分子シグネチャと機械学習駆動型のストレスバイオマーカーによるニジマスの養殖と気候変動への適応

Climate-induced stressors pose significant threats to fish growth, survival, and ecological stability. Identifying reliable molecular biomarkers is crucial for improving stress management and acclimation strategies. This study employed a comprehensive transcriptomic analysis to examine stress responses in rainbow trout (Oncorhynchus mykiss) exposed to five distinct environmental stressors-high and low temperatures, crowding, salinity, and low water quality (characterized by reduced dissolved oxygen and elevated CO₂)-over six hours. A total of 21,580 differentially expressed transcripts (DETs) were identified, including 16,959 unique DETs. Heat stress and salinity induced the most pronounced transcriptomic responses, with most DETs being stressor-specific, highlighting distinct physiological acclimation mechanisms. Only 39 DETs were consistently regulated across all stress conditions. Key DETs associated with heat stress were further analyzed using machine learning models to evaluate their predictive potential in distinguishing control and heat-stressed fish from natural Redband trout populations. The logistic model tree (LMT) classifier demonstrated the highest accuracy with a set of 234 DETs. When the dataset was reduced to 50 or 2 DETs, the Random Forest model achieved optimal classification, consistently identifying two heat shock protein transcripts, hsp47 and hspa4l, as primary predictors across both short- and long-term stress responses. In contrast, core DETs shared across stressors exhibited limited predictive power, achieving only 52.78% classification accuracy. These findings underscore the specificity of molecular signatures to individual stressors and highlight the potential of transcriptomic biomarkers for monitoring climate-induced stress in fish populations. The study recommends the integration of these biomarkers into selective breeding programs and conservation strategies to enhance fish resilience and welfare in the face of environmental change.