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Dryad

Simulations of gene regulatory networks with transcriptional adaptation

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Aug 02, 2024 version files 2.61 GB

Abstract

Background

Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Transcriptional adaptation has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Further, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear.

Results

In the corresponding manuscript, we analyze existing bulk and single-cell transcriptomic datasets to infer the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. In the data presented here, stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation.

Conclusions

We provide a formal quantitative framework to test and refine models of transcriptional adaptation.