Long-range interactions between topologically associating domains shape the four-dimensional genome during differentiation

Paulsen, Jonas; Ali, Tharvesh Moideen Liyakat; Nekrasov, Maxim; Delbarre, Erwan; Baudement, Marie-Odile; Kurscheid, Sebastian; Tremethick, David J.; Collas, Philippe

doi:10.1038/s41588-019-0392-0

Cited by 121 publications

(220 citation statements)

References 58 publications

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“…This pattern is observed genome-wide: MIA-Sig TADs have higher inactive mark (H3K27me3) in than InS TADs, and MIA-Sig gaps have higher active mark (H3K27ac) than InS gaps (Supplementary Figure 6). Most interactions occur within a single TAD, but 23% of significant complexes also cross 2 or more TADs, consistent with previous findings (Paulsen et al, 2019). Thus, we identify frequent interactions involving multiple TADs by counting the occurrences and performing a binomial test (Methods).…”

supporting

confidence: 84%

Multiplex chromatin interaction analysis by signal processing and statistical algorithms

Kim

Zheng

Tian

et al. 2019

Preprint

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The single-molecule multiplex chromatin interaction data generated by emerging non-ligationbased 3D genome mapping technologies provide novel insights into high dimensional chromatin organization, yet introduce new computational challenges. We developed MIA-Sig (https://github.com/TheJacksonLaboratory/mia-sig.git), an algorithmic framework to de-noise the data, assess the statistical significance of chromatin complexes, and identify topological domains and inter-domain contacts. On chromatin immunoprecipitation (ChIP)-enriched data, MIA-Sig can clearly distinguish the protein-associated interactions from the non-specific topological domains. Main textPrevious 3D genome-mapping efforts have suggested complex chromosomal folding structures. In particular, methods based on high-throughput sequencing capture bulk chromatin contacts (Hi-C (Lieberman-Aiden et al., 2009)) or enrich for chromatin contacts involving a specific protein (ChIA-PET (Fullwood et al., 2009)). Both of these methods rely on proximity ligation, and therefore can only reveal population averages of pairwise contacts. Thus, they lacked the ability to simultaneously capture multiple loci involved in a chromatin complex in an individual cell.To overcome these challenges, novel experimental methods have recently been developed to capture multiplex chromatin contacts with single-molecule resolution. For instance, GAM (Beagrie et al., 2017) identifies multi-way interactions by capturing multiple DNA elements co-existing in a given nuclear slice, SPRITE (Quinodoz et al., 2018) barcodes individual chromatin complexes via a split-pool strategy, and ChIA-Drop (Zheng et al., 2019) partitions each complex into a microfluidic droplets for barcoding and amplification. Collectively, these emerging 3D genome-mapping technologies are advancing the frontier of the nuclear architecture field. However, as with other genomic approaches prone to the background noise, the noisy and high-dimensional nature of the multiplex data poses unique computational challenges that cannot be readily addressed with existing tools that are tailored for pairwise interactions data. Thus, we developed MIA-Sig (Multiplex Interactions Analysis by Signal processing algorithms) with a set of Python modules tailored for ChIA-Drop and related datatypes.A central analytic challenge is to distinguish the true biological chromatin complexes from the experimental noise. A possible source of noise is an event that two or more chromatin complexes are potentially encapsulated in the same microfluidics droplet and then assigned the same barcode, yielding a multiplet (Figure 1a). The problem also prevails in microfluidics-based single-cell RNA-seq data, which is then resolved computationally via dimensionality-reduction and clustering (Wolock et al., 2019). However, methods developed for single-cell

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supporting

confidence: 84%

Multiplex chromatin interaction analysis by signal processing and statistical algorithms

Kim

Zheng

Tian

et al. 2019

Preprint

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“…Robust LAD 'marking' may strengthen the robustness of liver-specific gene expression, possibly by segregation of heterochromatin from euchromatin. Oscillations of subsets of LADs, regardless of periodicity, may establish local and dynamic radial chromatin states in regions that are in 3-dimensional proximity, but not necessarily in linear proximity, affecting gene expression in these regions (Paulsen et al 2019). Thus periodic LAD displacement may result in radial repositioning of topological chromatin domains (Robson et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Rhythmic chromatin interactions with lamin B1 reflect stochasticity in variable lamina-associated domains during the circadian cycle

