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Transcriptomic Analysis and Machine Learning Identify Cross-Pathogen Biomarkers for Bacterial and Parasitic Infections in Silver Pomfret (Pampus argenteus)
by
, and
Yunkang Wu
1,2,3,†, 
Yuanbo Li
1,2,3,†,
Ting Chen
4,
Wuqiang Xia
4,
Yajun Wang
1,2,3,4,
Xiaojun Yan
1,2,3 and
Jiabao Hu
1,2,3,4,* 1
School of Marine Sciences, Ningbo University, Ningbo 315211, China
2
Key Laboratory of Applied Marine Biotechnology, Ningbo University, Ministry of Education, Ningbo 315211, China
3
Key Laboratory of Marine Biotechnology of Zhejiang Province, Ningbo University, Ningbo 315211, China
4
Xiangshan Aquatic Seed Industry Innovation Research Institute, Ningbo 315211, China
*
Author to whom correspondence should be addressed.
†
The authors contributed equally to this work.
Animals 2026, 16(10), 1510; https://doi.org/10.3390/ani16101510
Submission received: 13 April 2026
/
Revised: 8 May 2026
/
Accepted: 12 May 2026
/
Published: 14 May 2026
(This article belongs to the Section Aquatic Animals)
Simple Summary
Silver Pomfret in aquaculture is frequently affected by infections caused by the Gram-negative bacterium Photobacterium damselae subsp. damselae, the Gram-positive bacterium Nocardia seriolae, and the protozoan parasite Cryptocaryon irritans, but cross-pathogen molecular markers remain poorly defined. By comparing transcriptomic responses and immune infiltration analysis across these three infection conditions, we identified six shared responsive genes, among which canx, angptl4, and rnd3 showed relatively consistent changes during infection and discriminated infected from healthy fish. These genes represent candidate cross-pathogen response biomarkers and have potential for the future aquaculture industry of Silver Pomfret.
Abstract
Silver Pomfret is increasingly threatened by many diseases under intensive artificial culturing conditions, yet conserved host biomarkers across different infections remain poorly defined. In this study, we integrated transcriptomic datasets from independent infections with Cryptocaryon irritans, Nocardia seriolae, and Photobacterium damselae subsp. damselae to identify shared host-response genes. By combining differential expression analysis with weighted gene co-expression network analysis, we prioritized six candidate genes associated with cross-pathogen infection responses. Random Forest and support vector machine analysis further supported their classification potential across the three infection models. Phylogenetic and structural analyses provided additional evidence for the conserved annotation of these proteins. GSVA-based signature analysis supported the cross-pathogen discriminatory capacity of the six-gene panel and suggested context-dependent contributions of individual genes across infection models. Immune signature analysis indicated distinct host immune response patterns under different pathogenic challenges, and candidate genes showed positive associations with inferred T cell-related signatures. Upstream regulatory prediction identified CTCF and the miR-17/20/93 family as potential regulators of these genes. Quantitative real-time PCR of the kidney further highlighted canx, rnd3, and angptl4 as the most robust infection-responsive candidates, with consistent temporal expression patterns observed from 0 to 24 h post-infection. These findings suggest a potential cross-pathogen host-response pattern in Silver Pomfret and provide preliminary support for future exploration of molecular markers for disease monitoring in aquaculture.
