Biological Analysis and Interpretation in IPA ®

1. Biological Analysis and Interpretation in IPA® October 2013 Gene Chen 陳冠文 Senior Specialist of GGA & IPA Certified Analyst

2. How can I analyze existing data …

3. How Researchers Ask Questions Now Spend time in the lab Read multiple articles Search multiple Websites Mine Internal Databases Wrangle multiple Excel sheets

4. Agenda • Introduction to Ingenuity Pathways Analysis (IPA) • Introduction to Ingenuity Knowledge Base • Questions Arise During ExperimentalProcess : • How Can IPA Help You? • Data Analysis & Interpretation in IPA • Case study for Cross Platform Integration of Metabolomics and Transcriptomics from a Diabetic Mouse Model • Q&A

5. Ingenuity Systemsis a pioneer and leading provider in capturing information, structuring information, building tools that turn information into knowledge

6. IPA IPA is an All-in-one, web-based software application Enables researchers to model, analyze, and understand the complex biological and chemical systemsat the core of life science research

7. IPA Applications: • Disease Mechanisms • Target Identification and Variation • Biomarker Discovery • Drug Mechanism of Action • Drug Mechanism of Toxicity ExperimentalPlatformSupported: • Gene Expression: (mRNA,miRNA, microarray platform, Next-gen sequencing,qPCR) • Proteomics • Genotyping • Metabolomics Identifiers

9. Peer-Reviewed Publications Citing IPA 9483 Expression, Proteomics, SNP, Copy Number, RNAi, miRNA, Oncology, Cardiovascular Disease, Neuroscience, Metabolic Disease, Inflammation/Immunology, Infectious Disease Basic, Translational, Drug Discovery & Development Research Proprietary and Confidential Full bibliography at www.ingenuity.com

10. 使用IPA之研究機構 麻省理工學院 波士頓大學 德國癌症研究中心 美國國家衛生研究院 加州大學舊金山分校 癌症研究中心 匹茲堡大學 哈佛醫學院 杜克大學 明尼蘇達大學 史丹佛大學醫學圖書館

11. 使用IPA之企業 葛蘭素史克藥廠 傑克森實驗室 賽諾菲安萬特藥廠 美國安進 默克化學 惠氏藥廠 阿斯特捷利康公司 輝瑞藥廠 默克雪蘭諾生物製藥 嬌生藥廠 必治妥藥廠 轉譯基因組學研究所

12. Introduction to Ingenuity Knowledge Base

13. Ingenuity Expert FindingsFrom full text, contextual detail, experimentally demonstrated ► Contextual details: Manual curation process captures relevant details • ► Experimentally demonstrated: Findings are from full text articles – includes tables and figures • ► Structured: Supports computation and answering in-depth biological questions in the relevant context • ► High quality: QC’d to ensure accuracy ► Timely information: Weekly updates so up to date information is captured

14. Ingenuity Content • Expert Extraction: Full text from top journals • Coverage of top journals, plus review articles and textbooks • Manually extracted by Ph.D. scientists • Import Annotations, Findings: • OMIM, GO, Entrez Gene • Tissue and Fluid Expression Location • Molecular Interactions (e.g. BIND, TarBase) • Internally curated knowledge: • Signaling & Metabolic Pathways • Drug/Target/Disease relationships • Toxicity Lists • All findings structured for computation and updated weekly

15. Synonyms, Protein Family, Domains GO, Entrez Gene, Pfam Tissue and Biofluid Expression & Location GNF, Plasma Proteome Molecular Interactions BIND, DIP, MIPS, IntAct, Biogrid, MINT, Cognia, etc. miRNA/mRNA target databases TargetScan, TarBase, miRBase Gene to Disease Associations OMIM, GWAS Exploratory Clinical Biomarkers Clinical Trial and drug information ClinicalTrials.gov, Drugs@FDA, Mosby’s Drug Consult,..etc Ingenuity® Supported Third Party Information

16. The Ingenuity OntologyStructures, translates, and integrates information • Helps you to find highly relevant and contextual information. ex: direction of change • Makes information computationally accessible and available for queries. ex: • Query over any type of connections (molecular, cellular, organism) • Make leaps from one concept to another and ask “Is there a path that might lead from A to B? • Ensures we are all talking about the same concept– regardless of your preferred nomenclature (semantically consistent). : THE INGENUITY ONTOLOGY ex : IL-1 beta increases regulation of COX1: Which COX1? cyclooxygenase or cytochrome c oxidase – both are enzymes

17. Extensive: Leverages knowledge in one place Largest scientific knowledge base of its kind with modeled relationships between proteins, genes, complexes, cells, tissues, drugs, pathways and diseases Structured:Captures relevant details Scientific statements are modeled into Findings (often causal) using the Ingenuity Ontology Expert Review Process: Checked for accuracy Findings go through extensive QC process Timely: Frequent updates and up-to-date knowledge Findings are added weekly Explore the Ingenuity Knowledge Base Ingenuity Expert Findings THE INGENUITY KNOWLEDGE BASE • From the full text • Contextual details • Timely • High-quality Ingenuity ExpertAssist Findings • High coverage (abstracts) • Timely • High-quality Ingenuity Expert Knowledge Ingenuity Supported Third Party Information

