Sarsani Vishal, Aldikacti Berent, Zhao Tingting, He Shai, Chien Peter, Flaherty Patrick
Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA 01002, USA.
Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, Amherst, MA 01002, USA.
PNAS Nexus. 2025 Jan 13;4(1):pgae574. doi: 10.1093/pnasnexus/pgae574. eCollection 2025 Jan.
Every protein progresses through a natural lifecycle from birth to maturation to death; this process is coordinated by the protein homeostasis system. Environmental or physiological conditions trigger pathways that maintain the homeostasis of the proteome. An open question is how these pathways are modulated to respond to the many stresses that an organism encounters during its lifetime. To address this question, we tested how the fitness landscape changes in response to environmental and genetic perturbations using directed and massively parallel transposon mutagenesis in . We developed a general computational pipeline for the analysis of gene-by-environment interactions in transposon mutagenesis experiments. This pipeline uses a combination of general linear models, statistical knockoffs, and a nonparametric Bayesian statistical model to identify essential genetic network components that are shared across environmental perturbations. This analysis allows us to quantify the similarity of proteotoxic environmental perturbations from the perspective of the fitness landscape. We find that essential genes vary more by genetic background than by environmental conditions, with limited overlap among mutant strains targeting different facets of the protein homeostasis system. We also identified 146 unique fitness determinants across different strains, with 19 genes common to at least two strains, showing varying resilience to proteotoxic stresses. Experiments exposing cells to a combination of genetic perturbations and dual environmental stressors show that perturbations that are quantitatively dissimilar from the perspective of the fitness landscape are likely to have a synergistic effect on the growth defect.
每种蛋白质都要经历从诞生到成熟再到死亡的自然生命周期;这个过程由蛋白质稳态系统协调。环境或生理条件会触发维持蛋白质组稳态的途径。一个悬而未决的问题是,这些途径如何被调节以应对生物体在其生命周期中遇到的诸多压力。为了解决这个问题,我们利用定向和大规模平行转座子诱变,测试了适应度景观如何响应环境和基因扰动。我们开发了一个通用的计算流程,用于分析转座子诱变实验中的基因与环境相互作用。该流程结合了通用线性模型、统计仿样和非参数贝叶斯统计模型,以识别在各种环境扰动中共享的关键遗传网络组件。这种分析使我们能够从适应度景观的角度量化蛋白毒性环境扰动的相似性。我们发现,关键基因在遗传背景上的差异大于在环境条件上的差异,针对蛋白质稳态系统不同方面的突变菌株之间重叠有限。我们还在不同菌株中鉴定出146个独特的适应度决定因素,其中至少有两个菌株共有19个基因,这些基因对蛋白毒性应激表现出不同的恢复力。将细胞暴露于基因扰动和双重环境应激源组合的实验表明,从适应度景观的角度来看,定量上不同的扰动可能对生长缺陷产生协同效应。