Overview:Overview:Functional Genomics Functional Genomics

Dissections of Transcriptional Dissections of Transcriptional NetworksNetworks

Rani ElkonRon Shamir, Yossi Shiloh

I. Reverse-Engineering Reverse-Engineering of Transcriptional Networks

‘Reverse engineering’ of transcriptional networks

Infers regulatory mechanisms from gene expression data Assumption:

co-expression → transcriptional co-regulation → common cis-regulatory promoter elements

Step 1: Identification of co-expressed genes using microarray technology (clustering algs)

Step 2: Computational identification of cis-regulatory elements that are over-represented in promoters of the co-expressed genes

Such methodologies were first demonstrated in yeast

Reverse-engineering of the Yeast Cell-Cycle

Expression profiles were recorded in synchronized yeast cells in 10 min intervals over 2 cell cycles.

~ 500 ORFs showed a periodic expression pattern

Reverse-engineering of the Yeast Cell-Cycle (II)

Reverse-engineering of the Human Cell-Cycle

Whitfield et al. recorded expression profiles during the progression of human cell cycle.

874 genes showed periodic expression patterns, and were partitioned into five clusters (G1/S, S, G2, G2/M and M/G1).

We applied promoter analysis to these 5 clusters

p = 1.2x10-8 (true positive)

78 promoters (92 hits)

p = 1.2x10-11 (152, 203)

p = 8x10-4 (20, 25)

Enriched TFs in cell cycle-regulated promoters

II. Cis-Regulatory Modules

Transcriptional Modules I: Co-occurrence

Transcriptional regulation is combinatorial

Promoter #1

Promoter #n

• Defining transcriptional modules:

• Co-occurrence

• Positional bias (distance)

• Orientational bias (order)

Positional and Orientational Bias for the RRPE-PAC Module

Modules II: Expression Coherence

III. Comparative Genomics

Conservation of Regulatory ElementsGene “DNA replication licensing factor MCM6”: (G1/S)

Human Cell Cycle Revisited We detected global enrichments that pointed to

major TFs in human cell cycle regulation.

However, we did not report on specific target genes due to high rate of false positive hits.

Comparative Genomics greatly boosts the specificity of in-silico detection of regulatory elements.

It now allows us to pinpoint TF targets with high confidence.

E2F Human-Mouse Conserved Hits

Cell Cycle Promoters

Rest of Genome

Total 69715,602

E2F hits75525

%11%3.3%

Enrichment Factor

3.3

P-valueE-17

16,299 human-mouse ortholog promoters (Ensembl)

E2F Conserved Hits: Phase Distribution

Num of targets

Enrich. Factor

P-val

Cell Cycle – Total

75x3.3E-17

G1/S33x7.3E-18

S15x3.9E-5

G2+M18------

M/G19------

CHR Regulatory Element

Cell-cycle Homology Region

To date, CHR was experimentally identified on 7 cell cycle-regulated promoters:

• including CDC2, CCNB1, CCNB2 and CDC25C (major regulators of G2-M)

Transcriptional Modules

Promoter #1

Promoter #n

CHR and NF-Y elements show significant co-occurrence rate (p<10-

11)

CHR-NF-Y Module16,299 Hs-Mm ortholog promoters; NFY-CHR putative targets: 71

Num of targets

Enrich. Factor

P-val

Cell Cycle – Total

42x32E-39

G1/S---------

S1------

G2+M40x64E-49

M/G11------

CHR-NFY: novel transcriptional module with a pivotal role in G2-M regulation

CHR-NFY Module Dictates Expression that is CHR-NFY Module Dictates Expression that is Specific to G2/MSpecific to G2/M

G2/MG1/S

CHR-NFY Module – False Positive Rate

Cell cycle promoters

Rest of genome (negative set)

Total 69715,602

CHR-NFY conserved hits

424229

False positives0.19%*697 =1.3

29/15602 = 0.19%

True positives~40/42 = 95%

Comparative genomics yields highly specific identification of novel CHR-NFY cell-cycle targets

Regulation of CyclinB-CDC2 activity

Rho GTPases pathways

Cytokinesis

Regulation of the kinetochore apparatus

Regulation of the mitotic spindle assembly

Novel CHR-NFY Targets in the G2-M Network

IV. Regulation of Gene Expression by

Micro-RNAs

He and Hannon, 2004

Mature miRNA (~ 22 bp) tend to: Start with a “U” base Bind their target mRNAs at sites of length 8 bp. Target site is complementary to positions 1-8 of

the mature miRNA. Assumed to play major regulatory function

during development (many show tissue-specific expression pattern)

