bioRxivMicrobiology
A mixed blessing of viruses in wastewater treatment plants
L.-D. Shi, X. Dong, Z. Liu, Y. Yang, J.-G. Lin, M. Li, J.-D. Gu, L.-Z. Zhu, H.-P. Zhao
Abstract
Activated sludge of wastewater treatment plants harbors a very high diversity of both microorganisms and viruses, wherein the latter control microbial dynamics and metabolisms by infection and lysis of cells. However, it remains poorly understood how viruses impact the biochemical processes of activated sludge, for example in terms of treatment efficiency and pollutant removal. Using metagenomic and metatranscriptomic deep sequencing, the present study recovered thousands of viral sequences from activated sludge samples of three conventional wastewater treatment plants. Gene-sharing network indicated that most of viruses could not be assigned to known viral genera, implying activated sludge as an underexplored reservoir for new viruses and viral diversity. in silico Predictions of virus-host linkages demonstrated that infected microbial hosts, mostly belonging to bacteria, were transcriptionally active and able to hydrolyze polymers including starches, celluloses, and proteins. Some viruses encode auxiliary metabolic genes (AMGs) involved in carbon, nitrogen, and sulfur cycling, and antibiotic resistance genes (ARGs) for resistance to multiple drugs. The former group of virus-encoded genes (i.e. AMGs) may enhance the biodegradation of contaminants like starches and celluloses, suggesting a positive role for viruses in strengthening the performance of activated sludge. However, the latter group (i.e. ARGs) would be disseminated to different microorganisms using viruses as gene shuttles, demonstrating the possibility for viruses to facilitate the spread of antibiotic resistance in the environment. Collectively, this study highlights the mixed blessing of viruses in wastewater treatment plants, and deciphers how they manipulate the biochemical processes in the activated sludge, with implications for both environmental protection and ecosystem security.
- assay: metagenomics
- research focus: cell type annotation
- research focus: gene regulatory network inference
- approach: statistical model
- approach: deep learning
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