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


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.


Dairy cattle Escherichia coli Functional genomics Gene network System biology Transcriptome 


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)


  1. Abdelrahim M, Safe S (2005) Cyclooxygenase-2 inhibitors decrease vascular endothelial growth factor expression in colon cancer cells by enhanced degradation of Sp1 and Sp4 proteins. Mol Pharmacol 68:317–329. Google Scholar
  2. Alanazi IO, Ebrahimie E (2016) Computational systems biology approach predicts regulators and targets of microRNAs and their genomic hotspots in apoptosis process. Mol Biotechnol 58:460–479. CrossRefGoogle Scholar
  3. Androulidaki A et al (2009) The kinase Akt1 controls macrophage response to lipopolysaccharide by regulating microRNAs. Immunity 31:220–231. CrossRefGoogle Scholar
  4. Arranz A et al (2012) Akt1 and Akt2 protein kinases differentially contribute to macrophage polarization. Proc Natl Acad Sci U S A 109:9517–9522. CrossRefGoogle Scholar
  5. Ashburner M et al. (2000) Gene ontology. Tool for the unification of biology. The Gene Ontology Consortium Accessed November 2017
  6. Bader GD, Christopher DB, Hogue WV (2003) BIND. The biomolecular interaction network database.
  7. Bakhtiarizadeh MR, Moradi-Shahrbabak M, Ebrahimie E (2013) Underlying functional genomics of fat deposition in adipose tissue. Gene 521:122–128. CrossRefGoogle Scholar
  8. Bannerman DD, Paape MJ, Jai-Wei L, Xin Z, Hope JC, Pascal R (2004) Escherichia coli and Staphylococcus aureus Elicit Differential Innate Immune Responses following Intramammary Infection. Clin Diagn Lab Immunol 11(3):463–472Google Scholar
  9. Bar D et al (2008) The cost of generic clinical mastitis in dairy cows as estimated by using dynamic programming. J Dairy Sci 91:2205–2214. CrossRefGoogle Scholar
  10. Beishline K, Azizkhan-Clifford J (2015) Sp1 and the ‘hallmarks of cancer’. FEBS J 282:224–258. CrossRefGoogle Scholar
  11. Black AR, Black JD, Azizkhan-Clifford J (2001) Sp1 and kruppel-like factor family of transcription factors in cell growth regulation and cancer. J Cell Physiol 188:143–160. CrossRefGoogle Scholar
  12. Bradley A (2002) Bovine mastitis: an evolving disease. Vet J 164:116–128CrossRefGoogle Scholar
  13. Busca A, Saxena M, Kryworuchko M, Kumar A (2009a) Anti-apoptotic genes in the survival of monocytic cells during infection. Curr Genomics 10:306–317. CrossRefGoogle Scholar
  14. Busca A, Saxena M, Kryworuchko M, Kumar A (2009b) Anti-apoptotic genes in the survival of monocytic cells during infection. Curr Genomics 10:306–317CrossRefGoogle Scholar
  15. Centola M et al (2013) Development of a multi-biomarker disease activity test for rheumatoid arthritis. PLoS One 8:e60635. CrossRefGoogle Scholar
  16. Cooks T et al (2013) Mutant p53 prolongs NF-kappaB activation and promotes chronic inflammation and inflammation-associated colorectal cancer. Cancer Cell 23:634–646. CrossRefGoogle Scholar
  17. Dandona P, Aljada A, Mohanty P, Ghanim H, Hamouda W, Assian E, Ahmad S (2001) Insulin inhibits intranuclear nuclear factor kappaB and stimulates IkappaB in mononuclear cells in obese subjects: evidence for an anti-inflammatory effect? J Clin Endocrinol Metab 86:3257–3265. Google Scholar
  18. Dandona P, Ghanim H, Bandyopadhyay A, Korzeniewski K, Ling Sia C, Dhindsa S, Chaudhuri A (2010) Insulin suppresses endotoxin-induced oxidative, nitrosative, and inflammatory stress in humans. Diabetes Care 33:2416–2423. CrossRefGoogle Scholar
  19. Dong C, Davis RJ, Flavell RA (2002) MAP kinases in the immune response. Annu Rev Immunol 20:55–72. CrossRefGoogle Scholar
