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Eisfeld, A. J.

Publications and source records attributed to Eisfeld, A. J..

3 recordsLinked to original sources

Evolutionary constraints limit additional mammalian adaptation of bovine-derived H5N1 influenza viruses in ferrets

The outbreak of clade 2.3.4.4b H5N1 viruses among U.S. dairy cattle has raised concerns that sustained circulation among agricultural mammals could facilitate viral adaptation toward efficient human transmission. However, the evolutionary dynamics governing such adaptation remain poorly understood. Here we investigated the evolution of two bovine-derived H5N1 B3.13 genotype viruses during infection and airborne transmission in ferrets, building on prior characterization of their robust replication and inefficient airborne transmission. Within hosts, viral genetic diversity was limited and viruses were subject to genetic drift and weak purifying selection. Transmission, when it occurred, was characterized by stringent bottlenecks that sharply reduced viral genetic diversity. We found no evidence of mammalian adaptation during infection or transmission. Together, these findings indicate that bovine-derived H5N1 viruses face evolutionary constraints during acute mammalian infection and transmission, limiting movement toward enhanced airborne spread. These constraints may help explain why efficient mammalian replication does not necessarily coincide with efficient transmission. Continued circulation of HA clade 2.3.4.4b viruses nevertheless creates repeated opportunities for rare but consequential evolutionary events, underscoring the importance of sustained surveillance and risk mitigation.

microbiology↗

Host network-based discovery of critical regulators of innate immunity, virus growth, and pathogenesis in influenza virus infection

Innate immunity is protective against viruses, but also can facilitate pathological infection responses. Despite intensive research, our understanding of the mechanisms that regulate innate immunity in virus infection remains incomplete. Systems biology-based data-driven modeling approaches hold substantial promise toward discovery of crucial innate immune signaling regulators, yet model-derived predictions are almost completely unexplored. Here, we carried out systematic experimental validation of candidate regulators predicted by a transcriptional association network model of influenza virus-infected cells. We identified dozens of novel innate immune signaling regulators with potent effects on the replication of influenza and other viruses, and importantly, we established the biological relevance of a validated regulator in vivo. Collectively, these findings aid in clarifying mechanisms of influenza virus pathogenicity and might lead to innovative approaches for treating influenza virus disease. Similar data-driven modeling strategies may be applicable for the study of other pathogen systems or immunological disorders.

microbiology↗