Journal of Animal Breeding and Genomics (J Anim Breed Genom)
Sangwon Yoon1,2, Yu-Sam Kim1, Hee Jung Baek1,4, Dae-Yeong Koh3, Ill-Joo Lee3, Myunghum Park1, Jung Woo Choi4*, Dongwon Seo1*
1Research Institute, TNT Research Ltd., Sejong 30141, Korea
2Department of Bio-AI Convergence, Chungnam National University, Daejeon 34134, Korea
3Darby Genetics Inc., Anseong 17529, Korea
4Department of Animal Science, Kangwon National University, Chuncheon 24341, Korea
Correspondence to Dongwon Seo, E-mail: dwseo@tntresearch.co.kr
Volume 8, Number 4, Pages 135-141, December 2024.
Journal of Animal Breeding and Genomics 2024, 8(4), 135-141. https://doi.org/10.12972/jabng.20240406
Received on 25 September, 2024, Revised on 17 December, 2024, Accepted on 20 December, 2024, Published on 31 December, 2024.
Copyright © 2024 Korean Society of Animal Breeding and Genetics.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0).
Studies have shown that consumers prioritize meat color, an easily evaluated trait, when selecting meat products. However, meat color traits are only observable post-slaughter, making them difficult to target for selective breeding. Therefore, this study aimed to identify potential candidate genes associated with pork meat color traits by conducting a transcriptome analysis on carcasses, measuring L*, a*, and b* values of meat color, and extracting differentially expressed genes (DEGs). Notably, previously reported candidate genes associated with meat color were not identified in this study, but the Gene Ontology (GO) analysis provided insights into functional information potentially linked to meat color. The limitations in detecting direct candidate genes for meat color may be attributed to challenges in meat sampling and the constraints of blood sample collection. This study suggests the need for comparative research between meat and blood, as similar functional genes could be identified in blood, and correlations with postmortem processes, such as rigor mortis, could be confirmed in future meat color studies.
Transcriptome, Pig, DEG, Meat color, Candidate genes
This research was funded by the Rural Development Administration, South Korea (grant number RS-2023-00231989)
No potential conflict of interest relevant to this article is reported.
Cardona, M., Izquierdo, D., Barat, J.M., Fernández-Segovia, I., 2023. Intrinsic and extrinsic attributes that influence choice of meat and meat products: techniques used in their identification. Eur. Food Res. Technol. 249, 2485-2514. doi:10.1007/s00217-023-04301-1
[DOI]
Ding, Z., Wei, Q., Liu, C., Zhang, H., Huang, F., 2022. The Quality Changes and Proteomic Analysis of Cattle Muscle Postmortem during Rigor Mortis. Foods 11, 217. doi:10.3390/foods11020217
[DOI][PubMed][PMC]
Faustman, C., Sun, Q., Mancini, R., Suman, S.P., 2010. Myoglobin and lipid oxidation interactions: Mechanistic bases and control. Meat Sci. 86, 86-94. doi:10.1016/j.meatsci.2010.04.025
[DOI][PubMed]
Harris, S.E., Huff-Lonergan, E., Lonergan, S.M., Jones, W.R., Rankins, D., 2001. Antioxidant status affects color stability and tenderness of calcium chloride-injected beef1. J. Anim. Sci. 79, 666-677. doi:10.2527/2001.793666x
[DOI][PubMed]
Kang, S., Gang, G., Go, G., 2024. Ambivalence towards pork belly: exploring its significance and contradictions from the perspectives of the food industry and nutritional science. Food Sci. Biotechnol. 33, 23-31. doi:10.1007/s10068-023-01429-4
[DOI][PubMed][PMC]
Kim, D., Langmead, B., Salzberg, S.L., 2015. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357-360. doi:10.1038/nmeth.3317
[DOI][PubMed][PMC]
Klont, R.E., Brocks, L., Eikelenboom, G., 1998. Muscle fibre type and meat quality. Meat Sci. 49, S219-S229. doi:10.1016/s0309-1740(98)90050-x
[DOI]
Ko, E., Jeong, K., Oh, H., Park, Y., Choi, J., Lee, E., 2023. A deep learning-based framework for predicting pork preference. Curr. Res. Food Sci. 6, 100495. doi:10.1016/j.crfs.2023.100495
[DOI][PubMed][PMC]
Kwon, D., Park, N., Wy, S., Lee, D., Park, W., Chai, H.-H., Cho, I.-C., Lee, J., Kwon, K., Kim, H., Moon, Y., Kim, Juyeon, Kim, Jaebum, 2024. Identification and characterization of structural variants related to meat quality in pigs using chromosome-level genome assemblies. BMC Genom. 25, 299. doi:10.1186/s12864-024-10225-1
[DOI][PubMed][PMC]
