Genome-wide Survey of Mixed MicroRNA / Transcription Factor - PowerPoint PPT Presentation

Genome-wide Survey of Mixed MicroRNA / Transcription Factor Feed-Forward Regulatory Circuits in Human Davide Corà – University of Torino and INFN cora@to.infn.it

Transcription Factors and miRNAs • Regulation of gene expression mainly mediated by: Transcription Factors (TFs) : proteins MicroRNAs (miRNAs) are a family of binding to specific recognition motifs small RNAs (typically 21 - 25 nucleotide (TFBSs) usually short (5-10 bp) and long) that negatively regulate gene located upstream of the coding region expression at the posttranscriptional of the regulated gene. level , (usually) thanks to the “seed” region in 3’-UTR regions. He L. , Hannon GJ . Nat. Rev. Genetics Wassermann , Nat. Rev. Genetics

Regulatory Networks 1 Key 1 --> TFs are themselves proteins produced by other genes, and they act in a combinatorial way, resulting in a complex network of interactions between genes and their products. --> Transcriptional Network miRNAs they also act in a combinatorial and one-to-many way, and, moreover, are transcribed from POL-II promotes. --> Post-Transcriptional Network Gene E Gene F miRNA X Protein E

Regulatory Networks 2 Key 2 --> difficult to understand the whole regulatory network …. Biological functions are performed by groups of genes which act in an interdependent and synergic way. A complex network can be divided into simpler, distinct regulatory patterns called network motifs , typically composed by 3 or 4 interacting components which are able to perform elementary signal processing functions. TF miRNA target gene . . . .

Our Project Several methods exist to elucidate TF-related and microRNA-related regulatory networks, but comparable information is lacking to explicitly connect them. • We conducted an investigation aimed at the systematic integration of transcriptional and post-transcriptional regulatory interactions. • We inferred and than combined the two networks looking in particular for Mixed Feed-Forward Regulatory Loops --> a network motif in which a master Transcription Factor (TF) regulates a miRNA and together with it a set of Joint Target coding genes. TF Joint miR Target Hornstein E, Shomron N, Nat Genet 38 Suppl:S20–4 (2006).

Mainstream 1. TFs act on the promoter region of protein coding genes. 2. The same TFs act on the promoter region of miRNA genes. 3. miRNAs act on the 3’-UTR region of protein-coding genes. through cis-binding DNA/RNA sites Infer the Mixed FFL network motifs , on the human genome, using only genome sequence and functional annotations. Through an ab-initio genome-wide sequence analysis … and investigate their properties

Pipeline human core promoters human 3’-UTR exons non-redundant set of non-redundant set of human core promoters full length 3’-UTRs -900 / +100 around TSS (protein-coding genes) (protein-coding + miRNA genes) Oligo analysis Oligo analysis sets of human genes sets of human genes mouse mouse 3’-UTRs promoters conserved conserved overrepresentation overrepresentation regulatory oligos in human promoters and 3’-UTRs Gene relevance Mixed Feed-Forward regulatory Loops Ontology to cancer external annotations

Dataset Promoter and 3’-UTRs definition: miRNAs : • clustering of the pre-miRNAs into Transcriptional Units by mirATLAS. For each TU, retain only the 5’-most. • pre-miRNAs genome positions according reference genes. Non_genic --> its own core promoter Genic --> Opp_strand: its own core promoter --> Same_strand: host gene’s core promoter miRNA core promoter is -900+100 around nt 1 of pre-miRNA protein-coding genes : core promoter only KNOWN-KNOWN max_length transcript, -900+100 around TSS, default RepeatMasked. protein-coding genes : known 3’UTR full-length regions, only KNOWN-KNOWN max_length transcript, default RepeatMasked. • evolutionary constrain: retain only human / mouse conserved one2one miRNAs and protein-coding genes .

