Search Advanced

AIMS Agriculture and Food

2020, Volume 5, Issue 1: 14-19. doi: 10.3934/agrfood.2020.1.14
Previous Article Next Article
Technical note

Development of real-time PCR assay for genotyping SNP rs41255693 in cattle SCD gene

  • 1.
    FSBSI Center of Experimental Embryology and Reproductive Biotechnologies, Moscow, Russian Federation
  • 2.
    Lomonosov Moscow State University, Faculty of Biology, Moscow, Russian Federation
  • Received: 08 August 2019 Accepted: 19 November 2019 Published: 09 December 2019

  • Increased content of monounsaturated fatty acids with low melting points in cattle milk and meat known to strongly affect their taste and nutritional value. Steroyl-CoA-desaturase (SCD) is a key enzyme in the biosynthesis of monounsaturated fatty acids. The gene encoding SCD in cattle is located on chromosome 26 and consists of 6 exons and 5 introns and is expressed in the cells of cow udder, adipose, muscles and other tissues. To date, several SNPs in cattle SCD gene have been found. For SNP rs41255693 (g.10329T > C), a correlation was shown between the content of monounsaturated fatty acids in cattle milk/meat and milk productivity. This article describes the development of a real-time PCR assay for genotyping SNP rs41255693 in cattle SCD gene with allele-specific TaqMan probes. The use of this assay allows to significantly reduce the time needed for cattle genotyping compared to the commonly used PCR-RFLP method.

    Citation: Svetlana N. Kovalchuk, Anna L. Arkhipova, Eugene A. Klimov. Development of real-time PCR assay for genotyping SNP rs41255693 in cattle SCD gene[J]. AIMS Agriculture and Food, 2020, 5(1): 14-19. doi: 10.3934/agrfood.2020.1.14

    Related Papers:

    [1] Kento Okuwa, Hisashi Inaba, Toshikazu Kuniya . Mathematical analysis for an age-structured SIRS epidemic model. Mathematical Biosciences and Engineering, 2019, 16(5): 6071-6102. doi: 10.3934/mbe.2019304
    [2] Cristeta U. Jamilla, Renier G. Mendoza, Victoria May P. Mendoza . Explicit solution of a Lotka-Sharpe-McKendrick system involving neutral delay differential equations using the r-Lambert W function. Mathematical Biosciences and Engineering, 2020, 17(5): 5686-5708. doi: 10.3934/mbe.2020306
    [3] Shuyang Xue, Meili Li, Junling Ma, Jia Li . Sex-structured wild and sterile mosquito population models with different release strategies. Mathematical Biosciences and Engineering, 2019, 16(3): 1313-1333. doi: 10.3934/mbe.2019064
    [4] Azmy S. Ackleh, Mark L. Delcambre, Karyn L. Sutton, Don G. Ennis . A structured model for the spread of Mycobacterium marinum: Foundations for a numerical approximation scheme. Mathematical Biosciences and Engineering, 2014, 11(4): 679-721. doi: 10.3934/mbe.2014.11.679
    [5] Rinaldo M. Colombo, Mauro Garavello . Optimizing vaccination strategies in an age structured SIR model. Mathematical Biosciences and Engineering, 2020, 17(2): 1074-1089. doi: 10.3934/mbe.2020057
    [6] Andrey V. Melnik, Andrei Korobeinikov . Lyapunov functions and global stability for SIR and SEIR models withage-dependent susceptibility. Mathematical Biosciences and Engineering, 2013, 10(2): 369-378. doi: 10.3934/mbe.2013.10.369
    [7] Jordi Ripoll, Jordi Font . Numerical approach to an age-structured Lotka-Volterra model. Mathematical Biosciences and Engineering, 2023, 20(9): 15603-15622. doi: 10.3934/mbe.2023696
    [8] Abdennasser Chekroun, Mohammed Nor Frioui, Toshikazu Kuniya, Tarik Mohammed Touaoula . Global stability of an age-structured epidemic model with general Lyapunov functional. Mathematical Biosciences and Engineering, 2019, 16(3): 1525-1553. doi: 10.3934/mbe.2019073
    [9] Hui Cao, Dongxue Yan, Ao Li . Dynamic analysis of the recurrent epidemic model. Mathematical Biosciences and Engineering, 2019, 16(5): 5972-5990. doi: 10.3934/mbe.2019299
    [10] Andrea Franceschetti, Andrea Pugliese, Dimitri Breda . Multiple endemic states in age-structured SIR epidemic models. Mathematical Biosciences and Engineering, 2012, 9(3): 577-599. doi: 10.3934/mbe.2012.9.577
  • Increased content of monounsaturated fatty acids with low melting points in cattle milk and meat known to strongly affect their taste and nutritional value. Steroyl-CoA-desaturase (SCD) is a key enzyme in the biosynthesis of monounsaturated fatty acids. The gene encoding SCD in cattle is located on chromosome 26 and consists of 6 exons and 5 introns and is expressed in the cells of cow udder, adipose, muscles and other tissues. To date, several SNPs in cattle SCD gene have been found. For SNP rs41255693 (g.10329T > C), a correlation was shown between the content of monounsaturated fatty acids in cattle milk/meat and milk productivity. This article describes the development of a real-time PCR assay for genotyping SNP rs41255693 in cattle SCD gene with allele-specific TaqMan probes. The use of this assay allows to significantly reduce the time needed for cattle genotyping compared to the commonly used PCR-RFLP method.




