Simulations of gene regulatory networks with transcriptional adaptation
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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 compe..., The dataset was generated through simulations of gene regulatory networks with transcriptional adaptation, where genes are represented as nodes and regulatory relationships as edges. Please see the corresponding manuscript for a complete description of the model, simulation conditions, and analyses. The regulatory networks were constructed using a modified telegraph model for transcriptional bursting, where each gene's alleles switch between active (transcribing) and inactive (quiescent) states, for an ancestral regulator gene (A), its paralogs (A'1 and A'2), and a downstream target gene (B). To capture the dynamics of gene regulation, we implemented stochastic simulations using Gillespieâs next reaction method. We accounted for differential regulatory effects of different gene products. We included models for both activating and repressing interactions, as well as models with more than one paralog gene. Parameter ranges were defined based on previously reported literature on transcrip..., , # **Simulations of Gene Regulatory Networks with Transcriptional Adaptation** [https://doi.org/10.5061/dryad.nk98sf82j](https://doi.org/10.5061/dryad.nk98sf82j) ## Project Description Outputs of simulations of gene regulatory networks with transcriptional adaptation (also known as nonsense-induced transcriptional compensation) as described in > Mellis IA, Melzer ME, Bodkin N, and Goyal Y. 2024. Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells. Genome Biology. We performed simulations of gene regulatory networks with transcriptional adaptation, where genes are represented as nodes and regulatory relationships as edges. The regulatory networks were constructed using a modified telegraph model for transcriptional bursting, where each gene's alleles switch between active (transcribing) and inactive (quiescent) states, for an ancestral regulator gene (A), its paralogs (A' for 1-paralog models, A'1 and A'2 for multiparalog models), and a dow...
# 研究背景 细胞与组织可通过多种分子机制，对遗传扰动产生显著的适应性响应。转录适配（transcriptional adaptation）作为此类机制之一，近年被证实可在基因发生无义突变时，触发同源基因的上调表达，进而可能在细胞乃至个体层面赋予机体稳健性。然而，目前除少数发育场景与经注释筛选的基因集外，尚未针对哺乳动物细胞类型与实验场景开展全基因组范围的此类行为系统性研究。此外，固有的随机性代偿基因调控网络所产生的调控层面效应，如何影响单细胞中的表型外显率，目前仍不明确。 # 研究结果 在本研究对应的论文中，我们通过分析现有批量转录组与单细胞转录组数据集，推断了不同场景与细胞类型下，哺乳动物系统中转录适配的普遍程度。本文所呈现的数据，通过对最小代偿系统的随机数学建模以及携带转录适配机制的基因调控网络模拟生成：将基因表示为节点，调控关系表示为边。完整的模型构建、模拟条件与分析流程，请参阅对应研究论文。 本研究采用改进的转录爆发电报模型构建调控网络：针对祖先调控基因（A）、其旁系同源基因（单旁系同源时记为A'，多旁系同源时记为A'1与A'2）以及下游靶基因（B），每个基因的等位基因会在活跃（转录中）与非活跃（静息）状态间切换。为捕捉基因调控的动态过程，我们采用吉莱斯皮（Gillespie）下一反应法开展随机模拟，并考量了不同基因产物间的差异化调控效应。我们同时构建了激活、抑制相互作用模型，以及包含多个旁系同源基因的模型。 参数范围的设定基于此前已发表的转录相关研究…… # **携带转录适配机制的基因调控网络模拟** [https://doi.org/10.5061/dryad.nk98sf82j](https://doi.org/10.5061/dryad.nk98sf82j) ## 项目简介 本数据集为携带转录适配（亦称无义突变诱导的转录代偿）机制的基因调控网络模拟输出结果，相关研究详见： > Mellis IA, Melzer ME, Bodkin N, 及 Goyal Y. 2024. 单细胞中转录适配的普遍程度与基因调控约束. 《Genome Biology》. 我们开展了携带转录适配机制的基因调控网络模拟：将基因表示为节点，调控关系表示为边。 本研究采用改进的转录爆发电报模型构建调控网络：针对祖先调控基因（A）、其旁系同源基因（单旁系同源时记为A'，多旁系同源时记为A'1与A'2）以及下游靶基因（B），每个基因的等位基因会在活跃（转录中）与非活跃（静息）状态间切换。……
