Bioinformatics for Targeted Metabolomics: Met and Unmet Needs - PowerPoint Presentation

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Bioinformatics for Targeted Metabolomics: Met and Unmet Needs

Klaus M. Weinberger

Biocrates

Life Sciences AG, Innsbruck, Austria

3

rd

Annual Forum for SMEs

Information

Workshop on European Bioinformatics Resources

Vienna,

September 3 – 4, 2009

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Agenda

Why (targeted) metabolomics?

Proof-of-concept in

routine clinical diagnosticsTechnology platformWorkflow integration & data analysisIssuesAcknowledgements

So

crates

470-399 BC

Hippocrates 460-377 BC

IntelligenceWisdom

MedicineHealth

BIOCRATES

“Creating Knowledge for Health”

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... the systematic

identification

and

quantitation of all/ biologically relevant small molecules* in a given compartment, cell, tissue or body fluid.

It represents the

functional end-point

of physiological and pathophysiological processes depicting both genetic predisposition and

environmental influences like nutrition, exercise or medication.

* no biopolymers (nucleic acids, polypeptides)Metabolomics is...

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Why

(

targeted

) metabolomics?

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Six systems biologists examining an elephant

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Transcription

Translation

PTM

DNA

2.5·10

4

RNA~105

Polypeptides~106

Proteins~107

~104Metabolites

EnzymaticactivityTransportetc.

Why metabolomics?

Functional

end-

point

of

physiology

and

pathophysiology

Reasonable

scale

of

the

analytical

challenge

Direct mirror of environmental influences

(Mal-)nutrition

Exercize

Medication

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Sample

cohorts

Metabolic

profiling

(e.g.

full scan LC-MS)

Differential patterninformation

Metabolomics approaches

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HPLC-

ToF

-MS

of urine samplesSample: mouse urine ID 0204029486 (3/8)

HPLC: Waters

Atlantis dC18 injection volume

: 10 µl detection: pos. ToF-MS m/z 100-1500mass accuracy: ~ 2

ppmdata content: c. 2500 features per spectrum for statistical assessment

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PCA of LC/MS profiling data

Candidate drug

vs.

Untreated

Untreated vs.

Rosiglitazone

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Sample

cohorts

Metabolic

profiling

(e.g.

full scan LC-MS)

Differential patterninformation

Identification of relevant metabolites

Targeted metabolomics(ID / quantitation

by SID on

MS/MS

)

Metabolite concentration

shifts

Functional annotation

Metabolomics approaches

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Pathway mapping of quantitative Mx

data

Cit

Arg

Orn

Argsucc

Fum

Urea

Asp

Carb-P

NO

NOS

ASL

ASS

ARG

OCT

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Basic research

Functional genomics in biochemistry, physiology, cell biology, microbiology, ecology, …

Agricultural & nutrition industry

Plant intermediary metabolismHealth effects of functional food products

Biotechnology

Optimization and monitoring of fermentation processes

Pharmaceutical R&DPathobiochemistry / characterization of

disease modelsSafety / toxicologyEfficacy /

pharmacodynamics and mode-of-actionClinical diagnostics & theranostics

Early diagnosis and accurate stagingSpecific monitoring of therapeutic effectsAreas of application

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History

and

proof-of-concept in clinical diagnostics

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Sir Archibald Edward Garrod

1857, London – 1936, Cambridge

Educated in Marlborough, Oxford, and London

Postgraduate studies at the AKH in Vienna in 1884/85Publications on chemical pathology (e.g. of alkaptonuria, cystinuria, pentosuria)One gene – one enzyme hypothesis

Concept of inborn errors of metabolism (Croonian lectures to the Royal College of Physicians, 1908

)

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Proof-of-concept

in

neonatology

Newborn screening for inborn

metabolic disorders

replaced expensive

monoparametric assayssimultaneous detection of 40 - 60 metabolites (amino acids,

acylcarnitines)simultaneous diagnosis of 20 - 30 monogenic diseases (AA metabolism, FATMO) with immediate

treatment optionstotal incidence > 1:2000unprecedented sensitivity

, specificity, ppvco-pioneered in the mid-90s by BIOCRATES founder Bert Roscher

> 1,300,000 newborns screened in Munich

similar

labs

worldwide

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Lessons

from

newborn screeningQuantitative tandem mass spectrometry (stable isotope dilution) is able to meet the most stringent quality criteria (precision, accuracy) for routine diagnostics

The concept of multiparametric biomarkers improving assay sensitivity and, particularly, specificity is

valid for many monogenic (and multifactorial) diseases

MS-based diagnostics can save costs despite a wider analytical panel and improved

diagnostic qualityAlso true for therapeutic drug

monitoring of immunosuppressants, antidepressants, antiretrovirals...

