Transcriptional Enhancers Looking out for the genes and each other Sridhar Hannenhalli Department of Cell Biology and Molecular Genetics Center for Bioinformatics.

Slides:



Advertisements
Similar presentations
Methods to read out regulatory functions
Advertisements

Epigenetics Xiaole Shirley Liu STAT115, STAT215, BIO298, BIST520.
Regulomics II: Epigenetics and the histone code Jim Noonan GENE760.
Functional Non-Coding DNA Part II DNA Regulatory Elements BNFO 602/691 Biological Sequence Analysis Mark Reimers, VIPBG.
Thermodynamic Models of Gene Regulation Xin He CS598SS 04/30/2009.
Combined analysis of ChIP- chip data and sequence data Harbison et al. CS 466 Saurabh Sinha.
Speaker: HU Xue-Jia Supervisor: WU Yun-Dong Date: 19/12/2013.
Epigenetics 12/05/07 Statisticians like data.
Teresa Przytycka NIH / NLM / NCBI RECOMB 2010 Bridging the genotype and phenotype.
Biology and Bioinformatics Gabor T. Marth Department of Biology, Boston College BI820 – Seminar in Quantitative and Computational Problems.
27803::Systems Biology1CBS, Department of Systems Biology Schedule for the Afternoon 13:00 – 13:30ChIP-chip lecture 13:30 – 14:30Exercise 14:30 – 14:45Break.
This presentation was originally prepared by C. William Birky, Jr. Department of Ecology and Evolutionary Biology The University of Arizona It may be used.
[Bejerano Spr06/07] 1 TTh 11:00-12:15 in Clark S361 Profs: Serafim Batzoglou, Gill Bejerano TAs: George Asimenos, Cory McLean.
27803::Systems Biology1CBS, Department of Systems Biology Schedule for the Afternoon 13:00 – 13:30ChIP-chip lecture 13:30 – 14:30Exercise 14:30 – 14:45Break.
[Bejerano Aut07/08] 1 MW 11:00-12:15 in Redwood G19 Profs: Serafim Batzoglou, Gill Bejerano TA: Cory McLean.
The Hardwiring of development: organization and function of genomic regulatory systems Maria I. Arnone and Eric H. Davidson.
ChIP-seq QC Xiaole Shirley Liu STAT115, STAT215. Initial QC FASTQC Mappability Uniquely mapped reads Uniquely mapped locations Uniquely mapped locations.
CS 374: Relating the Genetic Code to Gene Expression Sandeep Chinchali.
[Bejerano Fall09/10] 1 Thank you for the midterm feedback!
[Bejerano Aut08/09] 1 MW 11:00-12:15 in Beckman B302 Profs: Serafim Batzoglou, Gill Bejerano TAs: Cory McLean, Aaron Wenger.
“An integrated encyclopedia of DNA elements in the human genome” ENCODE Project Consortium. Nature 2012 Sep 6; 489: Michael M. Hoffman University.
[BejeranoFall13/14] 1 MW 12:50-2:05pm in Beckman B302 Profs: Serafim Batzoglou & Gill Bejerano TAs: Harendra Guturu & Panos.
Comparative Genomics II: Functional comparisons Caterino and Hayes, 2007.
ENCODE enhancers 12/13/2013 Yao Fu Gerstein lab. ‘Supervised’ enhancer prediction Yip et al., Genome Biology (2012) Get enhancer list away to genes DNase.
1 1 - Lectures.GersteinLab.org Overview of ENCODE Elements Mark Gerstein for the "ENCODE TEAM"
Epigenome 1. 2 Background: GWAS Genome-Wide Association Studies 3.
P300 Marks Active Enhancers Ruijuan LiChao HeRui Fu.
Current Topics in Genomics and Epigenomics – Lecture 2.
An Introduction to ENCODE Mark Reimers, VIPBG (borrowing heavily from John Stamatoyannopoulos and the ENCODE papers)
* only 17% of SNPs implicated in freshwater adaptation map to coding sequences Many, many mapping studies find prevalent noncoding QTLs.
