Microbes compose most of the biomass on the planet, yet the majority of taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter,” represent a major challenge for biology. To understand the ecological contributions of these Unknown taxa, it is essential to first understand the relationship between unknown species, neighboring microbes, and their respective environment. Here, we establish a method to study the ecological significance of “microbial dark matter” by building microbial co-occurrence networks from publicly available 16S rRNA gene sequencing data of four extreme aquatic habitats. For each environment, we constructed networks including and excluding unknown organisms at multiple taxonomic levels and used network centrality measures to quantitatively compare networks. When the Unknown taxa were excluded from the networks, a significant reduction in degree and betweenness was observed for all environments. Strikingly, Unknown taxa occurred as top hubs in all environments, suggesting that “microbial dark matter” play necessary ecological roles within their respective communities. In addition, novel adaptation-related genes were detected after using 16S rRNA gene sequences from top-scoring hub taxa as probes to blast metagenome databases. This work demonstrates the broad applicability of network metrics to identify and prioritize key Unknown taxa and improve understanding of ecosystem structure across diverse habitats.

微生物构成了地球上的大部分生物量，但大多数分类群仍然没有特征。这些未知的微生物，通常被称为“微生物暗物质”，代表了生物学的重大挑战。要了解这些未知类群的生态贡献，必须首先了解未知物种、邻近微生物及其各自环境之间的关系。在这里，我们通过从四个极端水生栖息地的公开可用 16S rRNA 基因测序数据构建微生物共生网络，建立了一种研究“微生物暗物质”生态意义的方法。对于每个环境，我们构建了包含和排除多个分类级别的未知生物的网络，并使用网络中心性度量来定量比较网络。当未知分类群被排除在网络之外时，观察到所有环境的程度和介数都显着降低。引人注目的是，未知分类群在所有环境中都是顶级枢纽，这表明“微生物暗物质”在各自的社区中发挥着必要的生态作用。此外，在使用来自得分最高的枢纽类群的 16S rRNA 基因序列作为爆炸宏基因组数据库的探针后，检测到了新的适应相关基因。这项工作证明了网络指标的广泛适用性，可以识别和优先考虑关键的未知分类群，并提高对不同栖息地生态系统结构的理解。表明“微生物暗物质”在各自的群落中发挥着必要的生态作用。此外，在使用来自得分最高的枢纽类群的 16S rRNA 基因序列作为爆炸宏基因组数据库的探针后，检测到了新的适应相关基因。这项工作证明了网络指标的广泛适用性，可以识别和优先考虑关键的未知分类群，并提高对不同栖息地生态系统结构的理解。表明“微生物暗物质”在各自的群落中发挥着必要的生态作用。此外，在使用来自得分最高的枢纽类群的 16S rRNA 基因序列作为爆炸宏基因组数据库的探针后，检测到了新的适应相关基因。这项工作证明了网络指标的广泛适用性，可以识别和优先考虑关键的未知分类群，并提高对不同栖息地生态系统结构的理解。