Genome Center, University of California, Davis, Davis, California, USA.
Department of Chemical and Biomolecular Engineering, University of Delawaregrid.33489.35, Newark, Delaware, USA.
mSystems. 2022 Dec 20;7(6):e0016522. doi: 10.1128/msystems.00165-22. Epub 2022 Oct 13.
Genotype-fitness maps of evolution have been well characterized for biological components, such as RNA and proteins, but remain less clear for systems-level properties, such as those of metabolic and transcriptional regulatory networks. Here, we take multi-omics measurements of 6 different E. coli strains throughout adaptive laboratory evolution (ALE) to maximal growth fitness. The results show the following: (i) convergence in most overall phenotypic measures across all strains, with the notable exception of divergence in NADPH production mechanisms; (ii) conserved transcriptomic adaptations, describing increased expression of growth promoting genes but decreased expression of stress response and structural components; (iii) four groups of regulatory trade-offs underlying the adjustment of transcriptome composition; and (iv) correlates that link causal mutations to systems-level adaptations, including mutation-pathway flux correlates and mutation-transcriptome composition correlates. We thus show that fitness landscapes for ALE can be described with two layers of causation: one based on system-level properties (continuous variables) and the other based on mutations (discrete variables). Understanding the mechanisms of microbial adaptation will help combat the evolution of drug-resistant microbes and enable predictive genome design. Although experimental evolution allows us to identify the causal mutations underlying microbial adaptation, it remains unclear how causal mutations enable increased fitness and is often explained in terms of individual components (i.e., enzyme rate) as opposed to biological systems (i.e., pathways). Here, we find that causal mutations in E. coli are linked to systems-level changes in NADPH balance and expression of stress response genes. These systems-level adaptation patterns are conserved across diverse E. coli strains and thus identify cofactor balance and proteome reallocation as dominant constraints governing microbial adaptation.
进化的基因型 - 适应性图谱在生物成分（如 RNA 和蛋白质）方面得到了很好的描述，但对于代谢和转录调控网络等系统水平的特性仍然不太清楚。在这里，我们对 6 种不同的大肠杆菌菌株在适应实验室进化（ALE）过程中的多组学测量结果进行了分析，以获得最大生长适应性。结果表明：（i）在所有菌株中，大多数整体表型测量结果趋同，只有 NADPH 产生机制的分歧是明显的例外；（ii）转录组适应性保守，描述为促进生长的基因表达增加，而应激反应和结构成分的表达减少；（iii）转录组组成调整的四个调控权衡组；（iv）将因果突变与系统水平适应性联系起来的相关性，包括突变 - 途径通量相关性和突变 - 转录组组成相关性。因此，我们表明 ALE 的适应性景观可以用两个因果关系层来描述：一个基于系统水平的特性（连续变量），另一个基于突变（离散变量）。了解微生物适应的机制将有助于对抗耐药微生物的进化，并实现预测性基因组设计。虽然实验进化使我们能够识别微生物适应的因果突变，但仍然不清楚因果突变如何使适应性提高，并且通常以单个组件（即酶速率）而不是生物系统（即途径）来解释。在这里，我们发现大肠杆菌中的因果突变与 NADPH 平衡和应激反应基因表达的系统水平变化有关。这些系统水平的适应模式在不同的大肠杆菌菌株中是保守的，因此确定了辅因子平衡和蛋白质组再分配作为控制微生物适应的主要限制因素。
