8954856055505db

فراتحلیل (متا-آنالیز) داده‌های بیان ژن بافت پستان آلوده‌شده با باکتری اشریشیاکلی در گاوهای شیری

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، دانشکده کشاورزی دانشگاه صنعتی اصفهان، اصفهان

2 دانشیار، دانشکده کشاورزی، دانشگاه صنعتی اصفهان، اصفهان

3 دانشیار، گروه بیوتکنولوژی دانشگاه شیراز و عضو گروه تحقیقاتی دانشگاه آدلاید- استرالیا

چکیده

تشخیص ژن‌های درگیر در صفات پیچیده مانند حساسیت به بیماری نه تنها می‌تواند موجب بهبود تشخیص و پیشگیری از بیماری مورد نظر می‌شود، بلکه در انتخاب راه‌های درمانی کارآمد و همچنین در انتخاب دام‌های مقاوم به اصلاحگران کمک خواهد کرد. در این تحقیق با هدف بالا بردن توان تجزیۀ آماری در شناسایی ژن‌ها و مسیرهای زیستی (بیولوژیکی) درگیر در بیماری ورم پستان، از فراتحلیل به روش Fisherبرای یکی کردن p-value‌های به‌دست‌آمده از تجزیۀ انفرادی داده‌های شش بررسی‌ ریزآرایه که بیان ژن بافت پستان هنگام درگیری با باکتری اشریشیاکلی (E. coli) در گاوهای شیری را بررسی کرده بودند، استفاده شد. شناسایی ژن‌هایی که در هیچ‌یک از بررسی‌های انفرادی معنی­دار نشده بودند می­تواند تأییدکنندۀ هدف بیان شده باشد که منجر به ارائۀ مجموعۀ کامل‌تری از مسیرهای زیستی مرتبط با سامانۀ ایمنی، التهاب، تجزیۀ پروتئین (پروتئولیز) و مسیرهای مرتبط با رشد و افزونش و مرگ یاخته‌ای شد."کنترل مثبت بیان پروموتور RNA پلیمراز II"مسیر جدیدی در رابطه با این بیماری است که با وجود در بر گرفتن بیشترین شمار ژن در این بررسی‌، در بررسی‌های گذشتۀ مرتبط با ورم پستان گزارش نشده است.

کلیدواژه‌ها


عنوان مقاله [English]

Meta-analysis of transcriptomic data of mammary gland infected by Escherichia coli Bacteria in dairy cows

نویسندگان [English]

  • Somayeh Sharifi 1
  • Abbas Pakdel 2
  • Esmaeil Ebrahimie 3
1 Ph. D. Candidate, Faculty of Agriculture, Isfahan University of Technology, Isfahan, Iran
2 Associate Professor, Faculty of Agriculture, Isfahan University of Technology, Isfahan, Iran
3 Associate Professor, Institute of Biotechnology, Shiraz Univeristy, Shiraz, Iran, and School of Biological Science, Faculty of Science and Engineering, Flinders University of Adelaide, Australia
چکیده [English]

Identification of disease-causing genes that underlie complex traits such as susceptibility to disease not only can improve diagnosis and the prevention of illness, but also help breeder to select resistance animals against diseases. In the current study to aim the higher power of statistical analysis to identification of genes and biological pathways related to mastitis disease, we used Fisher meta-analysis to combine p-values obtained from individual analysis of datasets extracted from 6 microarray-based studies which investigate transcriptomic data of mammary gland tissue infected by Escherichia coli (E. coli) in dairy cows. Identification of genes that did not show a significant p-value in any of the independent studies may confirm the aim and lead to introduce a more complete set of biological pathways involved in this disease such as the pathways related to immune response, inflammation, proteolysis, growth, and death of cell. Positive regulation of transcription from RNA polymerase II promoter, is new pathways related to this disease which despite of the enrichment by maximum number of up-regulated genes in this study, have not been reported in previous mastitis studies.

کلیدواژه‌ها [English]

