Systematic assessment of gene co-regulation within chromatin domains determines differentially active domains across human cancers

Abstract

Background Spatial interactions and insulation of chromatin regions are associated with transcriptional regulation. Domains of frequent chromatin contacts are proposed as functional units, favoring and delimiting gene regulatory interactions. However, contrasting evidence supports the association between chromatin domains and transcription. Result Here, we assess gene co-regulation in chromatin domains across multiple human cancers, which exhibit great transcriptional heterogeneity. Across all datasets, gene co-regulation is observed only within a small yet significant number of chromatin domains. We design an algorithmic approach to identify differentially active domains (DADo) between two conditions and show that these provide complementary information to differentially expressed genes. Domains comprising co-regulated genes are enriched in the less active B sub-compartments and for genes with similar function. Notably, differential activation of chromatin domains is not associated with major changes of domain boundaries, but rather with changes of sub-compartments and intra-domain contacts. Conclusion Overall, gene co-regulation is observed only in a minority of chromatin domains, whose systematic identification will help unravel the relationship between chromatin structure and transcription.

Transcription factor expression is the main determinant of variability in gene co-activity

“…However, while deletions of TAD boundaries or chromosomal inversions may disrupt TAD-contained regulatory wirings and cause gene dysregulation (Gröschel et al, 2014;Laugsch et al, 2019;Lupiáñez et al, 2015), the proper formation of TADs only has a marginal contribution to gene expression (Ghavi-Helm et al, 2019;Nora et al, 2017;Rao et al, 2017). This is reflected by only a minor agreement between gene co-regulation and TAD co-localization across active genes (Soler-Oliva et al, 2017;Zufferey et al, 2021).…”

“…Co-expression can, for instance, be measured by quantifying the correlation between expression levels of genes across cell types (Hawrylycz et al , 2012), assessing to what extent gene sets have a concordant differential expression between cell types or conditions (Zufferey et al , 2021) or to what degree they define cellular identity in single cell experiments (Crow & Gillis, 2018). While analysis of gene regulatory differences between cell types may successfully capture differential activity between TFs or domain repression, studying variation in gene expression between individuals within the same cell type has indicated other regulatory patterns, which may appear comparatively subtle in relation to between cell type effects.…”

Mapping nucleosome and chromatin architectures: A survey of computational methods

“…For example, researchers have recently observed differential responses at TADs, looping genes and expressed genes in endocrine-resistant breast cancer cells [81] . A new algorithmic approach DADo [82] , has been proposed to identify such differentially active domains between the two conditions, providing complementary information to a general differential expression analysis. However, there are not many publicly available tools for the aforementioned task.…”

SDEvelo: a deep generative approach for transcriptional dynamics with cell-specific latent time and multivariate stochastic modeling

“…Strikingly, SDEvelo leverages data on multiple genes simultaneously to infer the transcriptional dynamics with a cell-specific latent time that is shared across genes for a single cell/spot. This consideration can potentially capture candidate genes involved in co-interactions and co-regulation, which is crucial in understanding complex biological processes [32,33].…”