ISME communications · 2026
Quorum-sensing microbial dark matter enables core functions in wastewater microbiomes.
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Liu Qiuju, Su Han, Gong Sai, Jin Ying, Fan Yingzheng, Wu Mengxiong, Guo Jianhua, Ren Xinkun, Wang Jinfeng
Аннотация
Quorum-sensing bacteria (QSB) play a crucial role in coordinating microbial activities in activated sludge. However, our knowledge is primarily based on cultivated or taxonomically classified species, neglecting microbial dark matter (MDM), which comprises lineages lacking reference genomes and is prevalent in wastewater communities. Herein, 3569 high- and medium-quality metagenome-assembled genomes were obtained from 69 full-scale wastewater treatment plants. A total of 1289 MDM lineages were identified to possess quorum sensing (QS) capabilities, designated as MDM-QSB. Among them, 127 novel genera were identified. These organisms possess larger genomes enriched in genes for energy metabolism and environmental information processing, allowing a broader ecological niche compared to non-QS MDM (P < .001). They occupy central positions in co-occurrence networks and are predicted as dominant keystones by a deep learning model. Their robust metabolic potential and network keystone position enable coordinated community-level metabolic responses, as evidenced by stronger carbon, nitrogen, phosphorus, and sulfur element cycling functions and higher associations with chemical oxygen demand, total nitrogen, and ammonium nitrogen removal (P < .05). Furthermore, genome-scale metabolic modeling reveals they engage in extensive cross-feeding with nitrifiers, denitrifiers, and polyphosphate-accumulating organisms, supplying metabolites such as protoheme and serine alongside QS signals. Together, these findings identify MDM-QSB as previously overlooked multifunctional hubs of activated sludge microbiomes. Given their crucial roles in community stability and process performance, incorporating these previously overlooked populations into monitoring and control frameworks is expected to improve the resilience and predictability of biological wastewater treatment processes.