Table_6_Microbial dark matter sequences verification in amplicon sequencing and environmental metagenomics data.xlsx

Although microorganisms constitute the most diverse and abundant life form on Earth, in many environments, the vast majority of them remain uncultured. As it is based on information gleaned mainly from cultivated microorganisms, our current body of knowledge regarding microbial life is partial and does not reflect actual microbial diversity. That diversity is hidden in the uncultured microbial majority, termed by microbiologists as “microbial dark matter” (MDM), a term borrowed from astrophysics. Metagenomic sequencing analysis techniques (both 16S rRNA gene and shotgun sequencing) compare gene sequences to reference databases, each of which represents only a small fraction of the existing microorganisms. Unaligned sequences lead to groups of “unknown microorganisms” that are usually ignored and rarefied from diversity analysis. To address this knowledge gap, we analyzed the 16S rRNA gene sequences of microbial communities from four different environments—a living organism, a desert environment, a natural aquatic environment, and a membrane bioreactor for wastewater treatment. From those datasets, we chose representative sequences of potentially unknown bacteria for additional examination as “microbial dark matter sequences” (MDMS). Sequence existence was validated by specific amplification and re-sequencing. These sequences were screened against databases and aligned to the Genome Taxonomy Database to build a comprehensive phylogenetic tree for additional sequence classification, revealing potentially new candidate phyla and other lineages. These putative MDMS were also screened against metagenome-assembled genomes from the explored environments for additional validation and for taxonomic and metabolic characterizations. This study shows the immense importance of MDMS in environmental metataxonomic analyses of 16S rRNA gene sequences and provides a simple and readily available methodology for the examination of MDM hidden behind amplicon sequencing results.

尽管微生物是地球上多样性最高、丰度最大的生命形式，但在诸多环境中，绝大多数微生物仍未获得纯培养。当前我们对微生物生命的认知大多源自已培养微生物的研究，因此存在局限性，无法反映真实的微生物多样性。这类多样性隐藏在未培养的绝大多数微生物中，被微生物学家称为“微生物暗物质（microbial dark matter, MDM）”，该术语借鉴自天体物理学。宏基因组测序分析技术（涵盖16S rRNA基因测序与鸟枪法测序）会将基因序列与参考数据库进行比对，但现有参考数据库仅能覆盖现存微生物的极小一部分。无法比对上参考数据库的序列会形成“未知微生物”类群，这类类群通常会在多样性分析中被忽略或进行稀疏化处理。为弥补这一认知空白，本研究分析了四种不同环境中微生物群落的16S rRNA基因序列，分别为活体宿主、沙漠环境、自然水生环境以及污水处理膜生物反应器。从上述数据集中，我们筛选出潜在未知细菌的代表性序列，作为“微生物暗物质序列（microbial dark matter sequences, MDMS）”开展后续研究。我们通过特异性扩增与重测序验证了这些序列的真实性。随后，我们将这些序列与现有数据库进行比对，并结合基因组分类数据库（Genome Taxonomy Database, GTDB）构建全面的系统发育树以完成序列的进一步分类，由此发现了潜在的新候选门及其他演化支系。同时，我们还将这些推定的MDMS与本研究探索环境中获得的宏基因组组装基因组（metagenome-assembled genomes, MAGs）进行比对，以完成额外验证，并开展分类学与代谢特征分析。本研究证实了MDMS在16S rRNA基因序列环境宏分类学分析中的重要价值，并提供了一种简便易行的方法，用于解析扩增子测序结果中隐藏的微生物暗物质。