applying nmr- and lc/ms-based metabolite profiling to ......phenobarbital: hepatic transcriptional...
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Applying NMR- and LC/MS-BasedMetabolite Profiling to Predictive
and Investigative Toxicology
Lois Lehman-McKeeman, Ph.D.Discovery Toxicology
Bristol-Myers Squibb Co.
Determining Toxicant Effects at DifferentLevels of Biomolecular Organization
Metabolomics: Phenotype• Altered gene expression (constitutive or induced)• Environmental or dietary influence
– Contribution of intestinal microbiota• Biomarkers of toxicity or disease
– Causally-related to toxicity or result of toxicity
Gene Expression
Proteins MetabolitesMetabolomicsTranscriptomics Proteomics
Integrating Omics Data for HazardIdentification and Risk Assessment
RISK ASSESSMENT
Dose-responseExposure Analysis
Cross-Species AnalysisMechanism(s)
HAZARDIDENTIFICATION
Evaluating ToxicityPredicting Toxicity
Developing HypothesesInvestigating Mechanisms
Application of NMR and MS Methods inMetabolomics
Robertson et al. (Toxicol. Sci. 2011, in press)
Increasing use; uHPLC, GCWorkhorse; matureRapid, non-destructive
Status
Some available; varies withconditions
Databases available(more needed)
Annotation
Minimal; millions of ionsYesDeconvolution required
Overlappingpeaks
Ionization variable; extractionand chromatographic conditions
Yes: protonMinimal sample prep
ConsistencyMuch more than NMRLow (10-5 M)Sensitivity
Requires standardYesQuantitativeMS-basedNMRAttribute
Application of Metabolomics toUnderstand Phenotypic Changes
• Genetic Basis– Male and female mice distinguished by urinary
excretion of trimethylamine and trimethylamine-oxide
– FMO3 only expressed in females: TMAO>>TMA– Males: TMA>>>TMAO
NC H 3
C H 3C H 3
H O NC H 3
C H 3C H 3
+ NCH3
CH3CH3
OGutFlora
FMO3
Characterization of TransporterKnockout Models: Oatps
Oatp1a1 Oatp1a4
WildtypeOatp1a1-/-Oatp1a4-/-
050
100150200250300
E217G E3S TC Digoxin MPP+ CSA
Subs
trat
e U
ptak
e (p
mol
/mg/
min
)
+/- -/-
521 bp
273 bp476 bp
232 bp+/- -/-
Urinary Metabolite Profiles inOatp Null Mice
Serum LC-MS: Bile acidsmuricholic, deoxycholic
– Increased in nulls
Urinary NMRA: Hippurate
– Increased in nullsB, C:phenylpropionic acids
– Decreased in nulls
A B C
Wildtype
Oatp1a1-/-
Oatp1a4-/-
7.5 5.0 2.5 0.0ppm
A B C
7.90 7.80 7.70 7.60 7.50 7.50 7.40 7.30 7.20 7.102.90 2.80 2.70 2.60 2.50
Reciprocal Relationship betweenHippurate and Chlorogenic Acids
Wikoff W R et al. PNAS 2009;106:3698-3703
Distribution and quantity of gut anaerobes increased throughout entire gi tract
Integrating Metabonomic andTranscriptomic Data
Is it possible to integrate omics data to definecausal relationships?– Time course of gene expression changes relative to
effects on metabolites– Integrating global metabonomic outome with single
organ transcriptional data– Static and dynamic effects “merged”
• Multiple time points for metabonomic samples and singletime point for gene expression
• Urine sample collected over 24 hr with gene expressiondetermined only at the end of collection
Phenobarbital: Hepatic Transcriptionaland Urinary Metabonomic Data in Rats
• Phenobarbital: widely usedin transcriptional profilingstudies– Cytochrome P450 enzymes– Phase II enzymes and
xenobiotic transporters– Nuclear receptor activation
and gene expression– Hepatic transcription factors– Cell cycle regulation and cell
proliferation
Toxicol. Sci. 2002 67:219-231
Urinary NMR Metabolomic Profileafter Phenobarbital Treatment
Ascorbic Acid
GulonicAcid
Urinary Metabolomic Profile: MicrosomalEnzyme Inducers
GAAA
Control
GA
AA
DMP 904; 1 Day
DMP 904; 5 Day
PB; 1 Day
PB; 5 Day
Hepatic Ascorbic Acid SyntheticPathway
FEBS Journal Vol. 274: 1-22
Hepatic Transcriptional Profiles InformMetabolite Excretion
HO
HO
H
HHO
CH2OH
C
H
H
O
O
CHO
OHH
HHO
OHH
OHH
COO-
COO-
HHO
HHO
OHH
HHO
CH2OH
OHO
HO
H
HHO
CH2OH
C
O1: Glucuronate Reductase
2: Gulonolactonase
3: L-Gulonolactone Oxidase
+ 5.2 - 1.7
After 5 days of dosing; (p < 0.001)
1 2
3
Glucuronate Gulonic Acid Gulonolactone
- 2.9
Transcriptional Effects on HepaticRecycling of Ascorbic Acid
+ 1.7+ 3.5
+ 1.6
Integrating Metabolomic andTranscriptional Profiling Data
• Metabolomic and transcriptomic data inform eachother– Major urinary metabolomic changes reflected in transcriptional
profiling data• not most affected genes
