Transcriptomic Analysis and Machine Learning Identify Cross-Pathogen Biomarkers for Bacterial and Parasitic...

Area of Science:

• Aquaculture
• Fish immunology
• Genomics

Background:

• Silver Pomfret aquaculture faces significant disease threats.
• Lack of defined host biomarkers hinders effective disease management.
• Understanding cross-pathogen responses is crucial for disease resistance.

Purpose of the Study:

• To identify conserved host-response genes in Silver Pomfret across different infections.
• To discover potential molecular markers for cross-pathogen disease monitoring.
• To investigate the immune response patterns and regulatory mechanisms in infected fish.

Main Methods:

• Integrated transcriptomic data from three independent infections (Cryptocaryon irritans, Nocardia seriolae, Photobacterium damselae).
• Utilized differential expression analysis, weighted gene co-expression network analysis, Random Forest, and support vector machine.
• Performed phylogenetic, structural, GSVA-based signature, and immune signature analyses.
• Conducted upstream regulatory prediction and quantitative real-time PCR (qRT-PCR).

Main Results:

• Identified six candidate genes (e.g., canx, rnd3, angptl4) associated with cross-pathogen responses.
• Validated the classification potential and discriminatory capacity of the six-gene panel.
• Observed distinct immune response patterns and associations with T cell signatures.
• Confirmed robust infection-responsive expression for canx, rnd3, and angptl4 via qRT-PCR.

Conclusions:

• A potential cross-pathogen host-response pattern exists in Silver Pomfret.
• The identified gene panel shows promise as molecular markers for aquaculture disease monitoring.
• Further research can explore these biomarkers for improved disease surveillance and management.