for Reverse Engineering of Regulatory Networks

for Reverse Engineering of Regulatory Networks
Integration of BiblioSphere PE for Reverse Engineering of Regulatory Networks Christian Zinser Genomatix Software GmbH

Overview BiblioSphere concepts Analysis demo using BiblioSphere Outlook

Genomatix core competences
Genome Annotation and Promoter Analysis Pathway Mining and Literature Analysis Microarray Analysis

BiblioSphere PathwayEdition

Gene networks construction
BiblioSphere concepts Data mining solution to extract and analyse gene networks from literature databases Integration of multiple complementary lines of evidence Literature Genome wide promoter analysis Experimental data Entity metadata Goals: Providing biological context for genes and gene lists Generating hypotheses for gene regulation

Use cases Single Gene Query – Literature environment of a gene Gene List Analysis – Finding significant biology for hypothesis generation Free Text Search – Gene network generated from a PubMed query MeSH Term Query – Networks generated from literature on a certain topic Literature List Analysis – Rapid review of knowledge in a set of abstracts

Data sources and tools Literature database – PubMed Literature metadata – MeSH Manually curated expert knowledge on gene-gene relations Gene knowledge base – ElDorado Biological entity metadata - Gene Ontology Canonical pathways – BioCyc Expression data – Unigene Transcription factor database - MatBase Bioinformatic tools and services – Genomatix Portal

What is really required is an optimized combined approach
Gene networks construction Literature mining - two approaches Expert curated databases Text mining based databases Expert quality Deeper reach Focused high coverage up-to-date inexpensive low coverage hardly up-to-date expensive unchecked quality only abstract level missed details What is really required is an optimized combined approach © 2006 Genomatix Software GmbH

Literature/knowledge mining by BiblioSphere PE Literature based relations All relevant PubMed abstracts analyzed More than 265,000 gene synonyms Regulatory sequence analysis based relations 220,000 gene - TF interactions on sentence level 132,000 interactions confirmed by additional evidence from promoter sequence analysis Expert-curated gene-gene relations >69,000 gene-gene relations curated by Genomatix experts >56,000 hand-curated gene-gene relations from NetPro © 2006 Genomatix Software GmbH

Redundancy Independent lines of evidence
Gene networks construction Relevant networks are supported by multiple lines of evidence Lines of evidence need to be independent to avoid redundancy Literature co-citation Literature Genomics + co-regulation Expression + co-expression System 1 System 2 System 3 A B One System Redundancy Independent lines of evidence © 2006 Genomatix Software GmbH

abstracts Gene networks construction
Literature/knowledge mining by BiblioSphere PE Direct filtering on genes Gene Ontology Tissues Gene expression Indirect filtering on abstracts Cocitation filter Free text filter MeSH terms BiblioSphere gene-centric BiblioSphere connection-centric abstracts © 2006 Genomatix Software GmbH

Disease A is significantly associated with the genes
Gene networks construction Determining the significance of association Calculating the z-score BiblioSphere PE abstracts random Found abstracts about a set of input genes Found 17 times disease A Found 582 times disease A observed z-score: 136 expected Disease A is significantly associated with the genes © 2006 Genomatix Software GmbH

Finding biological relevance in your data Co-citation Literature Analysis Promoter Analysis Co-regulation MMP12 SSRP1 FUS ADAM10 MMP3 MMP1 CTNNB1 CLDN1 EPHB3 F2A TIMP1 PITRM1 CHI3L1 RUNX1 LEF1 MMP12 SSRP1 FUS ADAM10 MMP3 MMP1 CTNNB1 CLDN1 EPHB3 F2A TIMP1 PITRM1 CHI3L1 RUNX1 LEF1 MMP12 MMP3 MMP1 Experimental Data Co-expression Metadata Common biology

Outline of the General Analysis Strategy
SAM statistics on single probes Regulated Genes Sharing Common Framework Probe to transcript mapping Significant Transcripts Regulated Genes Regulated Genes Genes Sharing Common Framework Genes Sharing Common Framework Network/Pathway Mining Biologically Relevant Subgroups Promoter Database Scan Promoter Sequences Common TFBS Patterns (Frameworks)

Glucocorticoid Effects on the Lymphoblast Transcriptome in Patients with Acute Lymphoblastic Leukemia (ALL)

Data Basis Schmidt et al.: Identification of glucocorticoid-response genes in children with acute lymphoblastic leukemia. Blood 107(5), (2006) NCBI Gene Expression Omnibus (GEO) accession number GSE2677 Peripheral blood lymphoblasts of 13 juvenile patients with childhood acute lymphoblastic leukemia (ALL) Comparison of samples taken 24 h after onset of Prednisolone treatment with controls Affymetrix HG-U133 Plus 2 array, raw data (CEL files)

~1600 Significant Transcripts
Step 1: Analysis of Microarray Raw Data 24 hrs post-onset control X SAM statistics on single probes Probe quality asessment FDR estimation Probe to transcript mapping ~1600 Significant Transcripts FDR = 0.0% Probe Coverage = 3 log2 Ratio Threshold 0.3

Step 2: Data Driven Network Analysis ~1600 Significant Transcripts Mapping > 550 Regulated Genes Overrepresented Biological Categories Prominently Regulated Transcription Factors Cell Cycle Down-Regulated ZBTB16 (PLZF) Up-Regulated Transcriptional Repressor Cell Cycle Link?

