1. Integrative analysis of epigenomes illuminates differentiation and diseases of blood cells
Ross Hardison
Department of Biochemistry and Molecular Biology
Huck Institute for Genomics
Penn State University
9/29/16
Bioinformatics and Genomics, UNC Charlotte

2. Simplified scheme of hematopoiesis
HSC
CMP
MEP
GMP
CLP
MEG
ERY
EOS
Mast
GRA
MONO T B NK
2 M sec-1
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3. Differentiation and diseases of blood cells
Lineage specific binding of key transcription factors drives expression patterns that determine cell type
Maps of transcription factor occupancy inform models of regulation
Cell specific phenotypes arise from lineage-specific binding of transcription factors at distinct sites
ValIdated Systematic IntegratiON: A VISION for epigenomics in hematopoietic gene regulation
Measure distances between cell types by quantitative comparisons of chromatin accessibility landscapes and transcriptomes
Integrative analysis of epigenomics can improve prediction of enhancers
Formal modeling to understand regulation of a locus and regulatory output of each cis-regulatory module
Use this information to increase accuracy of search for genetic variants in regulatory regions to explain phenotypes
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4. E.H. Davidson, 1976, Gene Activity in Early Development, 2nd ed.
The guiding principle of developmental biology: Differential gene expression determines the distinctive properties of each cell type.
E.H. Davidson, 1976, Gene Activity in Early Development, 2nd ed.
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5. Lineage specific binding of key transcription factors drives expression patterns that determine cell type
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6. GATA1 is required for production of erythrocytes, megakaryocytes, mast cells, and eosinophils
ES cells, Gata1-
HSC
CMP
CLP
GMP
MEP
MEG
ERY
GRA
MONO T B NK
EOS
Mast
blastocyst
Chimeric mouse X X X X
Did the Gata1- ES cells contribute to specific lineages?
Pevny et al Nature 349:257;
Pevny et al Development 121:163
and subsequent papers,
multiple alleles of Gata1
S.H. Orkin (1995) J. Biol. Chem. 270:
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7. Lineage-restricted TFs determine hematopoietic cell fate
HSC
CMP
CLP
GMP
MEP
MEG
ERY
GRA
MONO T B NK
EOS
Mast
TAL1
GATA2
IKAROS
PU.1
PU.1
GATA1
PAX5
TAL1
GATA1
CEBPA
FLI1
LMO2
GATA3
GATA3
GATA1
PU.1
KLF1
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8. Cell-restricted transcription factors regulate target genes positively and negatively
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9. Erythroid differentiation in cultured and primary cells
G1E-ER4
BFU-E
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Weiss, Yu, Orkin (1997) Mol. Cell. Biol. 17: 1642
Wu et al Genome Res 21:
Welch et al.. (2004) Blood 104: 3146
Pilon, Subramanian, Kumar et al.2011 Blood Epub Sep 2011

10. Transcriptional response to GATA1-ER activation in G1E cellsB
Induced
Repressed
Differentially expressed genes 3 7 14 24 30 hr
Platform
Genes induced
Genes repressed
References
Affymetrix microarrays
1048
1568
Cheng et al Genome Res 19:2172
RNA-seq, polyA+ RNA
1416
1039
Jain, Mishra et al Genomics Data 4:1-7 A B
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11. TFs regulate lineage-specific genes
GATA1 +
Induced gene
WGATAR
GATA1 -
Repressed gene
WGATAR
Contexts must differ between induced and repressed:
Sequence, motifs?
Other TFs? Co-activators? Co-repressors?
Chromatin?
Nuclear location?
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12. GATA1 occupancy genome-wide: clues about regulation
Yong Cheng Ying Zhang G. Celine Han
GATA1 occupancy genome-wide: clues about regulation
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13. Locations of occupancy by GATA1
ChIP-chip
~3,558 sites
Cheng et al
ChIP-seq
~14,000 sites
Erythroblasts, Wu et al. 2011; Pimkin et al. 2014
ChIP-exo
~10,000 sites
Han et al Mol. Cell Biol.
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Jain, Mishra et al Genomics Data 4:1-7.

14. Distinguishing features of GATA1-mediated gene induction
GATA1 tends to bind close to the TSS
Most often in the first intron but frequently in the proximal flanking region
Multiple GATA1 OSs
58% of induced genes, 24% repressed
Evolutionary constraint on the GATA motif instances
Region around the TSS depleted of H3K27me3
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Cheng et al. (2009) Genome Res. 19:

15. TAL1 + GATA1 = induction Gerd Blobel Weisheng Wu
Tripic et al (2009) Blood 113: 2191
Cheng et al (2009) Genome Res. 19: 2172
Wu et al (2011) Genome Res. 21: 1659
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Weisheng Wu