    [1] Ladeira MM, Schoonmaker JP, Gionbelli MP, et al. (2016) Nutrigenomics and beef quality: A review about lipogenesis. Int J Mol Sci 17.
    [2] Gebreyesus G, Buitenhuis AJ, Poulsen NA, et al. (2019) Multi-population GWAS and enrichment analyses reveal novel genomic regions and promising candidate genes underlying bovine milk fatty acid composition. BMC Genomics 20: 178. doi: 10.1186/s12864-019-5573-9
    [3] Gamarra D, Aldai N, Arakawa A, et al. (2018) Distinct correlations between lipogenic gene expression and fatty acid composition of subcutaneous fat among cattle breeds. BMC Vet Res 14: 167. doi: 10.1186/s12917-018-1481-5
    [4] Mansilla MC, Banchio CE, de Mendoza D (2008) Signalling pathways controlling fatty acid desaturation. Subcell Biochem 49: 71-99. doi: 10.1007/978-1-4020-8831-5_3
    [5] Ntambi JM (1995) The regulation of stearoyl-CoA desaturase (SCD). Prog Lipid Res 34: 139-150. doi: 10.1016/0163-7827(94)00010-J
    [6] Ntambi JM (1999) Regulation of stearoyl-CoA desaturase by polyunsaturated fatty acids and cholesterol. J Lipid Res 40: 1549-1558.
    [7] Weiss K, Mihaly J, Liebisch G, et al. (2011) Effect of synthetic ligands of PPAR alpha, beta/delta, gamma, RAR, RXR and LXR on the fatty acid composition of phospholipids in mice. Lipids 46: 1013-1020. doi: 10.1007/s11745-011-3593-6
    [8] Bernard L, Leroux C, Hayes H, et al. (2001) Characterization of the caprine stearoyl-CoA desaturase gene and its mRNA showing an unusually long 3'-UTR sequence arising from a single exon. Gene 281: 53-61. doi: 10.1016/S0378-1119(01)00822-8
    [9] Barton L, Kott T, Bures D, et al. (2010) The polymorphisms of stearoyl-CoA desaturase (SCD1) and sterol regulatory element binding protein-1 (SREBP-1) genes and their association with the fatty acid profile of muscle and subcutaneous fat in Fleckvieh bulls. Meat Sci 85: 15-20. doi: 10.1016/j.meatsci.2009.11.016
    [10] Chung M, Ha S, Jeong S, et al. (2000) Cloning and characterization of bovine stearoyl CoA desaturasel cDNA from adipose tissues. Biosci Biotechnol Biochem 64: 1526-1530. doi: 10.1271/bbb.64.1526
    [11] Kelsey JA, Corl BA, Collier RJ, et al. (2003) The effect of breed, parity, and stage of lactation on conjugated linoleic acid (CLA) in milk fat from dairy cows. J Dairy Sci 86: 2588-2597. doi: 10.3168/jds.S0022-0302(03)73854-5
    [12] Lawless F, Murphy JJ, Harrington D, et al. (1998) Elevation of conjugated cis-9, trans-11-octadecadienoic acid in bovine milk because of dietary supplementation. J Dairy Sci 81: 3259-3267. doi: 10.3168/jds.S0022-0302(98)75890-4
    [13] Peterson DG, Kelsey JA, Bauman DE (2002) Analysis of variation in cis-9, trans-11 conjugated linoleic acid (CLA) in milk fat of dairy cows. J Dairy Sci 85: 2164-2172. doi: 10.3168/jds.S0022-0302(02)74295-1
    [14] Milanesi E, Nicoloso L, Crepaldi P (2008) Stearoyl CoA desaturase (SCD) gene polymorphisms in Italian cattle breeds. J Anim Breed Genet 125: 63-67. doi: 10.1111/j.1439-0388.2007.00697.x
    [15] Kgwatalala PM, Ibeagha-Awemu EM, Hayes JF, et al. (2009) Stearoyl-CoA desaturase 1 3'UTR SNPs and their influence on milk fatty acid composition of Canadian Holstein cows. J Anim Breed Genet 126: 394-403. doi: 10.1111/j.1439-0388.2008.00796.x
    [16] Carvajal AM, Huircan P, Dezamour JM, et al. (2016) Milk fatty acid profile is modulated by DGAT1 and SCD1 genotypes in dairy cattle on pasture and strategic supplementation. Genet Mol Res 15.
    [17] Houaga I, Muigai AWT, Ng'ang'a FM, et al. (2018) Milk fatty acid variability and association with polymorphisms in SCD1 and DGAT1 genes in White Fulani and Borgou cattle breeds. Mol Biol Rep 45: 1849-1862. doi: 10.1007/s11033-018-4331-4
    [18] Wathes DC, Clempson AM, Pollott GE (2012) Associations between lipid metabolism and fertility in the dairy cow. Reprod Fertil Dev 25: 48-61.
    [19] Kgwatalala PM, Ibeagha-Awemu EM, Mustafa AF, et al. (2009) Stearoyl-CoA desaturase 1 genotype and stage of lactation influences milk fatty acid composition of Canadian Holstein cows. Anim Genet 40: 609-615. doi: 10.1111/j.1365-2052.2009.01887.x
    [20] Pegolo S, Cecchinato A, Mele M, et al. (2016) Effects of candidate gene polymorphisms on the detailed fatty acids profile determined by gas chromatography in bovine milk. J Dairy Sci 99: 4558-4573. doi: 10.3168/jds.2015-10420
    [21] Glazko VI, Andreichenko IN, Kovalchuk SN, et al. (2017) Candidate genes for control of cattle milk production traits. Russ Agric Sci 42: 458-464.
    [22] Rasmussen HB (2012) Restriction fragment length polymorphism analysis of PCR-amplified fragments (PCR-RFLP) and gel electrophoresis-Valuable tool for genotyping and genetic fingerprinting. In: Magdeldin S, Gel Electrophoresis-Principles and Basics, 315-334.
    [23] Babii AV, Arkhipova AL, Andreichenko IN, et al. (2018) A TaqMan PCR assay for detection of DGAT1 K232A polymorphism in cattle. AIMS Agric Food 3: 306-312. doi: 10.3934/agrfood.2018.3.306