Lim, K.-S., Lee, K.-T., Lee, S.-W., Chai, H.-H., Jang, G., Hong, K.-C., Kim, T.-H., 2016. Genomic structure, expression and association study of the porcine FSD2. Mol. Biol. Rep. 43, 1011-1018. doi:10.1007/s11033-016-4029-4
[DOI][PubMed]
Love, M.I., Huber, W., Anders, S., 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. doi:10.1186/s13059-014-0550-8
[DOI][PubMed][PMC]
Mancini, R., 2013. The Science of Meat Quality 177-198. doi:10.1002/9781118530726.ch9
[DOI]
Mancini, R.A., Hunt, M.C., 2005. Current research in meat color. Meat Sci. 71, 100-121. doi:10.1016/j.meatsci.2005.03.003
[DOI][PubMed]
Miar, Y., Plastow, G., Wang, Z., 2015. Genomic Selection, a New Era for Pork Quality Improvement. Springer Sci. Rev. 3, 27-37. doi:10.1007/s40362-015-0029-3
[DOI]
Pérez-Andrés, J.M., Cropotova, J., Harrison, S.M., Brunton, N.P., Cullen, P.J., Rustad, T., Tiwari, B.K., 2020. Effect of Cold Plasma on Meat Cholesterol and Lipid Oxidation. Foods 9, 1786. doi:10.3390/foods9121786
[DOI][PubMed][PMC]
Pertea, M., Kim, D., Pertea, G.M., Leek, J.T., Salzberg, S.L., 2016. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 11, 1650-1667. doi:10.1038/nprot.2016.095
[DOI][PubMed][PMC]
Pertea, M., Pertea, G.M., Antonescu, C.M., Chang, T.-C., Mendell, J.T., Salzberg, S.L., 2015. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290-295. doi:10.1038/nbt.3122
[DOI][PubMed][PMC]
Poveda-Arteaga, A., Krell, J., Gibis, M., Heinz, V., Terjung, N., Tomasevic, I., 2023. Intrinsic and Extrinsic Factors Affecting the Color of Fresh Beef Meat-Comprehensive Review. Appl. Sci. 13, 4382. doi:10.3390/app13074382
[DOI]
Ramanathan | Recent Updates in Meat Color Research: Integrating Traditional and High-Throughput Approaches | Meat and Muscle Biology [WWW Document], n.d. URL https://www.iastatedigitalpress.com/mmb/article/id/9598/ (accessed 9.22.24).
Ramanathan, R., Hunt, M.C., Mancini, R.A., Nair, M.N., Denzer, M.L., Suman, S.P., Mafi, G.G., 2020. Recent Updates in Meat Color Research: Integrating Traditional and High-Throughput Approaches. Meat Muscle Biol. 4. doi:10.22175/mmb.9598
[DOI]
Raudvere, U., Kolberg, L., Kuzmin, I., Arak, T., Adler, P., Peterson, H., Vilo, J., 2019. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191-W198. doi:10.1093/nar/gkz369
[DOI][PubMed][PMC]
Saini, B.L., Gaur, G.K., Sahoo, N.R., Mendiratta, S.K., Kumar, A., Naha, B.C., Baranwal, A., Yadav, V., Jaiswal, R.K., 2018. Polymorphism distribution of RYR1, PRKAG3, HFABP, MYF-5 and MC4R genes in crossbred pigs. Mol. Biol. Rep. 45, 1575-1585. doi:10.1007/s11033-018-4263-z
[DOI][PubMed]
Sarup, P., Sørensen, J.G., Kristensen, T.N., Hoffmann, A.A., Loeschcke, V., Paige, K.N., Sørensen, P., 2011. Candidate Genes Detected in Transcriptome Studies Are Strongly Dependent on Genetic Background. PLoS ONE 6, e15644. doi:10.1371/journal.pone.0015644
[DOI][PubMed][PMC]
Sibut, V., Hennequet-Antier, C., Bihan-Duval, E.L., Marthey, S., Duclos, M.J., Berri, C., 2011. Identification of differentially expressed genes in chickens differing in muscle glycogen content and meat quality. BMC Genom. 12, 112. doi:10.1186/1471-2164-12-112
[DOI][PubMed][PMC]
Simunović, S., Tomasevic, I., Djordjevic, V.Ž., Baltić, T., Simunovic, S., Ćirić, J., Djekic, I., 2024. Meat Color, Marbling, and the Evaluation of Defects in Beef and Pork at the Point of Purchase. Appl. Sci. 14, 6797. doi:10.3390/app14156797
[DOI]
Skaperda, Z., Argyriadou, A., Nechalioti, P.M., Alvanou, M., Makri, S., Bouroutzika, E., Kyriazis, I.D., Tekos, F., Veskoukis, A.S., Kallitsis, T., Mesnage, R., Arsenos, G., Kouretas, D., 2021. Redox Biomarker Baseline Levels in Cattle Tissues and Their Relationships with Meat Quality. Antioxidants 10, 958. doi:10.3390/antiox10060958
[DOI][PubMed][PMC]
Terlouw, E.M.C., Picard, B., Deiss, V., Berri, C., Hocquette, J.-F., Lebret, B., Lefèvre, F., Hamill, R., Gagaoua, M., 2021. Understanding the Determination of Meat Quality Using Biochemical Characteristics of the Muscle: Stress at Slaughter and Other Missing Keys. Foods 10, 84. doi:10.3390/foods10010084
[DOI][PubMed][PMC]
Uimari, P., Sironen, A., 2014. A combination of two variants in PRKAG3is needed for a positive effect on meat quality in pigs. BMC Genet. 15, 29. doi:10.1186/1471-2156-15-29
[DOI][PubMed][PMC]
Zhang, D., Li, X., Chen, L., Hou, C., Wang, Z., 2021. Protein Phosphorylation and Meat Quality 23-47. doi:10.1007/978-981-15-9441-0_3
[DOI]