Biological Analysis and Interpretation in IPA ®

Comprehensive pathway and network analysis of complex 'omics data

Biological Analysis and Interpretation in IPA®

October 2013Gene Chen 陳冠文 Senior Specialist of GGA & IPA Certified Analyst

Proprietary and Confidential 2

How can I analyze existing data …


Search multiple Websites

Read multiple articles

Mine Internal Databases

Wrangle multiple Excel sheets

Spend time in the lab

How Researchers Ask Questions Now


Agenda

• Introduction to Ingenuity Pathways Analysis (IPA)

• Introduction to Ingenuity Knowledge Base

• Questions Arise During Experimental Process :

– 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


• Ingenuity Systems is a pioneer and leading provider in capturing information, structuring information, building tools that turn information into knowledge


IPAIPA is an All-in-one, web-based software applicationEnables researchers to model, analyze, and understand the complex biological and chemical systems at the core of life science research


IPA Applications:

• Disease Mechanisms

• Target Identification and Variation

• Biomarker Discovery

• Drug Mechanism of Action

• Drug Mechanism of Toxicity

Experimental Platform Supported :

• Gene Expression: (mRNA, miRNA, microarray platform, Next-gen sequencing, qPCR)

• Proteomics• Genotyping • Metabolomics Identifiers

9Proprietary and Confidential

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Peer-Reviewed Research Articles Citing IPA

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Peer-Reviewed Publications Citing IPA

9483

Full bibliography at www.ingenuity.com

Expression, Proteomics, SNP, Copy Number, RNAi,

miRNA,

Oncology, Cardiovascular Disease, Neuroscience,

Metabolic Disease, Inflammation/Immunology

, Infectious Disease

Basic, Translational, Drug Discovery & Development

Research


史丹佛大學醫學圖書館

麻省理工學院

癌症研究中心

哈佛醫學院

明尼蘇達大學

杜克大學

美國國家衛生研究院加州大學舊金山分校

匹茲堡大學

使用 IPA之研究機構

波士頓大學德國癌症研究中心


嬌生藥廠

惠氏藥廠默克化學

輝瑞藥廠

必治妥藥廠

默克雪蘭諾生物製藥

傑克森實驗室

美國安進

阿斯特捷利康公司

轉譯基因組學研究所

賽諾菲安萬特藥廠葛蘭素史克藥廠

使用 IPA之企業

http://www.pfizer.com/main.html

http://www.jnj.com/index.jsp

http://www.gsk.com/index.htm


Introduction to Ingenuity Knowledge Base


Ingenuity Expert FindingsFrom full text, contextual detail, experimentally demonstrated

Original sentence from publication

Ingenuity Expert Findings

nNOS overexpression mice showed

reduced myocardial contractility.

Transgenic nNOS in myocardium from mouse

heart decreases the contractility of

myocardium in left ventricle from mouse

heart.

Francisella organisms

efficiently induce IL-1beta

processing and release.

Francisella tularensis subsp. novicida U112 increases (in a time-dependent manner)

release of human IL1B protein from human

monocytes.

► 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


Ingenuity ContentExpert 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


Ingenuity® Supported Third Party Information•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


▶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). : ex : IL-1 beta increases regulation of COX1:

Which COX1? cyclooxygenase or cytochrome c oxidase – both are enzymes

THE INGENUITY ONTOLOGY

The Ingenuity OntologyStructures, translates, and integrates information


Explore the Ingenuity Knowledge Base• 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

THE INGENUITY KNOWLEDGE BASE

Ingenuity ExpertAssist Findings

• High coverage (abstracts)• Timely• High-quality

Ingenuity Expert Findings

• From the full text• Contextual details• Timely• High-quality

Ingenuity Supported Third Party Information

Ingenuity Expert Knowledge


ExperimentalPlatforms

Expression Arrays Proteomics Traditional Assays

Apoptosis Angiogenesis

Metastasis

Molecules Fas Vegf

CellularProcesses

DiseaseProcesses

Disease phenotype, physiological response

Cellular phenotypes, pathways

Molecular modules

Molecular “fingerprint” – cancer vs. normal cells

The Challenge Integrate – Interpret – Gain Therapeutic Insight from Experimental Data


Expression Arrays Proteomics Traditional Assays

Apoptosis Angiogenesis

Cancer

Molecules Fas VEGFA

CellularProcesses

DiseaseProcesses

Educate in vivo, in vitro assays

Search for genes implicated in disease

Identify related cellular processes, pathways

Generate hypothesis of molecular mechanismbevacizumab

The Challenge Rapid understanding and interpretation of experimental systems

ExperimentalPlatforms


Questions Arise During Experimental Process :

How Can IPA Help You?


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


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


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?


