在基因表达和环境组合变化的情况下,预测生长率的连续外显模型。

Cell systems Pub Date : 2024-02-21 Epub Date: 2024-02-09 DOI:10.1016/j.cels.2024.01.003
Ryan M Otto, Agata Turska-Nowak, Philip M Brown, Kimberly A Reynolds
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引用次数: 0

摘要

由于表观相互作用和可能扰动的巨大组合空间,在基因表达和环境变化的情况下量化和预测生长率表型变得非常复杂。我们开发了一种绘制表达-生长率景观的方法,它将稀疏采样的实验测量结果与可解释的机器学习模型相结合。我们使用错配 CRISPRi 跨基因对和基因三对,在不同环境背景下创建了超过 8000 个大肠杆菌基因表达的滴定变化,探索了多达 22 种不同环境中的表观性。我们的研究结果表明,以前用于描述药物相互作用的配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,并且当仅在成对扰动数据上进行训练时,可预测多达四种扰动的综合效应。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了解细菌基因表达的基本制约因素。本文的透明同行评审过程记录见补充信息。
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A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment.

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.

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