Gopalakrishnappa Chandana, Gowda Karna, Prabhakara Kaumudi H, Kuehn Seppe
Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA.
iScience. 2022 Jan 11;25(2):103761. doi: 10.1016/j.isci.2022.103761. eCollection 2022 Feb 18.
The metabolic activity of microbial communities plays a primary role in the flow of essential nutrients throughout the biosphere. Molecular genetics has revealed the metabolic pathways that model organisms utilize to generate energy and biomass, but we understand little about how the metabolism of diverse, natural communities emerges from the collective action of its constituents. We propose that quantifying and mapping metabolic fluxes to sequencing measurements of genomic, taxonomic, or transcriptional variation across an ensemble of diverse communities, either in the laboratory or in the wild, can reveal low-dimensional descriptions of community structure that can explain or predict their emergent metabolic activity. We survey the types of communities for which this approach might be best suited, review the analytical techniques available for quantifying metabolite fluxes in communities, and discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data.
微生物群落的代谢活动在整个生物圈中基本营养物质的流动中起着主要作用。分子遗传学揭示了模式生物用于产生能量和生物量的代谢途径，但我们对不同自然群落的代谢如何从其组成部分的集体行动中产生却知之甚少。我们提出，在实验室或野外，对不同群落集合中的基因组、分类学或转录变异进行测序测量时，对代谢通量进行量化和映射，可以揭示群落结构的低维描述，从而解释或预测它们的代谢活动。我们调查了最适合这种方法的群落类型，回顾了可用于量化群落中代谢物通量的分析技术，并讨论了哪些类型的数据分析方法可能有助于从这些数据中了解群落的结构-功能映射。