  • Dairy Cows
  • Differential express gene
  • Escherichia coli
  • gene ontology
  • Mastitis
  1. Abernethy, T. J. & Avery, O. T. (1941). The Occurrence during Acute Infections of a Protein Not Normally Present in the Blood: I. Distribution of the Reactive Protein in Patients' Sera and the Effect of Calcium on the Flocculation Reaction with C Polysaccharide of Pneumococcus. Journal of Experimental Medicine, 73, 173-82.
  2. Bannerman, D. D. (2009). Pathogen-dependent induction of cytokines and other soluble inflammatory mediators during intramammary infection of dairy cows. Journal of Animal Science, 87, 10-25.
  3. Bannerman, D. D., Paape, M. J., Hare, W. R. & Hope, J. C. (2004). Characterization of the bovine innate immune response to intramammary infection with Klebsiella pneumoniae. Journal of Dairy Science, 87, 2420-32.
  4. Blum, J. W., Dosogne, H., Hoeben, D., Vangroenweghe, F., Hammon, H. M., Bruckmaier, R. M. & Burvenich, C. (2000). Tumor necrosis factor-alpha and nitrite/nitrate responses during acute mastitis induced by Escherichia coli infection and endotoxin in dairy cows. Domestic Animal Endocrinology, 19, 223-35.
  5. Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. (2009). Introduction to Meta-Analysis. John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom.
  6. Brand, B., Hartmann, A., Repsilber, D., Griesbeck-Zilch, B., Wellnitz, O., Kuhn, C., Ponsuksili, S., Meyer, H. H. & Schwerin, M. (2011). Comparative expression profiling of E. coli and S. aureus inoculated primary mammary gland cells sampled from cows with different genetic predispositions for somatic cell score. Genetics Selection Evolution, 43, 24.
  7. Buitenhuis, B., Rontved, C. M., Edwards, S. M., Ingvartsen, K. L. & Sorensen, 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.
  8. Clark, D. P. & Pazdernik, N. J. (2012). Molecular Biology. Elsevier.
  9. Elmore, S. (2007). Apoptosis: A Review of Programmed Cell Death. Toxicologic Pathology, 35, 495-516.
  10. Fierro, A. C., Vandenbussche, F., Engelen, K., de Peer, Y. V. & Marchal, K. (2008). Meta analysis of gene expression data within and across species. Current genomics, 9(8), 525-534
  11. Fisher, R. A. (1950). Statistical methods for research workers. Edinburgh: Genesis Publishing, Oliver and Boyd.
  12. Fuda, N. J., Ardehali, M. B. & Lis, J. T. (2009). Defining mechanisms that regulate RNA polymerase II transcription in vivo. Nature, 461, 186-92.
  13. Genini, S., Badaoui, B., Sclep, G., Bishop, S. C., Waddington, D., Pinard van der Laan, M. H., Klopp, C., Cabau, C., Seyfert, H. M., Petzl, W., Jensen, K., Glass, E. J., de Greeff, A., Smith, H. E., Smits, M. A., Olsaker, I., Boman, G. M., Pisoni, G., Moroni, P., Castiglioni, B., Cremonesi, P., Del Corvo, M., Foulon, E., Foucras, G., Rupp, R. & Giuffra, E. (2011). Strengthening insights into host responses to mastitis infection in ruminants by combining heterogeneous microarray data sources. BMC Genomics, 12, 225.
  14. Gentleman, R., Carey, V., Huber, W., Irizarry, R. & Dudoit, S. (2005). Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer.
  15. Guenther, M. G., Levine, S. S., Boyer, L. A., Jaenisch, R. & Young, R. A. (2007). A chromatin landmark and transcription initiation at most promoters in human cells. Cell, 130, 77-88.
  16. Gunther, J., Esch, K., Poschadel, N., Petzl, W., Zerbe, H., Mitterhuemer, S., Blum, H. & Seyfert, H. M. (2011). Comparative kinetics of Escherichia coli- and Staphylococcus aureus-specific activation of key immune pathways in mammary epithelial cells demonstrates that S. aureus elicits a delayed response dominated by interleukin-6 (IL-6) but not by IL-1A or tumor necrosis factor alpha. Infect Immun, 79, 695-707.
  17. Gunther, J., Petzl, W., Zerbe, H., Schuberth, H. J., Koczan, D., Goetze, L. & Seyfert, H. M. (2012). Lipopolysaccharide priming enhances expression of effectors of immune defence while decreasing expression of pro-inflammatory cytokines in mammary epithelia cells from cows. BMC Genomics 13, 17.
  18. Hagiwara, S., Mori, K. & Nagahata, H. (2016). Predictors of fatal outcomes resulting from acute Escherichia coli mastitis in dairy cows. Journal of Veterinary Medical Science, 78(5), 905-908.‏
  19. Halasa, T., Huijps, K., Osteras, O. & Hogeveen, H. (2007). Economic effects of bovine mastitis and mastitis management. Veterinary Quarterly, 29, 18-31.
  20. Hedges L. & Olkin I. (1980) Vote-counting methods in research synthesis. Psychol Bull, 88, 359-69.
