Identification of genes affecting the amount of abdominal fat in broiler chickens ‎using microarray and RNA sequencing data

Document Type : Research Paper

Authors

1 M.Sc. Student, of Animal Breeding and Genetics, Department of Animal ‎Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

2 Associate Professor of Animal Breeding and Genetics, Department of Animal Science, ‎College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

3 Former Ph.D. Student of Animal Breeding and Genetics, Department of Animal Science, College of Agriculture and ‎Natural Resources, University of Tehran, Karaj, Iran

4 Professor of Animal Breeding and Genetics, Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, ‎Karaj, Iran

Abstract

Abdominal fat deposition and several other unique features in the metabolism of birds such as interaction between genetic and endocrine factors, fasting hyperglycemia and insulin resistance are signs of obesity and metabolic disorders in poultry, similar to humans. The main purpose of this study was to use transcript profile of fat tissue in two groups of broiler chickens with high and low abdominal fat deposition, to identify the genes involved in storage and metabolism of fat, as well as the signaling pathways associated with the endocrine glands. Based on the analysis of microarray and RNA-seq data, 2914 and 1867 genes were detected as differentially expressed genes, respectively. In total, 1835 genes were identified by comparing the genes with a significant difference in expression (P<0.000001). Then, by comparing the number of relevant genes among the transcript profiles, the most important related genes were THBS1, COLEC12, ANXA7, RGS19, TMEM258 and HTR7L, which in the main process of pathways controlling synthesis, fat metabolism and storage and the endocrine signaling pathways activated by adipokines, are involved. The analysis of the relevant tissue may indicate the role of ventricular fat as a metabolic and endocrine organ in broiler chickens.