Build and Grow Networks of Molecules

Grow Upstream from AKT1 to kinases and phosphatases


View Canonical Pathways and Link Additional Molecules


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


Upstream Regulators and Mechanistic Networks

Upstream Regulator

Dataset Molecules

Regulator

Upstream Regulator

Dataset Molecules

Additional Upstream Regulators

Algorithm chains interacting regulators together to create a “Mechanistic Network”

Mechanistic Network


Upstream Regulator AnalysisIdentify important signaling molecules for a more complete regulatory picture

Proprietary and Confidential

• Quickly filter by molecule type• Filter by biological context• Generate regulators-targets network to identify key relationships


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


Interpret Downstream Biological Functions

Identify over-represented biological functions and predict how those functions are increased or decreased in the experiment


Compare Canonical Pathways across analyses as a heatmap


• 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

BioProfiler*: Find, Filter and Explore

*Available for additional cost


miRNA Target Filter

miRNA Data

Molecule Type

Pathways (Cancer/ Growth)

88 data points

13,690 targets

1,090 targets

333 targets

?32

targets

mRNA↑↓↓↑

39 targets

Use Pathway tools to build hypothesis for microRNA to mRNA target association

Filter Datasets for Biomarkers or miRNA Targets


SummaryIPA 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


IPA: Unique Tools for Biological Analysis and Interpretation

Gene View & Chem View Summaries

Human Isoform Views

Interaction Networks

Biological Functions

Canonical Pathways

Upstream Regulators/ Causal Networks

BioProfiler

Build & Overlay Tools


Data Analysis & Interpretation in IPA

- Case study for Cross Platform Integration of

Metabolomics and Transcriptomics from a

Diabetic Mouse Model


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


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


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


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?





• Are there differences in transcript and metabolite levels relevant to gluconeogenesis (Hepatic Glucose Production)?



Metabolite upload and mapping

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


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


Network 1

Dysregulated metabolites and network associated proteins highlight:• Lipid metabolism• Carbohydrate metabolism• Branched Chain Amino

Acid metabolism


Key regulators of diabetes: ADIPOR2





• Are there differences in transcript and metabolite levels relevant to glucose metabolism?



Liver

Muscle

Network 1- metabolite and transcript data

Inclusion of Liver, Muscle and Adipose transcript data

Adipose








Carbohydrate metabolism


TCA Cycle

Upregulated Citrate Cycle feeding Pyruvate into Gluconeogenesis


Gluconeogenesis

Liver Muscle Adipose

Gluconeogenesis upregulated at transcript level in Liver, but not Muscle or Adipose








Pre-established Clinical Biomarkers


Putative novel biomarkers


Putative novel biomarkers• Hyperglycaemia

– Glucose– Creatinine

• Hyperinsulinaemia– Glucose

• Diabetes– Creatine

• All detectable in Serum/Urine and phenotypically tagged to Diabetes and/or co-morbidities


Summary• 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. – Molecular networks and functions from metabolite data align to a

range of carbohydrate, lipid and protein metabolism functions and pathways

• Can we integrate metabolite and transcript data into a concerted analysis of a dB/dB model?– Ready alignment of array data and metabolite data identifies key

metabolic pathways perturbed across gluconeogenesis, citrate cycle and branched chain amino acid metabolism


Summary• Are there differences in transcript and metabolite levels

relevant to glucose metabolism?– Gluconeogenesis upregulated in liver c.f. adipose and muscle

tissue. – Citrate cycle and Valine, Leucine and Isoleucine degradation

support this hypothesis• Can we identify putative serum/tissue biomarkers relevant

to a dB/dB model? – Established diagnosis (INS) and efficacy (Glucose) biomarkers for

T2DM, obesity & dislipidaemia – Putative biomarkers in metabolite profile (glucose, creatine &

creatinine) for diabetes, hyperglycaemia and hyperinsulinaemia

Office: +886-2-2795-1777#1169

Fax: +886-2-2793-8009

My E-mail: [email protected]

MSC Support: [email protected]

Thank you

歡迎與我們聯絡


Predicting upstream regulators of a dataset

↑ ↓↓ ↑ ↑ Differential Gene Expression (Uploaded Data)↑

Predicted activation state of TF/UR: 1 = Consistent with activation of UR -1 = Consistent with inhibition of UR

1 -11 1 1 1

+++-

Note that the actual z-score is weighted by the underlying findings, the relationship bias, and dataset bias

• z-score is a statistical measure of the match between expected relationship direction and observed gene expression

• z-score > 2 or < -2 is considered significant

Literature-based effect TF/UR has on downstream genes

Every possible TF & Upstream Regulator in theIngenuity Knowledge Base is analyzed

++

=(7-1)/√8 = 2.12 (=predicted activation)

↓

-

1

↑

1

+

UR


Single- vs. Mechanistic- vs. Causal NetworksLeveraging the network to create more upstream regulators

Single Upstream Regulator

Dataset Molecules

Regulator

Causal Network ScoringCasual connection to disease, function, or gene of interest

Upstream Regulator

Dataset Molecule

s

Transcription Factors Mechanistic Network of

Upstream Regulators


Peer-Reviewed Publications citing IPASorted by Research Area, Through September 2013

Oncology27%

Inflammation/Immunology12%

Neuroscience9%

Infectious Disease7%

Bioinformatics/General Methods5%

Toxicity/Safety Assessment5%

Reproductive Biology4%

Hematology4%

Genetics4%

Metabolic Disease3%

Cardiovascular Disease4%

Developmental Biology3%

Stem Cell Biology4%

Wound Healing/Injury3%

Eye Disease2%

Nutritional Sciences2%

Renal Disorders1%

Aging1%

Bone Development1%

Liver Disease1%


Peer-Reviewed Publications Citing IPA Sorted by Experimental Platforms Through September 2013

Gene Expression Profiling72%

Proteomics Profiling13%

RNAi4%

miRNA6%

Genotyping4%

Methylation Profiling3%

ChIP-on-Chip1%

Metabolomics1%

Next-Gen Sequencing2%

Chromatin Immunoprecipitation1%

Documents

Biological Analysis and Interpretation in IPA ®