  21. Hiss, S., Mielenz, M., Bruckmaier, R. M. & Sauerwein, H. (2004). Haptoglobin concentrations in blood and milk after endotoxin challenge and quantification of mammary Hp mRNA expression. Journa of Dairy Science, 87(11), 3778-84.
  22. Hochberg, Y. & Benjamini, Y. (1990). More powerful procedures for multiple significance testing. Statistics in medicine, 9(7), 811-818.‏
  23. Hong, F., Breitling, R., McEntee, C. W., Wittner, B. S., Nemhauser, J. L. & Chory, J. (2006). RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis. Bioinformatics, 22, 2825-7.
  24. Huang, D. W., Sherman, B. T. & Lempicki, R. A. (2009a). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37, 1-13.
  25. Huang, D. W., Sherman, B. T. & Lempicki, R. A. (2009b). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4, 44-57.
  26. Imtiyaz, H. Z. & Simon, M. C. (2010). Hypoxia-inducible factors as essential regulators of inflammation. Curr Top Microbiol Immunol, 345, 105-20.
  27. Irizarry, R. A., Bolstad, B. M., Collin, F., Cope, L. M., Hobbs, B. & Speed, T. P. (2003). Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Research, 31, e15.
  28. Jiang, L., Sorensen, P., Rontved, C., Vels, L. & Ingvartsen, K. L. (2008). Gene expression profiling of liver from dairy cows treated intra-mammary with lipopolysaccharide. BMC Genomics, 9, 443.
  29. Johnson, W. E., Li, C. & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118-27.
  30. Jorgensen, H. B., Buitenhuis, B., Rontved, C. M., Jiang, L., Ingvartsen, K. L. & Sorensen, P. (2012). Transcriptional profiling of the bovine hepatic response to experimentally induced E. coli mastitis. Physiol Genomics, 44, 595-606.
  31. Mitterhuemer, S., Petzl, W., Krebs, S., Mehne, D., Klanner, A., Wolf, E., Zerbe, H. & Blum, H. (2010). Escherichia coli infection induces distinct local and systemic transcriptome responses in the mammary gland. BMC Genomics, 11, 138.
  32. Moreau, Y., Aerts, S., De Moor, B., De Strooper, B. & Dabrowski, M. (2003). Comparison and meta-analysis of microarray data: from the bench to the computer desk. Trends Genetics, 19, 570-7.
  33. Mysorekar, I. U., Mulvey, M. A., Hultgren, S. J. & Gordon, J. I. (2002). Molecular regulation of urothelial renewal and host defenses during infection with uropathogenic Escherichia coli. Journal of Biological Chemistry, 277, 7412-9.
  34. Ramasamy, A., Mondry, A., Holmes, C. C. & Altman, D. G. (2008). Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Medicine, 5, e184.
  35. Rhodes, D. R., Barrette, T. R., Rubin, M. A., Ghosh, D. & Chinnaiyan, A. M. (2002). Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Research, 62, 4427-33.
  36. Riollet, C., Rainard, P. & Poutrel, B. (2000). Differential induction of complement fragment C5a and inflammatory cytokines during intramammary infections with Escherichia coli and Staphylococcus aureus. Clin Diagn Lab Immunol, 7, 161-7.
  37. Schukken, Y. H., Gunther, J., Fitzpatrick, J., Fontaine, M. C., Goetze, L., Holst, O., Leigh, J., Petzl, W., Schuberth, H. J., Sipka, A., Smith, D. G., Quesnell, R., Watts, J., Yancey, R., Zerbe, H., Gurjar, A., Zadoks, R. N., Seyfert, H. M. & Members of the Pfizer mastitis research c. (2011). Host-response patterns of intramammary infections in dairy cows. Vet Immunol Immunopathol. 144, 270-89.
  38. Sipka, A., Klaessig, S., Duhamel, G. E., Swinkels, J., Rainard, P. & Schukken, Y. (2014)/ Impact of intramammary treatment on gene expression profiles in bovine Escherichia coli mastitis. PLoS ONE, 9, e85579.
  39. Thulasiraman, P., Mukherjee, J., Banerjee, D., Pandiyan, G. D. V., Das, K., Ghosh, P. R. & Das, P. K. (2013). Acute Phase Proteins - a Potent Biomarker for Mastitis. Vetscan 7, 6-14.
  40. Uthaisangsook, S., Day, N. K., Bahna, S. L., Good, R. A. & Haraguchi, S. (2002). Innate immunity and its role against infections. Ann Allergy Asthma Immunol, 88, 253-64; quiz 65-6, 318.
  41. Wang, X., Kang, D. D., Shen, K., Song, C., Lu, S., Chang, L. C., Liao, S. G., Huo, Z., Tang, S., Ding, Y., Kaminski, N., Sibille, E., Lin, Y., Li, J. & Tseng, G. C. (2012). An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics, 28, 2534-6.
  42. Xia, J., Fjell, C. D., Mayer, M. L., Pena, O. M., Wishart, D. S. & Hancock, R. E. W. (2013). INMEX-a web-based tool for integrative meta-analysis of expression data. Nucleic Acids Res. 41, web server issue w63-w70.
  43. Yin, L., Coelho, S. G., Valencia, J. C., Ebsen, D., Mahns, A., Smuda, C., Miller, S. A., Beer, J. Z., Kolbe, L. & Hearing, V. J. (2015). Identification of Genes Expressed in Hyperpigmented Skin Using Meta-Analysis of Microarray Data Sets. Journal of Investigative Dermatology, 135, 2455-63.