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Prediction of key regulators and downstream targets of E. coli induced mastitis

  • Somayeh SharifiEmail author
  • Abbas Pakdel
  • Esmaeil Ebrahimie
  • Yalda Aryan
  • Mostafa Ghaderi Zefrehee
  • James M. ReecyEmail author
Animal Genetics • Original Paper

Abstract

Mastitis, an inflammatory response of mammary glands to invading bacteria, is one of the most economically costly diseases affecting dairy animals. Escherichia coli can be introduced as a major etiological agent of bovine mastitis in well-managed dairy farms. It is of great significance to understand the regulatory mechanisms by which the disease can be controlled. High-throughput technologies combined with novel computational systems biology tools have provided new opportunities for a better understanding of the molecular mechanisms that underlie disease. In the current study, the results of microarray meta-analysis research were used to perform a network analysis to potentially identify molecular mechanisms that regulate gene expression profile in response to E. coli mastitis. In our result, transcription factors, TP53, SP1, ligands, INS, IFNG, EGF, and protein kinases, MAPK1, MAPK14, AKT1, were identified as the key upstream regulators whereas protein kinases, MAPK3, MAPK8, MAPK14, ligands, VEGFA, IL10, an extracellular protein, MMP2, and a mitochondrial membrane protein, BCL2, were identified as the key downstream targets of differentially expressed genes. The results of this research revealed important genes that have the key functions in immune response, inflammation, or mastitis which can provide the basis for strategies to improve the diagnosis and treatment of mastitis in dairy cows.

Keywords

Dairy cattle Escherichia coli Functional genomics Gene network System biology Transcriptome 

Notes

Compliance with ethical standards

Research does not involve human participants and/or animals.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

13353_2019_499_MOESM1_ESM.xlsx (45 kb)
Online Resource 1 Differentially expressed genes identified after meta-analysis (one-tailed q < 0.005). (XLSX 44 kb)
13353_2019_499_Fig1_ESM.png (7.9 mb)
Online Resource 2

The network constructed by the Common Regulator algorithm. (PNG 8041 kb)

13353_2019_499_MOESM2_ESM.tif (197.5 mb)
High resolution image (TIF 202233 kb)
13353_2019_499_MOESM3_ESM.xlsx (269 kb)
Online Resource 3 The details of applying of Common Regulator algorithms on meta-genes. (XLSX 269 kb)
13353_2019_499_Fig2_ESM.png (6.9 mb)
Online Resource 4

The network constructed by the Common Targets algorithm. (PNG 7031 kb)

13353_2019_499_MOESM4_ESM.tif (219.9 mb)
High resolution image (TIF 225205 kb)
13353_2019_499_MOESM5_ESM.xlsx (176 kb)
Online Resource 5 The details of applying of Common Target algorithms on meta-genes. (XLSX 176 kb)

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Copyright information

© Institute of Plant Genetics, Polish Academy of Sciences, Poznan 2019

Authors and Affiliations

  1. 1.Department of Animal Science, College of AgricultureIsfahan University of TechnologyIsfahanIran
  2. 2.Department of Animal ScienceIowa State UniversityAmesUSA
  3. 3.School of Animal and Veterinary SciencesThe University of AdelaideAdelaideAustralia
  4. 4.Genomics Research Platform, School of Life SciencesLa Trobe UniversityMelbourneAustralia
  5. 5.Computer Engineering Payam Noor UniversityTehranIran
  6. 6.Department of Animal Science, College of AgricultureYasouj UniversityKohgiluyeh and Boyer-AhmadIran

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