A Novel SNPs of the SREBF1 and SCARB1 Genes and the Association with Fatty Acid Profile in Bali Cattle
Abstract
This study aimed to investigate the genetic impact of single nucleotide polymorphisms (SNPs) of the sterol regulating element binding factor 1 (SREBF1) and scavenger receptor class B member 1 (SCARB1) genes on carcass and meat characteristics, as well as fatty acid composition, in the Bali cattle. The blood and beef samples used for DNA sequencing, physical assessment, and fatty acid analysis were collected from 95 male Bali cattle. The ultrasound images were analyzed using the Image-J NIH software. A total of 4 SNPs were identified in the SREBF1 gene and 5 SNPs in the SCARB1 gene. The results showed that the 4 SNPs in the SREBF1 gene, namely g.12629T>C, g.12731T>C, g.12881A>G, and g.12986C>T, were associated with heptadecanoic acid (C17:0) and cis-11-eicosanoic acid (C20:1). The SNPs g.12731T>C of the SREBF1 gene was associated with fat content, palmitoleic acid (C16:1), stearic acid (C18:0), cis-11-eicosanoic acid (C20:1), and total fatty acids. Furthermore, 4 SNPs in the SCARB1 gene, including g.72219C>T, g.72380C>A, g.72517G>A, and g.72607C>T correlated with longissimus dorsi thickness (LDT). All SNPs in the SCARB1 gene showed significant associations with cis-10 heptadecanoic acid (C17:1) and cis 8,11,14-eicosatrienoic acid (C20:3n6). The SNP g.72400A>G of the SCARB1 gene was related to caprylic acid (C8:0), lauric acid (C12:0), arachidonic acid (C20:4n6), monounsaturated fatty acids (MUFA), and unsaturated fatty acids (UFA). These results suggested that the identified polymorphisms in the SREBF1 and SCARB1 genes could serve as valuable references for investigating similar genes in other cattle breeds, particularly concerning fatty acids.
References
AOAC. 2019. Officials Methods of Analysis of AOAC International. 21st ed. AOAC, Washington, DC.
Arshad, M. S., M. Sohaib, R. S. Ahmad, M. T. Nadeem, A. Imran, M. U. Arshad, J. Kwon, & Z. Amjad. 2018. Ruminant meat flavor is influenced by different factors with special reference to fatty acids. Lipids Health Dis. 17:223. https://doi.org/10.1186/s12944-018-0860-z
AUS-MEAT. 2018. Australian Beef Carcase Evaluatin. 9th ed. AUS-MEAT Limited Company, Murarri, AUS.
Bergman, B. C., D. Howard, I. E. Schauer, D. M. Maahs, J. K. Snell-Bergeon, T. W. Clement, R. H. Eckel, L. Perreault, & M. Rewers. 2013. The importance of palmitoleic acid to adipocyte insulin resistance and whole-body insulin sensitivity in type 1 diabetes. J. Clin. Endocrinol. Metab. 98:E40-E50. https://doi.org/10.1210/jc.2012-2892
Berg, J. M., J. L. Tymoczko, & G. J. Gatto. 2015. Biochemistry. 8th ed. W. H. Freeman and Company, New York, NY.
BIF. 2016. Guidelines for Uniform Beef Improvement Program. 9th ed. North Carolina State University, North Carolina.
Binia, A., C. V. Martinez, M. A. Moreno, L. M. Gosaniu, & I. Montoliu. 2017. Improvement of cardiometabolic markers after fish oil intervention in young Mexican adults and the role of PPARα L162V and PPARγ2 P12A. J. Nutr. Biochem. 43:98–106. https://doi.org/10.1016/j.jnutbio.2017.02.002
Bhuiyan, M. S. A., S. L. Yu, J. T. Jeon, Y. M. Cho, & E. W. Park. 2009. DNA polymorphisms in SREBF1 and FASN genes affect fatty acid composition in Korean cattle (Hanwoo). Asian-Australas. J. Anim. Sci. 22:765-773. https://doi.org/10.5713/ajas.2009.80573
Calder, P. C. 2013. Omega-3 polyunsaturated fatty acids and inflammatory processes: Nutrition or pharmacology. Br. J. Clin. Pharmacol. 75:645-662. https://doi.org/10.1111/j.1365-2125.2012.04374.x
Castelloe, J. 2018. Power Analysis for Generalized Linear Models Using the New Custom Statement in Proc Power. SAS Institute Inc, Cary, North Carolina, NC.
