon
ANGTTNNNNNNNNAACNT -19 -2 AAGTTGGAGGAGGAACAT
ATCATNNNATANATGAT -62 -46 ATCATATATTGGATAAA Has 2 substitutions when compared to the Dyad Sequence. Overlaps with -35 region of the 1st gene of the operon.
ATGNTNNCNGNNANCAT -46 -30 ATGATAACGGCGACCAT Overlaps with -10 region of the 1st gene
of the operon
ATTNNNNNAAT -55 -45 ATTGGATAAAT Overlaps with -35 region of the 1st gene
of the operon
TATCNNATATNNGATA -63 -48 TATCATATATTGGATA Overlaps with -35 region of the 1st gene
of the operon
Appendix 5. Inverted repeats found in the itcR intergenic region from Y. pseudotuberculosis.
Appendix 4 (continuation). TN{5,15}A motifs found in the itcR intergenic region from Y. pseudotuberculosis.
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Appendix 6. P. aeruginosa growth in 96-well plates with mineral medium (pH7) supplemented with 40 mM of glucose over a 48 hours period. Growth is represented by OD640nm.
- wild type strain grown in 40 mM of glucose; - ΔPA0877 grown in 40 mM of glucose; - ΔPA0883 grown in 40 mM of glucose;
Appendix 7. P. aeruginosa growth in mineral medium (pH7) without carbon source (A) and supplemented with 40 mM of glucose (B) over a 7 days period. Growth is represented by OD640nm.
- wild type strain grown with no carbon source; - ΔPA0877 grown with no carbon source; - wild type
strain grown in 40 mM of glucose; - ΔPA0877 grown in 40 mM of glucose;
A B
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Appendix 9. ItcR from P. aeruginosa codon frequency distribution in E. coli. In X-axis are represented the codon-quality groups and in Y-axys the percentage of those codons figuring in the ItcR from P. aeruginosa. Codons with values lower than 30 are likely to hamper the expression efficiency. Created with GenScript Rare Codon Analysis (www.genscript.com)
Appendix 8. Codon usage analysis between P. aeruginosa (in red) an E. coli (in black). Created with Graphical Codon Usage Analyser (www.gcua.de)
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Y. pseudotuberculosis YPIII P. aeruginosa PA0
Y. pseudotuberculosis YPIII P. aeruginosa PA0
Y. pseudotuberculosis YPIII P. aeruginosa PA0
Appendix 10. Alignment of the ItcR from Y. pseudotuberculosis and P. aeruginosa. Amino acid conservation, quality, occupancy and the consensus sequence can be found in the inferior part of the figure.
69
IA Glucose
(2h)
IA
(4h)
Appendix 11. Relative expression of P. aeruginosa PA0883 gene using the WT_PA0 grown for 2 hours in glucose as basal condition. Values were normalized using rpoD gene.
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Strain Peak Area
0 h
6 h 12 h 24 h 30 h 48 h
WT 1 0 24938 29925 0 0 0
3 0 18550 27136 8603 0 0
Δ78
1 0 22417 107651 41379768 54274683 82001082
2 0 24063 261130 42254688 55270994 84434328
3 0 24258 1023507 43776657 5530680 81788239
Δ79
1 0 25977 30602 13802 23908 0
2 0 28362 31490 0 0 0
3 0 29740 37880 0 0 0
Δ80
1 0 25250 151128 41892905 37579271 2676660
2 0 16217 510810 44107190 40874007 0
3 0 10878 1826949 50910139 45019520 0
Δ81
1 0 19711 2199777 58438152 74082793 82705169
2 0 0 28713 38290475 54087408 70544013
3 0 20787 263178 22107955 31704596 60122975
Δ82
1 0 18406 23320 1451949 2690186 6468527
2 0 18257 40350 2089528 3367527 7500209
3 0 23511 36474 1668597 2847585 5907560
Δ83 2 0 23384 27768 1895702 3207471 7669543
3 0 20137 20360 1512151 2676539 5560738
Appendix 12. Chromatogram exhibiting the ‘’unknown’’ peak with a retention time of 16 minutes (A) and area of the ‘’unknown’’ found growths performed with P. aeruginosa wildtype and mutant strains(B).
