a comprehensive mass spectrometry - based metabolomics ... · pdf filea comprehensive mass...
TRANSCRIPT
A Comprehensive Mass Spectrometry – Based Metabolomics Workflow in Biomarker DiscoveryWei Zou, Ph.D. Environmental Chemistry Lab, Berkeley, DTSC
What is a metabolome?
A metabolome is the completecollection of all endogenous low
molecular weight compounds in cells, tissues or fluids that are required for the maintenance, growth and normal
function of a biological system
What is metabolomics?
Metabolomics – next to phenotype
Phenotype
Genotype
mRNA expression
protein expression
metabolite expression
Metabolomics: Detect the Unexpected !
1. Target analysisfew metabolites
2. Metabolite profilingsome selected metabolites
3. Metabolomicsall metabolites
Metabolic fingerprintingclassifying samples
scopeac
cura
cy
Comp.Funct. Genom. 2 (155-168) 2001 Plant Mol. Biol. 48 (155-171) 2002
Metabolic Biomarkers: Endogenous Metabolites
• A “Biomarker” is a quantifiable biological variable that characterizes cellular, organ, physiological, pathological or clinical condition.
• Metabolic Biomarker is a quantifiable endogenous Metabolite.
• Panel of Metabolic Biomarkers is a Metabolic Signature.
N.I.Parikh & R.S. Vasan. Assessing the clinical utility of biomarkers in medicine. Biomarkers Med. (2007)1(3), pp 419-436
Case Study 1: Pancreatic Ductal Adenocarcinoma(PDAC)
Urayama, S., Zou, W., et al. Comprehensive mass spectrometry based metabolic profiling of blood plasma reveals potent discriminatory classifiers of pancreatic cancer. Rapid Commun. in Mass Spectrom. 2010, 24, 613–620.
sam
ples
hypotheses
data
experiments
inte
rpre
tatio
n
m/z 131
m/z 129
Data Acquisition ( Mass Spectrometry )
260 300 340 380
distance
Data Mining (Statistics, Visualization )
Experimental design
grow
th h
isto
ry
'control' 'mutant'
organ1 organ2 organ3 organ4
dose1
time1
N N
time 2 N N
time3
N N
time4
N N N N N N N N
dose2
time1
N N
time2
N N
time3
N N
time4
N N N N N N N N
dose1
time1
N N
time2
N N
time3
N N
time 4 N N N N N N N N
dose2
time1
N N
time2
N N
time3
N N
time4
N N N N N N N N
BioSource
treat
men
t 1tre
atm
ent 1
organ1 organ2 organ3 organ4
Sample Preparation ( Wet Chemistry )
Biomarker Discovery WorkflowBiomarker Discovery Workflow
UC Davis Genome Center UC Davis Genome Center Metabolomics Core LabMetabolomics Core Lab
LCLC--MS BranchMS Branch
HILIC versus RP separationsHILIC versus RP separations
0 2 4 6 8 10 12 14 16 18 20Time (min)
0
100
Relat
ive
Abu
ndan
ce
0.60
2.78
3.62
3.75
1.92
3.98 17.1713.579.54 19.785.81 10.707.394.35
8.45 14.73 18.776.61 16.8611.40
0 2 4 6 8 10 12 14Time (min)
0
100
Relat
ive
Abu
ndan
ce
1.46
8.36
8.65
7.785.372.80 6.77 12.078.763.83 4.30 9.191.219.97 14.9913.61
U P L C - R P - I T M S
H P L C - H I L I C - I T M S
H U M A N U R I N E L C / M S P R O F I L I N G
ElectrosprayElectrospray ionization (ESI)ionization (ESI)
Thermo Electron Corp. APPI/APCI combination probe operator’s manual.
Atmospheric pressure chemical Atmospheric pressure chemical ionization (APCI)ionization (APCI)
Thermo Electron Corp. APPI/APCI combination probe operator’s manual.
