RT Conference Proceedings
T1 An Efficient Machine Learning Method to Identify Genetic Drivers of Avian Influenza Virus Adaptation to Humans
A1 Leiva Rebollo, Rocío
A1 Patiño Galindo, Juan
A1 Villalon Letelier, Fernando
A1 Mena, Nacho
A1 Tansey, W
A1 Park, K
A1 Labella Vera, Alejandro Manuel
A1 Castro-López, María Dolores
A1 Borrego-García, Juan José
A1 Rabadán, Raúl
A1 García Sastre, Adolfo
K1 Microbiología
K1 Virus
AB In this work we developed a method, based on a logistic regression model, to identifymutations subject to directional selection. We tested the model analyzing thousands of AIV (H5N1, H7N9) sequencesfrom public datasets, to predict mutations facilitating the process of adaptation in host-switching. Additionally, theeffect of predicted mutations in the viral fitness and viral infectivity of influenza mutant viruses was performed tovalidate the bioinformatics tools. We found mutations significantly associated with the emergence into humans in allAIV segments, being 238 and 62 mutations detected in H5N1 and H7N9, respectively. Most of them were located inthe polymerase complex (PA, PB1 and PB2 genes). Interestingly, up to 18% of these mutations are known to beinvolved in AIV adaptive processes through host-switching. Related those influenza mutant viruses we reverted thecandidate mutation driving human adaptation to avian state. Using reverse genetics, we introduced the mutationsinto human IAV (H3N2) backbone for each specific segment. We studied the infectivity of mutant viruses in ovo andin vitro at different times post infection compared to the wild-type virus. The results obtained in ovo showed that the mostsignificant differences were observed in those viruses carrying the mutations in the PA, PB2, NP and PB1 segments.Regarding the in vitro study, we highlight that in the DF-1 cell line most of the mutant viruses reached higher titers atsome point during the viral growth compared to the wild-type, enhancing viral growth in those mutant viruses withthe mutations introduced in the viral polymerase and in the viral nucleoprotein. Consequently, the generatedpipeline exhibits fastness and robustness in discerning manifestations of directional selection. Its application in AIVcontexts suggests widespread adaptative trends in host-switching, thus exerting potential influence on all regions ofthe genome.
YR 2024
FD 2024-09-02
LK https://hdl.handle.net/10630/32597
UL https://hdl.handle.net/10630/32597
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026