Qian Yili, Menon Sarvesh D, Quinn-Bohmann Nick, Gibbons Sean M, Venturelli Ophelia S
Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.
Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
bioRxiv. 2025 Jan 4:2025.01.04.631316. doi: 10.1101/2025.01.04.631316.
Microbial communities play a central role in transforming environments across Earth, driving both physical and chemical changes. By harnessing these capabilities, synthetic microbial communities, assembled from the bottom up, offer valuable insights into the mechanisms that govern community functions. These communities can also be tailored to produce desired outcomes, such as the synthesis of health-related metabolites or nitrogen fixation to improve plant productivity. Widely used computational models predict synthetic community functions using species abundances as inputs, making it impossible to predict the effects of species not included in the training data. We bridge this gap using a data-driven community genotype function (dCGF) model. By lifting the representation of each species to a high-dimensional genetic feature space, dCGF learns a mapping from community genetic feature matrices to community functions. We demonstrate that dCGF can accurately predict communities in a fixed environmental context that are composed in part or entirely from new species with known genetic features. In addition, dCGF facilitates the identification of species roles for a community function and hypotheses about how specific genetic features influence community functions. In sum, dCGF provides a new data-driven avenue for modeling synthetic microbial communities using genetic information, which could empower model-driven design of microbial communities.
微生物群落对地球上的环境转变起着核心作用，推动着物理和化学变化。通过利用这些能力，自下而上组装的合成微生物群落为支配群落功能的机制提供了有价值的见解。这些群落还可以进行定制以产生预期的结果，例如合成与健康相关的代谢物或固氮以提高植物生产力。广泛使用的计算模型以物种丰度作为输入来预测合成群落功能，这使得无法预测未包含在训练数据中的物种的影响。我们使用数据驱动的群落基因型功能（dCGF）模型来弥补这一差距。通过将每个物种的表示提升到高维遗传特征空间，dCGF学习从群落遗传特征矩阵到群落功能的映射。我们证明，dCGF可以准确预测在固定环境背景下部分或完全由具有已知遗传特征的新物种组成的群落。此外，dCGF有助于识别群落功能中的物种作用以及关于特定遗传特征如何影响群落功能的假设。总之，dCGF为利用遗传信息对合成微生物群落进行建模提供了一条新的数据驱动途径，这可以推动微生物群落的模型驱动设计。