Keywords

References

  1. Abdollahi, M. R., Ravindran, V. & Svihus, B. (2013). Pelleting of broiler diets: an overview with emphasis on pellet quality and nutritional value. Animal Feed Science and Technology, 179(1), 1-23.
  2. Andrews S. (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc
  3. Blankenberg, D., Gordon, A., Von Kuster, G., Coraor, N., Taylor, J. & Nekrutenko, A. (2010). Galaxy team. Manipulation of FASTQ data with Galaxy. Bioinformatics, 26(14), 1783-5.
  4. Bolger, A. M., Lohse, M. & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30, 2114-2120.
  5. Bourneuf, E., Hérault, F., Chicault, C., Carré, W., Assaf, S., Monnier, A., ... & Diot, C. (2006). Microarray analysis of differential gene expression in the liver of lean and fat chickens. Gene, 372, 162-170.
  6. Burt, D. W. (2007). Emergence of the chicken as a model organism: implications for agriculture and biology. Poultry Science, 86(7), 1460-1471.
  7. Byerly, M. S., Simon, J., Cogburn, L. A., Le Bihan-Duval, E., Duclos, M. J., Aggrey, S. E. & Porter, T. E. (2010). Transcriptional profiling of hypothalamus during development of adiposity in genetically selected fat and lean chickens. Physiological Genomics, 42(2), 157-167.
  8. Caohuy, H. & Pollard, H. B. (2001). Activation of annexin 7 by protein kinase c in vitro and In vivo. Journal of Biological Chemistry, 276(16), 12813-12821.
  9. Chun, T. H. (2012). Peri-adipocyte ECM remodeling in obesity and adipose tissue fibrosis. Adipocyte, 1(2), 89-95.
  10. Cogburn, L. A. (2007). Hepatic transcriptional profiling of juvenile broiler chickens divergently selected for abdominal fatness or leanness, geo, V1. https://www.ncbi.nlm.nih.gov/geo/query/acc/cgi?acc=GSE8812
  11. Cogburn, L. A., Porter, T. E., Duclos, M. J., Simon, J., Burgess, S. C., Zhu, J. J. & Burnside, J. (2007). Functional genomics of the chicken—a model organism. Poultry Science, 86(10), 2059-2094.
  12. Crespo, N. & Esteve- Garcia, E. (2001). Dietary fatty acid profile modifies abdominal fat deposition in broiler chickens. Poultry Science, 80, 71-78.
  13. Davis S, Meltzer P (2007). “GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor.” Bioinformatics, 14, 1846–1847.
  14. Degenhardt, F., Niklowitz, P., Szymczak, S., Jacobs, G., Lieb, W., Menke, T. & Döring, F. (2016). Genome-wide association study of serum coenzyme Q10 levels identifies susceptibility loci linked to neuronal diseases. Human Molecular Genetics, 25(13), 2881-2891.
  15. Dodgson, J. B. (2007). The chicken genome: some good news and some bad news. Poultry Science, 86(7), 1453-1459.
  16. Du, P., Kibbe, W. A. & Lin, S. M. (2008). Lumi: a pipeline for processing Illumina microarray', Bioinformatics, 24, 1547-1548.
  17. Friedman-Einat, M., Cogburn, L. A., Yosefi, S., Hen, G., Shinder, D., Shirak, A. & Seroussi, E. (2014). Discovery and characterization of the first genuine avian leptin gene in the rock dove (Columba livia). Endocrinology, 155(9), 3376-3384.
  18. Graham, D. B., Lefkovith, A., Deelen, P., de Klein, N., Varma, M., Boroughs, A. & Petersen, C. P. (2016). TMEM258 is a component of the oligosaccharyltransferase complex controlling ER stress and intestinal inflammation. Cell Reports, 17(11), 2955-2965.
  19. Hassanzadeh, M., Gilanpour, H., Charkhkar, S., Buyse, J. & Decuypere, E. (2005). Anatomical parameters of cardiopulmonary system in three different lines of chickens: further evidence for involvement in ascites syndrome. Avian Pathology, 34(3), 188-193.
  20. Hermier, D. Q. B. A., Quignard-Boulange, A., Dugail, I., Guy, G., Salichon, M. R., Brigant, L., ... & Leclercq, B. (1989). Evidence of enhanced storage capacity in adipose tissue of genetically fat chickens. The Journal of Nutrition, 119(10), 1369-1375.
  21. Hoque, M., Rentero, C., Cairns, R., Tebar, F., Enrich, C. & Grewal, T. (2014). Annexins-Scaffolds modulating PKC localization and signaling. Cellular Signalling, 26(6), 1213-1225.
