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Molecular signatures and machine learning driven stress biomarkers for rainbow trout aquaculture and climate adaptation

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Abstract

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.

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Data availability

The raw sequencing data from three strains of Redband trout were downloaded from the NCBI Short Read Archive (SRA) under accession number PRJNA233945. The rainbow trout genome annotation was obtained from NCBI (GCA_013265735.3, https://www.ncbi.nlm.nih.gov/assembly/ GCF_013265735.2/). Additionally, RNA-seq datasets from fish exposed to five different stress conditions were retrieved from the NCBI Sequence Read Archive (SRA) using the accession number SRP070774.

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Authors and Affiliations

  1. Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742-231, USA

    Ali Ali, Youssef Ali, Guglielmo Raymo & Mohamed Salem

  2. College of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, MD, 20742-231, USA

    Asutosh Dalei

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  2. Youssef Ali
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Y.A., A.A., and M.S. Conceived the study. Y.A., A.A., G.R., A.D. and M.S. analyzed the data. Y.A. Drafted the manuscript. All authors read and approved the final manuscript. Y.A. and A.A. contributed equally.

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Ali, A., Ali, Y., Raymo, G. et al. Molecular signatures and machine learning driven stress biomarkers for rainbow trout aquaculture and climate adaptation.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-30120-3

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  • DOI: https://doi.org/10.1038/s41598-025-30120-3

Keywords

  • Environmental stressors
  • Heat stress
  • Fish welfare
  • Predictive modeling
  • Aquaculture stress breeding

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