Skip to main content

Advertisement

SpringerLink
Log in

Prediction of potential molecular markers of bovine mastitis by meta-analysis of differentially expressed genes using combined p value and robust rank aggregation

  • Regular Articles
  • Published:
Tropical Animal Health and Production Aims and scope Submit manuscript

Abstract

Bovine mastitis causes significant economic loss to the dairy industry by affecting milk quality and quantity. Escherichia coli and Staphylococcus aureus are the two common mastitis-causing bacteria among the consortia of mastitis pathogens, wherein E. coli is an opportunistic environmental pathogen, and S. aureus is a contagious pathogen. This study was designed to predict molecular markers of bovine mastitis by meta-analysis of differentially expressed genes (DEG) in E. coli– or S. aureus–infected mammary epithelial cells (MECs) using p value combination and robust rank aggregation (RRA) methods. High-throughput transcriptome of bovine MECs, infected with E. coli or S. aureus, were analyzed, and correlation of z-scores were computed for the expression datasets to identify the lineage profile and functional ontology of DEGs. Key pathways enriched in infected MECs were deciphered by Gene Set Enrichment Analysis (GSEA), following which combined p value and RRA were used to perform DEG meta-analysis to limit type I error in the analysis. The miRNA-gene networks were then built to uncover potential molecular markers of mastitis. Lineage profiling of MECs showed that the gene expression levels were associated with mammary tissue lineage. The up-regulated genes were enriched in immune-related pathways, whereas down-regulated genes influenced the cellular processes. GSEA analysis of DEGs deciphered the involvement of Toll-like receptor (TLR), and NF-kappa B signaling pathway during infection. Comparison after meta-analysis yielded with genes ZC3H12A, RND1, and MAP3K8 having significant expression levels in both E. coli and S. aureus dataset, and on evaluating miRNA-gene network, 7 pairs were common to both sets identifying them as potential molecular markers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this published article.

Code availability

R Code will be made available on request.

References

  • Agarwal, V., Bell, G.W., Nam, J.-W. and Bartel, D.P., 2015. Predicting effective microRNA target sites in mammalian mRNAs. eLife, 4, e05005.

  • Alhussien, M.N. and Dang, A.K., 2018. Milk somatic cells, factors influencing their release, future prospects, and practical utility in dairy animals: An overview Veterinary World, 11, 562–577

    Article  PubMed  PubMed Central  Google Scholar 

  • Bannerman, D.D., Paape, M.J., Lee, J.W., Zhao, X., Hope, J.C. and Rainard, P., 2004. Escherichia coli and Staphylococcus aureus elicit differential innate immune responses following intramammary infection Clinical and Diagnostic Laboratory Immunology, 11, 463–472

    PubMed  PubMed Central  Google Scholar 

  • Bhattarai, D., Worku, T., Dad, R., Rehman, Z.U., Gong, X. and Zhang, S., 2018. Mechanism of pattern recognition receptors (PRRs) and host pathogen interplay in bovine mastitis Microbial Pathogenesis, 120, 64–70 (Elsevier Ltd)

    Article  CAS  PubMed  Google Scholar 

  • Buehring, G.C., 1990. Culture of mammary epithelial cells from bovine milk. Journal of dairy science, 73, 956–963 (United States)

    Article  CAS  PubMed  Google Scholar 

  • Buitenhuis, B., Røntved, C.M., Edwards, S.M., Ingvartsen, K.L. and Sørensen, P., 2011. In depth analysis of genes and pathways of the mammary gland involved in the pathogenesis of bovine Escherichia coli-mastitis BMC Genomics, 12, 130 (BioMed Central Ltd)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Burvenich, C., Bannerman, D.D., Lippolis, J.D., Peelman, L., Nonnecke, B.J., Kehrli, M.E.J. and Paape, M.J., 2007. Cumulative physiological events influence the inflammatory response of the bovine udder to Escherichia coli infections during the transition period. Journal of dairy science, 90 Suppl 1, E39-54 (United States)

    Article  Google Scholar 

  • Chang, L.-C., Lin, H.-M., Sibille, E. and Tseng, G.C., 2013. Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline. BMC bioinformatics, 14, 368

    Article  PubMed  PubMed Central  Google Scholar 

  • Chang, L., Zhou, G., Soufan, O. and Xia, J., 2020. miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology. Nucleic acids research, 48, W244–W251

