A network approach to elucidate and prioritize microbial dark matter in microbial communities

• Published in: The ISME Journal Vol. 15; no. 1; pp. 228 - 244
• Main Authors: Zamkovaya, Tatyana, Foster, Jamie S, de Crécy-Lagard, Valérie, Conesa, Ana
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 01.01.2021; Nature Publishing Group UK
• Subjects: Aquatic environment; Aquatic habitats; Bacteria - genetics; Dark matter; Ecosystem; Ecosystem structure; Gene sequencing; Metagenome; Microbial activity; Microbial Consortia; Microbiota; Microorganisms; Networks; Planetary probes; RNA, Ribosomal, 16S - genetics; rRNA 16S; Taxa
• ISSN: 1751-7362; 1751-7370
• DOI: 10.1038/s41396-020-00777-x

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.