16. ~15,000 GATA1-bound sites
More than number of GATA1-responsive genes (~2,500)
Average of 6 bound sites per responsive gene
Far fewer than number of GATA1 binding site motif instances (~8 million)
About 1 bound site per 500 motif instances
Considering DNA segments (500bp) containing at least one motif instance, about 1 in 150 DNA segments are bound
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17. Determinants of GATA1 occupancy: Chromatin >> motifs
Study DNA segments comparable in size to ChIP-chip peaks (500bp) that also have a match to a GATA1 binding site motif
What distinguishes GATA1-bound from unbound segments?
Additional motifs increase discriminatory power only 2 fold.
Mark of active chromatin (H3K4me1) increases discrimination 25 fold.
Zhang et al Nucleic Acids Res 37:7024.
Ying Zhang
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18. Epigenetic features associated with transcriptional regulation, assayed genome-wide
Repressed chromatin
Enhancer
Promoter
Repressed chromatin
H3K27ac
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19. Changes in TF occupancy drive differential regulation
Maxim Pimkin, Chris Morrissey, Tejas Mishra, Deepti Jain, Weisheng Wu..
Changes in TF occupancy drive differential regulation
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20. Most GATA1 and TAL1 binding sites are distinctive to ERYs vs MEGs
The TFs GATA1 and TAL1 are required for production of both erythroblasts and megakaryocytes.
Pimkin et al. (2014) Genome Research 24: 1932
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21. Major shifts in TAL1 occupancy during hematopoiesis
Wu et al. (2014) Genome Research 24: 1945
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22. ValIdated Systematic IntegratiON: A VISION for epigenomics in hematopoietic gene regulation
Ross Hardison
Department of Biochemistry and Molecular Biology
Huck Institute for Genome Sciences
Penn State University
9/29/16

23. Rationale for the VISION project
Acquisition of genome-wide epigenetic data across hematopoiesis is no longer the major barrier to understanding mechanisms of gene regulation during normal and pathological tissue development
The chief challenges are how to
integrate epigenetic data in terms that are accessible and understandable to a broad community of researchers
build validated quantitative models explaining how the dynamics of gene expression relates to epigenetic features
translate information effectively from mouse models to potential applications in human health.
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24. VISION: ValIdated Systematic IntegratiON of epigenomics in hematopoietic gene regulation
Acquire
Integrate
Validate
Translate
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25. Initial VISION Resources
BX Browser: Visualize functional genomics data
3D Genome Browser
CODEX compendium
of functional genomics
Repository of hematopoietic transcriptomes
Jens Lichtenberg poster
IDEAS data integration
Single cell transcriptomes, HSC
Gottgens lab
ENCODE Element Browser
Translate between mouse and human
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26. Generate, compile, and curate epigenomic data
Work from individual labs
736 datasets
11,774 datasets
High quality,
high information tracks
Hematopoietic cells :
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27. Focus on myeloid-erythroid branches of hematopoiesis
HSC
HPC7
CMP
MEP
GMP
CLP
G1E
ER4
CFU-Mk
CFU-E
MEG
ERY
EOS
Mast
GRA
MONO T B NK
2 M sec-1
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28. ScriptSeq RNA-seq at Zfpm1 and neighbors
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29. Hierarchical clustering: Erythroid separates from others
Transcript levels of all genes (RNA-seq)
BG, July 22, 2016
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30. ATAC-seq in Zfpm1 and neighbors
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31. Hierarchical clustering: Erythroid separates from others
Nuclease accessibility (ATAC-seq)
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BG, Aug 03, 2016

32. General model for lineage choice
HSC
CMP
MEP
GMP
ERY
MEG
CFU-Mk
CFU-E
Lineage choice occurs with – or even via – establishment of permissive and repressive chromatin states
These chromatin states are relatively stable within a lineage – even when expression changes dramatically
Induction and repression within a lineage are largely a result of changes in patterns of TF binding on the stage of the permissive chromatin
Similar regulatory landscapes
Dynamic TF binding
= change in regulatory landscape
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33. Nergiz Dogan
Integrative analysis of epigenomics can improve prediction of enhancers
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34. Epigenetic signatures can predict enhancers with high accuracy: TAL1 occupancy
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Dogan et al (2015) Epigenetics & Chromatin 8: 16

35. TF occupancy: frequently active as enhancers HMs without TFs: rarely active as enhancers
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Dogan et al. (2015) Epigenetics & Chromatin 8: 16.

36. Integration of epigenetic signals in two dimensions simultaneously
Integration of epigenetic signals along chromosomes and across cell types
Yu Zhang (Statistics, PSU): Integrative and Discriminative Epigenome Annotation System (IDEAS)
Zhang, An, Yue, Hardison (2016) Nucleic Acids Research 44:
Joint characterization of epigenetic landscapes in many cell types and detection of differential regulatory regions
Preserves the position-dependent and cell type-specific information at fine scales
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37. Integrative analysis of histone modifications reveals little change during erythroid maturation
Ernst & Kellis (2012) Nature Methods
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Wu et al. (2011) Genome Research 21: 1659.