Ionization mode selectionIonization mode selection
Thermo Electron Corp. APPI/APCI combination probe operator’s manual.
IonIon--trap mass spectrometry (LTQ)trap mass spectrometry (LTQ)
Thermo Electron Corp. LTQ hardware manual.
RPRP--LCLC--ESIESI--ITMS Sample PreparationITMS Sample Preparation
100 uL of human plasma, add 400 uL of methanol
Shake in a ball mill (30 cycles/sec for 2 min), Sonicate (1 min)
Centrifuge (13000 rpm, 5 min), Transfer the supernatant to a new tube
Dry the supernatant in a Speed-Vac, reconstitute in 100 uL of water-acetonitrile (1:1)
Inject 5 uL of final volume onto RP-LC-ESI-ITMS
UPLC conditionsColumn: BEH C8 (150x2mm, 1.7 m)
Column temp: 70 °C
Inject solv: H2O-ACN (1:1)
Inject vol: 5 L
Flow rate : 0.5 mL/min
Solvent: A: 13 mM NH4OAc, pH 5.5; B: Acetonitrile/Acetone (9:1)
Gradient: 0-0.1 min, 1% B; 0.1-10 min, 100% B; 10.1-15 min, 100% B; 15.1-16 min, 1% B; 16-20 min, 1% B.
LTQ conditionsIonization: ESI
Parameters: tune compd-sucrose
Nitrogen sheath flow: 60
Auxiliary flow: 20
Ion transfer capillary: 350 C
Ion gauge pressure: 0.9 x 10-5
Maximum injection time: 200 ms
Scan events: 21: +,Full scan,m/z [100-1500]
2: -,Full scan,m/z [100-1500]
RPLC-ESI-ITMS parameters
HILICHILIC--LCLC--ESIESI--ITMS Sample PreparationITMS Sample Preparation
100 uL of human plasma, add 400 uL of methanol
Shake in a ball mill (30 cycles/sec for 2 min), Sonicate (1 min)
Centrifuge (13000 rpm, 5 min), Transfer the supernatant to a new tube
Dry the supernatant in a Speed-Vac, reconstitute in 100 uL of water-acetonitrile (1:1)
Inject 10 uL of final volume onto HILIC-LC-ESI-MS
HPLC conditionsColumn: Luna HILIC Diol (150x3mm, 3 m)
Column temp: 40 °C
Inject solv: H2O-ACN (1:1)
Inject vol: 10 L
Flow rate : 0.4 mL/min
Solvent: A: 100 mM Ammonium Formate, pH 4.0; B: Acetonitrile
Gradient: 0-2 min, 3% A; 2-6 min, 12% A; 6.1-31 min, 30% A; 31.1-32 min, 100% A; 32.1-33 min, 100% A; 33.1-34 min, 3% A; 34.1-45 min, 3%A.
LTQ conditionsIonization: ESI
Parameters: tune compd-sucrose
Nitrogen sheath flow: 60
Auxiliary flow: 20
Ion transfer capillary: 350 C
Ion gauge pressure: 0.9 x 10-5
Maximum injection time: 200 ms
Scan events: 21: +,Full scan,m/z [100-1500]
2: -,Full scan,m/z [100-1500]
HILIC-LC-ESI-ITMS parameters
UC Davis Genome Center UC Davis Genome Center Metabolomics Core LabMetabolomics Core Lab
GCGC--MS BranchMS Branch
GC-MS automated liner exchange to avoid cross-contamination
Imag
e so
urce
: ©G
ert W
ohlg
emut
h –
The
nice
lab
serie
s
Complex matrices (blood serum, many plant extracts) require new liner each run otherwise, peak degradation or cross-contamination with "sticky" compounds.
Use of Twister (stir bar sorptive extraction) for sample extraction and pre-concentration used in VOC analysis.