Chinetti, G., F. G. Gbaguidi, S. Griglio, Z. Mallat, M. Antonucci, P. Poulain, J. Chapman, J. C. Fruchart, A. Tedgui, J. N. Fruchart, & B. Staels. 2000. CLA-1/SR-BI is expressed in atherosclerotic lesion macrophages and regulated by activators of peroxisome proliferator-activated receptors. Circulation 101:2411–2417. https://doi.org/10.1161/01.CIR.101.20.2411
Crews, D., M. Dikeman, S. L. Northcutt, D. Garrick, T. T. Marston, M. MacNeil, L. W. Olson, J. C. Paschal, G. Rouse, B. Weaber, T. Wheeler S. Shackelford, R. E. Williams, & D. E. Wilson. 2016. Guidelines for uniform beef improvement program: Ultrasound scanning to measure body composition. Beef Improvement Federation. 9:16-56.
Crossley, B. M., J. Bai, A. Glaser, R. Maes, E. Porter, M. L. Killian, T. Clement, & K. Toohey-Kurth. 2020. Guidelines for sanger sequencing and molecular assay monitoring. J. Vet. Diagn. Invest. 32:767-775. https://doi.org/10.1177/1040638720905833
Cruz, M. M., J. J. Simão, R. D. C. C. Sá, T. S. M. Farias, V. S. Silva, F. Abdala, V. J. Antraco, L. Armelin-Correa, & M. I. C. Alonso-Vale. 2020. Palmitoleic acid decreases non-alcoholic hepatic steatosis and increases lipogenesis and fatty acid oxidation in adipose tissue from obese mice. Front. Endocrinol. 11:537061. https://doi.org/10.3389/fendo.2020.537061
Dairoh, D., J. Jakaria, M. F. Ulum, & C. Sumantri. 2022. A new SNP at 3’UTR region of calpain 1 gene and its association with growth and meat quality in beef cattle. J. Indones. Trop. Anim. Agric. 47:273-284. https://doi.org/10.14710/jitaa.47.1.17-28
Dairoh, D., J. Jakaria, M. F. Ulum, A. B. L. Ishak, & C. Sumantri. 2021. Association of SNP g.232 G>T calpain gene with growth and live meat quality prediction using ultrasound images in Bali cattle. Jurnal Ilmu Ternak Veteriner 26:49-56. https://doi.org/10.14334/jitv.v26i2.2701
Dinh, T. T., K. V. To, & M. V. Schilling. 2021. Fatty acid composition of meat animals as flavor precursors. Meat Muscle Biology 5:1–16. https://doi.org/10.22175/mmb.12251
Draper, N. 2008. Identification of SNPs, or Mutation in Sequence Chromatograms. In: M. Starkey & R. Elaswarapu (eds). Genomics Protocols. Methods in Molecular Biology™, vol 439. Humana Press, New Jersey, US. https://doi.org/10.1007/978-1-59745-188-8_3
Ellegren, H. & N. Galtier. 2016. Determinants of genetic diversity. Nat. Rev. Genet. 17:422-433. https://doi.org/10.1038/nrg.2016.58
Felder, T. K., K. Klein, W. Patsch, & H. Oberkofler. 2005. A novel SREBP-1 splice variant: tissue abundance and transactivation potency. Biochem. Biophys. Acta. 1731:41-47. https://doi.org/10.1016/j.bbaexp.2005.08.004
Gamarra, D., N. Aldai, A. Arakawa, M. M. de Pancorbo, & M. Taniguchi. 2021. Effect of a genetic polymorphism in SREBP1 on fatty acid composition and related gene expression in subcutaneous fat tissue of beef cattle breeds. Anim. Sci. J. 92:e13521. https://doi.org/10.1111/asj.13521
Gao, Y. Y., G. Cheng, Z. X. Cheng, C. Bao, T. Yamada, G. F. Cao, S. Q. Bao, N. M. Schreurs, L. S. Zan, & B. Tong. 2022. Association of variants in FABP4, FASN, SCD, SREBP1, and TCAP genes with intramuscular fat, carcass traits, and body size in Chinese Qinchuan cattle. Meat. Sci. 192:108882. https://doi.org/10.1016/j.meatsci.2022.108882
Garnier-Géré, P. & L. Chikhi. 2013. Population Subdivision, Hardy–Weinberg Equilibrium and the Wahlund Effect. American Cancer Society, New York. https://doi.org/10.1002/9780470015902.a0005446.pub3
Graffelman, J., D. Jain, & B. A. Weir. 2017. Genome-wide study of Hardy–Weinberg equilibrium with next-generation sequence data. Hum. Genet. 136:727–741. https://doi.org/10.1007/s00439-017-1786-7
Hall, N., S. C. Schonfeld, & B. Pretorius. 2016. Fatty acids in beef from grain and grass-fed cattle the unique South African scenario. South. African. J. Clin. Nutr. 29:55-62. https://doi.org/10.1080/16070658.2016.1216359
Hartl, D. L. & A. G. Clark. 2007. Principles of population genetics. Ecoscience 14:544–545.
