Machine Learning Ensemble Reveals Distinct Molecular Pathways of Retinal Damage in Spaceflown Mice
BackgroundSpaceflight-associated neuro-ocular syndrome (SANS) poses significant risks to astronaut visual health during long-duration missions, yet its underlying molecular mechanisms remain incompletely understood. Oxidative stress and apoptosis are candidate molecular drivers, but their transcriptomic signatures in spaceflight-exposed retinal tissue have not been systematically characterized. MethodsWe applied a machine learning ensemble of linear regression models to predict two ocular phenotypes: 4-hydroxynonenal (4-HNE) immunostaining as a marker of lipid peroxidation-mediated oxidative damage; and TUNEL positivity as a marker of apoptotic cell death. In this observational study, we use bulk retinal gene expression data obtained from a controlled experiment with ground control and spaceflown mice to predict these phenotypes. Gene Ontology pathway enrichment was performed on the most predictive gene sets for each phenotype. ResultsThe 4-HNE phenotype was predicted by genes that converge on membrane-associated pathways, photoreceptor protein modification, synaptic dysfunction, and extracellular matrix dysregulation, including B2m, Tf, Cnga1, mt-Nd1, Snap25, and Efemp1. The genes predicting the TUNEL phenotype revealed a distinct signature emphasizing stress-induced apoptosis, rod photoreceptor degeneration, and endoplasmic reticulum dysfunction, with Ddit4, Nrl, Rom1, Reep6, and Gabarapl1 emerging as central regulators. ConclusionsOxidative lipid peroxidation and apoptotic cell death represent complementary and molecularly distinct pathological mechanisms in spaceflight-exposed murine retinal tissue. The gene signatures provide a putative molecular framework for developing noninvasive biomarkers and therapeutic targets to monitor and protect astronaut visual health during long-duration and deep-space missions.