Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada.
Viome Inc, San Diego, California, United States of America.
PLoS Comput Biol. 2021 Nov 1;17(11):e1009060. doi: 10.1371/journal.pcbi.1009060. eCollection 2021 Nov.
The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.
微生物群落及其相互作用的研究引起了科学界的兴趣，因为它们在生物技术、生态学和医学中有潜在的应用。种间相互作用的复杂性是微生物群落宏观行为的关键，但很难通过实验来研究。出于这个原因，微生物群落的建模开始利用已建立的基于约束的方法的知识，这些方法长期以来一直用于基于微生物的基因组规模代谢重建来研究和分析单个物种的微生物代谢。基因组规模代谢重建的一个主要问题是，由于基因组错误注释和未知酶功能，它们通常包含代谢间隙。这个问题通常通过使用填补空白算法来解决，该算法将生化反应从外部数据库添加到代谢重建中，以恢复模型的生长。然而，填补空白算法可以通过考虑在微生物群落中共存的物种之间的代谢相互作用来进化。在这项工作中，开发了一种在群落水平上解决代谢间隙的填补方法。通过分析该算法在合成的营养缺陷型大肠杆菌菌株群落上解决代谢间隙的能力来测试算法的功效。随后，该算法被应用于解决双歧杆菌和粪肠球菌群落中的代谢间隙，并预测其代谢相互作用，这两种菌是人类肠道微生物群中的两种，以及在经过实验研究的脱硫杆菌和拟杆菌门 ACT-3 群落中。群落填补空白方法可以促进代谢模型的改进，并识别微生物群落中难以通过实验识别的代谢相互作用。
