Cracking the promoter code: Identifying regulatory modules for tissue-specific transcripts.

Chris Stoeckert, Ph.D.

Center for Bioinformatics & Dept. of Genetics

University of Pennsylvania School of Medicine

Nov. 15, 2005

University of Nebraska Medical Center

What is the code for determining where (and when) a gene is expressed?

http://molbio.info.nih.gov/molbio/gcode.html

Expression

TFBS1 TFBS4TFBS3

TFBS3

TFBS4

TFBS2

TFBS2

TFBS1

TFBS = transcription factor binding site

Goal is to Identify Combinations of TFBS (cis-Regulatory Modules or

CRMs) that Specify Tissue Expression

From Wasserman & Sandelin, NRG 2004

A Genomics Unified Schema approach to understanding

gene expression

Jennifer Dommer, Steve Fischer, Thomas Gan, Greg Grant, John Iodice, Junmin Liu, Elisabetta Manduchi, Joan

Mazzarelli, Debbie Pinney, Angel Pizarro, Mike Saffitz, Jonathan Schug, Chris Stoeckert, Trish Whetzel

Computational Biology and Informatics Laboratory (CBIL), Penn Center for Bioinformatics

Cryptospiridium Database

Beta Cell Biology Consortium

Plasmodium Genome Resource

Phytophthora SoybeanEST Database

GUS

GUS

GUS is an open source projectSanger Institute

U. Georgia

Flora Centromere

Database

U. ChicagoKansas U.

U. Penn

U. Toronto

Virginia BioinformaticsInsitiute

GUS Project Goals

• Provide:– A platform for broad genomics data integration– An infrastructure system for functional

genomics

• Support:– Websites with advanced query capabilities– Research driven queries and mining

GUS Project Resources• Website -- http://www.gusdb.org

– News, Documentation, Distributable, GUS-based Projects

GUS Components

• Schema

• Application Framework

– Object/Relational Layer

– Plugin API

– Pipeline API

• Plug-ins

• Web DevelopmentKit (WDK)

Schemas Domain Features

DoTS Sequence and annotation

EST clustersGene models

RAD Gene expression MIAME

Prot Protein expression Mass specMzdata/pepXML

Study Experiments FuGE

TESS Gene Regulation TFBS organization

SRes Shared resources Ontologies

Core Administration Documentation, Data Provenance

GUS 3.5 Schemas

RAD EST clustering and assembly

DoTS

Genomic alignmentand comparativesequence analysis

Identify sharedTF binding sites

TESS

BioMaterial annotation SRes, Study

DoTS integrates sequence annotation including where expressed

RAD Contains Detailed Expression Experiments Including Tissue Surveys

TESS Allows You to Find Potential TFBS

But there are too many potential sites!

Promoters Features Related to Tissue-Specificity as Measured by Shannon

Entropy

Jonathan Schug1, Winfried-Paul Schuller2, Claudia Kappen2, J. Michael Salbaum2, Maja Bucan1, Christian

J. Stoeckert Jr.1

1. Center for Bioinformatics, Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania

2. Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, Nebraska

Genome Biology 2005 6:R33

What is a Liver-Specific Gene?

*http://expression.gnf.org/

Assessing Tissue Specificity of Genes Using Shannon Entropy

Shannon entropy is a measure of the uniformity of a discrete probability distribution. Given a set of T tissues, H ranges from 0 for a gene expressed in a single tissue to lg T for a gene expressed uniformly in all T tissues. It works well as a measure of overall tissue-specificity.

To measure specificity to a particular tissue, we combine entropy H and the relative expression level in that tissue to get Q. Q = 0 for a tissue when the gene is expressed only in that tissue and Q = 2T for a typical tissue in uniform expression.

(a) Very specific liver expression: H=1.6 and Qliver = 2.2, 98612_at cytochrome p450

(b) Near uniform expression : H=4.3 and Qliver=10.2, 104391_s_at Clcn7 chloride channel 7

Agreement between Microarrays and ESTs on Tissue Specificity

Specificity Characteristics of Tissues

TissueProbe SetID

H Q RefSeq Description

96055_at 3.2 5.8 NM_031161 cholecystokinin

93178_at 2.7 5.8 NM_019867neuronal guanine nucleotideexchange factor

93273_at 3.7 5.8 NM_009221 synuclein, alpha

92943_at 3.5 6.0 NM_008165glutamate receptor, ionotropic,AMPA1 (alpha 1)

Amygdala

95436_at 3.3 6.1 NM_009215 somatostatin

98406_at 2.7 4.0 NM_013653chemokine (C-C motif) ligand5

98063_at 1.6 4.1 -glycosylation dependent celladhesion molecule 1

99446_at 2.5 4.1 NM_007641membrane-spanning 4-domains, subfamily A, member1

92741_g_at 3.3 4.5 -immunoglobulin heavy chain 4(serum IgG1)

