Journal of Medical GeneticsBioinformaticsPeer-reviewed journal article
Low tolerance for transcriptional variation at cohesin genes is accompanied by functional links to disease-relevant pathways
William S. SchierdingiD, Julia A. HorsfieldiD, Justin Martin O'SullivaniD
Abstract
Variants in DNA regulatory elements can alter the regulation of distant genes through spatial-regulatory connections. In humans, these spatial-regulatory connections are largely set during early development, when the cohesin complex plays an essential role in genome organisation and cell division. A full complement of the cohesin complex and its regulators is important for normal development, since heterozygous mutations in genes encoding these components are often sufficient to produce a disease phenotype. The implication that genes encoding the cohesin complex and cohesin regulators must be tightly controlled and resistant to variability in expression has not yet been formally tested. Here, we identify spatial-regulatory connections with potential to regulate expression of cohesin loci, including linking their expression to that of other genes. Connections that centre on the cohesin ring subunits (Mitotic: SMC1A, SMC3, STAG1, STAG2, RAD21/RAD21-AS; Meiotic: SMC1B, STAG3, REC8, RAD21L1), cohesin-ring support genes (NIPBL, MAU2, WAPL, PDS5A and PDS5B), and CTCF provide evidence of coordinated regulation that has little tolerance for perturbation. We identified transcriptional changes across a set of genes co-regulated with the cohesin loci that include biological pathways such as extracellular matrix production and proteasome-mediated protein degradation. Remarkably, many of the genes that are co-regulated with cohesin loci are themselves intolerant to loss-of-function. The results highlight the importance of robust regulation of cohesin genes, indicating novel pathways that may be important in the human cohesinopathy disorders.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 6
- FWCI
- 0.41
- Field percentile
- top 47%
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