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:
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
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”
... 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...
Why (targeted) metabolomics?
Six systems biologists examining an elephant
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
Sample cohorts Metabolic profiling (e.g. full scan LC-MS) Differential pattern information Metabolomics approaches
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
PCA of LC/MS profiling data Candidate drug vs. UntreatedUntreated vs. Rosiglitazone
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
Pathway mapping of quantitative Mx data Cit Arg Orn Argsucc Fum Urea Asp Carb-P NO NOS ASL ASS ARG OCT
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
History and proof-of-concept in clinical diagnostics
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)
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
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...
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
Technology, workflow integration & data analysis
Pathway visualization in KEGG (reference pathway)
Pathway visualization in KEGG (human)
Dynamic pathway visualization in MarkerIDQ
Exploring ‚metabolic shells‘ around metabolites
Route finding between metabolites across pathways Reactions vs. Reactant pairs!
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
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
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
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
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
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