Animal Bioscience

  • 제37권3호
  • /
  • Pages.522-535
  • /
  • 2024
  • /
  • 2765-0189(pISSN)
  • /
  • 2765-0235(eISSN)
아세아태평양축산학회 (Asian Australasian Association of Animal Production Societies)
권호 표지
DOI QR코드

DOI QR Code

Identification and validation of putative biomarkers by in silico analysis, mRNA expression and oxidative stress indicators for negative energy balance in buffaloes during transition period

  • Savleen Kour (Division of Veterinary Medicine, Faculty of Veterinary Sciences & Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences & Technology of Jammu) ;
  • Neelesh Sharma (Division of Veterinary Medicine, Faculty of Veterinary Sciences & Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences & Technology of Jammu) ;
  • Praveen Kumar Guttula (Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela) ;
  • Mukesh Kumar Gupta (Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela) ;
  • Marcos Veiga dos Santos (Department of Animal Sciences, School of Veterinary Medicine and Animal Sciences, University of Sao Paulo) ;
  • Goran Bacic (Clinic for Reproduction and Theriogenology, Faculty of Veterinary Medicine, University of Zagreb) ;
  • Nino Macesic (Clinic for Reproduction and Theriogenology, Faculty of Veterinary Medicine, University of Zagreb) ;
  • Anand Kumar Pathak (Division of Animal Nutrition, Faculty of Veterinary Sciences & Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences & Technology of Jammu) ;
  • Young-Ok Son (Department of Animal Biotechnology, Faculty of Biotechnology, College of Applied Life Sciences and Interdisciplinary Graduate Program in Advanced Convergence Technology and Science, Jeju National University)
  • 투고 : 2023.08.02
  • 심사 : 2023.11.20
  • 발행 : 2024.03.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Objective: Transition period is considered from 3 weeks prepartum to 3 weeks postpartum, characterized with dramatic events (endocrine, metabolic, and physiological) leading to occurrence of production diseases (negative energy balance/ketosis, milk fever etc). The objectives of our study were to analyze the periodic concentration of serum beta-hydroxy butyric acid (BHBA), glucose and oxidative markers along with identification, and validation of the putative markers of negative energy balance in buffaloes using in-silico and quantitative real time-polymerase chain reaction (qRT-PCR) assay. Methods: Out of 20 potential markers of ketosis identified by in-silico analysis, two were selected and analyzed by qRT-PCR technique (upregulated; acetyl serotonin o-methyl transferase like and down regulated; guanylate cyclase activator 1B). Additional two sets of genes (carnitine palmotyl transferase A; upregulated and Insulin growth factor; downregulated) that have a role of hepatic fatty acid oxidation to maintain energy demands via gluconeogenesis were also validated. Extracted cDNA (complementary deoxyribonucleic acid) from the blood of the buffaloes were used for validation of selected genes via qRTPCR. Concentrations of BHBA, glucose and oxidative stress markers were identified with their respective optimized protocols. Results: The analysis of qRT-PCR gave similar trends as shown by in-silico analysis throughout the transition period. Significant changes (p<0.05) in the levels of BHBA, glucose and oxidative stress markers throughout this period were observed. This study provides validation from in-silico and qRT-PCR assays for potential markers to be used for earliest diagnosis of negative energy balance in buffaloes. Conclusion: Apart from conventional diagnostic methods, this study improves the understanding of putative biomarkers at the molecular level which helps to unfold their role in normal immune function, fat synthesis/metabolism and oxidative stress pathways. Therefore, provides an opportunity to discover more accurate and sensitive diagnostic aids.

키워드

과제정보

This work was supported by Department of Biotechnology (DBT), (Grant No. BT/PR26321/SPD/9/1307/2017) Government of India, and National Research Foundation of Korea (Grant No.: 2020R1A2C2004128), and Basic Science Research Program, NRF (Grant No.: 2019R1A6A1A10072987), Ministry of Education, Government of South Korea.