  20. Endo K, Takino T, Miyamori H, Kinsen H, Yoshizaki T, Furukawa M, Sato H (2003) Cleavage of syndecan-1 by membrane type matrix metalloproteinase-1 stimulates cell migration. J Biol Chem 278:40764–40770. CrossRefGoogle Scholar
  21. Genini S et al (2011) Strengthening insights into host responses to mastitis infection in ruminants by combining heterogeneous microarray data sources. BMC Genomics 12:225. CrossRefGoogle Scholar
  22. Ghanim H et al (2008) Acute modulation of toll-like receptors by insulin. Diabetes Care 31:1827–1831. CrossRefGoogle Scholar
  23. Gholizadeh-Ghaleh Aziz S, Pashaei-Asl F, Fardyazar Z, Pashaiasl M (2016) Isolation, characterization, cryopreservation of human amniotic stem cells and differentiation to osteogenic and adipogenic cells. PLoS One 11:e0158281. CrossRefGoogle Scholar
  24. He X, Wei Z, Zhou E, Chen L, Kou J, Wang J, Yang Z (2015) Baicalein attenuates inflammatory responses by suppressing TLR4 mediated NF-kappaB and MAPK signaling pathways in LPS-induced mastitis in mice. Int Immunopharmacol 28:470–476. CrossRefGoogle Scholar
  25. Hogan J, Larry Smith K (2003) Coliform mastitis. Vet Res 34:507–519. CrossRefGoogle Scholar
  26. Hogeveen H, Huijps K, Lam TJ (2011) Economic aspects of mastitis: new developments. N Z Vet J 59:16–23. CrossRefGoogle Scholar
  27. Kanehisa M (2008) KEGG. Metabolic database. Accessed November 2017
  28. Kargarfard F, Sami A, Ebrahimie E (2015) Knowledge discovery and sequence-based prediction of pandemic influenza using an integrated classification and association rule mining (CBA) algorithm. J Biomed Inform 57:181–188. CrossRefGoogle Scholar
  29. Lamb DJ, Modjtahedi H, Plant NJ, Ferns GA (2004) EGF mediates monocyte chemotaxis and macrophage proliferation and EGF receptor is expressed in atherosclerotic plaques. Atherosclerosis 176:21–26. CrossRefGoogle Scholar
  30. Lee WR et al (2013) Effects of chimeric decoy oligodeoxynucleotide in the regulation of transcription factors NF-kappaB and Sp1 in an animal model of atherosclerosis. Basic Clin Pharmacol Toxicol 112:236–243. CrossRefGoogle Scholar
  31. Lewis JD (2011) The utility of biomarkers in the diagnosis and therapy of inflammatory bowel disease. Gastroenterology 140:1817–1826 e1812. CrossRefGoogle Scholar
  32. Li D et al (2013) Emodin ameliorates lipopolysaccharide-induced mastitis in mice by inhibiting activation of NF-kappaB and MAPKs signal pathways. Eur J Pharmacol 705:79–85. CrossRefGoogle Scholar
  33. Malarstig A, Eriksson P, Hamsten A, Lindahl B, Wallentin L, Siegbahn A (2008) Raised interleukin-10 is an indicator of poor outcome and enhanced systemic inflammation in patients with acute coronary syndrome. Heart 94:724–729. CrossRefGoogle Scholar
  34. McGuire JK, Li Q, Parks WC (2003) Matrilysin (matrix metalloproteinase-7) mediates E-cadherin ectodomain shedding in injured lung epithelium. Am J Pathol 162:1831–1843. CrossRefGoogle Scholar
  35. Mimoso C, Lee DD, Zavadil J, Tomic-Canic M, Blumenberg M (2014) Analysis and meta-analysis of transcriptional profiling in human epidermis. Methods Mol Biol 1195:61–97. CrossRefGoogle Scholar
  36. Minuti A et al (2015) Acute mammary and liver transcriptome responses after an intramammary Escherichia coli lipopolysaccharide challenge in postpartal dairy cows. Phys Rep 3.
  37. Mizia-Stec K, Gasior Z, Zahorska-Markiewicz B, Janowska J, Szulc A, Jastrzebska-Maj E, Kobielusz-Gembala I (2003) Serum tumour necrosis factor-alpha, interleukin-2 and interleukin-10 activation in stable angina and acute coronary syndromes. Coron Artery Dis 14:431–438. CrossRefGoogle Scholar
  38. Mohammadi A, Saraee MH, Salehi M (2011) Identification of disease-causing genes using microarray data mining and gene ontology. BMC Med Genet 4:12. Google Scholar