Gerstel Dual robotic arm withALEX automatic liner exchange and CIS4
Gerstel Dual robotic arm with ALEX, TWISTER and sample holder and ovens for derivatization
GC-MS mass spectral deconvolution
(Text File) Peak True - sample "080516bGCTOF344814_1", peak 77, a80 110 140 170 200 230 260 290
0
50
100
86
98
114 131
147
191
216 246 274
(Text File) Peak True - sample "080516bGCTOF344814_1", peak 76, a80 110 140 170 200 230 260 290
0
50
100
86
100
117
133
147
158
174
188 204
248
276
Deconvolution by ChromaTOF (plasma sample, two overlapping compounds)
GC-MS mass spectral and retention library
701Any (total number of structures)SA-T
22PurinesSA-8
53Carboxyl (acid, ester, salt) with aliphatic carbon chain (n>6)
SA-7
321Carboxylic acidsSA-6
58Nitrogen (n>0) in aromatic 6-ringSA-5
1Chlorine containing (non salt)SA-4
41Phosphate group containingSA-3
16General steroidsSA-2
7Aromatic steroidsSA-1
16Sugar pattern reducing sugarsS278
46Sugar pattern (multiple rings)S277
48AmidesS98
14LactonesS86
106KetonesS49
20AldehydesS48
130AminesS23
276AlcoholsS12
0AlkynesS6
96AlkenesS5
FiehnLib HitsFunctional groupID
Table and chemical hashed fingerprints calculated with ChemAxon JAVA API;
The library contains a diverse set of compoundsimportant for metabolic profiling and machine learning purposes (substructure and RIs).
Mass spectra + retention indices: 1400Unique compounds: 800GC-TOF-MS and Quadrupole-GC-MS Library
1300s di- & tri-saccharides
mono-saccharides
small acidsalcohols
free fattyacids
sterolshydroxy acidsamino acids
1300s di- & tri-saccharides
mono-saccharides
small acidsalcohols
free fattyacids
sterolshydroxy acidsamino acids
Retention Index Filter
Unique Mass Filter
s/n + Purity Filter
Similarity Filter
New DB Entry
Pass
Failed
Spectra import GCTOF
Isomer Filter
Automatic annotation BinBase DB
Purity Filter
s/n Filter Pass
80% Group Filter
Failed
Discard Spectrum
Annotate asDB Entry
GCGC--EIEI--TOF MS Sample PreparationTOF MS Sample Preparation
100 uL of human plasma, add 400 uL of methanol
Shake in a ball mill (30 cycles/sec for 2 min), Sonicate (1 min)
Centrifuge (13000 rpm, 5 min), Transfer the supernatant to a new tube
Dry the supernatant in a Speed-Vac; reconstitute in 20 uL of methoxylamine-HCl in pyridine, agitate at 30 C for 30 min; add 180 uL of MSTFA, agitate at 30 C for 30 min
Inject 1 uL of final volume onto GC-EI-TOF MS
Agilent 6890 GC conditionsColumn: Rtx-5Sil MS (30mx0.25mm, 0.25 m)
Inject vol: 1 L
Injector temperature program: initial, 50 C; ramp rate: 10 C; final temp, 330 C; hold 10 min.
Split ratio: 1:5
Flow rate : Helium, 1 mL/min
Oven temperature gradient: 0-1 min, 50 C; 1-15 min, 330 C ; 15-20 min, 330 C.
Transfer line: 250 C
Ion source: 250 C
Pegasus III TOF MSconditionsIonization: EI
Filament voltage: 70 eV
Solvent delay: 350 sec
Scan speed: 20 hz
Scan events: 1
1: +,Full scan,m/z [50-500]
GC-EI-TOF MS parameters
Data mining
• MarkerView (MDS Sciex)
• Statistica Data Miner (StatSoft)
• R Statistical Platform, Packages: XCMS + pcaMethods + GALGO Genetic Algorithms
Zou, W., Tolstikov, V. Probing Genetic Algorithms for Feature Selection in Comperhensive Metabolic Profiling. Rapid Commun. Mass Spectrom., 2008, 22, 1312–1324. Zou, W., Tolstikov, V. Pattern Recognition and Pathway Analysis with Genetic Algorithms in Mass Spectrometry Based Metabolomics. Algorithms 2009, 2, 638-666.