18. Metastasis Apoptosis Angiogenesis Expression Arrays Proteomics Traditional Assays The Challenge Integrate – Interpret – Gain Therapeutic Insight from Experimental Data Cellular phenotypes, pathways Disease phenotype, physiological response Molecular modules Molecular “fingerprint” – cancer vs. normal cells Disease Processes Cellular Processes Fas Vegf Molecules Experimental Platforms

19. Cancer Apoptosis Angiogenesis Expression Arrays Proteomics Traditional Assays The Challenge • Rapid understanding and interpretationof experimental systems Search for genes implicated in disease Disease Processes Identify related cellular processes, pathways Cellular Processes Generate hypothesis of molecular mechanism VEGFA bevacizumab Fas Molecules Educate in vivo, in vitro assays Experimental Platforms

20. Questions Arise During ExperimentalProcess : • How Can IPA Help You?

21. IPA Allows Scientists to Explore Biological Findings • Browse and Search the comprehensive Ingenuity Knowledge Base • Gene/Chemical Search • Functions Search • Pathway Search • Build Pathways; Build Hypotheses • Use Build Tools to explore which molecules have molecular interactions with molecule(s) of interest • Use the Overlay tools to layer additional functional, drug and biomarker information • Analyze Data; Interpret Cause and Effect; Discover the Biology • Gain insights into the Biological Functions, Canonical Pathways and Molecular Networks that involve dataset molecules • Predict Transcription Factors & Upstream Regulators involved in transcriptional changes and connect Regulators into Mechanistic and Causal Networks • Explore the Causal Effects of network changes • Filter Datasets • Biomarkers and Biofluid expression • microRNA Target Filter for miRNA-mRNA relationships

22. Search for Genes, Chemicals, Diseases, Functions, or Pathways

23. Build Pathways; Build HypothesesSearch and Explore Examples • Tell me about my gene of interest – Insulin / INS • What canonical signalling pathways does it appear in? • What are the transcriptional regulators of this gene? • What Ligand-Dependent Nuclear Receptors are regulated by these Transcription Factors? • What GPCRs are involved in diabetes? • How do they interconnect? • What other biological processes (functions) are these genes involved in? • What are the molecular connections that link these genes to cytokines involved in obesity? • What drugs target these genes? • Tell me about rosiglitazone? • What clinical trials are running with rosiglitazone? • How does rosiglitazone treatment affect the gene expression of these diabetes GPCRs and obesity cytokines? • What are the upstream regulators of the gene expression changes induced by rosiglitazone treatment?

24. Build and Grow Networks of Molecules Grow Upstream from AKT1 to kinases and phosphatases

25. View Canonical Pathways and Link Additional Molecules

26. Cause and Effect Analytics • Upstream Regulator Analysis (including Transcription Factors) • Predicts which Transcriptional Regulators and other upstream molecules are driving gene expression changes and predicts which are activated / inhibited to explain gene expression observed in a dataset • Create Mechanistic and Causal Networks • Connect upstream regulators into networks to help understand the regulatory control of the gene expression seen • Use the Ingenuity Knowledge Base of causal relationships to predict regulators that can be causally linked to the dataset molecules for unprecedented understanding of biological regulation • Downstream Effects Analysis • Predict increase or decreases in downstream biological processes (functions) and disease using the direction of change in your gene expression data • Molecule Activity Predictor • Visualize the predicted activity of causally connected molecules in Networks and Pathways

27. Upstream Regulators and Mechanistic Networks Upstream Regulator Regulator Dataset Molecules Mechanistic Network Algorithm chains interacting regulators together to create a “Mechanistic Network” Upstream Regulator Additional Upstream Regulators Dataset Molecules

28. Proprietary and Confidential Upstream Regulator AnalysisIdentify important signaling molecules for a more complete regulatory picture • Quickly filter by molecule type • Filter by biological context • Generate regulators-targets network to identify key relationships

29. Mechanistic NetworksHow might the upstream molecule drive the observed expression changes? • Hypothesis generation and visualization • Each hypothesis generated indicates the molecules predicted to be in the signaling cascade

30. Interpret Downstream Biological Functions Identify over-represented biological functions and predict how those functions are increased or decreased in the experiment

31. Compare Canonical Pathways across analyses as a heatmap

32. BioProfiler*: Find, Filter and Explore • Find molecules causally relevant to a disease, phenotype, or function • Filter by specific genetic evidence or species • Explore association with other similar diseases or phenotypes/symptoms leveraging the depth of the Ingenuity Ontology and the Human Phenotype Ontology *Available for additional cost

