3D Nuclear Organization and Transcriptional Gene Networks

Eurandom Workshop – Statistics for Complex Networks

January 30th -February 1st 2013

Geert Geeven

Nuclear organization of DNA is not randomNuclear organization of DNA is not random

Kosak & Groudine, 2004

Gene repositioning relative to nuclear landmarks influences gene expressionGene repositioning relative to nuclear landmarks influences gene expression

Kosak & Groudine, 2004

Spatial organization at different scales – the modular genomeSpatial organization at different scales – the modular genome

OutlineOutline

• 3C technology and its derivates : Powerful tools to study the 3D genomic context of nuclear DNA.

• .Genome-wide identification of Pericentromeric Heterochromatin (PCH) associated domains (PADs).

• Implications of 4C and Hi-C data for the identification of transcriptional gene regulatory networks.

3C and its derivatives – proximity based ligation3C and its derivatives – proximity based ligation

The role of nuclear organization in differentiationThe role of nuclear organization in differentiation

In ES cells, inactive regions show depletion of specific contacts.

Mapping promoter-enhancer (P-E) interactions with 4CMapping promoter-enhancer (P-E) interactions with 4C

Van de Werken et al. (2012), Nature Methods

OutlineOutline

• 3C technology and its derivates : Powerful tools to study the 3D genomic context of Nuclear DNA.

• .Genome-wide identification of Pericentromeric Heterochromatin (PCH) associated domains (PADs).

• Implications of 4C and Hi-C data for the identification of transcriptional gene regulatory networks.

UCSF School of Medicine

heterochromatin

condensed chromatin (“closed”)

low gene density

repeat-rich

associated with gene repression

eurochromatin

relaxed chromatin (“open”)

high gene density

repeat-poor

associated with gene expression

Gene positioning relative to nuclear landmarks is associated with changes in gene expression

Gene positioning relative to nuclear landmarks is associated with changes in gene expression

Repressive nuclear landmarks: nuclear periphery and pericentromeric heterochromatin (PCH)

Repressive nuclear landmarks: nuclear periphery and pericentromeric heterochromatin (PCH)

NUCLEAR PERIPHERY H3K4ME3(TRANSCRIPTION)

DAPI (PCH)

Luo et al., 2009

Pericentromeric heterochromatin (PCH) from different chromosomescluster together to form chromocenters

Pericentromeric heterochromatin (PCH) from different chromosomescluster together to form chromocenters

Adapted from Probst & Almouzni, 2007

pericentromeric clustering is dynamic and cell type-specificpericentromeric clustering is dynamic and cell type-specific

Mayer et al., 2005

Brown et al., 1997, Cell

transcriptionally inactive genes associate with heterochromatic foci in B cell lines

transcriptionally inactive genes associate with heterochromatic foci in B cell lines

B3 (immature) Bal17 (mature)

CD19 (active in both)

CD8a (inactive in both)

λ5 (active in B3)

CD2 (active in Bal17)

Mapping pericentromere-associated domains using a sat4CMapping pericentromere-associated domains using a sat4C

van de Werken et al., 2012

Mapping pericentromere-associated domains using a sat4CMapping pericentromere-associated domains using a sat4C

van de Werken et al., 2012

gSAT 4C ---- from raw data to PAD domainsgSAT 4C ---- from raw data to PAD domainsGene repositioning relative to nuclear landmarks influences gene expressionGene repositioning relative to nuclear landmarks influences gene expressiongSat 4C – From raw data to PCH associated domainsgSat 4C – From raw data to PCH associated domains

On each fragend we observe a number of reads. From the raw reads a domain structure is not immediately apparent. However, a two-state Hidden semi-MarkovModel can be used to uncover the underlying structure.

This can subsequently be visualized by plotting a zero-centered running mean over w fragends (4C signal).

Mapping pericentromere-associated domains using a sat4CMapping pericentromere-associated domains using a sat4Cle

ssas

soci

ated

mor

eas

soci

ated

'pericentromere-associated domains' (PADs)'pericentromere-associated domains' (PADs)

Tcell CB HEP ESC NPC AC constP

PAD genome % 48 42 45 49 68 49 33

non-PAD genome% 52 58 55 51 32 51 42

PAD genes % 24 20 24 39 67 32 14

non-PAD genes % 74 78 73 57 30 66 64

# PAD 845 682 917 1157 1125 942 784

# nPAD 838 676 911 1152 1116 935 826

Identification of PADs and non-PADs across different cell typesIdentification of PADs and non-PADs across different cell types

Validation by FISHValidation by FISH

sat4C reflects 3D nuclear organizationsat4C reflects 3D nuclear organization

asy

nch

ronous

DAPIsatellites

3T3 fibroblasts

sat4C reflects 3D nuclear organizationsat4C reflects 3D nuclear organization

asy

nch

ronous

puri

fied c

hro

moso

mes

DAPIsatellites

3T3 fibroblasts

DAPIsatellites

Proximity to PCH is associated with closed, repressive chromatinProximity to PCH is associated with closed, repressive chromatin

PADs and nPADs are tightly separatedPADs and nPADs are tightly separated

H3K9me2 which is enriched in heterochromatine, correlates positivelyH3K9me2 which is enriched in heterochromatine, correlates positively

hundreds of cell type-specific PADs existhundreds of cell type-specific PADs exist

What characterizes the genomic regions where tissue specific “switching” (relocation to/away from PCH) occurs ?

differential pericentromeric association can correlate with gene repressiondifferential pericentromeric association can correlate with gene repression

differential association does not always correlate with gene repressiondifferential association does not always correlate with gene repression

Pericentromeric association does not correlate with gene expression statusin tissue-specific PADs

Pericentromeric association does not correlate with gene expression statusin tissue-specific PADs

RNA/DNA FISH to confirm expression at PCH.

Summary of main resultsSummary of main results

• The 4C method can be modified to identify PCH associated domains (PADs) genome-wide.

•PADs are generally strongly associated with gene poor regions, closed chromatin and gene repression.

•PAD borders sharply separate PADs from open, active chromatin in non-PADs.

•PADs are largely conserved across cell types. Differential (tissue specific) PADs are not tightly associated with tissue specific gene repression.

OutlineOutline

• 3C technology and its derivates : Powerful tools to study the 3D genomic context of Nuclear DNA.

• .Genome-wide identification of Pericentromeric Heterochromatin (PCH) associated domains (PADs).

• Implications of 4C and Hi-C data for the identification of transcriptional gene regulatory networks.

Identifying transcriptional networks using a lasso approachIdentifying transcriptional networks using a lasso approach

Identifying transcriptional networks using a lasso approachIdentifying transcriptional networks using a lasso approach

Identifying transcriptional networks using a lasso approachIdentifying transcriptional networks using a lasso approach

Identifying transcriptional networks using a lasso approachIdentifying transcriptional networks using a lasso approach

Identifying transcriptional networks using a lasso approachIdentifying transcriptional networks using a lasso approach

Extension to mixture to allow component specific coef estimatesExtension to mixture to allow component specific coef estimates

Preliminary resultsPreliminary results

Preliminary resultsPreliminary results

HI-C data identifies topological domainsHI-C data identifies topological domains

HI-C data identifies topological domainsHI-C data identifies topological domains

AcknowledgementsAcknowledgements

Wouter de LaatPatrick Wijchers

Harmen van de WerkenElzo de Wit

Marjon Verstegen

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