3D PCA Plot 3D PCA Plot –– HILICHILIC--LCLC--ESIESI--MSMSRed circle – PDAC patients; Black rectangle - Controls
3D PCA Plot 3D PCA Plot –– RPRP--LCLC--ESIESI--MSMSRed circle – PDAC patients; Black rectangle - Controls
3D PCA Plot 3D PCA Plot -- GCGC--EIEI--MSMSRed circle – PDAC patients; Black rectangle - Controls
852.3560
850 851 852
MS^n
m/z
Workflow for LC-MS Structural Eluidation of Unknown Biomarkers
a) LC separation and fractionation w/o O2, light
b) constraints: isotope abundance, ms/ms, etc
c) potential candidates: structural databases
d) 2D NMR
f) 2D NMR
e ) ACD MS fragm. Prediction /Mass Frontier
tent. identified‚characterizied‘
de novo if IP
g ) ACD NMR pred.
tent. identified‚characterizied‘
Workflow for GC-MS Structure Elucidation
EI/CIGC-MS
SetupX BinBase DB
Plan experimentCollect MSI meta data
Acquire EI and CIAccurate mass, M+
Align true metabolitesuse spectral meta data
EI mass spectra
Substructure algorithmInterpret mass spectra
SetupX / BinBaseWeb GUI
MS, RI, RAWmeta data
CImass spectra
Rank and refine molecular formulas
Substructurealgorithm
Accurate massMolecular ion
Seven GoldenRules
DB
PubChem PUGChemspider API
Isomersstructures
Retention Indexprediction
Bellerophon
Set of possible isomer structures
Exclude wrong candidates
Combine + refineall informationassign MetaboScore
Highly reproducibleEI mass spectra
FTFT--ICRICR--MS MS Ultra High Resolution ~ 1,000,000Ultra High Resolution ~ 1,000,000
[ ]
406.0 406.5 407.0 407.5 408.0 408.5 409.0 409.5 410.0m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Rel
ativ
e A
bund
ance
407.15286R=1077201
408.15619R=1172604
409.15700R=1395404
[ ]
382.5 383.0 383.5 384.0 384.5 385.0 385.5m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Rel
ativ
e A
bund
ance
383.15445R=1088200
384.15910R=959100
384.74595R=642300
383.00625R=2811600
383.30194R=2388300
382.57637R=2477900
385.60247R=1360900
[M+Na]+ [M-H]-
AA
A+1
A+2A+1
A+2
MassWorksMassWorks
Cerno Biosciences
• Elemental Composition Assignment
• sCLIPS for very high resolution does not require calibration for Spectral Accuracy
• 100K resolution is more than sufficient for small molecules profile(continuous) data
FROM MASS ACCURACY TO SPECTRAL ACCURACY
MassWorksMassWorksLC-ESI-FT-ICR-MS data at 100,000 resolution
Optimization of Mass Accuracy, Spectral Accuracy and Resolution Optimization of Mass Accuracy, Spectral Accuracy and Resolution using LTQ FTusing LTQ FT
Zou, W., Wang, Y.D., Gu, M., Tolstikov, V.V. Optimization of mass accuracy, spectral accuracy, and resolution in metaboliteidentification using LTQ-FT Ultra hybrid mass spectrometer. 57th ASMS Conference, June 2009, Philadelphia, PA.