Transfected two miRNAs into Hela human cells and examined changes in mRNA expression profiles: miR-1: expressed in skeletal muscle miR-124: expressed in brain

96 and 174 genes were significantly down-regulated by miR-1 and miR-124, respectively

Comparison with human tissue expression atlas: Genes down-regulated by miR-1 are expressed at lower levels

in skeletal muscle and heart than in other tissues Genes down-regulated by miR-124 are expressed at lower

levels in the brain than in other tissues Searching for enriched signals in the 3’-UTRs of the

down-regulated genes discovered the cognate binding sites

Computational identification of putative miRNA targets – scan 3’-UTRs for putative target sites

Anti-Correlation between the expression pattern of miRs and their putative targets (using the human tissue gene expression atlas)

Genes expressed at the same time and place as a miRNA evolved to avoid sites matching the miRNA

Comparative analysis of promoter and 3’-UTR regulatory motifs using the human, mouse, rat and dog genomes.

Search for highly conserved motifs (degenerate strings, 6-18 bases) Motif Conservation Score (MCS): Z score of the

proportion of the conserved occurrences of a motif relative to the conservation rate of comparable random motifs.

PromotersPromoters (-2 kb to + 2kb relative TSS): 174 highly conserved motifs (MCS > 6):

69 – known (out of 123 TRANSFAC motifs, 56%) 105 potentially novel regulatory elements

Demonstrating biological function for the discovered motifs: Correlate the occurrence of a motif with

tissue-specific gene expression (using data from the human tissue expression atlas)

Target sets of 86% (59 out of 69) of the known motifs showed significant tissue-specific expression

53 out of the 105 (50%) novel motifs

Examine positional bias of the motif hits

3’ UTR3’ UTR signals: 106 highly conserved motifs (MCS > 6) Hypothesis: function as binding sites for miRNAs Many of the discovered motifs show features of

miRNA binding sites: Strong strand bias of the conservation rate

consistent with a role in post-transcriptional regulation, acting at the RNA rather than DNA level

Biased length distribution: strong peak at 8 bp High rate of “A” in position #8 Search for matches of the 8-mer motifs to the known human

miRs: in 95% of the cases the matches begins at position 1 or 2 of the

mature miRNAs.

V. Integrative Analysis

Systems-level analysis of the DNA damage response in yeast by an integrated approach that combines:

1. Genome-wide profiling of TF-promoter binding (ChIP-chip data)

2. Expression profiling (in deleted and w.t. strains)

3. Phenotyping sensitivity to DNA damage in deleted strains

4. Wide scale protein-protein interaction data

1. Systematic screen for TFs involved in the DNA damage response:

30 (out of 141) TFs based on either: Expression: differentially expressed after DNA

damage Binding: bind promoters of genes induced by

DNA damage Sensitivity: TF-mutant strain is hyper-sensitive to

DNA-damaging agent

2. TF-promoter binding profiling (Chip-chip) for each of these 30 TFs, without and after exposure to DNA damaging agent

3. Validation of functional roles of the measured TF-promoter binding interactions:

Gene expression profiling in w.t. and deleted strains (27 out of the 30 are non-essential) without and after exposure to DNA damaging agent

“Deletion Buffering”: genes that respond to the damage in w.t. but become unresponsive in a specific TF-deleted strain

Only 11% (37 out of 341) of the observed deletion-buffering events could be explained by direct TF-promoter interaction

The rest are probably mediated by longer, indirect, regulatory pathways linking the deleted TF and the buffered gene

4. Physical pathways that explain indirect deletion-buffering events were searched for using Bayesian modeling procedure

Utilized various data sources: TF-promoter binding data measured in this study Tf-promoter binding data measured for all yeast

TFs (in nominal conditions) 14K high-throughput protein-protein interactions

(in nominal conditions)

The inferred network explains a total of 82 deletion-buffering events.

and in human and in human cells?cells?

Small-interfering RNA(siRNA)

He and Hannon, 2004

RNA Interference (RNAi)RNA Interference (RNAi) A major technological breakthrough in

biomedical research

Allows rapid establishment of mammalian cell lines which are stably knocked-down for any gene of interest – pivotal tool in functional genomics

Efforts to establish cell lines in which specific genes are silenced, eventually spanning most of the genome

The combination of RNAi and microarrays holds promise as a powerful tool for a systematic, genome-wide, dissection of transcriptional networks in human cells

Experiment GoalExperiment Goal Proof of principle that

RNAi+microarrays can "deliver"

Focus on transcriptional network induced by DNA damage as a test case

AP-1 CREB NF-kB E2F1 p53

ATM

g3g13 g12 g10 g9 g1g8 g7 g6 g5 g4g11 g2

DNA Double Strand Breaks

Transcriptional network induced by DNA double strand breaks

26 genes whose activation is:

• Strongly reduced in the absence of ATM and Rel-A

• Partially reduced in the absence of p53

ATM-NFκB-dependent cluster, partial role for p53

Heatmap colors:

Red – above average induction

Black – average induction

Green – below average induction

46 genes whose activation is:

• Strongly attenuated in the absence of ATM and p53

• Not affected by the absence of Rel-A

ATM-p53-dependent cluster

Response of known NF-κB targets

GeneCLacZNF-κBp53ATMNFKBIA4.615.41.262.671.02

RELB3.72.890.822.950.91

TNFAIP38.265.341.153.021.18

TNFRSF94.013.51.12.071.21

CD833.452.9811.731.06

IER34.435.131.432.351.44

• Knocking down Rel-A subunit of NF-κB abolished the induction of known NF-κB targets

• ATM is required for the activation of the NF-κB mediated transcriptional response

• p53 plays a positive role in the activation of NF-κB targets (?)

Response of known p53 targets

GeneCLacZNF-κBp53ATM

GADD45A2.352.0721.081.21

ATF33.433.757.051.541.47

FOS1.711.422.221.061.21

JUND1.671.982.671.31.02

JUN2.011.452.711.361.26

• Knocking down p53 attenuated the induction of its known targets

• ATM is required for the activation of p53 targets

• NF-κB plays an inhibitory role in the induction of some components in the p53 pathway (?)

PRIMA - Results

p53 response is mediated by the p53 response is mediated by the activation of the ATF pathwayactivation of the ATF pathway

GeneCLacZNF-κBp53ATM

GADD45A2.352.0721.081.21

ATF33.433.757.051.541.47

FOS1.711.422.221.061.21

JUND*1.671.982.671.31.02

JUN*2.011.452.711.361.26

• ATF2 is an hetrodimeric transcription factor

• Its partners are ATF3 and Jun

ATM

p53NF-kB

Jun ATF3Others

ATF2

Conclusions

VI. Gene Expression and

Cancer Treatment

Some patient are responsive and other are resistant to certain chemotherapy modalities (although suffering from the same cancer)

Probably due to different deregulated oncogenic pathways underlying the cancer

Goal: Personalized/targeted therapies

Various oncogenes (e.g., Ras, Myc, E2F) were expressed in otherwise quiescent cells

Expression signatures characteristic of each oncogenic pathway were identified

Tested on various mouse cancer models: the oncogenic-pathway signatures successfully predicted the deregulated pathways

Prediction of oncogene-pathway deregulation leads to prediction of cancer sensitivity to therapeutic agents

Ionizing radiation (IR) is the most common cancer treatment

IR causes cell death mainly via the induction of DNA double strand breaks (DSBs)

The ATM protein is the master regulator of the cellular responses to DSBs.

We examined expression profiles in response to IR in w.t. and Atm-/- mice (lymph node).

Major Gene Clusters – Irradiated Lymph nodeAtm-dependent early responding genes

Major Gene Clusters – Irradiated Lymph nodeAtm-dependent 2nd wave of responding genes

PRIMA - Results

Transcription factor

Enrichment factor

P-value

PRIMA - Results

NF-B 5.1 3.8x10-8

p53 4.2 9.6x10-7

STAT-1 3.2 5.4x10-6

Sp-1 1.7 6.5x10-4

Biological endpoints of p53- and NF-B-mediated arms

Red – induction

Yellow – No change

Gray – N.A.

• In lymphoid cells pro- and anti-apoptotic pathways are activated in parallel in an Atm- dependent manner

•The pro-apoptotic arm is mediated by p53

•The pro-survival arm is mediated by NF-κB

Responses to IR in B-CLLResponses to IR in B-CLL

Stankovic et al. (Blood, 2004) used microarrays to profile IR responses in:ATM-mutant B-CLLsp53-mutant B-CLLsATM+/+, p53+/+ B-CLLs

B-CLLs Response to IR B-CLLs Response to IR ((Stankovic et al. Blood, Stankovic et al. Blood,

2004)2004)

ATM

?p53

Pro-apoptotic signals

Pro-survival signals

NF-κB

•Blocking NF-κB arm → increase radiosensitivity of lymphoid tumors

(79 genes)

(61 genes)

AcknowledgementsAcknowledgements Sharon Rashi-Elkeles Yaniv Lerenthal Tamar Tenne Yossi Shiloh

Chaim Linhart Roded Sharan Ron Shamir

Rita Vesterman Nira Amit Giora Sternberg Ran Blechman Jackie Assa

Nir Weisman Ari Barzilai

Ninette Amariglio Gidi Rechavi