Algorithms Genome-wide ab-initio oligos analysis : - binomial probability for overrepresentation - motifs from 5 to 9 - CG rich/poor regions treatment - discard overlapping matches - non redundant dataset as background vs real sequences for signal - count both strand/single strand (promoters/3’UTRs) Conserved-overrepresentation: - Human vs mouse - Hypergeometric model - Benjamini-Yekutiely FDR for both promoters and 3’-UTRs - motifs validation via Transfac known TFBSs and known miRNA seeds Chan et al, PloS Comput. Biol. 2005 Corà et al, BMC Bioinformatics 2005 Corà et al, BMC Bioinformatics 2007

Results Human Transcriptional Network --> Fixing 0.1 as FDR level, we obtained a catalogue of 2031 oligos that can be associated to known TFBSs for a total of 115 different TFs. --> target a total of 21159 genes (20972 protein-coding and 187 miRNAs) Human Post-Transcriptional Network --> Fixing 0.1 as FDR level, we obtained a catalogue of 3989 oligos (7-mers). 182 of them turned out to match with at least one seed present in 140 mature miRNAs. --> target a total of 17266 genes Human mixed FFLs catalogue --> We were able to obtain a list of 5030 different “ single target circuits ”, corresponding to 638 “ merged circuits ”. TF --> involving a total of 2625 joint target genes (JTs), 101 TFs and 133 miRNAs. miR JT 1 # of JTs ranged from 1 to 38. JT 2 JT …

Circuits assessment 1: functional analysis We analyzed each one of the 638 merged circuits looking for an enrichment in Gene Ontology categories in the set of their joint targets . To assess this enrichment we used the standard exact Fisher test with a p-value threshold p < 10 －4 .  we end with a list of 32 merged mixed Feed-Forward Loops (corresponding to 380 single-target FFLs). These circuits involve a total of 344 JT protein-coding genes, 24 TFs and 25 mature miRNAs.

Circuits assessment 1: functional analysis --> various aspects of organism differentiation and development

Circuits assessment 2: comparison with external databases We developed an annotation scheme , based on the existence of additional computational evidences for each circuit link . Joint TF ECRbase Target ECRbase, PMID:17447837 TF miR Joint miRBase4; PicTar; miR TargetScan4.2 Target

Circuits assessment 3: looking for cancer related FFLs In these last few years it is becoming increasingly clear that miRNAs play a central role in cancer development (e.g. Blattener Mol Syst. Biol. 2008 ).  We filtered our results looking for FFLs containing at least two cancer related miRNA or target gene. Sources: oncomiRs reported in - Esquela-Kerscher and FJ Slack, Nat Rev Cancer 2006 - Zhang et al, Dev Biol, 2007 cancer genes reported in - Cancer Gene Census database.

Example of an interesting circuit 1 MYC|hsa-mir-17-5p|E2F1 MYC The “merged” circuit contains 11 joint targets among which, the E2F1. The FFL involving E2F1 is well known in hsa- the literature. E2F1 mir-17-5p EDD1 It was discussed for the first time in TAF5L O’Donnell et al. (Nature 2005) and plays a HIF1A role in the control of cell proliferation, Q6ZR74 growth and apoptosis. OSBPL10 ACP1 NFAT5 which is known to play a critical MYNN role in heart, vasculature, muscle and CENTB5 nervous tissue development. GDA This circuit is experimentally validated !

Example of an interesting circuit 2 Cancer related circuit HSF2 HSF2|hsa-let-7f|MYCN hsa-let-7f MYCN HSF2 role in cancer is being elucidated by the observation ESPL1 that it functions as bookmarking factor for heat shock PLSCR3 Responsive genes and also for genes that are involved in PDCD4 regulation of cell apoptosis and proliferation. MTO1 The MYCN oncogene is crucial in neuronal development FMO2 and its amplification is currently one of the molecular marker adopted in neuroblastoma clinical treatments. The MYC family oncogenes are known to deregulate cell cycle progression, apoptosis and genomic instability. let-7f belongs to the let-7 family of oncomiRs and, in particular, let-7f has been found involved in cell aging and various other aspects of cancer biology. In this case, the interplay in a mixed FFL is novel.

Analysis of the mixed FFLs in term of network motifs Elementary regulatory circuits (the so called ”network motifs”) were shown to be over-represented in transcriptional networks. (Milo et a., Science 2002 , Shen-Orr et., Nat Genetics 2002 ) In order to quantify the overrepresentation we perfomed various randomization tests. - Random miRNA promoters and seeds, Z = 8.1 Mixed FFLs are genuine - Edge Switching , Z = 8.4 Network motifs - Complete node replacement, Z = 9.2