An Example An Example –– Feature SelectionFeature SelectionPhosphatidylCholinePhosphatidylCholine (34:2)(34:2)
An Example An Example –– Extracted Ion ChromatogramsExtracted Ion ChromatogramsPhosphatidylCholinePhosphatidylCholine (34:2)(34:2)
An Example An Example –– Proposed MS/MS Fragmentation MechanismProposed MS/MS Fragmentation MechanismPhosphatidylCholinePhosphatidylCholine (34:2)(34:2)
Putative Biomarkers for PDACPutative Biomarkers for PDAC
Pathway Analysis using Putative BiomarkersPathway Analysis using Putative Biomarkers
Conclusions on Case Study 1
• Putative small molecule Biomarkers and Signatures are found.
• Biomarker Signatures rather than individual ones should be used for diagnostic test development.
Case Study 2: Renal Cell Carcinoma (RCC)
Zou, W., Tolstikov, V. (2009) Predictive multiple reactions monitoring (MRM) as a functional test of hepatocyte-like human embryonic stem cells (ESC). Orally presented at the Fifth Annual MetabolomicsSociety International Conference, August 2009, Edmonton, Alberta, Canada.
Zou, W., Tolstikov, V. Pattern Recognition and Pathway Analysis with Genetic Algorithms in Mass Spectrometry Based Metabolomics. Algorithms 2009, 2, 638-666.
Neat urine HILIC LC-MS profiling 1-femaleRCC 2-male RCC 3-female control 4-male control
Neat urine RP LC-MS profiling 1-femaleRCC 2-male RCC 3-female control 4-male control
Categ. Box & Whisker Plot: 204.165_5.51Acetylcarnitine
3.5E7
4E7
4.5E7
5E7
5.5E7
6E7
6.5E7
7E7
7.5E7
8E7
8.5E7
204.
165_
5.51
Upregulated in RCC patients
1 2 3 4
Low abundant Metabolites
• Single MS experiments of either TOF or trap analyzers are suitable for medium to high level metabolite detection. Low abundant metabolites are missing.
• Overload of the separation unit (GC-MS/LC-MS) deteriorate quality of separation.
• Artificial sample enrichment (targeted solid phase extraction, etc.) induce loss of unbound analytes.
Extending QTrap technique to Metabolomics: a hard way
Reasons: High selectivity. Gain in sensitivity ~ 100 folds.
Focus on certain pathways:
Leucine degradation pathway – failed.
Arginine metabolism – marginal response, poor reproducibility.
Limitations: ESI and/or APCI ionization efficiency for certain metabolites is not sufficient.
Focus on certain chemical classes:
Ribonucleosides – low abundant and difficult urinary metabolites are found.
4000 Qtrap MS
Hybrid instrument
Neutral Loss (NL) Scan Using 4000 Qtrap MS
UPLC conditionsColumn: BEH C18 (100x2mm, 1.7 m)Column temp: 50 °CInject solv: neat urineInject vol: 10 LFlow rate : 0.4 mL/minSolvent: A: 0.1% formic acid in H2O; B: 0.1% formic acid in acetonitrileGradient: 0-2 min, 0% B; 2.1-8 min, 40% B; 8.1-9 min, 95% B; 9.1-10 min, 100 %B; 10.1-11 min, 0%B; 11.1-15 min, 0% B.
