A Comprehensive Mass Spectrometry - Based Metabolomics ... · PDF fileA Comprehensive Mass...

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.

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data

experiments

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m/z 129

Data Acquisition ( Mass Spectrometry )

260 300 340 380

distance

Data Mining (Statistics, Visualization )

Experimental design

grow

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'control' 'mutant'

organ1 organ2 organ3 organ4

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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)

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0.60

2.78

3.62

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3.98 17.1713.579.54 19.785.81 10.707.394.35

8.45 14.73 18.776.61 16.8611.40

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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)


Ionization mode selectionIonization mode selection


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




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

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: ©G

ert W

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The

nice

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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

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(Text File) Peak True - sample "080516bGCTOF344814_1", peak 76, a80 110 140 170 200 230 260 290

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86

100

117

133

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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




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

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407.15286R=1077201

408.15619R=1172604

409.15700R=1395404

[ ]

382.5 383.0 383.5 384.0 384.5 385.0 385.5m/z

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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

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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

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2.3e6

2.4e6

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Inte

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, 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-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

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



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



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)


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)


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!

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