33. Filter Datasets for Biomarkers or miRNA Targets miRNA Data miRNA Target Filter Molecule Type Pathways (Cancer/ Growth) ? ↑↓ ↓↑ mRNA 88 data points 13,690 targets 1,090 targets 333 targets 39 targets 32 targets Use Pathway tools to build hypothesis for microRNA to mRNA target association

34. Summary IPA is a powerful data analysis and reference tool used by thousands of scientists worldwide • Browse and search the comprehensive Ingenuity Knowledge Base • Build pathways • Build hypotheses • Analyze and filter data • Discover and interpret cause and effect • Build enterprise knowledge base and results repository

35. IPA: Unique Tools for Biological Analysis and Interpretation Gene View & ChemView Summaries Human Isoform Views Interaction Networks Biological Functions Canonical Pathways Upstream Regulators/ Causal Networks BioProfiler Build & Overlay Tools

36. Data Analysis & Interpretation in IPA • - Case study for Cross Platform Integration of Metabolomics and Transcriptomics from a Diabetic Mouse Model

37. Background • T2DM is one of the most common diseases of the western world • 150 million afflicted worldwide. • Animal models can aid discovery of biomarkers and clinical compounds. • However no animal model reflects all aspects of the human form of the disease. • Omics analysis across model systems could provide supporting evidence of the value of those animal models. • Metabolic manifestations of diabetes associated with insensitivity to insulin include: • Uncontrolled lipogenesis • Hepatic glucose production • Mitochondrial dysfunction • Altered protein turnover

38. Dataset used • Integration of Metabolomics and Transcriptomics Data to Aid Biomarker Discovery in Type 2 Diabetes. Connor S et al. 2009 • Metabolites identified from Urine of dB/dB mice compared to dB/+ controls using a Non Targeted NMR based approach. • Transcriptomic analysis performed on tissue from liver, adipose and muscle using Affymetrix arrays

39. dB/dB mouse model • Lack functional Leptin receptor (LEPR-) • Leads to defective leptin-mediated signal transduction. • Results in: • Chronic overeating • Obesity • Severe hyperinsulinaemia • Hyperglycaemia and dyslipidaemia

40. Aim of case study • What diabetes aligned phenotypes are highlighted by the IPA Metabolite analysis e.g changes in lipid, glucose and protein metabolism, mitochondrial dysfunction and oxidative stress. • Can we integrate metabolite and transcript data into a concerted analysis of a dB/dB model? • Are there differences in transcript and metabolite levels relevant to gluconeogenesis (Hepatic Glucose Production)? • Can we identify putative serum/tissue biomarkers relevant to a dB/dB model?

41. Aim of case study • What diabetes aligned phenotypes are highlighted by the IPA Metabolite analysis e.g changes in lipid, glucose and protein metabolism, mitochondrial dysfunction and oxidative stress. • Can we integrate metabolite and transcript data into a concerted analysis of a dB/dB model? • Are there differences in transcript and metabolite levels relevant to gluconeogenesis (Hepatic Glucose Production)? • Can we identify putative serum/tissue biomarkers relevant to a dB/dB model?

42. Metabolite upload and mapping Includes some phase-1 and phase-2 type transformed metabolites. 68 out of 74 metabolites mapped.

43. Summary of metabolic analysis • Networks built around the metabolites also include key protein regulators of relevant functions and pathways • T2DM and Insulin receptor signalling. • Hyperglycemia, hyperinsulinemia and quantity of lipid

44. Network 1 • Dysregulated metabolites and network associated proteins highlight: • Lipid metabolism • Carbohydrate metabolism • Branched Chain Amino Acid metabolism

45. Key regulators of diabetes: ADIPOR2

46. Aim of case study • What diabetes aligned phenotypes are highlighted by the IPA Metabolite analysis e.g changes in lipid, glucose and protein metabolism, mitochondrial dysfunction and oxidative stress. • Can we integrate metabolite and transcript data into a concerted analysis of a dB/dB model? • Are there differences in transcript and metabolite levels relevant to glucose metabolism? • Can we identify putative serum/tissue biomarkers relevant to a dB/dB model?

47. Network 1- metabolite and transcript data Liver Muscle Adipose Inclusion of Liver, Muscle and Adipose transcript data

48. Aim of case study • What diabetes aligned phenotypes are highlighted by the IPA Metabolite analysis e.g changes in lipid, glucose and protein metabolism, mitochondrial dysfunction and oxidative stress. • Can we integrate metabolite and transcript data into a concerted analysis of a dB/dB model? • Are there differences in transcript and metabolite levels relevant to glucose metabolism? • Can we identify putative serum/tissue biomarkers relevant to a dB/dB model?

49. Carbohydrate metabolism

50. TCA Cycle Upregulated Citrate Cycle feeding Pyruvate into Gluconeogenesis