4000 QTrap conditionsIonization: ESIParameters: tune compd-adenosineCurtain gas (CUR): 20 psiCollision gas (CAD): highIon spray voltage (IS): 5.2 kVTemprature: 300 °CIon source gas 1 (GS1): 50 psiIon source gas 2 (GS2): 50 psiInterface heater (IHE): onScan events: 3
1: +, Neutral loss scan, m/z 1322: IDA criteria, >1000 counts3: +, EPI, profile, scan rate 4000 amu/sec
RPLC-ESI-4000 QTrap MS parameters
Nucleosides Found in Urine from a RCC Cancer Patient Using Neutral Loss Scan of 132 (loss of the ribose moiety)
2.52
3.65
2.82
4.962.191.06
11.84
3.31
9.22
4.49
6.850.79 6.08
8.93
5.70
8.16
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Time, min
0.0e0
2.0e5
4.0e5
6.0e5
8.0e5
1.0e6
1.2e6
1.4e6
1.6e6
1.8e6
2.0e6
2.2e6
2.4e6
2.6e6
2.8e6
3.0e6
3.2e6
3.4e6
3.6e6
Inte
nsity
, cps
+TIC
M12 (2.52)
2.19
M24 (3.65)
M17 (3.04)
4.52
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Time, min
0.0e0
1.0e5
2.0e5
3.0e5
4.0e5
5.0e5
6.0e5
7.0e5
8.0e5
9.0e5
1.0e6
1.1e6
1.2e6
1.3e6
1.4e6
1.5e6
1.6e6
1.7e6
1.8e6
1.9e6
2.0e6
2.1e6
2.2e6
2.3e6
2.4e6
2.5e6
Inte
nsity
, cps
XIC of 271.8 (Gain of 139.7/+139.7)
140.0
96.1
167.2
97.1
112.2209.0 226.0
100 200 300
m/z, Da
0.0e0
5.0e4
1.0e5
Inte
nsity
, cps
+MSMS of 271.8 (Gain of 139.7)
N-formyl-cytidine NO
OH
OHOH
N
NN
O
N
CH3
CH3
NO
OH
OHOH
N
NN
O
N
CH3
CH3
N-formyl-cytidine
N-succinyl-adenosine
N-succinyl-adenosine
N-glutamyl-adenosine
162.0136.1
119.0
281.2137.0120.1 162.8
281.694.1
268.1210.1
100 200 300 400
m/z, Da
0.0e0
5.0e4
1.0e5
1.5e5
Inte
nsity
, cps
+MSMS of 413.5 (Gain of 281.5)
N-glutamyl-adenosine
N-butyl-guanosine
N-butyl-guanosine
Conclusions on Case Study 2• HILIC-LCMS method is found capable of
providing the most prominent putative biomarkers.
• Low abundant modified nucleosides cancer biomarkers are found and identified using neutral loss scan techniques.
• Metabolic Signatures offer more confidence for RCC diagnostic test development.
• Characterize metabolic detoxification of hepatocytes derived from human embryonic stem cells (hESC).
• Develop LC-MS/MS test allowing detection and monitoring drug biotransformations.
• Pharmacokinetics.
Case Study 3: Stem Cells Derived Hepatocytes
Duan, Y.Y., Ma, X.C., Zou, W., Wang, C., Saramipoor, I., Ahuja, T., Tolstikov, V., Zern, M.A. (2010) Differentiation and Characterization of Metabolically Functioning Hepatocytes from Human Embryonic Stem Cells. Stem Cells DOI 10.1002/stem.315.
• hESC-derived hepatocyte-like cells demonstrated partial liver function such as secretion of albumin, accumulation of glycogen, etc. However, metabolism is a crucial function in the detoxification of drugs and/or other xenobiotics, as well as endogenous substrates in the liver.
• General Profiling of endogenous metabolites in cell lysate and cell media.
• Targeting exogenous metabolites: Biotransformations. More than 80 types of biothransformations are described so far.Predictive MRM driven LC-MS/MS analysis.
Methods are Created in LightSight™ Software with IDA for Automatic MS/MS Collection
• What is predictive MRM - “pMRM”?
• pMRM is the next generation of theoretically predicted metabolite MRM transitions.
• Combining selective quadrupole scans with sensitive trap mode dependent scans facilitates comprehensive detection of metabolites.
• LightSightTM software used to create and submit acquisition methods, as well as interpret the data.
Biotransfomation – hydroxylation and methylation
Drug Bufuralolbiotransformations monitored
in Stem Cells media with pMRM
• Oxidation
• Dehydrogenation
• Glucuronidation
• Glycosilation
O
CH3OH NH2
+CH3
CH3CH3
O
CH3OH NH2
+CH3
CH3CH3
O
CH3OH NH2
+CH3
CH3CH3
OH
Bufuralol Oxidation. Stem Cells Media.
