Tracking the genetic diversity of SARS-CoV-2 in wastewater, rather than just viral abundance, dramatically improves the ability to monitor and predict COVID-19 outbreaks, researchers report. Their study suggests that the new approach to wastewater pathogen surveillance could serve as a powerful predictive tool for public health, providing earlier and more accurate insight into emerging waves of infection. Monitoring pathogens in wastewater has become a powerful public health tool since the COVID-19 pandemic, offering a fast, cost-efficient, and potentially less biased way to track infectious diseases across scales. Because wastewater collects biological material from an entire population, a single sample can provide a broad snapshot of community-wide infection dynamics. Yet the standard methods used to estimate disease prevalence, which rely on measuring the amount of viral genetic material recovered from a given volume of wastewater, are vulnerable to limitations. Some measurements cannot be meaningfully compared across different pathogens or settings, while others are easily distorted by environmental factors like rainfall. According to Dustin Hill and colleagues, analyzing pathogen genetic diversity through whole-genome sequencing has the potential to overcome several of the limitations of traditional wastewater surveillance methods. They argue that changes in viral genetic diversity itself may serve as a meaningful indicator of shifts in disease spread within a population.

Here, Hill et al. introduce and validate a method for estimating the prevalence of SARS-CoV-2 by analyzing the virus’s genetic diversity in wastewater. To evaluate this idea, Hill et al. retroactively applied and analyzed genetic diversity within SARS-CoV-2 from 12,290 wastewater samples collected between 2023 and 2025 from across New York state. They found that genetic diversity within a specific region of the viruses’ spike protein – the S1 NTD region – closely tracked real-world COVID-19 infection trends and, in many cases, correlated with disease activity more strongly than traditional wastewater metrics. Moreover, the authors’ statistical analyses revealed that diversity patterns in wastewater consistently preceded increases in COVID-related hospital admissions by one to two weeks, suggesting robust early warning signals of worsening disease spread. “The use of wastewater surveillance as a primary tool for monitoring population health is still a developing area,” write Justin Lessle and Ariel Christensen in a related Perspective. “Nevertheless, [this approach] has the potential to revolutionize infectious disease research and public health practice. Viral sequencing approaches such as that proposed by Hill et al. will be an important component of that success.”

Overcoming a major hurdle in the use of microbes as medicine, researchers have introduced an implantable “living material” that contains bacteria that sense infections. It can release these therapeutic molecules on demand, while keeping them physically separated from the surrounding tissue. The findings represent a shift from passive drug depots to autonomous, responsive – and living – therapeutic systems. Engineered living cells are emerging as a new class of medicine that can autonomously sense disease and deliver treatment directly at affected sites. Unlike conventional drugs, these “living therapeutics” can sustain themselves in vivo and survive in many biological environments, including tumors, inflamed tissues, infected tissues, and even within human cells. Bacteria are particularly attractive because they can be genetically programmed to release therapeutic molecules in response to specific biological signals. However, this versatility also raises an important safety concern: therapeutic bacteria must be physically contained to prevent uncontrolled spread and potential toxicity. Previous implantable biomaterial systems, such as hydrogels and capsule-like enclosures, have shown some success in confining microbes, but only for short periods – typically no more than 2 weeks.

Tetsuhiro Harimoto and colleagues discovered that bacterial growth can be halted when the surrounding material reaches a sufficient level of stiffness, preventing the internal pressure of bacterial overgrowth from causing escape. At the same time, the material also needs to be tough enough to withstand the constant mechanical stress from surrounding tissues without cracking. To achieve this balance, Harimoto et al. created an implantable living material (ILM) consisting of a hierarchical hydrogel composed of bacteria-filled gelatin microgels embedded within a reinforced polyvinyl alcohol framework. In laboratory testing, the authors show that the material remained intact for 6 months with no detectable bacterial leakage, even under conditions designed to mimic long-term physiological stress. To evaluate the material’s clinical potential, Harimoto et al. transformed the ILM into an active therapeutic system by engineering bacteria to detect chemical signals from Pseudomonas aeruginosa, a common cause of implant-related infections. In response, the bacteria autonomously self-destructed to release an antibacterial protein that killed the pathogen. In a mouse model of joint infection, the system successfully reduced bacterial burden, demonstrating the potential of durable, programmable ILM-based therapeutics for long-term disease treatment. “Rather than treating the scaffold as a passive vehicle, Harimoto et al. treat it as an active determinant of whether contained bacteria can function safely over time,” write Kaige Chen and Quanyin Hu in a related Perspective. “This reframing brings living therapeutics closer to a model in which long-term, in vivo embedded therapeutic function replaces repeated drug administration.”

Podcast: A segment of Science's weekly podcast with Tetsuhiro Harimoto, related to this research, will be available on the Science.org podcast landing page after the embargo lifts. Reporters are free to make use of the segments for broadcast purposes and/or quote from them – with appropriate attribution (i.e., cite "Science podcast"). Please note that the file itself should not be posted to any other Web site.

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