Cell morphology deep learning guides discovery of mitochondrial host defense against influenza

Abstract Influenza A virus exploits host cellular machinery across subcellular compartments, yet the organelle-level changes that distinguish infected from uninfected cells and the molecular players driving them remain poorly defined. Here, we combine organelle image-based deep learning with proximity labeling chemoproteomics to address this gap. A convolutional neural network identified mitochondrial morphology as the strongest single-cell predictor of infection status (precision = 84.9%). Proximity labeling profiling of mitochondrial matrix proteome revealed 99 proteins with significantly altered upon infection, of which five (CH60, ETHE1, LONM, MPPB, and SQOR) were validated as host restriction factors whose depletion elevated interferon-β expression, enhanced viral RNA accumulation, or increased progeny virus production. Notably, two of these factors, ETHE1 and SQOR, operate within the mitochondrial hydrogen sulfide oxidation pathway, and pharmacological scavenging of H 2 S by hydroxocobalamin dose-dependently reversed their knockdown phenotypes, directly linking mitochondrial sulfide metabolism to antiviral defense against influenza.