Bioinformatics Program, Boston University, Boston, MA, 02215, USA.
Department of Biology, Boston University, Boston, MA, 02215, USA.
Nat Commun. 2017 Nov 16;8(1):1563. doi: 10.1038/s41467-017-01407-5.
Metabolite exchanges in microbial communities give rise to ecological interactions that govern ecosystem diversity and stability. It is unclear, however, how the rise of these interactions varies across metabolites and organisms. Here we address this question by integrating genome-scale models of metabolism with evolutionary game theory. Specifically, we use microbial fitness values estimated by metabolic models to infer evolutionarily stable interactions in multi-species microbial "games". We first validate our approach using a well-characterized yeast cheater-cooperator system. We next perform over 80,000 in silico experiments to infer how metabolic interdependencies mediated by amino acid leakage in Escherichia coli vary across 189 amino acid pairs. While most pairs display shared patterns of inter-species interactions, multiple deviations are caused by pleiotropy and epistasis in metabolism. Furthermore, simulated invasion experiments reveal possible paths to obligate cross-feeding. Our study provides genomically driven insight into the rise of ecological interactions, with implications for microbiome research and synthetic ecology.
微生物群落中的代谢物交换产生了控制生态系统多样性和稳定性的生态相互作用。然而，这些相互作用在代谢物和生物体中的出现方式尚不清楚。在这里，我们通过将代谢组学的基因组规模模型与进化博弈论相结合来解决这个问题。具体来说，我们使用代谢模型估计的微生物适应性值来推断多物种微生物“博弈”中的进化稳定相互作用。我们首先使用一个特征良好的酵母骗子-合作者系统来验证我们的方法。接下来，我们进行了超过 80,000 次的计算机模拟实验，以推断大肠杆菌中由氨基酸泄漏介导的代谢物相互依存关系如何在 189 对氨基酸之间变化。虽然大多数对都显示出物种间相互作用的共享模式，但由于代谢中的多效性和上位性，出现了多种偏差。此外，模拟入侵实验揭示了可能导致必需的交叉喂养的途径。我们的研究为生态相互作用的出现提供了基于基因组的见解，对微生物组研究和合成生态学具有重要意义。
