m+Medelevator.RU
← Поиск публикаций
Poultry science · 2026

Genomic signatures of host adaptation in Pasteurella multocida from poultry, cattle, swine, and cats: insights from interpretable machine learning.

СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Li Qianlong, Fan Yan, Wang Mingshu, Chen Shun, Liu Mafeng, Zhu Dekang, Zhao Xinxin, Wu Ying, Yang Qiao, Huang Juan, Ou Xumin, Sun Di, Tian Bin, He Yu, Wu Zhen, Cheng Anchun, Zhang Shaqiu

Аннотация

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.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты