Mellis Ian A, Bodkin Nicholas, Melzer Madeline E, Goyal Yogesh
Department of Pathology and Cell Biology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
bioRxiv. 2023 Nov 30:2023.08.14.553318. doi: 10.1101/2023.08.14.553318.
Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene can 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, to date, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and contexts. Moreover, how the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. Here we combine computational analysis of existing datasets with stochastic mathematical modeling and machine learning to uncover the widespread prevalence of transcriptional adaptation in mammalian systems and the diverse single-cell manifestations of minimal compensatory gene networks. Regulon gene expression analysis of a pooled single-cell genetic perturbation dataset recapitulates important model predictions. Our integrative approach uncovers several putative hits-genes demonstrating possible transcriptional adaptation-to follow up on experimentally, and provides a formal quantitative framework to test and refine models of transcriptional adaptation.
细胞和组织具有通过多种分子机制适应基因扰动的显著能力。无义诱导的转录补偿作为一种转录适应形式，最近已成为这样一种机制，即基因中的无义突变可触发相关基因的上调，这可能在细胞和机体水平赋予稳健性。然而，除了少数发育背景和经过筛选的基因集外，迄今为止，尚未针对哺乳动物细胞类型和背景对这种行为进行全面的全基因组研究。此外，内在随机补偿基因网络的调控水平效应如何促成单细胞中的表型外显率仍不清楚。在这里，我们将现有数据集的计算分析与随机数学建模和机器学习相结合，以揭示转录适应在哺乳动物系统中的广泛存在以及最小补偿基因网络的多样单细胞表现。对汇集的单细胞基因扰动数据集进行调控子基因表达分析，再现了重要的模型预测。我们的综合方法揭示了几个可能的命中基因——显示出可能的转录适应——以便进行后续实验跟进，并提供了一个正式的定量框架来测试和完善转录适应模型。
