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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.

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Presentation on theme: "Bioinformatics for Targeted Metabolomics: Met and Unmet Needs Klaus M. Weinberger Biocrates Life Sciences AG, Innsbruck, Austria 3 rd Annual Forum for."— Presentation transcript:

1 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

2 Agenda Why (targeted) metabolomics? Proof-of-concept in routine clinical diagnostics Technology platform Workflow integration & data analysis Issues Acknowledgements Socrates BC Hippocrates BC Intelligence Wisdom Medicine Health BIOCRATES “Creating Knowledge for Health”

3 ... 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...

4 Why (targeted) metabolomics?

5 Six systems biologists examining an elephant

6 Transcription Translation PTM DNA 2.5·10 4 RNA ~10 5 Polypeptides ~10 6 Proteins ~10 7 ~10 4 Metabolites Enzymatic activity Transport etc. Why metabolomics? Functional end-point of physiology and pathophysiology Reasonable scale of the analytical challenge Direct mirror of environmental influences (Mal-)nutrition Exercize Medication

7 Sample cohorts Metabolic profiling (e.g. full scan LC-MS) Differential pattern information Metabolomics approaches

8 HPLC-ToF-MS of urine samples Sample:mouse urine ID (3/8) HPLC:Waters Atlantis dC18 injection volume:10 µl detection:pos. ToF-MS m/z mass accuracy:~ 2 ppm data content: c features per spectrum for statistical assessment

9 PCA of LC/MS profiling data Candidate drug vs. UntreatedUntreated vs. Rosiglitazone

10 Sample cohorts Metabolic profiling (e.g. full scan LC-MS) Differential pattern information Identification of relevant metabolites Targeted metabolomics (ID / quantitation by SID on MS/MS) Metabolite concentration shifts Functional annotation Metabolomics approaches

11 Pathway mapping of quantitative Mx data Cit Arg Orn Argsucc Fum Urea Asp Carb-P NO NOS ASL ASS ARG OCT

12  Basic research -Functional genomics in biochemistry, physiology, cell biology, microbiology, ecology, …  Agricultural & nutrition industry -Plant intermediary metabolism -Health effects of functional food products  Biotechnology -Optimization and monitoring of fermentation processes  Pharmaceutical R&D -Pathobiochemistry / characterization of disease models -Safety / toxicology -Efficacy / pharmacodynamics and mode-of-action  Clinical diagnostics & theranostics -Early diagnosis and accurate staging -Specific monitoring of therapeutic effects Areas of application

13 History and proof-of-concept in clinical diagnostics

14 Sir Archibald Edward Garrod 1857, London – 1936, Cambridge Educated in Marlborough, Oxford, and London Postgraduate studies at the AKH in Vienna in 1884/85 Publications 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)

15 Proof-of-concept in neonatology Newborn screening for inborn metabolic disorders replaced expensive monoparametric assays simultaneous detection of metabolites (amino acids, acylcarnitines) simultaneous diagnosis of monogenic diseases (AA metabolism, FATMO) with immediate treatment options total incidence > 1:2000 unprecedented sensitivity, specificity, ppv co-pioneered in the mid-90s by BIOCRATES founder Bert Roscher > 1,300,000 newborns screened in Munich similar labs worldwide

16 Lessons from newborn screening 1)Quantitative tandem mass spectrometry (stable isotope dilution) is able to meet the most stringent quality criteria (precision, accuracy) for routine diagnostics 2)The concept of multiparametric biomarkers improving assay sensitivity and, particularly, specificity is valid for many monogenic (and multifactorial) diseases 3)MS-based diagnostics can save costs despite a wider analytical panel and improved diagnostic quality Also true for therapeutic drug monitoring of immunosuppressants, antidepressants, antiretrovirals...

17 Goals in clinical diagnostics Conventional diagnostics genetic predisposition healthy latent ill Multiparametric diagnostics Early diagnosis Prophylaxis instead of therapy Subtyping / Staging Therapeutic drug monitoring Phenotypic pharmacogenomics Individualized (and more cost- efficient) medicine

18 Technology, workflow integration & data analysis

19 Automated extraction and derivatization SPE Sample preparation Technical validation Statistical analysis Data visualization Biochemical interpretation BioInformatics Clinical & experimental samples Diagnoses & lab data BioBank LIMS/Database Separation (LC, GC) Quantitation (MRM, SID) QA/QC Analytics Integrated technology platform

20 Workflow overview

21

22 Staging of diabetic and non-diabetic nephropathy by PCA-DA MarkerView TM

23 Identifying marker candidates: stage 3 vs. stage 5 kidney disease (loadings)

24 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

25 Decreasing ADMA secretion in progressing CKD Regression analysis to identify correlation of marker candidates with continous (clinical) variables instead of discrete (=artificial) stages

26 Membrane phospholipids (GPC, GPE, GPS,...) LysophospholipidsFree fatty acids PUFAs AA 20:4  6LA 18:2  6 DHA 22:6  3 EPA 20:5  3 9-HODE12-HETE15-HETEPGD2LTB4TXB213-HODE SPL2 PGE2 LOXCOX ROS Orchestration of fatty acid oxidation

27 Pathway visualization in KEGG (reference pathway)

28 Pathway visualization in KEGG (human)

29 Dynamic pathway visualization in MarkerIDQ

30 Exploring ‚metabolic shells‘ around metabolites

31 Route finding between metabolites across pathways Reactions vs. Reactant pairs!

32 Issues I: Databases  Parallel / competing initiatives with incompatible / proprietary data formats  KEGG  MetaCyc, HumanCyc, etc.  Reactome  HMDB  OMIM  Lipidomics consortia ...  Compartmentalization not well depicted  Incompleteness / generic entries (phospholipids, acylcarnitines, etc.)  Lack of curation  Lack of publication

33  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 normalization

34  Overfitting & correction  Suitable clustering algorithms for multivariate data sets?  Metabolites are no equivalent independent variables  Analytical validity/variability are usually not considered  Often, 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

35 Summary I Metabolomics depicts the functional end-point of genetics and environment Targeted metabolomics data are analytically reproducible and allow immediate biochemical interpretation Proof-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

36 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 biostatistics Biomarker candidates Diligent study design Validated biomarkers

37 Selected partners

38 Acknowledgements Bioinformatics Daniel Andres Olivier Lefèvre Paolo ZaccariaFlorian Bichteler Marc BreitManuel Gogl Bernd HaasMattias Bair Robert EllerHamza Ovacin Gerd Lorünser Yi Zao Analytics Stefanie GstreinSascha Dammeier Hai Pham TuanCornelia Röhring Therese KoalAli Alchalabi Verena ForcherInes Unterwurzacher Stefan UrbanDoreen Kirchberg Ralf Bogumil Patrizia Hofer Lisa Körner Peter Enoh Statistics & Biochemistry Ingrid OsprianMarion Beier Vera Neubauer Oliver Lutz Matthias Keller Denise Sonntag Hans-Peter DeignerUlrika Lundin Admin, IT & BizDev Brad Morie Anton GronesIngrid Sandner Doris Gigele Georg DebusWolfgang Samsinger Elgar SchneggPatricia Aschacher


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