Lymph Node

102940_at 2.8 4.6 NM_008518 lymphotoxin B94777_at 1.3 2.1 - albumin 1101287_s_at 1.6 2.2 NM_010005 cytochrome P450, 2d1099269_g_at 1.5 2.2 NM_019911 tryptophan 2,3-dioxygenase100329_at 1.4 2.3 NM_009246 serine protease inhibitor 1-4

Liver

94318_at 1.6 2.3 NM_013475 apolipoprotein H

CpG Islands are Associated with the Start Sites of Genes with Wide-Spread Expression

CpG island = minimum 200 bp, C+G > 0.6, obs./expect. >=0.5

Tissue-Specific and Non-Specific Promoters Have Distinct Base Compositions

CpG+ CpG-

Multi-TissueH >= 4.4

TissueSpecificH <= 3.5

Promoters based on DBTSS (http://dbtss.hgc.jp)

TATA Boxes are Associated with Tissue-Specific Genes

p h = 0.13; p m = 0.15

p h = 0.00007; p m = 0.00087

p h = 0.00005; p m = 0.00001

0

10

20

30

40

50

60

70

80

90

0-2 2-4 4-6 6-8 8-10 >10

Q-Value

% with TATAA Box

human

mouse

(7/9)

(8/9)

(4/8)

(8/28)

(16/80)

(3/8) (10/28)

(16/80)

genes with

TATAA Box

human 18.8%

mouse: 22.9%

(4/31)

(2/27)

(9/35)

(3/27)

CellularComponent

BiologicalProcess

Human Only Mouse Only

extracellular,extracellular space

microsome, vesicular fraction intermediate filament(cytoskeleton)

CGI-/TATA+

response tostimulus

organismal physiological processinflammatory responseinnate immune responsecell motilitydefense responseresponse to pest/pathogen/parasiteresponse to woundingresponse to biotic stimuluscell-cell signalingmorphogenesisdigestionmuscle contraction

chemotaxis,taxis,response to chemicalsubstance,response to abioticstimulus,muscle development

cell, cytoplasm,intracellular,mitochondrion

nucleus, ribonucleoproteincomplex

CGI+/TATA-

nucleobase, nucleoside, nucleotideand nucleic acid metabolismintracellular transportmetabolismprotein transportintracellular protein transportRNA processingRNA metabolismcell cyclemitotic cell cycle

(integral to)(plasma)membrane

extracellular,extracellular space

CGI-/TATA-

organismalphysiologicalprocess, defenseresponse, immuneresponse, responseto biotic stimulus,response tostimulus

response to pest/ pathogen/parasite, cell communication,response to wounding, cellulardefense response, signaltransduction

complement activation,complement activation(classical pathway),humoral defensemechanism (sensuVertebrata),humoral immuneresponse

Functional relationships of promoter classes based on over-represented GO terms (EASE)

First Clues: TATA Box indicates Tissue Specific;

CpG indicates Wide Spread Expression

Additional clues: CpG-/TATA+ indicates high expression, secreted proteins while CpG+/TATA- indicates cellular and mitchondrial proteins.

Expanding the Mammalian CArGome

Qiang Sun1, Guang Chen2, Jeffrey W. Streb1, Xiaochun Long1, Yumei Yang1, Christian J. Stoeckert, Jr.2 and

Joseph M. Miano1

1. Cardiovascular Research Institute, University of Rochester School of Medicine, Rochester, New York

2. Center for Bioinformatics, University of Pennsylvania, Philadelphia, Pennsylvania

Genome Research (in press)

Serum Response Factor (SRF) Target Genes

Finding Novel CArG elements

• Expect 1 CArG element about every kb just by chance. – CCWWWWWWGG with one

mismatch allowed• Use conservation to reduce false

positives.– 188 associated with 4362

orthologous genes– 116 had orthologous CArGs– 10/62 known genes found– Repeated with 9169 orthologous

genes• 489 predictions• 32/62 known genes found

• 60 of 83 predictions were experimentally validated– Transfection assays– Binding assays– Knockdown assays

Serum Response Factor (SRF) Target Genes

More Clues: Human-mouse conservation enables

identification of valid CArG elements

CArG elements associated with many cytoskeletal genes suggesting role of SRF in cytoskeletal dynamics.

Using Bounded Collection Grammars to Identify cis-

Regulatory Modules in Tissue Specific Genes

Jonathan Schug

Max Mintz (CIS, U Penn)

Bounded Collection Grammars

Collection production rules for the GR response element in the PEPCK promoter

Rules are evaluated using the receiver operating characteristic (ROC)

Each point is a different parameter setting for a rule applied to training sets. Typically use area under the curve (AUC) to rank rules.