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cintio, M., Polacchini, G., Scarsella, E., Montanari, T., Stefanon, B. and Colitti, M., 2020. Microrna milk exosomes: From cellular regulator to genomic marker Animals, 10, 1–19

    Article  Google Scholar 

  • Connelly, L., Barham, W., Pigg, R., Saint-Jean, L., Sherrill, T., Cheng, D.-S., Chodosh, L.A., Blackwell, T.S. and Yull, F.E., 2010. Activation of nuclear factor kappa B in mammary epithelium promotes milk loss during mammary development and infection. Journal of cellular physiology, 222, 73–81

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Emig, D., Salomonis, N., Baumbach, J., Lengauer, T., Conklin, B.R. and Albrecht, M., 2010. AltAnalyze and DomainGraph: analyzing and visualizing exon expression data. Nucleic acids research, 38, W755-62

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Enright, A.J., John, B., Gaul, U., Tuschl, T., Sander, C. and Marks, D.S., 2003. MicroRNA targets in Drosophila. Genome biology, 5, R1

    Article  PubMed  PubMed Central  Google Scholar 

  • Ferronatto, J.A., Ferronatto, T.C., Schneider, M., Pessoa, L.F., Blagitz, M.G., Heinemann, M.B., Della Libera, A.M.M.P. and Souza, F.N., 2018. Diagnosing mastitis in early lactation: use of Somaticell®, California mastitis test and somatic cell count Italian Journal of Animal Science, 17, 723–729 (Informa Healthcare USA, Inc)

    Article  CAS  Google Scholar 

  • Fu, Y., Zhou, E., Liu, Z., Li, F., Liang, D., Liu, B., Song, X., Zhao, F., Fen, X., Li, D., Cao, Y., Zhang, X., Zhang, N. and Yang, Z., 2013. Staphylococcus aureus and Escherichia coli elicit different innate immune responses from bovine mammary epithelial cells. Veterinary immunology and immunopathology, 155, 245–252 (Netherlands)

    Article  CAS  PubMed  Google Scholar 

  • Han, H., 2019. Identification of several key genes by microarray data analysis of bovine mammary gland epithelial cells challenged with Escherichia coli and Staphylococcus aureus. Gene, 683, 123–132 (Netherlands)

    Article  CAS  PubMed  Google Scholar 

  • Han, Z., Fan, Y., Yang, Z., Loor, J.J. and Yang, Y., 2020. Mammary Transcriptome Profile during Peak and Late Lactation Reveals Differentially Expression Genes Related to Inflammation and Immunity in Chinese Holstein. Animals : an open access journal from MDPI, 10(3), 510.

  • Hong, F. and Breitling, R., 2008. A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments. Bioinformatics (Oxford, England), 24, 374–382 (England)

    Article  CAS  Google Scholar 

  • Huma, Z.I., Sharma, N., Kour, Sarabpreet, Tandon, S., Guttula, P.K., Kour, Savleen, Singh, A.K., Singh, R. and Gupta, M.K., 2020. Putative biomarkers for early detection of mastitis in cattle Animal Production Science, 60, 1721–1736

    Article  CAS  Google Scholar 

  • Irizarry, R.A., Hobbs, B., Collin, F., Beazer-Barclay, Y.D., Antonellis, K.J., Scherf, U. and Speed, T.P., 2003. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics (Oxford, England), 4, 249–264 (England)

    Article  Google Scholar 

  • Islam, M.A., Takagi, M., Fukuyama, K., Komatsu, R., Albarracin, L., Nochi, T., Suda, Y., Ikeda-Ohtsubo, W., Rutten, V., Eden, W. van, Villena, J., Aso, H. and Kitazawa, H., 2020. Transcriptome Analysis of The Inflammatory Responses of Bovine Mammary Epithelial Cells: Exploring Immunomodulatory Target Genes for Bovine Mastitis. Pathogens (Basel, Switzerland), 9(3), 200

  • Jensen, K., Günther, J., Talbot, R., Petzl, W., Zerbe, H., Schuberth, H.J., Seyfert, H.M. and Glass, E.J., 2013. Escherichia coli- and Staphylococcus aureus-induced mastitis differentially modulate transcriptional responses in neighbouring uninfected bovine mammary gland quarters BMC Genomics, 14, 36.