• Evidence that– Microsomal enzyme induction increases the demand for ascorbic
acid– Ascorbate is required to support microsomal enzyme induction
• Urinary excretion of gulonic and ascorbic acid consistentlyincreased by microsomal enzyme induction in rodents
Skeletal Muscle Fiber Type andBiochemistry
Fiber selective toxicity– Statins: Fast twitch (Type II)– PPARα agonists: slow twitch (Type I)
Variable response to stress or insult– Cell death, atrophy, hypertrophy
CP, glycogenTriglyceridesFuel sourceHighLowGlycolytic capacityLowHighMyoglobinLowHighOxidative capacityLowHighMitochondriaHighLowForce productionLowHighResistance to fatigueFastSlowContraction Type
Type IIType IFiber Type
Preclinical and Clinical Biomarkers ofSkeletal Muscle Toxicity
• Creatine kinase– Used widely clinically; limited utility in preclinical species
• Non-specific markers– AST, Aldolase, LDH
• Muscle-specific proteins– Troponins, myoglobin, myosin light chain 1 (Myl3), fatty
acid binding protein 3 (Fabp3)• Tissue specificity: skeletal and cardiac muscle
– Some expressed in other tissues (liver, kidney)• Short half-life can limit utility
• Combinatorial measurements as best approach
Skeletal Muscle Biomarkers of Toxicity:Fast Twitch Troponin I and Myoglobin
• Cerivastatin: severe skeletal muscle necrosis(Type II) in female rats– FsTnI increased markedly– AST highly variable
• Isoproterenol: cardiac (ventricular) necrosis
25 ± 7*< 6 ± 2ndnd157 ± 17135 ± 19Isoproterenol< 4 ± 1< 5 ± 111 ± 3< 7 ± 1151 ± 10130 ± 21Cerivastatin-M
141 ± 75*nd733 ± 224*< 9 ± 11838 ± 772*114 ± 6Cerivastatin-FTreatedControlTreatedControlTreatedControl
uMB (ng/mg Cr)fsTnI (ng/ml)AST (U/L)
Vassallo et al. Toxicol. Sci 111, 402-412, 2009Cerivastatin dosed for 14 d at 1 mg/kgIsoproterenol dosed to male rats at 0.5 mg/kg (single dose)
3-Methylhistidine
Urine NMR Spectral Characteristics:Cerivastatin
Aranibar et al. Anal Biochem (in press)
Carnosine and MethylhistidineMetabolism
Methylation
Carnosine
Anserine
β-alanine + histidine
H2N CH C
CH2
OH
O
N
N
H3C
β-alanine +
HN CH CH
CH2
OH
OH
HN
N
H2C C
O
H2CH2N
HN CH CH
CH2
OH
OH
N
N
H2C C
O
H2CH2N
H3C
1-Methylhistidine
• Highly specific to skeletal muscle (anserine)– mM levels– Used to study muscle protein turnover
• Rat serum concentrations at µM levels
010002000300040005000600070008000
0100200300400500600700800
3-Methylhistidine
• Post-translational modification of actin and myosin• Rat skeletal muscle concentrations ≥ 500 µM
– 3-MH > 1-MH in smooth muscle, heart
Effect of Skeletal Muscle Toxicity onMethylhistidine Levels
11 ± 3230 ± 18381 ± 45452 ± 34Cerivastatin-M522 ± 88*256 ± 263142 ±666*494 ± 52Cerivastatin-F
TreatedControlTreatedControl
3-MH(nmol/mg Cr)
1-MH(nmol/mg Cr)
Urinary Excretion
2.8 ± 0.13.0 ± 0.214.1 ± 0.815.8 ± 2.1Cerivastatin-M5.5 ± 0.9*1.9± 0.269.7 ± 12.8*6.0 ± 0.9Cerivastatin-F
TreatedControlTreatedControl
3-MH(µM)
1-MH(µM)
Serum
Time Course of Biomarker Changes
Control Day 9 Day 15
** *
The Paradox of Isoproterenol:Methylhistidines and Muscle Biology
• Cardiac necrosis– Myoglobin increased
• Dose-dependent effectson skeletal muscle– Low dose increases muscle
growth– 1- and 3-MH decrease due
to anabolic effect
• Methylhistidines mayreport hypertrophy,atrophy and necrosis
0
100
200
300
400
1-MH 3-MH
ControlIsoproterenol
Urin
ary
MH
(nm
ol/m
g C
r)
* *
Human Relevance of Methylhistidines
• All mammals except humans synthesize (much) anserine– 1-MH may only be applicable to preclinical species
• 3-MH is conserved across all species• Meat consumption can affect constitutive levels
HN CH CH
CH2
OH
OH
HN
N
H2C C
O
H2CH2NHN CH C
H
CH2
OH
OH
N
N
H2C C
O
H2CH2N
H3C
Integrating Omics Data for HazardIdentification and Risk Assessment
RISK ASSESSMENT
Dose-responseExposure Analysis
Cross-SpeciesAnalysis
Mechanism(s)
HAZARDIDENTIFICATION
Evaluating ToxicityPredicting Toxicity
Developing HypothesesInvestigating Mechanisms
Toxicology will progress from predominantly individual chemical studiesinto a knowledge-based science in which computational and informaticstools will play a significant role in deriving new understanding oftoxicant-related disease.
Acknowledgements
BMSJeff VassalloBrenda Lehman
Applied and Investigative MetabolomicsNelly AranibarPetia ShipkovaDon RobertsonMike Reily
Iowa State UniversityJohn Rathmacher
University of Kansas Medical CenterYoucai ZhangCurt Klaassen