Flamholz Avi I, Goyal Akshit, Fischer Woodward W, Newman Dianne K, Phillips Rob
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125.
Physics of Living Systems, Department of Physics, Massachusetts Institute of Technology, Cambridge MA 02139.
Proc Natl Acad Sci U S A. 2025 Jan 7;122(1):e2404048121. doi: 10.1073/pnas.2404048121. Epub 2025 Jan 3.
Microbial metabolism is impressively flexible, enabling growth even when available nutrients differ greatly from biomass in redox state. , for example, rearranges its physiology to grow on reduced and oxidized carbon sources through several forms of fermentation and respiration. To understand the limits on and evolutionary consequences of this metabolic flexibility, we developed a coarse-grained mathematical framework coupling redox chemistry with principles of cellular resource allocation. Our models inherit key qualities from both of their antecedents: i) describing diverse metabolic chemistries and ii) enforcing the simultaneous balancing of atom (e.g., carbon), electron, and energy (adenosine triphosphate) flows, as in redox models, while iii) treating biomass as both the product and catalyst of the growth process, as in resource allocation models. Assembling integrated models of respiration, fermentation, and photosynthesis clarified key microbiological phenomena, including demonstrating that autotrophs grow more slowly than heterotrophs because of constraints imposed by the intracellular production of reduced carbon. Our model further predicted that heterotrophic growth is improved by matching the redox state of biomass to the nutrient environment. Through analysis of [Formula: see text]60,000 genomes and diverse proteomic datasets, we found evidence that proteins indeed accumulate amino acid substitutions promoting redox matching. We therefore propose an unexpected mode of genome evolution where substitutions neutral or even deleterious to the individual biochemical or structural functions of proteins can nonetheless be selected due to a redox-chemical benefit to the population.
微生物代谢具有令人惊叹的灵活性，即使可用营养物质在氧化还原状态上与生物量有很大差异，也能实现生长。例如，[具体微生物名称未给出]通过多种发酵和呼吸形式重新调整其生理机能，以便在还原态和氧化态碳源上生长。为了理解这种代谢灵活性的限制及其进化后果，我们开发了一个粗粒度数学框架，将氧化还原化学与细胞资源分配原则相结合。我们的模型继承了其两个前身的关键特性：i）描述多种代谢化学过程；ii）像在氧化还原模型中一样，强制实现原子（如碳）、电子和能量（三磷酸腺苷）流的同时平衡；iii）像在资源分配模型中一样，将生物量视为生长过程的产物和催化剂。整合呼吸、发酵和光合作用的模型阐明了关键的微生物学现象，包括证明自养生物由于细胞内还原碳产生的限制而比异养生物生长得更慢。我们的模型进一步预测，通过使生物量的氧化还原状态与营养环境相匹配，可以改善异养生长。通过分析60000个基因组和各种蛋白质组数据集，我们发现有证据表明蛋白质确实积累了促进氧化还原匹配的氨基酸替换。因此，我们提出了一种意想不到的基因组进化模式，即对蛋白质的个体生化或结构功能而言中性甚至有害的替换，由于对种群具有氧化还原化学益处，仍可能被选择。