  • Kolde, R., Laur, S., Adler, P. and Vilo, J., 2012. Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics (Oxford, England), 28, 573–580

    Article  CAS  Google Scholar 

  • Krüger, J. and Rehmsmeier, M., 2006. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic acids research, 34, W451-4

    Article  PubMed  PubMed Central  Google Scholar 

  • Kulkarni, A.G. and Kaliwal, B.B., 2013. Bovine Mastitis: A Review International Journal of Recent Scientific Research, 4, 543–548

  • Kusebauch, U., Hernández-Castellano, L.E., Bislev, S.L., Moritz, R.L., Røntved, C.M. and Bendixen, E., 2018. Selected reaction monitoring mass spectrometry of mastitis milk reveals pathogen-specific regulation of bovine host response proteins Journal of Dairy Science, 101, 6532–6541

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lai, Y.C., Fujikawa, T., Maemura, T., Ando, T., Kitahara, G., Endo, Y., Yamato, O., Koiwa, M., Kubota, C. and Miura, N., 2017. Inflammation-related microRNA expression level in the bovine milk is affected by mastitis PLoS ONE, 12, 1–13

    Article  Google Scholar 

  • Lai, Y.C., Lai, Y.T., Rahman, M.M., Chen, H.W., Husna, A.A., Fujikawa, T., Ando, T., Kitahara, G., Koiwa, M., Kubota, C. and Miura, N., 2020. Bovine milk transcriptome analysis reveals microRNAs and RNU2 involved in mastitis FEBS Journal, 287, 1899–1918

    Article  CAS  PubMed  Google Scholar 

  • Li, T., Gao, J., Zhao, X. and Ma, Y., 2019. Digital gene expression analyses of mammary glands from meat ewes naturally infected with clinical mastitis. Royal Society open science, 6, 181604

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liao, Y., Wang, J., Jaehnig, E.J., Shi, Z. and Zhang, B., 2019. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic acids research, 47, W199–W205

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liu, J., Li, L., Chen, X., Lu, Y. and Wang, D., 2019. Effects of heat stress on body temperature, milk production, and reproduction in dairy cows: A novel idea for monitoring and evaluation of heat stress — A review Asian-Australasian Journal of Animal Sciences, 32, 1332–1339

    Article  PubMed  PubMed Central  Google Scholar 

  • Loher, P. and Rigoutsos, I., 2012. Interactive exploration of RNA22 microRNA target predictions. Bioinformatics (Oxford, England), 28, 3322–3323 (England)

    Article  CAS  Google Scholar 

  • Lorenz, R., Bernhart, S.H., Höner Zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P.F. and Hofacker, I.L., 2011. ViennaRNA Package 2.0. Algorithms for molecular biology : AMB, 6, 26

    Article  PubMed  PubMed Central  Google Scholar 

  • Ma, S., Tong, C., Ibeagha-Awemu, E.M. and Zhao, X., 2019. Identification and characterization of differentially expressed exosomal microRNAs in bovine milk infected with Staphylococcus aureus BMC Genomics, 20, 1–13 (BMC Genomics)

    Article  Google Scholar 

  • Matsushita, K., Takeuchi, O., Standley, D.M., Kumagai, Y., Kawagoe, T., Miyake, T., Satoh, T., Kato, H., Tsujimura, T., Nakamura, H. and Akira, S., 2009. Zc3h12a is an RNase essential for controlling immune responses by regulating mRNA decay. Nature, 458, 1185–1190 (England)

    Article  CAS  PubMed  Google Scholar 

  • Naeem, A., Zhong, K., Moisá, S.J., Drackley, J.K., Moyes, K.M. and Loor, J.J., 2012. Bioinformatics analysis of microRNA and putative target genes in bovine mammary tissue infected with Streptococcus uberis Journal of Dairy Science, 95, 6397–6408 (Elsevier)

    Article  CAS  PubMed  Google Scholar 

  • Prada C, Lima D, N.H., 2020. MetaVolcanoR: Gene Expression Meta-analysis Visualization Tool R package version 1.4.0.