참고문헌

  1. Arfuso F, Fazio F, Levanti M, et al. Lipid and lipoprotein profile changes in dairy cows in response to late pregnancy and the early postpartum period. Arch Anim Breed 2016;59:429-34. https://doi.org/10.5194/aab-59-429-2016
  2. Ashmawy NA. Blood metabolic profile and certain hormones concentrations in Egyptian buffalo during different physiological states. Asian J Anim Vet Adv 2015;10:271-80. https://doi.org/10.3923/ajava.2015.271.280
  3. Sharma N, Singh NK, Singh OP, Pandey V, Verma PK. Oxidative stress and antioxidant status during transition period in dairy cows. Asian-Australas J Anim Sci 2011;24: 479-84. https://doi.org/10.5713/AJAS.2011.10220
  4. Dimri U, Ranjan R, Sharma MC, Varshney VP. Effect of vitamin E and selenium supplementation on oxidative stress indices and cortisol level in blood in water buffaloes during pregnancy and early postpartum period. Trop Anim Health Prod 2010;42:405-10. https://doi.org/10.1007/s11250-009-9434-4
  5. Spears JW, Weiss WP. Role of antioxidants and trace elements in health and immunity of transition dairy cows. Vet J 2008;176:70-6. https://doi.org/10.1016/j.tvjl.2007.12.015
  6. Miltenburg C. Management of peripartum dairy cows for metabolic health and immune function [Doctoral dissertation]. Ontario, Canada: University of Guelphl; 2015.
  7. Ingvartsen KL, Dewhurst RJ, Friggens NC. On the relationship between lactational performance and health: is it yield or metabolic imbalance that cause production diseases in dairy cattle? A position paper. Livest Prod Sci 2003;83:277-308. https://doi.org/10.1016/S0301-6226(03)00110-6
  8. Vanholder T, Papen J, Bemers R, Vertenten G, Berge ACB. Risk factors for subclinical and clinical ketosis and association with production parameters in dairy cows in the Netherlands. J Dairy Sci 2015;98:880-8. https://doi.org/10.3168/jds.2014-8362
  9. Holcomb CS, Van Horn HH, Head HH, Hall MB, Wilcox CJ. Effects of prepartum dry matter intake and forage percentage on postpartum performance of lactating dairy cows. J Dairy Sci 2001;84:2051-8. https://doi.org/10.3168/jds.S0022-0302(01)74649-8
  10. Weber C, Hametner C, Tuchscherer A, et al. Hepatic gene expression involved in glucose and lipid metabolism in transition cows: effects of fat mobilization during early lactation in relation to milk performance and metabolic changes. J Dairy Sci 2013;96:5670-81. https://doi.org/10.3168/jds.2012-6277
  11. Fiore E, Arfuso F, Gianesella M, et al. Metabolic and hormonal adaptation in Bubalus bubalis around calving and early lactation. PLoS One 2018;13:e0193803. https://doi.org/10.1371/journal.pone.0193803
  12. Serrapica F, Masucci F, Romano R, et al. Effects of chickpea in substitution of soybean meal on milk production, blood profile and reproductive response of primiparous buffaloes in early lactation. Animals 2020;10:515. https://doi.org/10.3390/ani10030515
  13. Purohit GN, Ruhil S, Daga M, Gaur M, Bihani DK, Ahuja A. Parturition related metabolic disorder in buffaloes: a 10 year case analysis. Rumin Sci 2014;3:123-6.
  14. Ospina PA, Nydam DV, Stokol T, Overton TR. Associations of elevated nonesterified fatty acids and βhydroxybutyrate concentrations with early lactation reproductive performance and milk production in transition dairy cattle in the northeastern United States. J Dairy Sci 2010;93:1596-603. https://doi.org/10.3168/jds.2009-2852
  15. Oetzel GR. Herd-level ketosis-diagnosis and risk factors. In: Preconference Seminar 7C: Dairy Herd Problem Investigation Strategies, Transition Cow Trouble shooting American association of bovine practitioners 40th Annual Conference; 2007 September 19, Vancouver, Canada.
  16. Madreseh-Ghahfarokhi S, Dehghani-Samani A. Ketosis (acetonaemia) in dairy cattle farms: practical guide based on importance, diagnosis, prevention and treatments. J Dairy Vet Anim Res 2018;7:299-302.
  17. Lockstone HE. Exon array data analysis using Affymetrix power tools and R statistical software. Brief Bioinform 2011;12:634-44. https://doi.org/10.1093/bib/bbq086
  18. Zambon AC, Gaj S, Ho I, et al. GO-Elite: a flexible solution for pathway and ontology over-representation. Bioinformatics 2012;28:2209-10. https://doi.org/10.1093/bioinformatics/bts366
  19. Emig D, Salomonis N, Baumbach J, Lengauer T, Conklin BR, Albrecht M. AltAnalyze and DomainGraph: analyzing and visualizing exon expression data. Nucleic Acids Res 2017;38:W755-62. https://doi.org/10.1093/nar/gkq405