  • This article has been cited by:

    1. Benjamin Wacker, Jan Schlüter, Time-continuous and time-discrete SIR models revisited: theory and applications, 2020, 2020, 1687-1847, 10.1186/s13662-020-02995-1
    2. Yanyan Du, Qimin Zhang, Anke Meyer-Baese, The positive numerical solution for stochastic age-dependent capital system based on explicit-implicit algorithm, 2021, 165, 01689274, 198, 10.1016/j.apnum.2021.02.015
    3. Benjamin Wacker, Jan Christian Schlüter, A cubic nonlinear population growth model for single species: theory, an explicit–implicit solution algorithm and applications, 2021, 2021, 1687-1847, 10.1186/s13662-021-03399-5
    4. Benjamin Wacker, Jan Christian Schlüter, Qualitative analysis of two systems of nonlinear first‐order ordinary differential equations for biological systems, 2022, 45, 0170-4214, 4597, 10.1002/mma.8056
    5. Eleonora Messina, Mario Pezzella, Antonia Vecchio, Nonlocal finite difference discretization of a class of renewal equation models for epidemics, 2023, 20, 1551-0018, 11656, 10.3934/mbe.2023518
    6. Benjamin Wacker, Framework for solving dynamics of Ca2+ ion concentrations in liver cells numerically: Analysis of a non‐negativity‐preserving non‐standard finite‐difference method, 2023, 46, 0170-4214, 16625, 10.1002/mma.9464
    7. Haoyu Wu, David A. Stephens, Erica E. M. Moodie, An SIR‐based Bayesian framework for COVID‐19 infection estimation, 2024, 52, 0319-5724, 10.1002/cjs.11817
    8. Benjamin Wacker, Jan Christian Schlüter, A non-standard finite-difference-method for a non-autonomous epidemiological model: analysis, parameter identification and applications, 2023, 20, 1551-0018, 12923, 10.3934/mbe.2023577
    9. Benjamin Wacker, Revisiting the classical target cell limited dynamical within-host HIV model - Basic mathematical properties and stability analysis, 2024, 21, 1551-0018, 7805, 10.3934/mbe.2024343
    10. Benjamin Wacker, Qualitative Study of a Dynamical System for Computer Virus Propagation—A Nonstandard Finite‐Difference‐Methodological View, 2025, 0170-4214, 10.1002/mma.10798
    11. Benjamin Wacker, Analysis of a Finite‐Difference Method Based on Nonlocal Approximations for a Nonlinear, Extended Three‐Compartmental Model of Ethanol Metabolism in the Human Body, 2025, 0170-4214, 10.1002/mma.10858
    12. Gerbert Romijn, Niek Stadhouders, Johan Polder, Application of an Epi-Econ-Model to Analyze COVID-19 Lockdown Policies in the Netherlands: Lessons and Limitations, 2025, 2194-5888, 1, 10.1017/bca.2025.10
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
AIMS Agriculture and Food
1.3 3.9

Metrics

Article views(4872) PDF downloads(501) Cited by(1)

Preview PDF
Download XML
Export Citation
Article outline
Abstract
References

Figures and Tables

Figures(1)

Other Articles By Authors

Related pages

Tools

Copyright © AIMS Press

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog