Indiana University, Bloomington, IN, United States.
Front Cell Infect Microbiol. 2022 Sep 28;12:933516. doi: 10.3389/fcimb.2022.933516. eCollection 2022.
The human gut microbiome is composed of a diverse consortium of microorganisms. Relatively little is known about the diversity of the bacteriophage population and their interactions with microbial organisms in the human microbiome. Due to the persistent rivalry between microbial organisms (hosts) and phages (invaders), genetic traces of phages are found in the hosts' CRISPR-Cas adaptive immune system. Mobile genetic elements (MGEs) found in bacteria include genetic material from phage and plasmids, often resultant from invasion events. We developed a computational pipeline (BacMGEnet), which can be used for inference and exploratory analysis of putative interactions between microbial organisms and MGEs (phages and plasmids) and their interaction network. Given a collection of genomes as the input, BacMGEnet utilizes computational tools we have previously developed to characterize CRISPR-Cas systems in the genomes, which are then used to identify putative invaders from publicly available collections of phage/prophage sequences. In addition, BacMGEnet uses a greedy algorithm to summarize identified putative interactions to produce a bacteria-MGE network in a standard network format. Inferred networks can be utilized to assist further examination of the putative interactions and for discovery of interaction patterns. Here we apply the BacMGEnet pipeline to a few collections of genomic/metagenomic datasets to demonstrate its utilities. BacMGEnet revealed a complex interaction network of the pangenome with its phage invaders, and the modularity analysis of the resulted network suggested differential activities of the different ' CRISPR-Cas systems (Type I-C and Type II-C) against some phages. Analysis of the phage-bacteria interaction network of human gut microbiome revealed a mixture of phages with a broad host range (resulting in large modules with many bacteria and phages), and phages with narrow host range. We also showed that BacMGEnet can be used to infer phages that invade bacteria and their interactions in wound microbiome. We anticipate that BacMGEnet will become an important tool for studying the interactions between bacteria and their invaders for microbiome research.
人类肠道微生物组由多种微生物组成。相对而言，人们对噬菌体种群的多样性及其与人类微生物组中微生物的相互作用知之甚少。由于微生物（宿主）和噬菌体（入侵者）之间持续存在竞争，噬菌体的遗传痕迹存在于宿主的 CRISPR-Cas 适应性免疫系统中。细菌中的移动遗传元件（MGEs）包括噬菌体和质粒的遗传物质，通常是由于入侵事件而产生的。我们开发了一种计算管道（BacMGEnet），可用于推断和探索微生物之间的假定相互作用以及 MGE（噬菌体和质粒）及其相互作用网络。给定基因组集合作为输入，BacMGEnet 使用我们之前开发的工具来表征基因组中的 CRISPR-Cas 系统，然后利用这些系统从公开的噬菌体/原噬菌体序列集合中识别假定的入侵者。此外，BacMGEnet 使用贪婪算法来总结识别出的假定相互作用，以生成标准网络格式的细菌-MGE 网络。推断出的网络可用于协助进一步检查假定的相互作用并发现相互作用模式。在这里，我们将 BacMGEnet 管道应用于几个基因组/宏基因组数据集的集合，以展示其用途。BacMGEnet 揭示了 pangenome 与其噬菌体入侵者之间的复杂相互作用网络，而所得网络的模块化分析表明，不同的“CRISPR-Cas 系统（I-C 型和 II-C 型）对某些噬菌体具有不同的活性。对人类肠道微生物组的噬菌体-细菌相互作用网络的分析揭示了具有广泛宿主范围的噬菌体（导致具有许多细菌和噬菌体的大模块）和具有窄宿主范围的噬菌体的混合物。我们还表明，BacMGEnet 可用于推断入侵细菌及其在伤口微生物组中的相互作用的噬菌体。我们预计，BacMGEnet 将成为研究微生物组研究中细菌与其入侵者之间相互作用的重要工具。
