Transcriptomic in silico analysis of bovine Escherichia coli mastitis highlights its immune-related expressed genes as an effective biomarker.

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dc.citation.issue1
dc.citation.volume19
dc.contributor.authorFarmanullah F
dc.contributor.authorLiang X
dc.contributor.authorKhan FA
dc.contributor.authorSalim M
dc.contributor.authorRehman ZU
dc.contributor.authorKhan M
dc.contributor.authorTalpur HS
dc.contributor.authorSchreurs NM
dc.contributor.authorGouda M
dc.contributor.authorKhan SU
dc.contributor.authorShujun Z
dc.coverage.spatialNetherlands
dc.date.accessioned2024-04-17T23:52:37Z
dc.date.accessioned2024-07-25T06:34:47Z
dc.date.available2021-10-12
dc.date.available2024-04-17T23:52:37Z
dc.date.available2024-07-25T06:34:47Z
dc.date.issued2021-12
dc.description.abstractBACKGROUND: 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.
dc.description.confidentialfalse
dc.edition.editionDecember 2021
dc.format.pagination153-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/34637035
dc.identifier.citationFarmanullah F, Liang X, Khan FA, Salim M, Rehman ZU, Khan M, Talpur HS, Schreurs NM, Gouda M, Khan SU, Shujun Z. (2021). Transcriptomic in silico analysis of bovine Escherichia coli mastitis highlights its immune-related expressed genes as an effective biomarker.. J Genet Eng Biotechnol. 19. 1. (pp. 153-).
dc.identifier.doi10.1186/s43141-021-00235-x
dc.identifier.eissn2090-5920
dc.identifier.elements-typejournal-article
dc.identifier.issn1687-157X
dc.identifier.number153
dc.identifier.pii10.1186/s43141-021-00235-x
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70459
dc.languageeng
dc.publisherElsevier B.V.
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S1687157X23006297
dc.relation.isPartOfJ Genet Eng Biotechnol
dc.rights(c) 2021 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDairy cow
dc.subjectDifferentially expressed genes
dc.subjectEscherichia coli
dc.subjectMastitis
dc.subjectMicroarray
dc.subjectTranscriptomic analysis
dc.titleTranscriptomic in silico analysis of bovine Escherichia coli mastitis highlights its immune-related expressed genes as an effective biomarker.
dc.typeJournal article
pubs.elements-id449013
pubs.organisational-groupCollege of Health
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