38. Integrative and Discriminative Epigenome Annotation System (IDEAS)
Zhang, An, Yue, Hardison (2016) Nucleic Acids Research 44:
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39. IDEAS to integrate histone modifications and ATAC-seq across cell types
ATAC: Hardison & Bodine, Amit lab
Histone Mod iChIP: Amit lab
IDEAS: Integrative and Discriminative Epigenome Annotation System: 2D segmentation
Yu Zhang et al. 2016
NAR14:
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Promoter
Active chromatin
Quiescent

40. Nascent VISION gives new insights
Previous studies:
Autoregulation by GFI1B binding to promoter proximal CRM
Moroy et al NAR 33:987.
Multipotent progenitor cells
Maturing erythroid cells
Structural TFs
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41. Interpreting the maps as testable hypotheses
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42. Try to integrate all the epigenomic and expression information to derive rules for regulation that apply globally
rules = equations
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43. Modeling different aspects of regulation in VISION
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44. Functional output from distal CRMs measured for Hbb locus
Blood, 2012
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45. Locus models for Hbb and Hba
Locus model: States the functional output Xi,j from each of the cis-regulatory modules (CRMs) contributing to the expression level of the target gene (T).
E.g. here is a formal statement of results from Bender et al. 2012:
THbb = XHS1 + XHS2 + XHS3 + XHS4 + XHS5,6 =
For the Hba complex of enhancers (Hay et al Nature Genetics 48: 898):
THba = XR1 + XR2 + XR3 + XRm + XR4 =
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46. Models for cis-regulatory modules (CRMs)
CRM model: Quantitative estimates of the contribution of epigenomic features, sequence, conservation, etc. to the functional output Xi,j from each of the CRMs
XHS2, Hbb-b1 = 0.41= combination of f(chromatin state), f(TF occupancy), …
XHS1, Hbb-b1 = 0.22
HS1
HS2
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47. Global application of models
Once you have a CRM model, you can apply it globally
It is an equation using variables for which you have measurements genome-wide
H3K27ac, GATA1 occupancy, TAL1 occupancy, motifs, etc.
So you can predict Xi,j for all candidate CRMs
We learned it from a few CRMs in a few loci, and of course it should work there. But what about other loci?
Test these predictions! Genome editing in additional, reference loci
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48. Epigenome maps provide a guide to noncoding variants associated with phenotype
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49. Variants affecting gene regulation play a prominent role in complex traits
The majority of genomic variants associated with complex traits are not in protein-coding exons
Hindorff et al (2009) PNAS 106:9362.
Phenotype-associated, noncoding variants are highly enriched in DNA with epigenetic signatures of regulatory regions.
Maurano et al. (2012) Science 337: 1190
Schaub et al. (2012) Genome Research
ENCODE Consortium (2012) Integrated Encyclopedia … Nature
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50. From GWAS results to allele-specific regulation
CRM = cis regulatory module, e.g. enhancer
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Hardison (2012) JBC 287: Minireview on Epigenetic data as guide to interpret GWAS

51. Cluster of SNPs associated with inflammatory diseases are close to sites occupied by GATA factors
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ENCODE Consortium (2014) Integrated Encyclopedia … Nature

52. Strategy for linking regulatory variation to phenotype
Locus with phenotype-associated variants
Identify candidate CRMs from epigenomic data
Find common and rare variants in CRMs for in cohorts of patients
Predict those likely to affect regulation
Test for allele-specific effects
Candidate enhancers
Candidate loop bases
DNase FL ERY
DNase Multipot prog
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53. Differentiation and diseases of blood cells
Lineage specific binding of key transcription factors drives expression patterns that determine cell type
Maps of transcription factor occupancy inform models of regulation
Cell specific phenotypes arise from lineage-specific binding of transcription factors at distinct sites
ValIdated Systematic IntegratiON: A VISION for epigenomics in hematopoietic gene regulation
Measure distances between cell types by quantitative comparisons of chromatin accessibility landscapes and transcriptomes
Integrative analysis of epigenomics can improve prediction of enhancers
Formal modeling to understand regulation of a locus and regulatory output of each cis-regulatory module
Use this information to increase accuracy of search for genetic variants in regulatory regions to explain phenotypes
9/29/16

54. Thanks to the VISION team
Cheryl Keller
Yu Zhang
Gerd Blobel
James Taylor
Berthold Gottgens
Amber Miller
Feng Yue
Mitch Weiss
David Bodine
Doug Higgs
Belinda Giardine
Jim Hughes
Hardison Lab
Supported by
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55. Deliverables from VISION
Comprehensive catalogs of cis-regulatory modules utilized during hematopoiesis
Built by integration of multiple data types
Validated by extensive experimental tests
Quantitative models for gene regulation
Built by machine learning
Extensively tested by genome editing approaches in ten reference loci
Predictions applied genome-wide.
A guide for investigators to translate insights from mouse models to human clinical studies.
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