  39. Nikitin A, Egorov S, Daraselia N, Mazo I (2003) Pathway studio--the analysis and navigation of molecular networks. Bioinformatics 19:2155–2157CrossRefGoogle Scholar
  40. Panahi B, Mohammadi SA, Ebrahimi Khaksefidi R, Fallah Mehrabadi J, Ebrahimie E (2015) Genome-wide analysis of alternative splicing events in Hordeum vulgare: highlighting retention of intron-based splicing and its possible function through network analysis. FEBS Lett 589:3564–3575. CrossRefGoogle Scholar
  41. Papageorgiou AP, Heymans S (2012) Interactions between the extracellular matrix and inflammation during viral myocarditis. Immunobiology 217:503–510. CrossRefGoogle Scholar
  42. Parks WC, Wilson CL, Lopez-Boado YS (2004) Matrix metalloproteinases as modulators of inflammation and innate immunity. Nat Rev Immunol 4:617–629. CrossRefGoogle Scholar
  43. Pathi S, Jutooru I, Chadalapaka G, Nair V, Lee SO, Safe S (2012) Aspirin inhibits colon cancer cell and tumor growth and downregulates specificity protein (Sp) transcription factors. PLoS One 7:e48208. CrossRefGoogle Scholar
  44. Portt L, Norman G, Clapp C, Greenwood M, Greenwood MT (2011) Anti-apoptosis and cell survival: a review. Biochim Biophys Acta 1813:238–259. CrossRefGoogle Scholar
  45. Ramasamy A, Mondry A, Holmes CC, Altman DG (2008) Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med 5:e184. CrossRefGoogle Scholar
  46. Rech J et al (2016) Prediction of disease relapses by multibiomarker disease activity and autoantibody status in patients with rheumatoid arthritis on tapering DMARD treatment. Ann Rheum Dis 75:1637–1644. CrossRefGoogle Scholar
  47. Rinaldi M, Li RW, Capuco AV (2010) Mastitis associated transcriptomic disruptions in cattle. Vet Immunol Immunopathol 138:267–279. CrossRefGoogle Scholar
  48. Shafi S, Lamb D, Modjtahedi H, Ferns G (2010) Periadventitial delivery of anti-EGF receptor antibody inhibits neointimal macrophage accumulation after angioplasty in a hypercholesterolaemic rabbit. Int J Exp Pathol 91:224–234. CrossRefGoogle Scholar
  49. Sharifi S, Pakdel A, Ebrahimi M, Reecy JM, Fazeli Farsani S, Ebrahimie E (2018) Integration of machine learning and meta-analysis identifies the transcriptomic bio-signature of mastitis disease in cattle. PLoS One 13:e0191227. CrossRefGoogle Scholar
  50. Sipka A, Klaessig S, Duhamel GE, Swinkels J, Rainard P, Schukken Y (2014) Impact of intramammary treatment on gene expression profiles in bovine Escherichia coli mastitis. PLoS One 9:e85579. CrossRefGoogle Scholar
  51. Soehnlein O et al (2010) Anesthesia aggravates lung damage and precipitates hypotension in endotoxemic sheep. Shock 34:412–419. CrossRefGoogle Scholar
  52. Song X, Guo M, Wang T, Wang W, Cao Y, Zhang N (2014) Geniposide inhibited lipopolysaccharide-induced apoptosis by modulating TLR4 and apoptosis-related factors in mouse mammary glands. Life Sci 119:9–17. CrossRefGoogle Scholar
  53. Subramanian A et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550. CrossRefGoogle Scholar
  54. Tan KW et al (2013) Neutrophils contribute to inflammatory lymphangiogenesis by increasing VEGF-A bioavailability and secreting VEGF-D. Blood 122:3666–3677. CrossRefGoogle Scholar
  55. Turner MD, Nedjai B, Tara Hurst T, Pennington DJ (2014) Cytokines and chemokines: at the crossroads of cell signalling and inflammatory disease. Biochim Biophys Acta 1843:2563–2582CrossRefGoogle Scholar
  56. Younis S, Javed Q, Blumenberg M (2016) Meta-analysis of transcriptional responses to mastitis-causing Escherichia coli. PLoS One 11:e0148562. CrossRefGoogle Scholar
  57. Zhang WR et al (2015) Plasma IL-6 and IL-10 concentrations predict AKI and long-term mortality in adults after cardiac surgery. J Am Soc Nephrol 26:3123–3132. CrossRefGoogle Scholar

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

Personalised recommendations