LightSightTM
O
CH3OH NH2
+CH3
CH3CH3
O
CH3O NH2
+CH3
CH3CH3
O
OHOHOH
OHO
Bufuralol Glucuronidation. Stem Cells Media.
LightSightTM
Determination of differential clomazone biotransformations in herbicide resistant Echinochloaphyllopogon biotype. Phase I metabolism study using mass spectrometry-based metabolomicsapproach.
1
2
3
4
5
1’
3’
4’
5’
6’
2’
Case Study 4 Clomazone
Zou, W., Yasuor, H., Fischer, A.J., Tolstikov, V.V. (2011) Trace metabolic profiling and pathway analysis of clomazone using liquid chromatography coupled with triple quadruple-linear ion trap mass spectrometry in predictive multiple reaction monitoring mode. LCGC. (In press)
Yasuor, H., Zou, W., Tolstikov, V.V., Tjeerdema, R.S., Fischer, A.J. (2010) Differential oxidative metabolism and 5-ketoclomazone accumulation are involved in Echinochloaphyllopogon resistance to clomazone. Plant Physiol. Mar 5. 10.1104/pp.110.153296.
Tomco, P., Holstege, D., Zou, W., Tjeerdema, R. (2010) Microbial Degradation of Clomazoneunder Simulated California Rice Field Conditions. J. Agric. Food Chem. 58, 3674–3680.
The predictive MRM (pMRM) transition list and compound structural description used with LightSightTM 2.0
m/z (Q3/Q1) Metabolite structural descriptions References240/125 Clomazone parent compound Vencill et al., 1990204/125 Clomazone in-source fragmentation Vencill et al., 1990242/125 Clomazone open ring derivative Liu et al., 1996242/125 Clomazone chlorine signature Liu et al., 1996254/125 5-ketoclomazone Liu et al., 1996
256/125 Hydroxyl group on isoxazolidinone ringElNaggar et al., 1992; Liu et al., 1996
256/141 Hydroxyl group on aromatic ringElNaggar et al., 1992; Liu et al., 1996
272/125 Two hydroxyl groups on isoxazolidinonering
----
272/141 Two hydroxyl groups on both ring Liu et al., 1996272/157 Two hydroxyl groups on aromatic ring Liu et al., 1996
268/125 unknown found in the preliminary screen
----
320/125 unknown found in the preliminary screen
----
Clomazone biotransformation clusters demonstrated by PCA-DA
growth medium microorganisms resistant plants susceptible plants
Clomazone Metabolic Patterns in Susceptible (S) and Resistant (R) Plants
Case Study 5: Plasma Ketoisocaproic Acid (KIC) as a
True Precursor Measurement in the Liver
Zou, W., Lu, G.J., Thomas-Geevarghese, A., Ormsbya, B., Berglund, L. The kinetics of plasma leucine, alpha-ketoisocaproate, VLDL apolipoprotein B-100: effects of HIV. 7th Annual Conference on Arteriosclerosis, Thrombosis, and Vascular Biology, April 2006, Denver, CO.
Zou, W. & Berglund, L. (2007) HIV and Highly Active Antiretroviral Therapy (HAART): dyslipidemia, metabolic aberrations and cardiovascular risk. Prev. Cardiol. 10:96-103.
Introduction
• Deuterum labeled leucine (2H3Leu)
has been used as a tracer for
lipoprotein kinetics.
• Plasma ketoisocaproic acid (KIC), a
catabolic product of Leu, is better
than plasma Leu on representing
true precursor in the liver.
Principles
GC-MS measurement of KIC
Human plasma or serum/standards
Add pure ethanol, Centrifuge to separate
Elute on a pre-washed SPE column with 1 ml of water
Extract with hexane-diethyl ether (1:1, v/v)
The supernatant was dried under nitrogen gas at 40 °C, then added 40 mM HCl
The elute was added 1 ml of PDA (a derivatization chemical), heated at 100 °C for 1 h
Zipser et al. (1998) Carbohydr Res. 308: 47-55.