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Goals in

clinical

diagnosticsConventionaldiagnostics

genetic

predisposition

healthy

latentill

Multiparametric

diagnostics

Early diagnosisProphylaxis instead

of

therapy

Subtyping

/

Staging

Therapeutic

drug

monitoring

Phenotypic

pharmacogenomics

Individualized

(

and

more

cost-efficient

)

medicine

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Technology,

workflow integration & data analysis

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Automated

extraction and

derivatization

SPE

Sample preparation

Technical validation

Statistical analysisData visualizationBiochemical interpretationBioInformatics

Clinical & experimental

samplesDiagnoses & lab dataBioBank

LIMS/Database

Separation (LC, GC)

Quantitation

(

MRM, SID)

QA/QC

Analytics

Integrated technology platform

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Workflow overview

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Staging of diabetic and non-diabetic

nephropathy by PCA-DA

MarkerView

TM

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Identifying marker candidates:

stage 3 vs. stage 5 kidney disease

(loadings

)

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Increasing oxidative stress in progressing CKD

Oxidation of methionine is highly indicative for oxidative stress

Ratio of Met-SO to Met quantitative measure for this biomarker

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Decreasing ADMA secretion in progressing CKD

Regression analysis to identify correlation of marker candidates with continous (clinical) variables instead of discrete (=artificial) stages

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Membrane phospholipids (GPC, GPE, GPS, ...)

Lysophospholipids

Free fatty acids

PUFAs

AA 20:4

w

6

LA 18:2w6

DHA 22:6w3

EPA 20:5w3

9-HODE

12-HETE

15-HETE

PGD2

LTB4

TXB2

13-HODE

SPL2

PGE2

LOX

COX

ROS

Orchestration of fatty acid oxidation

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Pathway visualization in KEGG (reference pathway)

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Pathway visualization in KEGG (human)

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Dynamic pathway visualization in MarkerIDQ

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Exploring ‚metabolic shells‘ around metabolites

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Route finding between metabolites across pathways

Reactions vs. Reactant pairs!

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Issues I: Databases

Parallel / competing initiatives with incompatible / proprietary data formats

KEGG

MetaCyc, HumanCyc, etc.Reactome

HMDB

OMIM

Lipidomics consortia...Compartmentalization not well depictedIncompleteness / generic entries (phospholipids, acylcarnitines, etc.)Lack of curation

Lack of publication

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Standardization

Instrument vendors oppose common data formats

What meta-data to record?

No valid guidelines for quantitation of endogenous metabolites (FDA guidance was developed for xenobiotics)Nomenclature vs. analytical reality (sum signals, isomers, etc.)

Normalization

Absolute quantitation overcomes the need for analytical normalization

Role of sample types (plasma, CSF, urine, tissue homogenates, cell extracts, ...)How can biological normalization work? Are there ‚house-keeping metabolites‘?Issues II: Standardization and n

ormalization

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Overfitting & correction

Suitable clustering algorithms for multivariate data sets?

Metabolites

are no equivalent independent variablesAnalytical validity/variability are usually not consideredOften

, groups of metabolites are synthesized or degraded by the same enzyme(s)

Consecutive reactions within a pathway/network depend on each

other (flux analysis!)How to incorporate this in biostatistics? Weighting? Derived parameters, ratios, etc.?How to exploit this in (automated) plausibility checks?Issues III: Biostatistics

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Summary I

Metabolomics

depicts

the functional end-point of

genetics and

environment

Targeted metabolomics data are analytically reproducible and allow

immediate biochemical interpretationProof-of-concept has

been achieved in routine

diagnostics of inborn

errors of

metabolism

Many

metabolic biomarkers are valid across species and enable translational research

Comprehensive targeted metabolomics

bridges the

gap

to open

profiling approaches

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Summary II : Success factors for biomarker development

Validated quantitative assays

Well-documented biobanking

Patent strategy and experience

Clinical & scientific experts

Biochemical plausibility & understanding

Solid multi-variate biostatisticsBiomarker candidatesDiligentstudy design

Validated biomarkers

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Selected

partners

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Acknowledgements

Bioinformatics

Daniel Andres Olivier

LefèvrePaolo Zaccaria

Florian Bichteler

Marc Breit Manuel Gogl

Bernd Haas Mattias BairRobert Eller Hamza Ovacin Gerd Lorünser Yi Zao

AnalyticsStefanie Gstrein Sascha DammeierHai Pham Tuan Cornelia RöhringTherese Koal Ali

AlchalabiVerena Forcher Ines UnterwurzacherStefan Urban Doreen Kirchberg

Ralf BogumilPatrizia HoferLisa KörnerPeter Enoh

Statistics & BiochemistryIngrid Osprian

Marion Beier

Vera Neubauer Oliver Lutz

Matthias Keller Denise Sonntag

Hans-Peter

Deigner

Ulrika

Lundin

Admin, IT &

BizDev

Brad

Morie

Anton

Grones

Ingrid Sandner

Doris

Gigele

Georg Debus

Wolfgang

Samsinger

Elgar

Schnegg

Patricia

Aschacher