ArticlePoultry science2026
Genomic signatures of host adaptation in Pasteurella multocida from poultry, cattle, swine, and cats: insights from interpretable machine learning.
Article in Poultry science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Pasteurella multocida is a major zoonotic pathogen causing fowl cholera and other animal-derived infectious diseases, posing significant threats to the poultry industry and public health. Rapidly identifying its host preference is critical for disease control and understanding cross-species transmission. This study collected genomic data of P. multocida from four key hosts (chicken, cat, pig, cattle) in the NCBI Pathogen Database, yielding 814 qualified strains. Phylogenetic analysis showed host-correlated branching: cat strains formed a distinct clade, while cattle, pig, and chicken strains clustered with host-specific sub-clades. Multi-locus sequence typing (MLST) revealed strong host-ST associations (cattle with ST1, pig with ST11/10/3) and weaker links (cat with ST30, chicken with ST124). This single nucleotide polymorphism (SNP) matrix, alongside the host origin data, served as the feature set for training and evaluating five machine learning models: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). Models performed excellently on test sets (receiver operating characteristic area under the curve, ROC-AUC: 0.962-0.973; accuracy: 0.8528-0.865), with lower precision for chicken strains. SHAP (SHapley Additive exPlanations) analysis identified high-impact SNPs: XGBoost relied on key SNPs, while RF showed distributed contributions. Genes with these SNPs enriched in metabolic processes, quorum sensing, and beta-lactam resistance, indicating common and host-specific adaptation strategies. Misclassifications (cat-chicken, cattle-pig) suggested cross-host transmission risks. This study integrates phylogenetics, machine learning, and functional genomics to decipher the genetic basis of P. multocida host adaptation, providing insights for fowl cholera control and cross-species transmission early warning with implications for poultry health management.
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