  22. Hoyer, D., Clarke, D. E., Fozard, J. R., Hartig, P. R., Martin, G. R., Mylecharane, E. J., ... & Humphrey, P. P. (1994). International union of pharmacology classification of receptors for 5-hydroxytryptamine (Serotonin). Pharmacological Reviews, 46(2), 157-203.
  23. Huang, D. W., Sherman, B. T. & Lempicki, R. A. (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocol, 4(1), 44-57.
  24. Huang, G. & Greenspan, D. S. (2012). ECM roles in the function of metabolic tissues. Trends in Endocrinology & Metabolism, 23(1), 16-22.
  25. Huber, W., Carey, V.J., Gentleman, R., Anders, S., Carlson, M., Carvalho, B.S., Bravo, H.C., Davis, S., Gatto, L., Girke, T. & Gottardo, R. (2015). Orchestrating high-throughput genomic analysis with Bioconductor. Nature methods, 12(2), 115. http://www.nature.com/nmeth/journal/v12/n2/full/nmeth.3252.html.
  26. Ikeobi, C. O. N., Woolliams, J. A., Morrice, D. R., Law, A., Windsor, D., Burt, D. W. & Hocking, P. M. (2002). Quantitative trait loci affecting fatness in the chicken. Animal Genetics, 33(6), 428-435.
  27. Inoue, M., Jiang, Y., Barnes, R. H., Tokunaga, M., Martinez-Santibañez, G., Geletka, L. & Chun, T. H. (2013). Thrombospondin 1 mediates high-fat diet-induced muscle fibrosis and insulin resistance in male mice. Endocrinology, 154(12), 4548-4559.
  28. Ji, B., Ernest, B., Gooding, J. R., Das, S., Saxton, A. M., Simon, J. & Voy, B. H. (2012). Transcriptomic and metabolomic profiling of chicken adipose tissue in response to insulin neutralization and fasting. BMC Genomics, 13(1), 441.
  29. Ji, B., Middleton, J. L., Ernest, B., Saxton, A. M., Lamont, S. J., Campagna, S. R. & Voy, B. H. (2014). Molecular and metabolic profiles suggest that increased lipid catabolism in adipose tissue contributes to leanness in domestic chickens. Physiological Genomics, 46(9), 315-327.
  30. Julian, R. J. (1997). Causes and prevention of ascites in broilers. Zootec International, 452-53.
  31. Ka, S., Albert, F. W., Denbow, D. M., Pääbo, S., Siegel, P. B., Andersson, L. & Hallböök, F. (2011). Differentially expressed genes in hypothalamus in relation to genomic regions under selection in two chicken lines resulting from divergent selection for high or low body weight. Neurogenetics, 12(3), 211.
  32. Khan, T., Muise, E. S., Iyengar, P., Wang, Z. V., Chandalia, M., Abate, N. & Scherer, P. E. (2009). Metabolic dysregulation and adipose tissue fibrosis: role of collagen VI. Molecular and Cellular Biology, 29(6), 1575-1591.
  33. Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R. & Salzberg, S. L. (2013). TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology, 14(4), pp. R36.
  34. Le Mignon, G., Pitel, F., Gilbert, H., Le Bihan‐Duval, E., Vignoles, F., Demeure, O., ... & Douaire, M. (2009). A comprehensive analysis of QTL for abdominal fat and breast muscle weights on chicken chromosome 5 using a multivariate approach. Animal Genetics, 40(2), 157-164.
  35. Leclercq, B., Blum, J. C. & Boyer, J. P. (1980). Selecting broilers for low or high abdominal fat: initial observations. British Poultry Science, 21(2), 107-113.
  36. Leclercq, B., Hermier, D. & Salichon, M. R. (1984). Effects of age and diet on plasma lipid and glucose concentrations in genetically lean or fat chickens. Reproduction Nutrition Développement, 24(1), 53-61.
  37. Leheska, J. M., Montgomery, J. L., Krehbiel, C. R., Yates, D. A., Hutcheson, J. P., Nichols, W. T., ... & Miller, M. F. (2009). Dietary zilpaterol hydrochloride. II. Carcass composition and meat palatability of beef cattle. Journal of Animal Science, 87(4), 1384-1393.
  38. Martin, G. R. (1995). Operational characteristics of a 5-HT receptor mediating direct vascular relaxation: identity with 5-HT_7 receptors?. British Journal of Pharmacology, 114, 383.
  39. Merkel, M., Loeffler, B., Kluger, M., Fabig, N., Geppert, G., Pennacchio, L. A. & Heeren, J. (2005). Apolipoprotein AV accelerates plasma hydrolysis of triglyceriderich lipoproteins by interaction with proteoglycan-bound lipoprotein lipase. Journal of Biological Chemistry, 280(22), 21553-21560.
