Farmanullah Farmanullah, Liang Xianwei, Khan Faheem Ahmed, Salim Mohammad, Rehman Zia Ur, Khan Momen, Talpur Hira Sajjad, Schreurs N M, Gouda Mostafa, Khan Sami Ullah, Shujun Zhang
Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Education Ministry of China, College of Animal Sciences and Technology, Huazhong Agricultural University, Wuhan, 430070, People's Republic of China.
Faculty of Veterinary and Animal Sciences, National Center for Livestock Breeding Genetics and Genomics LUAWMS, Uthal, Pakistan.
J Genet Eng Biotechnol. 2021 Oct 12;19(1):153. doi: 10.1186/s43141-021-00235-x.
Mastitis is one of the major diseases causing economic loss to the dairy industry by reducing the quantity and quality of milk. Thus, the objective of this scientific study was to find new biomarkers based on genes for the early prediction before its severity.
In the present study, advanced bioinformatics including hierarchical clustering, enrichment analysis, active site prediction, epigenetic analysis, functional domain identification, and protein docking were used to analyze the important genes that could be utilized as biomarkers and therapeutic targets for mastitis.
Four differentially expressed genes (DEGs) were identified in different regions of the mammary gland (teat cistern, gland cistern, lobuloalveolar, and Furstenberg's rosette) that resulted in 453, 597, 577, and 636 DEG, respectively. Also, 101 overlapped genes were found by comparing 27 different expressed genes. These genes were associated with eight immune response pathways including NOD-like receptor signaling pathway (IL8, IL18, IL1B, PYDC1) and chemokine signaling pathway (PTK2, IL8, NCF1, CCR1, HCK). Meanwhile, 241 protein-protein interaction networks were developed among overlapped genes. Fifty-seven regulatory events were found between miRNAs, expressed genes, and the transcription factors (TFs) through micro-RNA and transcription factors (miRNA-DEG-TF) regulatory network. The 3D structure docking model of the expressed genes proteins identified their active sites and the binding ligands that could help in choosing the appropriate feed or treatment for affected animals.
The novelty of the distinguished DEG and their pathways in this study is that they can precisely improve the detection biomarkers and treatments techniques of cows' Escherichia coli mastitis disease due to their high affinity with the target site of the mammary gland before appearing the symptoms.
乳腺炎是导致乳制品行业经济损失的主要疾病之一，它会降低牛奶的产量和质量。因此，本科学研究的目的是基于基因寻找新的生物标志物，以便在乳腺炎病情严重之前进行早期预测。
在本研究中，运用了包括层次聚类、富集分析、活性位点预测、表观遗传分析、功能域鉴定和蛋白质对接在内的先进生物信息学方法，来分析可作为乳腺炎生物标志物和治疗靶点的重要基因。
在乳腺的不同区域（乳头池、腺池、小叶腺泡和弗斯滕贝格氏玫瑰花结）鉴定出四个差异表达基因（DEGs），分别导致453、597、577和636个DEG。此外，通过比较27个不同表达的基因发现了101个重叠基因。这些基因与八条免疫反应途径相关，包括NOD样受体信号通路（IL8、IL18、IL1B、PYDC1）和趋化因子信号通路（PTK2、IL8、NCF1、CCR1、HCK）。同时，在重叠基因之间构建了241个蛋白质-蛋白质相互作用网络。通过微RNA和转录因子（miRNA-DEG-TF）调控网络，在miRNA、表达基因和转录因子（TFs）之间发现了57个调控事件。表达基因蛋白质的三维结构对接模型确定了它们的活性位点和结合配体，这有助于为受影响的动物选择合适的饲料或治疗方法。
本研究中独特的DEG及其途径的新颖之处在于，它们在症状出现前与乳腺靶位点具有高亲和力，能够精确地改进奶牛大肠杆菌乳腺炎疾病的检测生物标志物和治疗技术。
