Ambient hydrogen sulfide exposure increases the severity of influenza A virus infection in swine

A review of the influence of environmental pollutants (microplastics, pesticides, antibiotics, air pollutants, viruses, bacteria) on animal viruses

Microorganisms, especially viruses, cause disease in both humans and animals. Environmental chemical pollutants including microplastics, pesticides, antibiotics sand air pollutants arisen from human activities affect both animal and human health. This review assesses the impact of chemical and biological contaminants (virus and bacteria) on viruses including its life cycle, survival, mutations, loads and titers, shedding, transmission, infection, re-assortment, interference, abundance, viral transfer between cells, and the susceptibility of the host to viruses. It summarizes the sources of environmental contaminants, interactions between contaminants and viruses, and methods used to mitigate such interactions. Overall, this review provides a perspective of environmentally co-occurring contaminants on animal viruses that would be useful for future research on virus-animal-human-ecosystem harmony studies to safeguard human and animal health.

Application of a maximal-clique based community detection algorithm to gut microbiome data reveals driver microbes during influenza A virus infection

Influenza A Virus (IAV) infection followed by bacterial pneumonia often leads to hospitalization and death in individuals from high risk groups. Following infection, IAV triggers the process of viral RNA replication which in turn disrupts healthy gut microbial community, while the gut microbiota plays an instrumental role in protecting the host by evolving colonization resistance. Although the underlying mechanisms of IAV infection have been unraveled, the underlying complex mechanisms evolved by gut microbiota in order to induce host immune response following IAV infection remain evasive. In this work, we developed a novel Maximal-Clique based Community Detection algorithm for Weighted undirected Networks (MCCD-WN) and compared its performance with other existing algorithms using three sets of benchmark networks. Moreover, we applied our algorithm to gut microbiome data derived from fecal samples of both healthy and IAV-infected pigs over a sequence of time-points. The results we obtained from the real-life IAV dataset unveil the role of the microbial families Ruminococcaceae, Lachnospiraceae, Spirochaetaceae and Prevotellaceae in the gut microbiome of the IAV-infected cohort. Furthermore, the additional integration of metaproteomic data enabled not only the identification of microbial biomarkers, but also the elucidation of their functional roles in protecting the host following IAV infection. Our network analysis reveals a fast recovery of the infected cohort after the second IAV infection and provides insights into crucial roles of Desulfovibrionaceae and Lactobacillaceae families in combating Influenza A Virus infection. Source code of the community detection algorithm can be downloaded from https://github.com/AniBhar84/MCCD-WN.