Analysis of gene expression dynamics and differential expression in viral infections using generalized linear models and quasi-likelihood methods - Rezapour, Mostafa; Walker, Stephen J.; Ornelles, David A.; McNutt, Patrick M.; Atala, Anthony; Gurcan, Metin Nafi (Frontiers in Microbiology, 2024)
In this study, we explore the gene expression dynamics within human lung organ tissue equivalents (OTEs) in response to infections by Influenza A virus (IAV), Human metapneumovirus (MPV), and Parainfluenza virus type 3 (PIV3). By deploying Generalized Linear Models (GLMs) with Quasi-Likelihood F-tests (GLMQL) to address the discrete and overdispersed nature of RNA-Seq data, and introducing the novel Magnitude-Altitude Score (MAS) and Relaxed Magnitude-Altitude Score (RMAS) algorithms, we navigate the intricate landscape of RNA-Seq data from 19,671 genes to unearth those significantly altered by viral infection. This analytical approach enables us to pinpoint potential biomarkers with unprecedented precision, highlighting the host's reliance on innate immune mechanisms, such as interferon-stimulated genes, to combat viral threats. Our study unveils key insights into the host's comprehensive defense mechanisms and the viral strategies that exploit host cellular functions, emphasizing the critical need for targeted therapeutic interventions. The Gene Ontology (GO) analysis enriches our understanding by detailing the impact of IAV, MPV, and PIV3 on critical biological processes and cellular components, illustrating a strategic shift in cellular priorities towards supporting viral replication. Notably, the activation of interferon-stimulated genes (e.g., IFIT1, IFIT2, IFIT3, OAS1) and changes in cellular functions like cilium assembly and mitochondrial ribosome assembly signal a robust antiviral response and a manipulation of host machinery, respectively. The stability of the MAS/RMAS ranking method, even under stringent statistical corrections, demonstrates the reliability of our approach in identifying key biomarkers, further validated by the 92% mean accuracy achieved in classifying respiratory virus infections using multinomial logistic regression.