Context-dependent perturbations in chromatin folding and the transcriptome by cohesin and related factors

Cohesin regulates gene expression through context-specific chromatin folding mechanisms such as enhancer–promoter looping and topologically associating domain (TAD) formation by cooperating with factors such as cohesin loaders and the insulation factor CTCF. We developed a computational workflow to explore how three-dimensional (3D) structure and gene expression are regulated collectively or individually by cohesin and related factors. The main component is CustardPy, by which multi-omics datasets are compared systematically. To validate our methodology, we generated 3D genome, transcriptome, and epigenome data before and after depletion of cohesin and related factors and compared the effects of depletion. We observed diverse effects on the 3D genome and transcriptome, and gene expression changes were correlated with the splitting of TADs caused by cohesin loss. We also observed variations in long-range interactions across TADs, which correlated with their epigenomic states. These computational tools and datasets will be valuable for 3D genome and epigenome studies.

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All studies must disclose on these points even when the disclosure is negative.The Custom code for the analysis is available on Zenodo (https://doi.org/10.5281/zenodo.8218447).
The human reference genome hg38 was obtained from the UCSC Genome Browser (https://genome.ucsc.edu/).The raw sequencing data and processed files for the Hi-C, RNA-seq, and ChIP-seq data from this study have been submitted to the Gene Expression Omnibus (GEO) under the accession number GSE196450.The .hic files of the merged Hi-C samples and the reference TAD and loop files are also available on GSE196034.The reference data of TAD and loops obtained from the merged control sample are available on Zenodo (https://doi.org/10.5281/zenodo.8218447).
not applicable not applicable not applicable not applicable Sample sizes were chosen to provide sufficient material for multi-omics data according to common practice in the field.
The sample of siNIPBL (24-h treatment) was excluded from the downstream analysis due to the insufficient depletion efficiency.
RNA-seq analysis was performed in duplicates.The principle samples in Hi-C and ChIP-seq data have two or more replicates.We have confirmed the sufficient similarity among replicates, as shown in Figures 1C and 6E.All blots were repeated for at least two biological replicates.All replicated experiments were successful.
Sample randomization is not relevant to this study because we did not use experiment groups.
Blinding is not relevant to this study because we did not use experiment groups.