Step 2: Data Driven Network Analysis ~1600 Significant Transcripts > 550 Regulated Genes Mapping Overrepresented Biological Categories Prominently Regulated Transcription Factors Biologically Relevant Subgroup Cell Cycle Down-Regulated ZBTB16 (PLZF) Up-Regulated Transcriptional Repressor Link? Down-regulated Co-cited with ZBTB16 Promoter with matching TFBS Green: matching TF binding site found by Promoter Analysis

Biologically Relevant Subgroup
Step 3: Promoter Analysis What are the potentially relevant promoter structures for the regulation of ZBTB16 targets? Biologically Relevant Subgroup Promoter Sequences Common TFBS Patterns (Frameworks) cell cycle

Step 3: Promoter Analysis
Possible model for regulation of ZBTB16 targets PLZF PLZF ZBTB16 HOX MYC MYC HOX CCNA2 CCNA2

Step 4: Promoter Database Scan
Which other genes share the identified promoter frameworks and are thus potential regulatory targets of ZBTB16? Common TFBS Patterns (Frameworks) Promoter Database Scan Genes Sharing Common Framework

Step 5: Merging of Database Scan and Network Analysis
new network > 550 significantly regulated genes ~ 470 genes sharing common PLZF-HOXF frameworks 20 genes belonging to both groups

? Cell Cycle Associated Genes as Potential PLZF Targets
But what about glucocorticoid action ? M ABL1 RAD17 WEE1 KIF23 DCTN3 FGF7 APC ACVR1 ANAPC1 CDC123 CDC16 DMTF1 ESR1 GAS2 HBP1 MPHOSPH1 PARD3 SUPT3H SYCP3 TRRAP VPRBP ASPM G2 MYC G1 G0 KIF14 CCNA2 CDK6 PLZF-HOXF framework Downregulated + PLZF-HOXF framework Downregulated + PLZF-HOXF framework + ZBTB16 co-cited S

Potential Glucocorticoid Receptor Action on ZBTB16
Could ZBTB16 be a direct target of the Glucocorticoid Receptor (GR)? NR3C1 (coding for GR) is upregulated after Prednisolone treatment

Potential Glucocorticoid Receptor Action on ZBTB16
Conserved cis-regulatory module upstream of ZBTB16 includes a GRE

Summary: Cell Cycle Repression by Glucocorticoids
Prednisolone Treatment Acute Lymphoblastic Leukemia GC GR NR3C1 PLZF PLZF ZBTB16 G1 S G2 M MYC MYC MYC MYC CCNA2 CCNA2 CCNA2 CCNA2

Upcoming new features in BiblioSphere PE

Integrated UI for ChipInspector and BiblioSphere
Upcoming new features Integrated UI for ChipInspector and BiblioSphere Project manager (common)

Upcoming new features Customizable report generator E.g. ranking of biological categories in a gene set

Upcoming new features Other improvements Compatibility to data exchange standards BioPAX SBML (Systems Biology Markup Language) Collaboration server Share results and collaborate online in data analysis Full access control on local server

Upcoming new features Automated reverse engineering of regulatory networks Identification of potential upstream regulators of genes and networks Based on multiple lines of evidence Genomics (promoter analysis) Transcriptomics (expression data) Proteomics (protein-protein interactions) Hypothesis generation: inference engine (JBoss Rules) Pre-defined rules for automated hypothesis generation User-editable

Automated reverse engineering of regulatory networks
Upcoming new features Automated reverse engineering of regulatory networks Identify promoters of all input transcripts Identify all TFBSs in corresponding promoters Identify over-represented TFBSs Identify linked transcriptional modules TFs B C A D Transcripts © 2007 Genomatix Software GmbH

Automated reverse engineering of regulatory networks
Upcoming new features Automated reverse engineering of regulatory networks Group transcripts by over-represented TFBSs & modules Identify potential regulators and pathways from expert curated data P TFs B C D A Transcripts © 2007 Genomatix Software GmbH

Signaling intermediates
Upcoming new features Automated reverse engineering of regulatory networks Upstream regulators P gene A gene C gene B gene D Signaling intermediates Thank you! TFs / modules Input genes/transcripts

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