Machine learning ensemble reveals distinct molecular pathways of retinal damage in spaceflown mice

Spaceflight-associated neuro-ocular syndrome (SANS) poses significant ocular health risks in long-duration missions, yet its molecular mechanisms remain incompletely understood. Oxidative stress and apoptosis are candidate drivers, but their transcriptomic-phenotypic relationships in spaceflight-exposed retinal tissue have not been systematically characterized. We applied a machine learning ensemble to predict two ocular phenotypes: 4-hydroxynonenal (4-HNE) endothelial cell density as a marker of oxidative damage, and TUNEL endothelial cell density as a marker of apoptosis. In this observational study, we use transcriptomic data from a controlled experiment with ground control and spaceflown mice to predict these phenotypes. Gene Ontology pathway enrichment was performed using the most predictive genes for each phenotype. Genes predicting 4-HNE converge on membrane-associated pathways, photoreceptor modification, synaptic dysfunction, and extracellular matrix dysregulation, including B2m , Trf , Cnga1 , mt-Nd1 , Snap25 , and Efemp1 . Genes predicting TUNEL emphasize stress-induced apoptosis, rod photoreceptor degeneration, and endoplasmic reticulum dysfunction, with Ddit4 , Nrl , Rom1 , Reep6 , and Gabarapl1 emerging as central regulators. Oxidative 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.