Rules are built by increasing complexity when AUC improves

Reduce search space by not pursuing unproductive paths.e.g., If (A,B) not better than A or B then don’t need to look at (A,B,C) or (A,B,D) or (A,B,C,D)

The 3-set rule for the PEPCK GR element

Note improvements of 2-sets over solos and the 3-set over 2-sets.

Discovering regulatory modules by creating profiles for Gene

Ontology Biological Processes based on tissue-specificity scores

Elisabetta Manduchi, Jonathan Schug

Klaus Kaestner (Genetics, U Penn)

If we focus on biological processes that are predominantly taking place in a given tissue, can we identify regulatory modules common to genes involved in these processes?

TissueBiological Process

Genes

For a given tissue survey, we attach “tissue-specificity” profiles to gene sets defined by GO BPs, based on the ranked lists of genes in each tissue according to increasing Q.

• To this end, we use an Enrichment Score (ES) in the spirit of that described in Mootha et al. (2003), as a measure of tissue-specificity for that gene set.

• The ES turns out to be equivalent (i.e. equal up to a multiplicative constant) to a Kolmogorov-Smirnov statistic.

......

...

liver muscle brain

**

*

*

*

*

***

*steroid metabolism

• Have applied to two different Affymetrix-based datasets– Schmueli et al. C R Biol 2003. GeneNote

(human)– Su et al. PNAS 2004. GEA2 (human and

mouse)

• We looked at ~ 2000 GO BPs that we could map to probe sets

Application to Tissue Surveys

GO BPs having significantly specific profiles for each tissue can be identified

significant in liver significant in heartand skeletal muscle

Mouse Tissue Survey Human Tissue Survey

Tissue-specific GO BPs Tissue-specific GO BPs

Reduce and Intersect

Training Setof Promoter Sequences

Training Setof Promoter Sequences

Ortholog Pairs(Homologene)

Learning Tissue-Specific Promoter Motifs

Mm-based consensusconserved sequences

Hs-based consensusconserved sequences

32 POS, 365 NEGUCSC conserved

sequences

Positive Solos Positive Solos

Liver-specificSteroid metabolism

GEA2

ROC area > 0.5

Common solos(31)

Mm-based collections(30)

Hs-based collections(83)

(13) Common collections

GATA MYCMAC/USF AIRE CAAT ER-LEFT TCF11TTAC/EFC/NCX/VBP PBX INIAATC SREBP DBPForkhead E4B EBOXCCAA CREB/ATF S8/CART1/CHX10/NKX25G_AA/CEBP/HLF TAACC LXRHNF1 ALX4 HNF4/TCF4/COUP/PPAR PPAR-LEFT ROAZ AML/PEBPBACH/NFE2/NRF2 ZTAP53 GNCF/SF1

Liver-specific from Krivan and Wasserman (2001)Known Liver TFs

Learning Liver Specific CRMs for Steroid Metabolism

• AIRE • P53• ER-LEFT • {CREB/ATF, GATA}• {GATA, GNCF/SF1}• {Forkhead, GATA} • {GATA, G_AA/CEBP/HLF}• {GATA, SREBP}• {GATA, TAACC}• {GATA, ZTA}• {AATC, SREBP}• {CAAT, SREBP}• {BACH/NFE2/NRF2, G_AA/CEBP/HLF}

Without imposing prior knowledge, end up with rules that are highly enriched for TFs expected to play a role in liver-specific streroid metabolism.

Testing a learned CRM for liver steroid metabolism using a liver HNF3-beta (FoxA2)

knock out mouse study

• {Forkhead, GATA} set rule applies to HNF3-beta/FoxA2

• Search promoters of genes down-regulated in liver as measured on PancChip microarray– Pancreas-focused array with 7356 known genes.

• 52 (0.7%) map to steroid metabolism.

– 71 genes down-regulated• 7 (10%) map to steroid metabolism.

Genes down-regulated by knockout of a forkhead protein (Hnf3-beta) are significantly enriched in steroid metabolism

More Clues: We can identify candidate CRMs from top-ranking GO Biological Processes for tissues

Tested a candidate CRM for liver steroid metabolism with a knock-out mouse. Support for role of one of the factors but not enough sensitivity for seeing both factors.

Future Directions

• Apply learning methods to many tissues and processes incorporating multiple surveys

• Add novel motifs to learning process

• Use ChIP and tissue-focused expression datasets to better evaluate

Our goal is to make inferences of the form: "The gene set G shows specificity for tissue T and is regulated by module M in this context".

http://www.cbil.upenn.edu