Potential role of MicroRNA as a diagnostic tool in the detection of bovine mastitis
Potential role of MicroRNA as a diagnostic tool in the detection of bovine mastitis
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DOI：
10.1016/j.prevetmed.2020.105101
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发表时间：
2020-09-01
影响因子：
2.6
通讯作者：
Chuammitri, Phongsakorn
中科院分区：
文献类型：
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作者：
Srikok, Suphakit;Patchanee, Prapas;Chuammitri, Phongsakorn
牛乳房炎是影响奶牛的主要健康问题，对牛奶生产有负面影响。血液和牛奶等生物体液中microRNAs的存在可能在检测牛乳房炎方面发挥关键作用。本研究的目的是确定牛奶中microRNA基因的表达水平，并结合其他已报道的乳房炎指标，作为牛乳房炎的生物标志物。从113头已知疾病状态的奶牛(即健康奶牛；23头奶牛，亚临床型乳房炎；45头奶牛，或临床型乳房炎；45头奶牛)采集乳样(n=171)，并使用实时荧光定量聚合酶链式反应(QPCR)方法分析Mir24-2、MIR29B-2、MIR146A、MIR148A、MIR155、MIR181A1、MIR184和MIR223的表达。然后利用表情数据创建接收者操作员特征曲线(ROC)，并通过机器学习(ML)方法进一步分析。MIR29B-2、MIR146A、MIR148A和MIR155在三组间的表达水平有显著差异。这些潜在的乳房炎microRNA生物标志物表现出较高的敏感性和特异性。接下来，我们应用ML算法，特别是基于MIR29B-2和MIR146A表达水平的决策树(DT)模型来预测牛奶的状态。结果表明，MIR29B-2与加利福尼亚乳房炎试验(CMT)和乳汁中天数(DIM)数据相结合，适用于奶牛乳样健康、亚临床型乳房炎或乳房炎的筛选和分类。MIR29B-2似乎具有足够的鉴别力，使其能够在不能根据CMT结果确定牛奶样本状态的情况下用作生物标记物。
Bovine mastitis is a major health problem that affects dairy cows and has a negative impact on milk production. The presence of microRNAs in biofluids, such as blood and milk, could play a pivotal role in the detection of bovine mastitis. The purpose of the current study was to determine the levels of microRNA gene expression in milk, in combination with other reported mastitis indicators, as a biomarker of bovine mastitis. Milk samples (n = 171) were obtained from 113 dairy cows with known disease status (i.e., healthy; n = 23 cows, subclinical mastitis; n = 45 cows, or clinical mastitis; n = 45 cows) and analyzed for the presence of MIR24-2, MIR29B-2, MIR146A, MIR148A, MIR155, MIR181A1, MIR184, and MIR223 expression using the real-time PCR (qPCR) method. The expression data were then utilized in the creation of receiver operator characteristic curves (ROC) and further analyzed by the machine learning (ML) methods. MIR29B-2, MIR146A, MIR148A, and MIR155 expression levels differed significantly among the three groups. These potential microRNA biomarkers of mastitis exhibited high sensitivity and specificity. Next, we applied ML algorithm, specifically, a decision tree (DT) model to predict the status of milk based on MIR29B-2 and MIR146A expression levels. The results suggested that MIR29B-2, when used in combination with the California mastitis test (CMT) and days in milk (DIM) data, was applicable for screening and classification of milk samples from cows as healthy, subclinical mastitis, or mastitis. MIR29B-2 appears to have sufficient discriminatory power to enable it to be utilized as a biomarker in cases where the status of a milk sample cannot be determined based on CMT results.