从大型微生物群落中设计功能特异的最小微生物群落

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY NPJ Systems Biology and Applications Pub Date : 2024-05-03 DOI:10.1038/s41540-024-00373-1
Aswathy K. Raghu, Indumathi Palanikumar, Karthik Raman
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引用次数: 0

摘要

微生物存在于由不同物种组成的大型群落中,具有各种功能。例如,哺乳动物肠道微生物群具有消化膳食纤维和产生不同短链脂肪酸的功能。并不是一个群落中的所有微生物都对某一特定功能做出贡献;有可能找到一个最小微生物群落,它是大型微生物群落的一个子集,能够在发挥功能的同时保持群落的其他特性,如生长速度和代谢物的产生。这种最小微生物群还将包含该群落中产生 SCFA 的关键物种。在这项工作中,我们提出了一种基于约束的系统方法,从大型群落中识别出用户提出功能的最小微生物组。我们采用了一种自上而下的方法,先进行顺序删除,然后求解一个混合整数线性规划问题,目标是最小化成员向量的 L1-norm。值得注意的是,我们考虑了群落增长率和代谢物产生率的量化指标。我们通过识别与三个肠道模型群落相对应的最小微生物群落,展示了我们算法的实用性,并根据群落中关键物种的存在讨论了其有效性。我们的方法通用、灵活,可用于研究各种微生物群落。该算法可从 https://github.com/RamanLab/minMicrobiome 上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Designing function-specific minimal microbiomes from large microbial communities

Microorganisms exist in large communities of diverse species, exhibiting various functionalities. The mammalian gut microbiome, for instance, has the functionality of digesting dietary fibre and producing different short-chain fatty acids. Not all microbes present in a community contribute to a given functionality; it is possible to find a minimal microbiome, which is a subset of the large microbiome, that is capable of performing the functionality while maintaining other community properties such as growth rate and metabolite production. Such a minimal microbiome will also contain keystone species for SCFA production in that community. In this work, we present a systematic constraint-based approach to identify a minimal microbiome from a large community for a user-proposed function. We employ a top-down approach with sequential deletion followed by solving a mixed-integer linear programming problem with the objective of minimising the L1-norm of the membership vector. Notably, we consider quantitative measures of community growth rate and metabolite production rates. We demonstrate the utility of our algorithm by identifying the minimal microbiomes corresponding to three model communities of the gut, and discuss their validity based on the presence of the keystone species in the community. Our approach is generic, flexible and finds application in studying a variety of microbial communities. The algorithm is available from https://github.com/RamanLab/minMicrobiome.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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