Department of Life Science and Medical Bioscience, Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsucho, Shinjuku-ku, Tokyo, 162-8480, Japan.
Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology, 3-4-1 Okubo, Shinjuku-ku, Tokyo, 169-8555, Japan.
Microbiome. 2020 Jan 23;8(1):5. doi: 10.1186/s40168-019-0779-2.
The gut microbiota can have dramatic effects on host metabolism; however, current genomic strategies for uncultured bacteria have several limitations that hinder their ability to identify responders to metabolic changes in the microbiota. In this study, we describe a novel single-cell genomic sequencing technique that can identify metabolic responders at the species level without the need for reference genomes, and apply this method to identify bacterial responders to an inulin-based diet in the mouse gut microbiota.
Inulin-feeding changed the mouse fecal microbiome composition to increase Bacteroides spp., resulting in the production of abundant succinate in the mouse intestine. Using our massively parallel single-cell genome sequencing technique, named SAG-gel platform, we obtained 346 single-amplified genomes (SAGs) from mouse gut microbes before and after dietary inulin supplementation. After quality control, the SAGs were classified as 267 bacteria, spanning 2 phyla, 4 classes, 7 orders, and 14 families, and 31 different strains of SAGs were graded as high- and medium-quality draft genomes. From these, we have successfully obtained the genomes of the dominant inulin-responders, Bacteroides spp., and identified their polysaccharide utilization loci and their specific metabolic pathways for succinate production.
Our single-cell genomics approach generated a massive amount of SAGs, enabling a functional analysis of uncultured bacteria in the intestinal microbiome. This enabled us to estimate metabolic lineages involved in the bacterial fermentation of dietary fiber and metabolic outcomes such as short-chain fatty acid production in the intestinal environment based on the fibers ingested. The technique allows the in-depth isolation and characterization of uncultured bacteria with specific functions in the microbiota and could be exploited to improve human and animal health. Video abstract.
肠道微生物组对宿主代谢有显著影响；然而，目前用于未培养细菌的基因组策略存在一些限制，这些限制阻碍了它们识别微生物组代谢变化的响应者的能力。在这项研究中，我们描述了一种新的单细胞基因组测序技术，该技术可以在不需要参考基因组的情况下在物种水平上识别代谢响应者，并应用该方法来识别小鼠肠道微生物群中菊粉基饮食的细菌响应者。
菊粉喂养改变了小鼠粪便微生物组的组成，增加了拟杆菌属，导致小鼠肠道中产生丰富的琥珀酸。使用我们的大规模平行单细胞基因组测序技术，名为 SAG-gel 平台，我们从喂食菊粉前后的小鼠肠道微生物中获得了 346 个单扩增基因组（SAG）。经过质量控制，SAG 被分类为 267 种细菌，跨越 2 个门、4 个纲、7 个目和 14 个科，31 个不同的 SAG 菌株被评为高质量和中质量的草案基因组。从中，我们成功获得了优势菊粉响应者拟杆菌属的基因组，并鉴定了它们的多糖利用基因座及其特定的琥珀酸生产代谢途径。
我们的单细胞基因组学方法产生了大量的 SAG，使我们能够对肠道微生物组中未培养细菌进行功能分析。这使我们能够根据摄入的纤维估计参与细菌发酵膳食纤维的代谢谱系以及肠道环境中短链脂肪酸产生等代谢结果。该技术允许对具有特定功能的未培养细菌进行深入分离和表征，并且可以用于改善人类和动物的健康。视频摘要。