  • Puerto, M.A., Shepley, E., Cue, R.I., Warner, D., Dubuc, J. and Vasseur, E., 2021. The hidden cost of disease: I. Impact of the first incidence of mastitis on production and economic indicators of primiparous dairy cows. Journal of dairy science, 104, 7932–7943 (United States)

    Article  CAS  PubMed  Google Scholar 

  • Radostits, O.M. and Done, S.H., 2007. Veterinary medicine : a textbook of the diseases of cattle, sheep, pigs, goats, and horses, 10th ed. (Elsevier Saunders: Edinburgh)

    Google Scholar 

  • Reshi, A.A., Husain, I., Bhat, S.A., Rehman, M.U., Razak, R., Bilal, S. and Mir, M.R., 2015. Bovine Mastitis As an Evolving Disease and Its Impact on the Dairy Industry Int J Cur Res Rev, 7, 48–55

    Google Scholar 

  • Schukken, Y.H., Günther, J., Fitzpatrick, J., Fontaine, M.C., Goetze, L., Holst, O., Leigh, J., Petzl, W., Schuberth, H.-J., Sipka, A., Smith, D.G.E., Quesnell, R., Watts, J., Yancey, R., Zerbe, H., Gurjar, A., Zadoks, R.N. and Seyfert, H.-M., 2011. Host-response patterns of intramammary infections in dairy cows. Veterinary immunology and immunopathology, 144, 270–289 (Netherlands)

    Article  PubMed  Google Scholar 

  • Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B. and Ideker, T., 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research, 13, 2498–2504

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Singh, S. V, Singh, J.P., Niyogi, D. and Kumar, Y.D., 2021. Analysis of Economic Losses due to Mastitis in Cattle of Uttar Pradesh , India 10, 1571–1576

    Google Scholar 

  • Sun, H.Z., Chen, Y. and Guan, L.L., 2019. MicroRNA expression profiles across blood and different tissues in cattle Scientific Data, 6, 1–8 (The Author(s))

    Article  Google Scholar 

  • Swanson, K.M., Stelwagen, K., Dobson, J., Henderson, H. V, Davis, S.R., Farr, V.C. and Singh, K., 2009. Transcriptome profiling of Streptococcus uberis-induced mastitis reveals fundamental differences between immune gene expression in the mammary gland and in a primary cell culture model. Journal of dairy science, 92, 117–129 (United States)

    Article  CAS  PubMed  Google Scholar 

  • Tong, J., Zhang, H., Zhang, Y., Xiong, B. and Jiang, L., 2019. Microbiome and Metabolome Analyses of Milk From Dairy Cows With Subclinical Streptococcus agalactiae Mastitis—Potential Biomarkers Frontiers in Microbiology, 10, 1–14

    Article  Google Scholar 

  • Tseng, G.C., Ghosh, D. and Feingold, E., 2012. Comprehensive literature review and statistical considerations for microarray meta-analysis Nucleic Acids Research, 40, 3785–3799

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Vejnar, C.E., Blum, M. and Zdobnov, E.M., 2013. miRmap web: Comprehensive microRNA target prediction online. Nucleic acids research, 41, W165-8

    Article  PubMed  PubMed Central  Google Scholar 

  • Wenz, J.R., Fox, L.K., Muller, F.J., Rinaldi, M., Zeng, R. and Bannerman, D.D., 2010. Factors associated with concentrations of select cytokine and acute phase proteins in dairy cows with naturally occurring clinical mastitis. Journal of dairy science, 93, 2458–2470 (United States)

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

This work was supported by a grant from the Department of Biotechnology, Government of India.

Author information

Authors and Affiliations

  1. Department of Biotechnology and Medical Engineering / Center for Bioinformatics and Computational Biology, National Institute of Technology Rourkela, Odisha, 769008, India

    Anushri Umesh, Praveen Kumar Guttula & Mukesh Kumar Gupta

  2. Gene Manipulation Laboratory, Department of Biotechnology and Medical Engineering, Center for Bioinformatics and Computational Biology, National Institute of Technology Rourkela, Odisha, 769008, India

    Mukesh Kumar Gupta

Authors
  1. Anushri Umesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Praveen Kumar Guttula
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mukesh Kumar Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Anushri Umesh: Conceptualization, data collection, analysis, writing-original draft. Praveen Kumar Guttula: Conceptualization and writing-original draft. Mukesh Kumar Gupta: Conceptualization, supervision, fund acquisition, review, and editing.

Corresponding author

Correspondence to Mukesh Kumar Gupta.

Ethics declarations

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Umesh, A., Guttula, P.K. & Gupta, M.K. Prediction of potential molecular markers of bovine mastitis by meta-analysis of differentially expressed genes using combined p value and robust rank aggregation. Trop Anim Health Prod 54, 269 (2022). https://doi.org/10.1007/s11250-022-03258-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11250-022-03258-9

Keywords

Search

Navigation