Heather, J. M. & B. Chain. 2016. The sequence of sequencer: The history of sequencing DNA. Genomics 107:1-8. https://doi.org/10.1016/j.ygeno.2015.11.003
Horcada, A., O. Polvillo, P. González-Redondo, A. López, D. Tejerina, & S. García-Torres. 2020. Stability of fatty acid composition of intramuscular fat from pasture- and grain-fed young bulls during the first 7 d postmortem. Arch. Anim. Breed. 63:45-52. https://doi.org/10.5194/aab-63-45-2020
Hung, J. H. & Z. Weng. 2016. Designing polymerase chain reaction primers using primer3plus. Cold. Spring. Harb. Protoc. 9:821-826. https://doi.org/10.1101/pdb.prot093096
Hussein, H. A., A. Westphal, & R. Staufenbiel. 2013. Relationship between body condition score and ultrasound measurement of backfat thickness in multiparous Holstein dairy cows at different production phases. Aust. Vet. J. 19:185–189. https://doi.org/10.1111/avj.12033
Iida, R., K. Saitou, T. Kawamura, S. Yamahguchi, & T. Nishimura. 2015. Effect of fat content on sensory characteristics of marbled beef from Japanese Black steers. Anim. Sci. J. 86:707-715. https://doi.org/10.1111/asj.12342
Jakaria, H. Khasanah, R. Priyanto, M. Baihaqi, & M. F. Ulum. 2017. Prediction of meat quality in Bali cattle using ultrasound imaging. J. Indones. Trop. Anim. Agric. 42:59-65. https://doi.org/10.14710/jitaa.42.2.59-65
Kanehisa, M., S. Goto, Y. Sato, M. Furumichi, & M. Tanabe. 2019. KEGG for the identification and annotation of pathways and modules in genomic datasets. Nucleic Acids Res. 47:590-596. https://doi.org/10.1093/nar/gky962
Koshy, L., A. L. Anju, S. Harikrishnan, V. R. Kutty, V. T. Jissa, I. Kurikesu, P. Jayachandran, A. J. Nair, A. Gangaprasad, G. M. Nair, & P. R. Sudhakaran. 2017. Evaluating genomic DNA extraction methods from human whole blood using endpoint and real-time PCR assays. Mol. Biol. Rep. 44:97-108. https://doi.org/10.1007/s11033-016-4085-9
Ladeira, M. M., J. P. Schoonmaker, K. C. Swanson, S. K. Ducket, M. P. Gionbelli, L. M. Rodrigues, & P. D. Teixeira. 2018. Review: Nutrigenomics of marbling and fatty acid profile in ruminant meat. Animals 12:s282-s294. https://doi.org/10.1017/S1751731118001933
Lee, S. H., B. H. Choi, D. Lim, C. Gondro, Y. M. Cho, & C. G. Dang. 2013. A genome-wide association study identifies major loci for carcass weight on BTA14 in Hanwoo (Korean cattle). PLoS ONE 8:e74677. https://doi.org/10.1371/journal.pone.0074677
Li, X., Z. Q. Du, & J. H. Yao. 2018. Polymorphisms in candidate genes related to lipid metabolism and their association with meat quality traits in Simmental cattle. Anim. Sci. J. 89:167-174.
Li, X., S. Yang, Z. Tang, K. Li, M. F. Rothschild, & B. Liu. 2014. Genome-wide scans to detect positive selection in Chinese Holstein cattle. PLoS ONE 9:e83752.
Liang, C., L. Qiao, Y. Han, J. Liu, J. Zhang, & W. Liu. 2020. Regulatory roles of SREBF1 and SREBF2 in lipid metabolism and deposition in two Chinese representative fat-tailed sheep breeds. Animals 10:1317. https://doi.org/10.3390/ani10081317
Littler, B. 2007. Live beef cattle assessment [internet]. Department of Primary Industries, NSW. Primefact 622. http://www.dpi.nsw.gov.au/_data/assets/pdf_file/0008/148355/Live-beef-cattle-assessment.pdf [January 05, 2023].
Long, K., L. Cai, & L. He. 2018. DNA sequencing data analysis. Methods Mol. Biol. 1754:1-13. https://doi.org/10.1007/978-1-4939-7717-8_1
Nei, M. & S. Kumar. 2000. Molecular Evolution and Phylogenetics. Oxford University Press, New York, NY.
Ng, P. C. & S. Henikoff. 2006. Predicting deleterious amino acid substitutions. Genome Res. 17:138-145.
