School of Molecular Biosciences, College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow, UK.
Scotland's Rural College (SRUC), Edinburgh, UK.
Vet Rec. 2024;195(11):e4798. doi: 10.1002/vetr.4798. Epub 2024 Nov 19.
Johne's disease, caused by Mycobacterium avium subspecies paratuberculosis (MAP), is a chronic enteritis that adversely affects welfare and productivity in cattle. Screening and subsequent removal of affected animals is a common approach for disease management, but efforts are hindered by low diagnostic sensitivity. Expression levels of small non-coding RNA molecules involved in gene regulation (microRNAs), which may be altered during mycobacterial infection, may present an alternative diagnostic method.
The expression levels of 24 microRNAs affected by mycobacterial infection were measured in sera from MAP-positive (n = 66) and MAP-negative cattle (n = 65). They were then used within a machine learning approach to build an optimal classifier for MAP diagnosis.
The method provided 72% accuracy, 73% sensitivity and 71% specificity on average, with an area under the curve of 78%.
Although control samples were collected from farms nominally MAP-free, the low sensitivity of current diagnostics means some animals may have been misclassified.
MicroRNA profiling combined with advanced predictive modelling enables rapid and accurate diagnosis of Johne's disease in cattle.
由鸟分枝杆菌副结核亚种（MAP）引起的约翰氏病是一种慢性肠炎，会对牛的福利和生产力产生不利影响。筛选和随后淘汰受感染动物是疾病管理的常见方法，但由于诊断灵敏度低，这项工作受到阻碍。参与基因调控的小非编码 RNA 分子（microRNAs）的表达水平可能在分枝杆菌感染期间发生改变，这可能提供一种替代的诊断方法。
在来自 MAP 阳性（n = 66）和 MAP 阴性牛（n = 65）的血清中测量了 24 种受分枝杆菌感染影响的 microRNAs 的表达水平。然后，他们在机器学习方法中构建了一个用于 MAP 诊断的最佳分类器。
该方法的平均准确率为 72%，灵敏度为 73%，特异性为 71%，曲线下面积为 78%。
尽管对照样本是从名义上无 MAP 的农场采集的，但目前诊断方法的灵敏度较低意味着一些动物可能被错误分类。
microRNA 分析与先进的预测模型相结合，可以快速准确地诊断牛的约翰氏病。
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