Convergent Autoencoder and Machine Learning Methodologies for SARS-CoV-2 Essential Host Factor Identification based on Interferon Pathway Loss-of-Function Perturb-seq Data Mining

Abstract

The COVID-19 pandemic has highlighted the importance of understanding host-virus interactions, particularly the role of interferon (IFN) signaling pathways in viral pathogenesis. This study employed a comprehensive machine learning framework to analyze Perturb-seq data from SARS-CoV-2-infected Calu-3 respiratory epithelial cells to identify key host factors associated with viral infection under various IFN signaling conditions. We implemented two complementary feature selection strategies: Raw Feature Ranking using ten algorithms combined with IFS technology, and Autoencoder-based Feature Reconfiguration for dimensionality reduction, followed by ranking with the same algorithms combined with IFS. We compared expression differences between infected and uninfected cells in both IFN-knockout and control conditions to investigate distinct responses to SARS-CoV-2 infection. In control conditions, Raw Feature Ranking identified important genes including FOS, JUN, B2M, PPIA, and MALAT1, known for their roles in viral pathogenesis, antigen processing, and inflammatory response. Under IFN knockout conditions, numerous mitochondrial genes (MT-ND3, MTCO1, MT-ND4L, MT-ND4, MT-CO3, MT-ATP6, MT-CO2, MT-CYB) emerged as key discriminatory features, suggesting mitochondrial dysfunction as a crucial mechanism when IFN signaling is compromised. Autoencoder-based methods showed close alignment with raw feature ranking, identifying 63 and 64 features in the IFN and Control groups, respectively, with 46 and 32 overlapping genes. Additional key factors included EEF1A1, KRT8, KRT19, MIF, SPINT2, and S100A10. Enrichment analysis revealed consistent enrichment of oxidative phosphorylation and thermogenesis pathways across conditions, with IFN knockout cells specifically enriched for coronavirus disease pathways. This comprehensive approach provided robust biomarker identification and molecular insights into SARS-CoV-2 immune evasion mechanisms.