Environmental Monitoring
Humans and animals shed viruses via various routes, including respiratory secretions, feces, and urine. These viruses ultimately end in environmental samples, such as wastewater and indoor aerosols. The assemblage of viruses (i.e., virome) in the environmental samples may shift when individuals are infected by contagious viruses. Therefore, environmental samples are of great value to provide signals of diseases at the community level. We are particularly interested in developing robust but low-cost methods to enrich viruses from complex environmental matrices, understanding the dynamics of environmental virome in response to infectious diseases, and discovering new biomarkers to predict future endemics and pandemics. We collaborate closely with our community stakeholders, industry partners, academic collaborators, and government agencies to make our impacts.
Funded projects:
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NIH U01DE035632, Hiding in plain sight: "integrating AI with targeted bench methods to discover and characterize viruses in the human body"
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NSF CCF #2200173, ”Predictive Intelligence for Pandemic Prevention Phase I: Center for ecosystems data integration and pandemic early warning”
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CDC Epidemiology and Laboratory Capacity (ELC), ”Wastewater surveillance to support COVID-19 response and expand New York State health security”
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Erie County Department of Health award #91287, ”SARS-CoV-2 wastewater monitoring program for Erie County”
VIRUS inactivation
Human-pathogenic viruses can preserve their infectivity in the environments, including water, air, and soils. It is important to apply effective engineering countermeasures to inactivate or remove these viruses before they encounter the next hosts. However, it is challenging to study inactivation kinetics of viruses in laboratory because viruses are constantly mutated and many of them might be difficult or too dangerous to handle. To address these issues, we innovate proteomic and genomic analysis to advance mechanistic understanding of the molecular features that drive virus inactivation. We leverage molecular structures simulation and machine-learning tools to develop models that predict the effectiveness of virus inactivation.
Funded projects:
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NSF CBET #2212779, ”Insights into biomolecular reactivity and structure for virus inactivation prediction”
Microbial interactions
Environmental variables in nature and engineered systems, such as oxygen, temperatures, and nutrients, shape microbial pathogenicity and virus-host interactions. Our research addresses two fundamental questions: (1) How does the environmental stressors drive the dissemination of virulence factors via extracellular vesicles from pathogens? and (2) How do phages infect and lyse host microorganisms within engineered systems? To answer these questions, we develop high-sensitive multi-omics workflows to monitor and quantify associated biomolecules, while leveraging AI models to predict these complex biological behaviors. A better understanding of microbial functional dynamics would inform novel strategies for pathogen control and bioenergy production.
Funded projects:
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DOE Genesis Mission, "PHOCUS: PHage-host interaction programming for anaerobic micrObiome Control Using AI-guided deSign"
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NSF CAREER #2338677, "CAREER: Bacterial extracellular vesicles in wastewater systems: Persistence and production to disseminate virulence proteins"
Funding Sources
