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.