A
B
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72
Compound Similarity Pathways
(R)-2-Methylmalate 0,81 Valine, leucine and isoleucine biosynthesis
Oxaloacetate 0,74 Alanine, aspartate and glutamate metabolism
L-Aspartate 0,74
Arginine biosynthesis Alanine, aspartate and glutamate metabolism
Glycine, serine and threonine metabolism Lysine biosynthesis Histidine metabolism
D-Aspartate 0,74 Alanine, aspartate and glutamate metabolism
2-Methylmaleate 0,74 Valine, leucine and isoleucine biosynthesis
Glutarate 0,7 Lysine degradation
alpha-Isopropylmalate 0,67 Valine, leucine and isoleucine biosynthesis
D-erythro-3-Methylmalate 0,65 Valine, leucine and isoleucine biosynthesis
L-Glutamate 0,63
Arginine biosynthesis Alanine, aspartate and glutamate metabolism
Arginine and proline metabolism Histidine metabolism
2-Oxoglutarate 0,63
Arginine biosynthesis Alanine, aspartate and glutamate metabolism
Lysine biosynthesis Lysine degradation
Histidine metabolism
(S)-2-Hydroxyglutarate 0,63 Lysine degradation
Succinate semialdehyde 0,63 Alanine, aspartate and glutamate metabolism
Tyrosine metabolism
Citrate 0,61 Alanine, aspartate and glutamate metabolism
N-Formyl-L-aspartate 0,61 Histidine metabolism
2-Isopropylmaleate 0,61 Valine, leucine and isoleucine biosynthesis
N-Formimino-L-aspartate 0,61 Histidine metabolism
Methylmalonate 0,6 Valine, leucine and isoleucine degradation
2-Aminomuconate 0,59 Tryptophan metabolism
2-Oxoadipate 0,58 Lysine biosynthesis Lysine degradation
Tryptophan metabolism
L-2-Aminoadipate 0,58 Lysine biosynthesis Lysine degradation
gamma-Oxalocrotonate 0,58 Tryptophan metabolism
Appendix 14. Compounds belonging to the Amino acid.
73
Compound Similarity Pathways
2-Hydroxyhepta-2,4-dienedioate
0,57 Tyrosine metabolism
3-Hydroxyisovalerate 0,56 Valine, leucine and isoleucine degradation
5-Carboxymethyl-2-hydroxymuconate
0,56 Tyrosine metabolism
N-Carbamoyl-L-aspartate 0,56 Alanine, aspartate and glutamate metabolism
N-Acetyl-L-aspartate 0,56 Alanine, aspartate and glutamate metabolism
L-Valine 0,55 Valine, leucine and isoleucine degradation Valine, leucine and isoleucine biosynthesis
L-Homoserine 0,55 Glycine, serine and threonine metabolism
Cysteine and methionine metabolism Lysine biosynthesis
L-Aspartate 4-semialdehyde
0,55
Glycine, serine and threonine metabolism Cysteine and methionine metabolism
Lysine biosynthesis Arginine and proline metabolism
Phenylalanine, tyrosine and tryptophan biosynthesis
Appendix 13 (continuation). Compounds belonging to the Amino acid.
74
0.55
0.67
0.61
0.81 0.74 0.61
Appendix 14. Valine, leucine and isoleucine biosynthesis – E. coli DH1 pathway from KEGG. Squares in green represent enzymes encoded in the genome of the host strain, being the codes the respective Enzyme Commission (EC) number of the enzymes. Red squares and the numbers in bold indicate the potentially interesting intermediate species and their similarity with IA.
75
0.56
0.60
Appendix 15. Valine, leucine and isoleucine degradation – E. coli DH1 pathway from KEGG. Squares in green
represent enzymes encoded in the genome of the host strain, being the codes the respective EC number of the enzymes. Red squares and the numbers in bold indicate the potentially interesting intermediate species and their similarity with IA.