Brunet

Forsberg

Collas

2019

Preprint

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Many mammalian genes exhibit circadian expression patterns concordant with periodic binding of transcription factors, chromatin modifications and chromosomal interactions. Here, we report periodic interactions of chromatin with nuclear lamins, suggesting rhythmic associations with the nuclear lamina.Entrainment of the circadian clock is accompanied in mouse liver by a gain of lamin B1-chromatin interactions, followed by oscillations in these interactions at hundreds of lamina-associated domains (LADs). A subset of these oscillations exhibit distinct 12, 18, 24 or 30-h periodicity in our dataset, and affect one or both LAD borders or entire stand-alone LADs. However, most LADs are conserved during the circadian cycle, and periodic LADs are seldom occurrences rather than dominant features of variable LADs. Periodic LADs display oscillation asynchrony between 5' and 3' LAD borders, and are uncoupled from periodic gene expression within or in vicinity of these LADs. Accordingly, periodic genes, including central clock-control genes, are often located megabases away from LADs, suggesting residence in a transcriptionally permissive environment throughout the circadian cycle. Autonomous oscillatory associations of the genome with nuclear lamins provide new evidence for rhythmic spatial chromatin configurations. Nevertheless, our data suggest that periodic LADs reflect stochasticity in lamin-chromatin interactions underlying chromatin dynamics in the liver during the circadian cycle. They also argue that periodic gene expression is by and large not regulated by rhythmic chromatin associations with the nuclear lamina.

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“…In fact, such ChREBP-LXR contacts might function as bridging point for smaller or larger chromatin loops (Mora et al, 2016, West et al, 2002, like promoter-enhancer loops (Siersbaek et al, 2017), possibly within topologically associated domains (TADs) (Paulsen et al, 2019). A tantalizing scenario could be that ChREBPα and LXRα are involved in bridging the exon 1A other hand, will activate LXR and drive cholesterol metabolism and efflux (Naik et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Open the LID: LXRα regulates ChREBPα transactivity in a target gene-specific manner through an agonist-modulated LBD-LID interaction

Fan

Nørgaard

Grytten³

et al. 2019

Preprint

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The cholesterol-sensing nuclear receptor liver X receptor (LXR) and the glucose-sensing transcription factor carbohydrate responsive element-binding protein (ChREBP) are central players in regulating glucose and lipid metabolism in liver. We have previously shown that LXR regulates ChREBP transcription and activity; however, the underlying mechanisms are unclear. In the current study, we demonstrate that LXRα and ChREBPα interact physically, and show a high co-occupancy at regulatory regions in the mouse genome. LXRα co-activates ChREBPα, and regulates ChREBP-specific target genes in vitro and in vivo. This co-activation is dependent on functional recognition elements for ChREBP, but not for LXR, indicating that ChREBPα recruits LXRα to chromatin in trans. The two factors interact via their key activation domains; ChREBPα's low glucose inhibitory domain (LID) and the ligand-binding domain (LBD) of LXRα. While unliganded LXRα co-activates ChREBPα, ligand-bound LXRα surprisingly represses ChREBPα activity on ChREBP-specific target genes. Mechanistically, this is due to a destabilized LXRα:ChREBPα interaction, leading to reduced ChREBP-binding to chromatin and restricted activation of glycolytic and lipogenic target genes. This liganddriven molecular switch highlights an unappreciated role of LXRα that was overlooked due to LXR lipogenesis-promoting function.developed, such as the nonsteroidal agonists GW3965 and T0901317 (Collins et al, 2002,

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Long-range interactions between topologically associating domains shape the four-dimensional genome during differentiation

Cited by 121 publications

References 58 publications

Multiplex chromatin interaction analysis by signal processing and statistical algorithms

Multiplex chromatin interaction analysis by signal processing and statistical algorithms

Rhythmic chromatin interactions with lamin B1 reflect stochasticity in variable lamina-associated domains during the circadian cycle

Open the LID: LXRα regulates ChREBPα transactivity in a target gene-specific manner through an agonist-modulated LBD-LID interaction

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