Bioinformatics analysis of E. coli causing mastitis in Holstein dairy cattle by usingmicroarray data

authors:

avatar Samad Vajdi , avatar Sadegh Alijani , avatar Hossein Daghigh kia , avatar Hakimeh Zali ORCID , * , avatar Soheila Khoda karim , avatar Mohsen Bigdeli

Koomesh: Vol.17, issue 1; 214-223
published online: September 28, 2015
article type: Research Article
received: August 01, 2015
accepted: September 09, 2015

how to cite: Vajdi S, Alijani S, Daghigh kia H, Zali H, Khoda karim S, et al. Bioinformatics analysis of E. coli causing mastitis in Holstein dairy cattle by usingmicroarray data. koomesh. 2015;17(1):e150794. 

Abstract

 Introduction: Mastitis is the inflammation of the mammary gland. One of its major pathogens is Escherichia coli. In order to develop new strategies for prevention of E. coli causing mastitis, it is necessary to have a clear understanding of the details and molecular mechanisms involved in host immunological responses to the pathogen. In this study by using available transcriptomic data, bioinformatics analysis of the diseases was performed. Materials and Methods: The data in this study was extracted from GEO web site, based on Gilbert’s microarray study on the mammary tissue transcriptome. Protein-protein interactions (PPIs) were identified by using String database and PPI networks generated by using Cytoscape software. In order to recognize and cluster the hubs based on GO, the network analysis was carried out by using ClueGO-Clue-Pedia. Results: The PPI network resulted from differentially expressed genes (DEGs) in contaminated mammary tissue with E. coli crude lipo-polysaccharide (LPS) were determined after 3 and 6 hours. PPI network was performed from common segments of the two PPI networks. Analysis of this new network showed that genes (IL6, IFIH1, PARP14, IL1B, ISG15, GRO1IFIT3, CCL5, ICAM1, IRF9 and NOS2) had the greatest degree and function as hubs. The most significant biological processes (BP), molecular functions (MF), cellular compartments (CC), immunological system and dominant pathways based on KEGG database were identified through GO-based cluster analysis of the network. Conclusion: This study suggests that expansion of pathogen presence in the host tissue would lead to the increase in the number of interactions in the hub proteins. So these proteins can be introduced as drug targets.