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Mining for the association of bovine mastitis linked genes to pathological signatures and pathways

Open Access
|May 2022

Abstract

Background: Bovine mastitis is a common infectious disease with a serious threat to the dairy industry and public health. Mastitis is a polygenetic trait under the control of many genes. In the current study, our research attempted to address the role of mastitis-associated genes in various signalings including parasitic, viral, cancer and fungal diseases by using online bioinformatics software. Methods: We selected mastitis-associated genes from already published data and using online bioinformatics tools including DAVID and String classified the pathological role of relevant genes. A Venn diagram was used to show the status of overlapping genes among different biological function processes. Result: This study revealed that the genes gathered in published resources of mastitis were significantly correlated with influenza A, chagas disease, leishmaniasis, toxoplasmosis, tuberculosis, cancer signaling, hepatitis B, type I & II diabetes mellitus and prion diseases biological pathways. Based on our findings, we concluded that mastitis-linked genes could be used as markers for many other diseases. Moreover, the bioinformatics tools applied in the current study might be helpful in screening the genes involved in one disease and their association with other diseases as well.

DOI: https://doi.org/10.2478/aoas-2021-0049 | Journal eISSN: 2300-8733 | Journal ISSN: 1642-3402
Language: English
Page range: 583 - 591
Submitted on: Jan 5, 2021
Accepted on: Jun 15, 2021
Published on: May 12, 2022
Published by: National Research Institute of Animal Production
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2022 Muhammad Zahoor Khan, Saadet Belhan, Nebi Cetin, Adnan Ayan, Adnan Khan, Irshad Ahmad, Yulin Ma, Jianxin Xiao, Jamal Muhammad Khan, Muhammad Kamal Shah, Shakeeb Ullah, Zhijun Cao, published by National Research Institute of Animal Production
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.