Hidden viruses in wastewater treatment may shape public health risks, study finds
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Decoding pathogen-virus-metabolic gene networks in full-scale wastewater treatment: from virus diversity to hosts interaction
view moreCredit: Yanmei Zhao, Xinyi Wang, Fang Huang, Rui Gao, Bin Liang, Lu Fan, Aijie Wang & Shu-Hong Gao
Viruses are everywhere in wastewater treatment plants, quietly interacting with bacteria as sewage is cleaned and reused. A new study reveals that these viral communities are far more complex and influential than previously recognized, with implications for water safety, antibiotic resistance, and how treatment performance is monitored.
In research published in Biocontaminant, scientists used advanced metagenomic sequencing to track viruses and their microbial hosts across full scale wastewater treatment plants in China and Singapore. By analyzing samples from influent to final effluent, the team uncovered persistent viral populations that survive treatment and interact closely with disease causing bacteria.
“Wastewater treatment plants are designed to remove pollutants and known pathogens, but viruses have largely been overlooked,” said corresponding author Shu Hong Gao of Harbin Institute of Technology. “Our results show that viruses are not just passive passengers. They actively shape microbial processes and may influence both treatment efficiency and health risks.”
The researchers identified 99 families of viruses across 28 wastewater and sludge samples. Two viral groups, Peduoviridae and Casjensviridae, were consistently abundant throughout all treatment stages, from raw sewage to treated effluent. Their persistence suggests they could serve as reliable biological indicators of treatment performance.
Traditionally, wastewater monitoring relies on bacterial indicators such as Escherichia coli. However, the study found that E. coli did not track viral dynamics well. Instead, the abundances of Pseudomonas aeruginosa and Aeromonas caviae, both opportunistic pathogens, closely mirrored the behavior of dominant viruses.
“This challenges the idea that one or two standard bacteria can represent overall biological risk,” Gao said. “Our findings suggest that alternative indicators linked to viral populations may provide a more accurate picture of treatment effectiveness.”
Beyond identifying viruses, the team explored what these viruses can do. Many carried auxiliary metabolic genes, which can alter the metabolism of their bacterial hosts. These genes were linked to carbohydrate metabolism, pollutant degradation, and xenobiotic breakdown, processes that may help wastewater systems remove contaminants more efficiently.
At the same time, the study uncovered a potential downside. Some viral genes may enhance the competitiveness of antibiotic resistant bacteria, indirectly promoting the spread of antibiotic resistance genes.
“These viral functions act like a double edged sword,” Gao explained. “They may support pollutant removal, but they can also increase the risk of resistance spreading among pathogens.”
Using machine learning to predict virus host relationships, the researchers found that most viruses targeted bacteria within the phylum Pseudomonadota, which includes many multidrug resistant pathogens commonly detected in wastewater. This highlights wastewater treatment plants as hotspots where viral host interactions could influence microbial evolution before water is released back into the environment.
Importantly, disinfection steps did not eliminate all viral functions. In some plants, viral metabolic genes persisted even after final treatment, suggesting that current processes may not fully address viral associated risks.
The authors say their work supports expanding wastewater surveillance beyond traditional indicators and incorporating viral monitoring into routine assessments.
“Understanding virus host networks gives us new tools to manage biological risks,” Gao said. “With better monitoring and targeted process optimization, wastewater treatment can be made safer and more resilient in a world facing growing public health challenges.”
The study provides a foundation for improving wastewater reuse safety and for developing next generation monitoring strategies that reflect the true biological complexity of engineered water systems.
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Journal reference: Zhao Y, Wang X, Huang F, Gao R, Liang B, et al. 2025. Decoding pathogen-virus-metabolic gene networks in full-scale wastewater treatment: from virus diversity to hosts interaction. Biocontaminant 1: e013
https://www.maxapress.com/article/doi/10.48130/biocontam-0025-0015
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About Biocontaminant:
Biocontaminant is a multidisciplinary platform dedicated to advancing fundamental and applied research on biological contaminants across diverse environments and systems. The journal serves as an innovative, efficient, and professional forum for global researchers to disseminate findings in this rapidly evolving field.
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Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Decoding pathogen-virus-metabolic gene networks in full-scale wastewater treatment: from virus diversity to hosts interaction
Article Publication Date
14-Dec-2025
Contaminated bathing water easier to detect
Urbanisation and a warmer climate means that more people want to swim in canals, harbours, and urban beaches. However, this means that they may swimming close to where treated wastewater and stormwater are discharged – including bacteria, viruses, and other pathogens that might make people sick. A new method tested in Sweden by Lund University, can provide both faster and more complete answers on whether the water is safe for swimming or not.
An innovation from researchers at Lund University, Sweden Water Research, and Kristianstad University has been successfully tested in Helsingborg, where the response time has been reduced from several days to just a few hours.
“Regular monitoring is essential to ensure that the water is safe. E. coli bacteria are so-called indicator bacteria, and are used as a warning signal that microorganisms that can cause illness might be in the water” says Catherine Paul, associate professor in water resources engineering at Lund University.
Currently, the most common method is to culture E. coli. The results take a few days, which increases the risk of unnecessary exposure to bacteria and potentially also viruses and other pathogens.
The new method instead looks at the community of all bacteria in the water that changes if there is a problem and connects this to the usual measurements for E. coli using machine learning and flow cytometry (a type of liquid laser scanner used to detect cells and other particles).
Together with two other experts in waterborne bacteria, doctoral student Isabel Erb, from technical microbiology at Lund University and Sweden Water Research, and computer scientist Niklas Gador at Kristianstad University, the team has tested the idea in practice.
Several advantages
According to the researchers, the method has several advantages compared with the alternatives on the market:
- Faster – it only takes about 20 minutes to analyse a water sample
- Cheaper and simpler – requires less work and fewer chemicals than, for example, PCR analyses
- Automated – the entire process can be handled by a machine, saving both time and staff
- More sustainable – uses less resources than traditional methods
Open source
Another advantage is that the method uses open-source code.
“The method is free to try for anyone who wants to. However, you need access to a flow cytometer, so it’s not for personal use,” says Isabel Erb.
Flow cytometry, as mentioned, is a technique that uses lasers to scan cells and small particles in water samples. Within minutes, it creates a “fingerprint” that describes all the bacteria in the sample.
Using code, software interprets the data and provides an 80% reliable answer on whether E. coli is present in the water and in what quantity.
The plan: locate the origins of the water pollution
Unlike traditional methods, the researchers focus on entire bacterial communities, or microbiomes, which makes it possible to detect changes even if E. coli is absent.
“With an overview of the entire microbiome, we may in the future be able to identify the contamination source. The microbiome of bird droppings, for example, looks completely different from that of treated wastewater.”
The advantages are many: the analysis takes about 20 minutes, requires fewer chemicals, and can be automated. The machine can measure water samples every 30 minutes. In the future, the goal is for the results to be sent directly to a data system that decides whether a warning should be issued. Expensive and time-consuming tests, such as PCR tests, could then be added for more details and for confirmation.
“The next step is to test the method in more situations, for example in drinking water, and to improve the algorithms for even more reliable predictions,” concludes Catherine Paul.
Journal
Water Research X
Article Title
A data-driven early warning system for Escherichia coli in water based on microbial community analysis using flow cytometry 2D histograms