  40. Moody, D.E., Hancock, D.L., Anderson. D.B. & Mello, J. P. D. (2000). Phenethanolamine Repartitioning Agents In: Farm Animal Metabolism and Nutrition, CAB pub., New York. Endocrinology and Metabolism, 281:449.454. . PP: 65-96.
  41. Nilsson, C., Raun, K., Yan, F. F., Larsen, M. O. & Tang-Christensen, M. (2012). Laboratory animals as surrogate models of human obesity. Acta Pharmacologica Sinica, 33(2), 173.
  42. Ohtani, K., Suzuki, Y., Eda, S., Kawai, T., Kase, T., Keshi, H. & Suzutani, T. (2001). The membrane-type collectin CL-P1 is a scavenger receptor on vascular endothelial cells. Journal of Biological Chemistry, 276(47), 44222-44228.
  43. Rankinen, T., Zuberi, A., Chagnon, Y. C., Weisnagel, S. J., Argyropoulos, G., Walts, B. & Bouchard, C. (2006). The human obesity gene map: the 2005 update. Obesity, 14(4), 529-644.
  44. Resnyk, C. W., Carré, W., Wang, X., Porter, T. E., Simon, J., Le Bihan-Duval, E. & Cogburn, L. A. (2013). Transcriptional analysis of abdominal fat in genetically fat and lean chickens reveals adipokines, lipogenic genes and a link between hemostasis and leanness. BMC Genomics, 14(1), 557.
  45. Resnyk, C. W., Carré, W., Wang, X., Porter, T. E., Simon, J., Le Bihan-Duval, E. & Cogburn, L. A. (2017). Transcriptional analysis of abdominal fat in chickens divergently selected on bodyweight at two ages reveals novel mechanisms controlling adiposity: validating visceral adipose tissue as a dynamic endocrine and metabolic organ. BMC Genomics, 18(1), 626.
  46. Resnyk, C. W., Chen, C., Huang, H., Wu, C. H., Simon, J., Le Bihan-Duval, E. & Cogburn, L. A. (2015). RNA-Seq analysis of abdominal fat in genetically fat and lean chickens highlights a divergence in expression of genes controlling adiposity, hemostasis, and lipid metabolism. PloS One, 10(10), e0139549.
  47. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W. & Smyth, G. K. (2015). Limma powers di erential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47.
  48. Sakomura, N. K., Longo, F. A., Oviedo-Rondon, E. O., Boa-Viagem, C. & Ferraudo, A. (2005). Modeling energy utilization and growth parameter description for broiler chickens. Poultry Science, 84, 1363-1369.
  49. Simon, J. (1989). Chicken as a usefull species for the comprehension of insulin action. CRC Critical Review in Poultry Biology, 2, 121-148.
  50. Simon, J. & Leclercq, B. (1982). Longitudinal study of adiposity in chickens selected for high or low abdominal fat content: further evidence of a glucose-insulin imbalance in the fat line. The Journal of Nutrition, 112(10), 1961-1973.
  51. Simon, J., Milenkovic, D., Godet, E., Cabau, C., Collin, A., Métayer-Coustard, S., ... & Cailleau-Audouin, E. (2012). Insulin immuno-neutralization in fed chickens: effects on liver and muscle transcriptome. Physiological Genomics, 44(5), 283-292.
  52. Stern, C. D. (2005). The chick: a great model system becomes even greater. Developmental Cell, 8(1), 9-17.
  53. Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J. & Pachter, L. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology, 28(5), 511-515.
  54. Tumova, E. & Teimouri, A. (2010). Fat deposition in the broiler chicken: A review. Scientia Agricultura and Bohemica, 41, 121-128.
  55. Ullmer, C., Schmuck, K., Kalkman, H. O. & Lübbert, H. (1995). Expression of serotonin receptor mRNAs in blood vessels. FEBS Letters, 370(3), 215-221.
  56. Varma, V., Yao-Borengasser, A., Bodles, A. M., Rasouli, N., Phanavanh, B., Nolen, G. T. & Fried, S. K. (2008). Thrombospondin-1 is an adipokine associated with obesity, adipose inflammation, and insulin resistance. Diabetes, 57(2), 432-439.
  57. Zheng, Q., Zhang, Y., Chen, Y., Yang, N., Wang, X. J. & Zhu, D. (2009). Systematic identification of genes involved in divergent skeletal muscle growth rates of broiler and layer chickens. BMC Genomics, 10(1), 87.
Iranian Journal of animal Science
Volume 50, Issue 4
February 2020
Pages 259-269

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History

  • Receive Date: 21 May 2019
  • Revise Date: 20 August 2019
  • Accept Date: 15 September 2019

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