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Bioinformatics for Targeted Metabolomics: Met and Unmet Needs

Bioinformatics for Targeted Metabolomics: Met and Unmet Needs

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Bioinformatics for Targeted Metabolomics: Met and Unmet Needs

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  1. Bioinformatics for Targeted Metabolomics: Met and Unmet Needs Klaus M. Weinberger Biocrates Life Sciences AG, Innsbruck, Austria 3rd Annual Forum for SMEs Information Workshop on European Bioinformatics Resources Vienna, September 3 – 4, 2009

  2. Agenda BIOCRATES • Why (targeted) metabolomics? • Proof-of-concept in routine clinical diagnostics • Technology platform • Workflow integration & data analysis • Issues • Acknowledgements Socrates 470-399 BC Hippocrates 460-377 BC Intelligence Wisdom Medicine Health “Creating Knowledge for Health”

  3. Metabolomics is... ... 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)

  4. Why (targeted) metabolomics?

  5. Six systems biologists examining an elephant

  6. Why metabolomics? Polypeptides Proteins ~106 ~107 Enzymatic activity Transport etc. PTM Translation RNA ~105 Metabolites ~104 • Functional end-pointofphysiologyandpathophysiology • Reasonablescaleoftheanalyticalchallenge • Direct mirror of environmental influences • (Mal-)nutrition • Exercize • Medication Transcription DNA 2.5·104

  7. Metabolomics approaches Sample cohorts Metabolicprofiling (e.g. fullscan LC-MS) Differential pattern information

  8. HPLC-ToF-MS of urine samples Sample: mouse urine ID 0204029486 (3/8) HPLC: Waters Atlantis dC18 injection volume: 10 µl detection: pos. ToF-MS m/z100-1500 mass accuracy:~ 2 ppm data content: c. 2500 features per spectrum for statistical assessment

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

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

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

  12. Areas of application • 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 / characterizationofdiseasemodels • Safety / toxicology • Efficacy / pharmacodynamics and mode-of-action • Clinical diagnostics & theranostics • Early diagnosis and accurate staging • Specific monitoring of therapeutic effects

  13. Historyandproof-of-concept in clinicaldiagnostics

  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 • Newbornscreeningforinbornmetabolicdisorders • replaced expensive monoparametricassays • simultaneousdetectionof 40 - 60 metabolites (aminoacids, acylcarnitines) • simultaneousdiagnosisof 20 - 30 monogenicdiseases (AA metabolism, FATMO) withimmediatetreatmentoptions • total incidence > 1:2000 • unprecedentedsensitivity, specificity, ppv • co-pioneered in the mid-90s by BIOCRATES founder Bert Roscher • > 1,300,000 newbornsscreened in Munich • similarlabsworldwide

  16. Lessonsfromnewbornscreening Quantitative 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-baseddiagnosticscan save costsdespite a wider analyticalpanelandimproveddiagnosticquality Also truefortherapeuticdrugmonitoringofimmunosuppressants, antidepressants, antiretrovirals...

  17. Goals in clinicaldiagnostics ill Conventional diagnostics Multiparametric diagnostics latent • Early diagnosis • Prophylaxisinsteadoftherapy • Subtyping / Staging • Therapeuticdrugmonitoring • Phenotypicpharmacogenomics • Individualized (andmorecost-efficient) medicine healthy geneticpredisposition

  18. Technology, workflow integration & data analysis

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

  20. Workflow overview

  21. Staging of diabetic and non-diabetic nephropathy by PCA-DA MarkerViewTM

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

  23. 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

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

  25. Orchestration of fatty acid oxidation Membrane phospholipids (GPC, GPE, GPS, ...) SPL2 Lysophospholipids Free fatty acids PUFAs LA 18:2w6 AA 20:4w6 EPA 20:5w3 DHA 22:6w3 LOX COX ROS 9-HODE 13-HODE 12-HETE 15-HETE LTB4 TXB2 PGD2 PGE2

  26. Pathway visualization in KEGG (reference pathway)

  27. Pathway visualization in KEGG (human)

  28. Dynamic pathway visualization in MarkerIDQ

  29. Exploring ‚metabolic shells‘ around metabolites

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

  31. 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

  32. Issues II: Standardization and normalization • 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‘?

  33. Issues III: Biostatistics • 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?

  34. Summary I • Metabolomics depictsthefunctional end-pointofgeneticsandenvironment • Targeted metabolomics dataareanalyticallyreproducibleandallowimmediatebiochemicalinterpretation • Proof-of-concepthasbeenachieved in routinediagnosticsofinbornerrorsofmetabolism • Many metabolic biomarkers are valid across species and enable translational research • Comprehensive targeted metabolomics bridges the gap to open profiling approaches

  35. Summary II : Success factors for biomarker development Validated biomarkers Patent strategy and experience Biomarker candidates Well-documented biobanking Diligent study design Solid multi-variate biostatistics Biochemical plausibility & understanding Clinical & scientific experts Validated quantitative assays

  36. Selected partners

  37. Analytics Stefanie Gstrein Sascha Dammeier Hai Pham Tuan Cornelia Röhring • Therese KoalAli Alchalabi • Verena Forcher Ines Unterwurzacher Stefan Urban Doreen Kirchberg Ralf Bogumil Patrizia Hofer Lisa Körner Peter Enoh Bioinformatics Daniel Andres Olivier Lefèvre Paolo Zaccaria Florian Bichteler Marc Breit Manuel Gogl Bernd Haas Mattias Bair Robert Eller Hamza Ovacin Gerd Lorünser Yi Zao Statistics & Biochemistry Ingrid Osprian Marion Beier Vera Neubauer Oliver Lutz Matthias Keller Denise Sonntag Hans-Peter Deigner Ulrika Lundin Acknowledgements Admin, IT & BizDev • Brad Morie Anton GronesIngrid Sandner • Doris GigeleGeorg Debus Wolfgang Samsinger • Elgar Schnegg Patricia Aschacher