The Innovative Genomics Institute at the University of California, Berkeley, CA, USA.
The Department of Earth and Planetary Science, University of California, Berkeley, CA, USA.
Nat Commun. 2023 Aug 8;14(1):4768. doi: 10.1038/s41467-023-40360-4.
Metagenomic or metabarcoding data are often used to predict microbial interactions in complex communities, but these predictions are rarely explored experimentally. Here, we use an organism abundance correlation network to investigate factors that control community organization in mine tailings-derived laboratory microbial consortia grown under dozens of conditions. The network is overlaid with metagenomic information about functional capacities to generate testable hypotheses. We develop a metric to predict the importance of each node within its local network environments relative to correlated vitamin auxotrophs, and predict that a Variovorax species is a hub as an important source of thiamine. Quantification of thiamine during the growth of Variovorax in minimal media show high levels of thiamine production, up to 100 mg/L. A few of the correlated thiamine auxotrophs are predicted to produce pantothenate, which we show is required for growth of Variovorax, supporting that a subset of vitamin-dependent interactions are mutualistic. A Cryptococcus yeast produces the B-vitamin pantothenate, and co-culturing with Variovorax leads to a 90-130-fold fitness increase for both organisms. Our study demonstrates the predictive power of metagenome-informed, microbial consortia-based network analyses for identifying microbial interactions that underpin the structure and functioning of microbial communities.
宏基因组或代谢条形码数据常用于预测复杂群落中的微生物相互作用,但这些预测很少通过实验来探索。在这里,我们使用一种生物体丰度相关网络来研究控制在数十种条件下生长的矿山尾矿衍生实验室微生物共生体群落组织的因素。该网络覆盖了有关功能能力的宏基因组信息,以生成可测试的假设。我们开发了一种度量标准,以预测每个节点与其相关维生素营养缺陷型之间在局部网络环境中的重要性,并预测一种鞘氨醇单胞菌属( Variovorax )物种是作为硫胺素重要来源的中心。在鞘氨醇单胞菌属在最小培养基中生长期间对硫胺素的定量表明,其产生了高水平的硫胺素,最高可达 100mg/L。我们预测了一些相关的硫胺素营养缺陷型会产生泛酸,我们发现这是鞘氨醇单胞菌属生长所必需的,这支持了一组维生素依赖性相互作用是互利共生的。一种隐球菌酵母( Cryptococcus )产生维生素 B 泛酸,与鞘氨醇单胞菌属共培养会使两种生物的适应性提高 90-130 倍。我们的研究表明,基于宏基因组信息和微生物共生体的网络分析具有预测能力,可以识别构成微生物群落结构和功能的微生物相互作用。