Park, H., S. Seo, Y. M. Cho, S. J. Oh, H. H. Seong, S. H. Lee, & D. Lim. 2012. Identification of candidate genes associated with beef marbling using QTL and pathway analysis in Hanwoo (Korean Cattle). Asian-Australas. J. Anim. Sci. 25:613-620. https://doi.org/10.5713/ajas.2011.11347
Pecina, M. & A. Ivankovic. 2021. Candidate genes and fatty acids in beef meat - a review. Ital. J. Anim. Sci. 20:1716-1729. https://doi.org/10.1080/1828051X.2021.1991240
Sakowski, T., G. Grodkowski, M. Golebiewski, J. Slosarz, P. Kostusiak, P. Solarczyk, & K. Puppel. 2022. Genetic and environmental determinant of beef quality - a review. Front. Vet. Sci. 9:819605. https://doi.org/10.3389/fvets.2022.819605
Salamena, F. J. & J. Papilaja. 2010. Characterization and genetic relationships analysis of buffalo population in MOA island of South-East West Maluku regency of Maluku Province. J. Indones. Trop. Anim. Agric. 35:75-82. https://doi.org/10.14710/jitaa.35.2.75-82
Siachos, N., G. Oikonomou, N. Panousis, G. Banos, G. Arsenos, & G. E. Valergakis. 2021. Association of body condition score with ultrasound measurements of backfat and longissimus dorsi muscle thickness in periparturient Holstein cows. Animals 11:818. https://doi.org/10.3390/ani11030818
Silva, S. L., J. U. Tarouco, J. B. S. Ferraz, R. C. Gomes, P. R. Leme, & E. A. Navajas. 2012. Prediction of retail beef yield, trim fat and proportion of high-valued cuts in Nellore cattle using ultrasound live measurements. Revista Brasileira Zootecnia 41:2025-2031. https://doi.org/10.1590/S1516-35982012000900009
Schumacher, M., H. DelCurto-Wyffels, J. Thomson, & J. Boles. 2022. Fat deposition and fat effects on meat quality - a review. Animals 12:1550. https://doi.org/10.3390/ani12121550
Shramko, V. S., Y. V. Polonskaya, E. V. Kashtanova, E. M. Stakhneva, & Y. I. Ragino. 2020. The short overview on the relevance of fatty acids for human cardiovascular disorders. Biomolecules 20:1127. https://doi.org/10.3390/biom10081127
Stachowiak, M., J. Nowacka-Woszuk, M. Szydlowsky, & M. Switonski. 2013. The ACAC and SREBF1 genes are promising markers for pig carcass and performance traits, but not for fatty acid content in the longissimus dorsi and adipose tissue. Meat. Sci. 95:64-71. https://doi.org/10.1016/j.meatsci.2013.04.021
Suryanto, E., B. Bulkaini, A. Ashari, & I. W. Karda. 2014. Carcass quality, marbling, and cholesterol content of male Bali cattle fed fermented cocoa shell. J. Indones. Trop. Anim. Agric. 39:249-255. https://doi.org/10.14710/jitaa.39.4.249-255
Sutarno, & A. D. Setyawan. 2016. The diversity of local cattle in Indonesia and the effort to develop superior indigenous cattle breeds. Biodiversitas 17:275-295. https://doi.org/10.13057/biodiv/d170139
Sutikno, S., R. Priyanto, C. Sumantri, & J. Jakaria. 2018. Polymorphism of ADIPOQ and EDG1 genes in Indonesian beef cattle. J. Indones. Trop. Agric. 43:323-332. https://doi.org/10.14710/jitaa.43.4.323-332
Tahuk, P. K., S. P. S. Budhi, P. Panjono, & E. Baliarti. 2018. Carcass and meat characteristics of male Bali cattle in Indonesian smallholder farms fed ration with different protein levels. Trop. Anim. Sci. J. 41:215-223. https://doi.org/10.5398/tasj.2018.41.3.215
Talib, C. 2022. Bali cattle in the breeding stock areas and their future development. Wartazoa. 12:100-107.
Waples, R. S. 2015. Testing for hardy–weinberg proportions: Have we lost the plot? J. Hered. 106:1–19. https://doi.org/10.1093/jhered/esu062
Webb, A., J. Knolblauch, N. Sabankar, A. S. Kallur, J. Hey, & A. Sethuraman. 2021. The popgen pipeline platform: A software platform for population genomic analyses. Mol. Biol. Evol. 38:3478-3485. https://doi.org/10.1093/molbev/msab113
Ye, J., G. Coulouris, I. Zaretskaya, I. Cutcutache, S. Rozen, & T. L. Madden. 2012. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics 13:134. https://doi.org/10.1186/1471-2105-13-134
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