  20. Fendri K, Patten SA, Kaufman GN, et al. Microarray expression profiling identifies genes with altered expression in Adolescent Idiopathic Scoliosis. Eur Spine J 2013;22:1300-11. https://doi.org/10.1007/s00586-013-2728-2
  21. Hulin A, Hortells L, Gomez-Stallons MV, et al. Maturation of heart valve cell populations during postnatal remodeling. Development 2012;146:173047.
  22. Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative CT method. Nat Protoc 2008;3:1101-8. https://doi.org/10.1038/nprot.2008.73
  23. Rehman SU. Lead induced regional lipid peroxidation in brain. Toxicol Lett 1984;21:333-7. https://doi.org/10.1016/0378-4274(84)90093-6
  24. Aebi H. Catalase in vitro. Methods Enzymol 1984;105:121-6. https://doi.org/10.1016/S0076-6879(84)05016-3
  25. Marklund S, Marklund G. Involvement of the superoxide anion radical in the autoxidation of pyrogallol and a convenient assay for superoxide dismutase. Eur J Biochem 1974;47:469-74. https://doi.org/10.1111/j.1432-1033.1974.tb03714.x
  26. Hafeman DG, Sunde RA, Hoekstra WG. Effect of dietary selenium on erythrocyte and liver glutathione peroxidase in the rat. J Nutr 1974;104:580-7. https://doi.org/10.1093/jn/104.5.580
  27. Loor JJ. Genomics of metabolic adaptations in the peripartal cow. Animal 2010;4:1110-39. https://doi.org/10.1017/S1751731110000960
  28. Lucy MC, Jiang H, Kobayashi Y. Changes in the somatotrophic axis associated with the initiation of lactation. J Dairy Sci 2001;84:E113-9. https://doi.org/10.3168/jds.S0022-0302(01)70205-6
  29. Bell AW. Regulation of organic nutrient metabolism during transition from late pregnancy to early lactation. J Anim Sci 1995;73:2804-19. https://doi.org/10.2527/1995.7392804x
  30. Bryers DI. Controlling metabolic diseases. In: Paper presented at Tri-state dairy nutrition conference, held at Michigan State University, Grand Wayne Center Fort Wayne; 1999, April 20-21, IN, USA.
  31. Accorsi PA, Govoni N, Gaiani R, Pezzi C, Seren E, Tamanini C. Leptin, GH, PRL, insulin and metabolic parameters throughout the dry period and lactation in dairy cows. Reprod Domest Anim 2005;40:217-23. https://doi.org/10.1111/j.1439-0531.2005.00581.x
  32. Ambrosio R, Sannino ML, Cortese L, Nappi C, Ara D, Cioffi M. Validation and application of an immunofluorimetric assay for detection of serum free triiodothyronine and free thyroxine concentrations in buffalo (Bubalus Bubalis) under various physiological conditions. J Vet Diagn Invest 2009;21:668-73. https://doi.org/10.1177/104063870902100511
  33. Singh R, Randhawa SNS, Randhawa CS. Oxidative stress, Hemato-biochemical and plasma mineral profile in transition buffaloes. Proc Natl Acad Sci 2017;87:1091-9. https://doi.org/10.1007/s40011-015-0684-9
  34. Huang Y, Zhao C, Kong Y, et al. Elucidation of the mechanism of NEFA-induced PERK-eIF2α signaling pathway regulation of lipid metabolism in bovine hepatocytes. J Steroid Biochem Mol Biol 2021;211:105893. https://doi.org/10.1016/j.jsbmb.2021.105893
  35. Rhoads ML, Meyer JP, Kolath SJ, Lamberson WR, Lucy MC. Growth hormone receptor, insulin-like growth factor (IGF)-1, and IGF-binding protein-2 expression in the reproductive tissues of early postpartum dairy cows. J Dairy Sci 2008;91:1802-13. https://doi.org/10.3168/jds.2007-0664
  36. Fenwick MA, Fitzpatrick R, Kenny DA, et al. Interrelationships between negative energy balance (NEB) and IGF regulation in liver of lactating dairy cows. Domest Anim Endocrinol 2008;34:31-44. https://doi.org/10.1016/j.domaniend.2006.10.002
  37. Sordillo LM, Raphael W. Significance of metabolic stress, lipid mobilization, and inflammation on transition cow disorders. Vet Clin North Am Food Anim Pract 2013;29:267-78. https://doi.org/10.1016/j.cvfa.2013.03.002
  38. Abuelo A, Hernandez J, Benedito JL, Castillo C. Oxidative stress index (OSi) as a new tool to assess redox status in dairy cattle during the transition period. Animal 2013;7:1374-8. https://doi.org/10.1017/S1751731113000396
  39. Herdt TH. Ruminant adaptation to negative energy balance: influences on the etiology of ketosis and fatty liver. Vet Clin North Am Food Anim Pract 2000;16:215-30. https://doi.org/10.1016/S0749-0720(15)30102-X
  40. Castillo C, Hernandez J, Valverde I, et al. Plasma malonaldehyde (MDA) and total antioxidant status (TAS) during lactation in dairy cows. Res Vet Sci 2006;80:133-9. https://doi.org/10.1016/j.rvsc.2005.06.003