The hexane extracts were dried under nitrogen gas at 40 °C, reconstitued in 100 l of hexane-diethyl ether (1:1, v/v)
HP 6890 GC-5971 MSD with Chemstation Software, electronic ionization (EI)
Extract with hexane-diethyl ether (1:1, v/v)
Agilent 6890 GC conditionsColumn: SPB-1 (15mx0.25mm, 0.25 m)
Inject vol: 1 L
Flow rate : Helium, 2 mL/min
Agilent 5971 MSDconditionsIonization: EI
Filament voltage: 70 eV
Scan events: 2
1: +, SIM, m/z 278 (D0-KIC)
2: +, SIM, m/z 281 (D3-KIC)
GC-EI-SingleQ MS parameters
GC/MS chromatogram of blood samples
5.76 5.77 5.78 5.79 5.80 5.81 5.82 5.83 5.84
300000
Abundance
D0-KIC Ion 278.00 (278.00 to 278.00): 15APR005.D5.80
5.76 5.77 5.78 5.79 5.80 5.81 5.82 5.83 5.84
40000D3-KIC Ion 281.00 (281.00 to 281.00): 15APR005.D
5.79
Time-->
Calculations
D3-KIC (m/z 281)• TTR = --------------------------
D0-KIC (m/z 278)
Time course of label incorporation into the apoB moiety of plasma lipoproteins during a primed constant infusion of 2H3-Leu in a HIV patient (#201)
Subject 201 Lipoprotein kinetics
0.100
1.000
10.000
100.000
0 400 800 1200 1600
Infusion time (min)
D3/
D0
TTR
(%) Free Leu
Free KIC
VLDL
IDL
LDL
Time course of label incorporation into the apoB moiety of plasma lipoproteins during a primed constant infusion of 2H3-Leu in a HIV patient (#202)
Subject 202 Lipoprotein kinetics
0.100
1.000
10.000
100.000
0 400 800 1200 1600
Infusion time (min)
D3/
D0
TTR
(%) Free Leu
Free KIC
VLDL
IDL
LDL
Time course of label incorporation into the apoB moiety of plasma lipoproteins during a primed constant infusion of 2H3-Leu in a HIV patient (#204)
Subject 204 Lipoprotein kinetics
0.100
1.000
10.000
100.000
0 200 400 600 800 1000
Infusion time (min)
D3/
D0
TTR
(%) Free Leu
Free KIC
VLDL
IDL
LDL
Time course of label incorporation into the apoB moiety of plasma lipoproteins during a bolus infusion of 2H3-Leu in a HIV patient (#701)
Subject 701 Lipoprotein kinetics
0.100
1.000
10.000
100.000
0 200 400 600 800 1000
Infusion time (min)
D3/
D0
TTR
(%) Free Leu
Free KIC
VLDL
IDL
LDL
Time course of label incorporation into the apoB moiety of plasma lipoproteins during a bolus infusion of 2H3-Leu in a HIV patient (#702)
Subject 702 Lipoprotein kinetics
0.100
1.000
10.000
100.000
0 200 400 600 800 1000
Infusion time (min)
D3/
D0
TTR
(%) Free Leu
Free KIC
VLDL
IDL
LDL
Conclusions on Case Study 5
Blood ketoisocaproic acid (KIC) can be used as a true precursor measurement of hepatic 2H3-Leu.
AcknowledgmentsAcknowledgments• Vladimir Tolstikov, PhD,
• Oliver Fiehn, PhD,
• Tobias Kind, PhD,
UC Davis Genome Center Metabolomics Group
• Shiro Urayama, MD, Professor
• Mark Zern, MD, Professor
• Yuyou Duan, PhD
• Robert Weiss, MD
UC Davis, Department of Internal Medicine
• Albert Fischer, PhD
• Hagai Yasuor, PhD
UC Davis, Department of Plant Sciences
Thank You!