Back

Unraveling Candidatus Dermatophostum as a Novel Genus of Polyphosphate-Accumulating Organisms for High-Strength Wastewater Treatment

Wang, H.; Zhao, Z.; Lin, L.; Dong, A.; Deng, Y.; Zhou, J.; Ju, F.

2025-12-29 microbiology
10.64898/2025.12.29.696836 bioRxiv
Show abstract

Dermatophilaceae polyphosphate-accumulating organisms (PAOs), formerly classified as Tetrasphaera PAOs, play pivotal roles in enhanced biological phosphorus removal (EBPR). However, their phylogenetic diversity, ecological preferences, and metabolic traits remain poorly characterized, and a robust marker gene for their classification is lacking. Here, we performed an extensive phylogenomic and metabolic analysis of Dermatophilaceae PAOs utilizing 46 newly recovered metagenome-assembled genomes (MAGs) from a laboratory-scale EBPR reactor treating high-strength wastewater and full-scale wastewater treatment plants. These analyses revealed a previously uncharacterized PAO genus, named Candidatus Dermatophostum, which is adapted to high-phosphorus environments. Its representative species, Ca. D. ammonifactor, was enriched in the EBPR reactor and its PAO phenotype was confirmed by polyphosphate staining and fluorescence in situ hybridization. Genomic, transcriptomic, and protein structure analyses revealed its specialized metabolic capabilities for phosphate metabolism, glycogen synthesis and dissimilatory nitrate reduction to ammonium. Moreover, Ca. Dermatophostum was found to be widely distributed across WWPTs worldwide, underscoring both its ecological importance and its potential role in mitigating nitrous oxide (N2O) emissions. Finally, we propose a ppk1-based classification framework that resolves Dermatophilaceae PAOs into six distinct clades, consistent with whole-genome phylogeny, and demonstrates that ppk1 can serve as a reliable marker gene for tracking these populations. Together, these findings expand the ecological and functional understanding of Dermatophilaceae PAOs and highlight their promise for advancing sustainable wastewater treatment and resource recovery.

Published in The ISME Journal (predicted rank #1) · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.