Otto Ryan M, Turska-Nowak Agata, Brown Philip M, Reynolds Kimberly A
Green Center for Systems Biology - Lyda Hill Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX 75230, USA.
Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, TX 75230, USA.
Cell Syst. 2024 Feb 21;15(2):134-148.e7. doi: 10.1016/j.cels.2024.01.003. Epub 2024 Feb 9.
Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.
鉴于基因表达和环境的变化，由于上位性相互作用以及可能扰动的巨大组合空间，对生长率表型进行量化和预测变得很复杂。我们开发了一种绘制表达-生长率景观的方法，该方法将稀疏采样的实验测量与可解释的机器学习模型相结合。我们使用跨基因对和三联体的错配CRISPRi在不同环境背景下在大肠杆菌基因表达中产生了超过8000种滴定变化，探索了多达22种不同环境中的上位性。我们的结果表明，先前用于描述药物相互作用的成对模型很好地描述了这些数据。该模型产生了与通路结构相关的可解释参数，并且在仅基于成对扰动数据进行训练时能够推广以预测多达四种扰动的联合效应。我们预计这种方法将广泛应用于优化细菌生长条件、生成药物基因组学模型以及理解对细菌基因表达的基本限制。本文透明同行评审过程的记录包含在补充信息中。
