State Key Laboratory of Microbial Technology, Shandong University, Qingdao, 266000, China.
State Key Laboratory of Microbial Resources, and Environmental Microbiology Research Center (EMRC), Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
Sci China Life Sci. 2024 Aug;67(8):1751-1762. doi: 10.1007/s11427-023-2537-0. Epub 2024 Apr 7.
Association networks are widely applied for the prediction of bacterial interactions in studies of human gut microbiomes. However, the experimental validation of the predicted interactions is challenging due to the complexity of gut microbiomes and the limited number of cultivated bacteria. In this study, we addressed this challenge by integrating in vitro time series network (TSN) associations and co-cultivation of TSN taxon pairs. Fecal samples were collected and used for cultivation and enrichment of gut microbiome on YCFA agar plates for 13 days. Enriched cells were harvested for DNA extraction and metagenomic sequencing. A total of 198 metagenome-assembled genomes (MAGs) were recovered. Temporal dynamics of bacteria growing on the YCFA agar were used to infer microbial association networks. To experimentally validate the interactions of taxon pairs in networks, we selected 24 and 19 bacterial strains from this study and from the previously established human gut microbial biobank, respectively, for pairwise co-cultures. The co-culture experiments revealed that most of the interactions between taxa in networks were identified as neutralism (51.67%), followed by commensalism (21.67%), amensalism (18.33%), competition (5%) and exploitation (3.33%). Genome-centric analysis further revealed that the commensal gut bacteria (helpers and beneficiaries) might interact with each other via the exchanges of amino acids with high biosynthetic costs, short-chain fatty acids, and/or vitamins. We also validated 12 beneficiaries by adding 16 additives into the basic YCFA medium and found that the growth of 66.7% of these strains was significantly promoted. This approach provides new insights into the gut microbiome complexity and microbial interactions in association networks. Our work highlights that the positive relationships in gut microbial communities tend to be overestimated, and that amino acids, short-chain fatty acids, and vitamins are contributed to the positive relationships.
关联网络广泛应用于人类肠道微生物组研究中细菌相互作用的预测。然而，由于肠道微生物组的复杂性和可培养细菌的数量有限，预测相互作用的实验验证具有挑战性。在这项研究中，我们通过整合体外时间序列网络 (TSN) 关联和 TSN 分类群对的共培养来解决这一挑战。收集粪便样本，用于在 YCFA 琼脂平板上培养和富集肠道微生物组，培养时间为 13 天。富集的细胞用于提取 DNA 和宏基因组测序。共回收了 198 个宏基因组组装基因组 (MAG)。使用细菌在 YCFA 琼脂上的时间动态来推断微生物关联网络。为了实验验证网络中分类群对的相互作用，我们从这项研究和之前建立的人类肠道微生物生物库中分别选择了 24 和 19 株细菌用于成对共培养。共培养实验表明，网络中大多数分类群之间的相互作用被鉴定为中性（51.67%），其次是共生（21.67%）、偏利共生（18.33%）、竞争（5%）和剥削（3.33%）。基于基因组的分析进一步表明，共生肠道细菌（助手和受益者）可能通过交换高生物合成成本的氨基酸、短链脂肪酸和/或维生素来相互作用。我们还通过在基本 YCFA 培养基中添加 16 种添加剂来验证了 12 种受益者，发现其中 66.7%的菌株的生长显著促进。这种方法为肠道微生物组的复杂性和关联网络中的微生物相互作用提供了新的见解。我们的工作强调了肠道微生物群落中的正相关关系往往被高估，而氨基酸、短链脂肪酸和维生素有助于正相关关系。
