Developing molecular classifiers to detect environmental stressors and smolt stages in sockeye salmon, Oncorhynchus...

Climate change is increasingly affecting Pacific salmon to combinations of thermal, hypoxic, and osmotic stressors that can impair survival. Although genomic tools have enabled detailed insight into stressor responses under controlled conditions, field applications remain limited by the lack of species-specific classifiers capable of resolving physiological condition across multiple stressor contexts. In this study, we developed and experimentally validated transcriptional classifiers for juvenile sockeye salmon (Oncorhynchus nerka) using controlled multi-stressor challenges. Fish exposed to a full factorial design of three temperatures (10 °C, 14 °C, 18 °C), three salinities (0, 20, 28 ppt), and two dissolved oxygen levels (> 8 mg L^-1, 3-3.5 mg L^-1) over six days, across smolt and de-smolt stages; pre-smolts were additionally challenged with seawater (28 ppt). Gill tissue, a primary interface between fish and their environment, was analyzed using the "Salmon Fit-Chips" microfluidic qPCR tool to quantify gene expression responses. Random Forest models were then used to develop classifiers detecting transcriptional signatures of thermal stress, hypoxic stress, salinity acclimation, and smolt stage. Classifiers achieved high predictive accuracy (85.5%-100%) across all stressors and life stages. Recovery analysis showed that transcriptional stress signals diminished within three days for hypoxia, but lingered longer for thermal stress. By developing sockeye salmon-specific molecular classifiers within a multi-stressor experimental framework, this study advances the Fit-Chip approach and provides a foundation for future field applications, including potential non-lethal monitoring using gill biopsies.