文献类型： 外文期刊

作者： Farmanullah, Farmanullah ¹ ; Liang, Xianwei ³ ; Khan, Faheem Ahmed ¹ ; Salim, Mohammad ⁴ ; Rehman, Zia Ur ¹ ; Khan, Momen ⁶ ; Talpur, Hira Sajjad ¹ ; Schreurs, N. M. ⁷ ; Gouda, Mostafa ⁸ ; Khan, Sami Ullah ¹⁰ ; Shujun, Zhang ¹ ;

作者机构： 1.Huazhong Agr Univ, Coll Anim Sci & Technol, Minist China, Key Lab Agr Anim Genet Breeding & Reprod Educ, Wuhan 430070, Peoples R China

2.Natl Ctr Livestock Breeding Genet & Genom LUAWMS, Fac Vet & Anim Sci, Uthal, Pakistan

3.Chinese Acad Agr Sci, Buffalo Res Inst, Minist Agr, Key Lab Buffalo Genet Breeding & Reprod Technol, Nanning 530001, Peoples R China

4.Univ Haripur, Dept Forestry & Wildlife Management, Haripur, Khyber Pakhtunk, Pakistan

5.UAP, Dept Anim Sci, Fac Anim Husb & Vet Sci, Peshawar, Pakistan

6.Livestock & Dairy Dev, Peshawar, Khyber Pakhtunk, Pakistan

7.Massey Univ, Sch Agr & Environm, Anim Sci, Private Bag 11222, Palmerston North, New Zealand

8.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China

9.Natl Res Ctr, Dept Nutr & Food Sci, Giza 12622, Egypt

10.Univ Gadjah Mada, Fac Vet Med, Dept Internal Med, Yogyakarta, Indonesia

关键词： Mastitis; Escherichia coli; Microarray; Transcriptomic analysis; Dairy cow; Differentially expressed genes

期刊名称：JOURNAL OF GENETIC ENGINEERING AND BIOTECHNOLOGY （ 影响因子：3.5； ）

ISSN： 2090-5920

年卷期： 2021 年 19 卷 1 期

页码：

收录情况： SCI

摘要： Background: 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. Methods: 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. Results: 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. Conclusions: 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.