GWAS Hits and Functional Implications Peter Castaldi February 1, 2013.
MEME homework: probability of finding GAGTCA at a given position in the yeast genome, based on a background model of A = 0.3, T = 0.3, G = 0.2, C = 0.2.
Recombination breakpoints Family Inheritance Me vs. my brother My dad (my Y)Mom’s dad (uncle’s Y) Human ancestry Disease risk Genomics: Regions  mechanisms.
Molecular Basis for Relationship between Genotype and Phenotype DNA RNA protein genotype function organism phenotype DNA sequence amino acid sequence transcription.
Overview of ENCODE Elements
Jason Ernst Broad Institute of MIT and Harvard
Biol 456/656 Molecular Epigenetics Lecture #5 Wed. Sept 2, 2015.
Genomics 2015/16 Silvia del Burgo. + Same genome for all cells that arise from single fertilized egg, Identity?  Epigenomic signatures + Epigenomics:
Genetics of Gene Expression BIOS Statistics for Systems Biology Spring 2008.
A high-resolution map of human evolutionary constraints using 29 mammals Kerstin Lindblad-Toh et al Presentation by Robert Lewis and Kaylee Wells.
Enhancers and 3D genomics Noam Bar RESEARCH METHODS IN COMPUTATIONAL BIOLOGY.
Understanding GWAS SNPs Xiaole Shirley Liu Stat 115/215.
Integrative Genomics. Double-helix DNA strands are separated in the gene coding region Which enzyme detects the beginning of a gene ? RNA Polymerase (multi-subunit.
Additional high-throughput sequencing techniques (finding all functional elements of genome) June 15, 2017.
High-throughput data used in bioinformatics
EQTLs.
Epigenetics Continued
Functional Elements in the Human Genome
Complex disease and long-range regulation: Interpreting the GWAS using a Dual Colour Transgenesis Strategy in Zebrafish.
Functional Mapping and Annotation of GWAS: FUMA
Gene Hunting: Design and statistics
Structure of proximal and distant regulatory elements in the human genome Ivan Ovcharenko Computational Biology Branch National Center for Biotechnology.
Albert Xue, Binbin Huang, Jianrong Wang
Relationship between Genotype and Phenotype
Relationship between Genotype and Phenotype
A Zero-Knowledge Based Introduction to Biology
Genetic-Variation-Driven Gene-Expression Changes Highlight Genes with Important Functions for Kidney Disease  Yi-An Ko, Huiguang Yi, Chengxiang Qiu, Shizheng.
Relationship between Genotype and Phenotype
A Phase Separation Model for Transcriptional Control
In these studies, expression levels are viewed as quantitative traits, and gene expression phenotypes are mapped to particular genomic loci by combining.
Genetic variation in DREs could be a causative factor in dysregulation of distal target gene expression. Genetic variation in DREs could be a causative.
Relationship between Genotype and Phenotype
Presented by, Jeremy Logue.
Are Interactions between cis-Regulatory Variants Evidence for Biological Epistasis or Statistical Artifacts?  Alexandra E. Fish, John A. Capra, William.
GWAS-eQTL signal colocalisation methods
Adam C. Wilkinson, Hiromitsu Nakauchi, Berthold Göttgens  Cell Systems 
Presented by, Jeremy Logue.
Integrative analysis of 111 reference human epigenomes
Relationship between Genotype and Phenotype
Presentation transcript:

Transcriptional Enhancers Looking out for the genes and each other Sridhar Hannenhalli Department of Cell Biology and Molecular Genetics Center for Bioinformatics and Computational Biology UM Institute for Advance Computer Studies University of Maryland

Transcriptome – a key mediator of diversity Genotype Transcriptome Phenotype “their macromolecules are so alike that regulatory mutations may account for their biological differences.” King and Wilson, Science, 1975

Transcriptional Regulation TF-DNA binding TF interactions Chromatin structure Posttranslational modification Promoter

Enhancers – key mediators of context-specific gene regulation Enhancer Shh Lmbr1 1 Mb A long-range Shh enhancer regulates expression in the developing limb and fin and is associated with preaxial polydactyly, Lettice et al. HMG 2003

(In a 3D chromatin context)

Avinash

Genome-wide association studies Collins 2007 Wang et al. Nature

Expression Quantitative Trait Loci (eQTL) CC CG GG Trait eQTL :: Trait = Expression GWAS :: Trait = Phenotype Linkage disequilibrium – Association versus causality Functional interpretation of detected SNPs

Enhancer-mediated eQTL (eQTeL) Nat Comm, 2015

MAGNet >300 human hearts Gene expression Genotype MAGNet >300 human hearts Gene expression Genotype ENCODE & Epigenome roadmap Histone modifications Transcription factors Chromatin accessibility…. ENCODE & Epigenome roadmap Histone modifications Transcription factors Chromatin accessibility…. eQTeL application

Biology: Protein binding

Motif disruption Biological evidence II:

15 Allelic imbalance

Spatial proximity to target

Justin Malin

Correlated “expression” Tissues Gene Network Correlated “activity” Tissues Enhancer Network Malin et al. NAR 2013

~100K candidate enhancers DNAse HS as a proxy for enhancer “activity” Genome-wide DHS profiles for 72 tissue types

o Shared TF binding sites Correlated enhancers tend to share common transcription factor binding motifs. This trend is stronger for enhancer pairs at greater distances. Presence of shared motifs can predict correlated activity with 73% accuracy. o Chromatin modification enzymes preferentially interact with the TF with enriched motifs in correlated enhancers o Correlated enhancers tend to be spatially proximal in multiple cell lines Properties of correlated enhancers Motif Sharing

Putative targets (nearest gene) of correlated enhancers Have correlated expression consistent with enhancer activity Enriched for specific molecular functions

Motif Sharing 3D Proximity Correlated enhancer activity Correlated gene expression Putting it together ….

(In a 3D chromatin context) Justin Malin a.k.a. Crowdsourcing of transcription factor binding by a spatially clustered collective of binding sites

DNA motif (~20 bases) Cooperative binding (~100 bases) GC content of flanking region (~200 bases) Local chromatin state (~500 bases) Homotypic clusters of BS (~500 bases) Determinants of TF-DNA interaction In vivo determinants In vitro determinant: motif

Low spatial proximity High spatial proximity Degenerate TFSpecific TF HCT Spatial homotypic clusters of TF BS

Computing occupancy ‘boost’ in archipelagos (APs) with digital footprint data (Neph et al 2012)

Occupancy boost scales with |BS|

1 2 Number of homotypic sites per enhancer Number of enhancer per archipelago 8 4 AP occupancy simulated (Facilitated TF Diffusion) Boris Adryan, Daphne Ezer, Xiaoyan Ma, Cambridge

Downstream impact of crowdsourcing occupancy boost AP enhancers enriched for degenerate motifs AP enhancers depleted for degenerate motifs Evolutionary conservation* 120% higher20% higher Target (neighbor) gene expression* ~3-fold higher ~3-fold lower Enhancer activity/ accessibility 9-fold higher DHS 3.5-fold higher H3K27Ac in enriched than depleted enhancers^ *Relative to matched non-AP enhancers ^ FG, BG controlled relative to matched non-AP enhancers

TF occupancy Chromatin accessibility

As expression of degenerate TFs ↑ (across 11 tissues) mean AP activity level (DHS) ↑ No change for non-degenerate TFs, non-AP AP Non-AP Degenerate TFs Non-degenerate TFs

Crowdsourcing emergent in gene complexes

(In a 3D chromatin context) Incorporating enhancer-mediated regulatory mechanisms in the eQTL model is critical to identify causal SNPs Much like genes, enhancers form functionally cohesive co- active clusters Spatial enhancer clusters have a group-level effect on in vivo occupancy

Avinash Das Justin Malin Cambridge Daphne Ezer Xiaoyan Ma Boris Adryan NIH