Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality
Abstract
:1. Introduction
2. Functional Mutations and Commercialized DNA Tests for Beef Quality
3. Genome-Wide Association Studies for Beef Quality Traits
4. Genomic Prediction and Selection for Beef Quality
5. Transcriptomics of Beef Quality
6. Proteomics of Beef Quality
7. Metabolomics of Beef Quality
8. Challenges and Future Directions
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- Integrating Multi-Omics Data: Combining genomics, transcriptomics, proteomics, and metabolomics data to elucidate the complex biological networks underlying meat quality is a challenging task that requires robust bioinformatic pipelines and systems biology approaches.
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- Implementing Integromics: Integromics, which uses advanced computational and statistical methods to integrate diverse data types, offers a promising platform for advancing beef quality research. However, the implementation of an integromics approach is still in its early stages and requires further development and validation.
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- Identifying Causal Functional Mutations: The identification and validation of causal functional mutations through gene editing techniques is crucial for precise genomic selection and breeding programs. While gene editing technologies like CRISPR/Cas9 have been developed, their application in beef quality research is still limited.
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- Overcoming Challenges through Interdisciplinary Research: Addressing the challenges in applying omics technologies to beef quality research will require interdisciplinary research efforts and public-private partnerships. The lack of collaboration between different disciplines and stakeholders has hindered progress in this field.
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- Translating Multi-Omics Findings into Practical Applications: While multi-omics findings have the potential to improve breeding strategies and genomic predictions for beef quality, the translation of these findings into practical applications is still limited. More research is needed to bridge the gap between research and industry.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Gene Symbol | Beef Attribute | Discovered by | Commercialized by |
---|---|---|---|
TG | Marbling | CSIRO/MLA | Genetic Solutions Pty Ltd. (Albion, QLD, Australia) |
CAST | Meat tenderness | CSIRO/MLA/Beef CRC | Genetic Solutions Pty Ltd. |
CAPN1 | Meat tenderness | USDA/AgResearch NZ | Open |
GH1 | Marbling | NIAS, Japan | Prescribe Genomics Co. (Ibaraki, Japan) |
LEP | Marbling/fat traits | Univ. of Saskatchewan | Merial, Inc. (Duluth, GA, USA) |
Multiple tests | Marbling | - | Genetic Solutions Pty Ltd. |
CAPN3 | Meat tenderness | CSIRO/MLA/Beef CRC | Genetic Solutions Pty Ltd. |
SCD | Fatty acid composition | Kobe University | Prescribe Genomics CO |
Beef Attribute | Population | Sample Size | Genotyping Platform | Significant Genomic Regions/Genes | Reference |
---|---|---|---|---|---|
Tenderness | Angus cattle | 1833 | Illumina BovineSNP50 BeadChip | CAST and CAPN1 for tenderness | [30] |
Marbling score | Simmental bulls | 785 | Illumina BovineHD BeadChip | TUBB1 and RPL27A for marbling score | [31] |
Warner–Bratzler Shear Force (WBSF), marbling, cooking loss, tenderness, juiciness, connective tissue and flavor | Multibreed Angus–Brahman steers | 672 | GGP Bovine F-250 chip containing 221,077 SNPs | LRP5, COL3A1, GRIP1, RECQL5, ANO2, NTF3, CD36, GPR98, MMRN2 and GOSR2. | [32] |
Marbling score, meat texture, meat color, and fat color | Hanwoo steers | 2110 | Illumina Bovine SNP50 BeadChip imputed to a higher density of 15,536,497 SNPs | SFT2D3 (marbling) located on BTA2, ENPP2 (meat color) on BTA14, CPAMD8 on BTA7 and RHCG on BTA21 for fat color | [26] |
Tenderness, marbling, and flavor, marbling, Warner–Bratzler shear force (WBSF), tenderness, and connective tissue | Angus-sired population of steers, bulls, and cows progeny | 2268 | Bovine SNP50 Infinium II BeadChip imputed to 44.3 million SNPs | Tenderness: CAST and CAPN1; WBSF: CAPN1, AGAP1, ANXA10, CCDC80, Connective Tissue: UTRN, TMX1, TMEM170B; Marbling: EGR2, RNF130, C1QTNF8, SOX8, SSTR5, TEKT4, SLC20A2 | [33] |
Meat color, purge loss, cooking loss, meat pH, Warner–Bratzler shear force. | Piedmontese young bulls | 1166 | GeneSeek Genomic Profiler Bovine LD’ (GGP Bovine LD) array containing 30,111 SNPs | SNPs on BTA4 (at ~112.51 Mb), BTA23 (at ~3.91 and ~7.25 Mb), BTA24 (at ~19.87 Mb), and BTA25 (at ~11.96 Mb) for meat color. Water holding capacity: one SNP located on BTA9 (at ~48.33 Mb) for purge loss, and two SNPs located on BTA6 (at ~29.23 Mb) and on BTA10 (at ~14.57 PMb) for cooking loss, one SNP on BTA8 (at ~28.46 Mb) for meat pH. | [34] |
Color, aroma, tenderness, juiciness, and palatability | Hanwoo steers | 250 | Affymetrix Bovine Axiom Array 640K SNP chip | Three pleiotropic SNPs (AX-26703353 and AX-26742891 on BTA6, and AX-18624743 on BTA10) influenced multiple traits like tenderness, juiciness, and palatability | [35] |
Oleic acid (C18:1) content in the intramuscular fat | Japanese Black cattle | 160 | BovineSNP50 BeadChip | A total of 32 SNPs, including the FASN gene, had significant effects on C18:1 levels, with 30 SNPs located between 49 and 55 Mbp on chromosome 19 | [36] |
Fatty acid composition | Chinese Simmental beef cattle | 723 | Illumina BovineHD BeadChip | SNPs near the FASN gene on BTA19 for C14:0 and C14:1, and the ELOVL5 gene on BTA23 for C14:0. | [37] |
Marbling score and tenderness | Crossbred beef cattle | 747 | BovineSNP50 BeadChip | One SNP (BTA-60019) on BTA25 accounted for 2.67% of the variation in tenderness. | [38] |
Fatty acid composition | Japanese Black cattle | 461 | BovineSNP50 BeadChip | FASN gene on BTA19, one SNP for C18:1 on BTA23, two SNPs for C16:0 on BTA25, and two SNPs for C14:1 near the SCD gene on BTA26. | [39] |
Fatty acid composition | Angus beef cattle | 1713 | BovineSNP50 BeadChip | FASN, SCD and THRSP genes | [40] |
Intramuscular fat deposition and composition | Nellore steers | 585 | Illumina BovineHD BeadChip | SNPs near the FASN gene on BTA19 for C16:0 and C18:1 fatty acids, and SNPs on BTA7 for intramuscular fat percentage | [41] |
Fatty acid composition | American Black Angus calves | 2177 | 574,662 SNPs imputed from BovineSNP50 BeadChip and BovineHD BeadChip | Candidate genes FABP2, FASN, FADS2, FADS3 and SCD | [42] |
Fatty acid composition | Nellore cattle | 1057 | Illumina BovineHD BeadChip | SNPs near the FASN gene on BTA19 for C16:0 and C18:1 and the SCD gene on BTA26 for C14:1 and C16:1., THRSP, ELOVL6 and FADS2 | [43] |
Eating quality traits: scores for tenderness, juiciness, flavor overall liking | Steers, heifers, and bulls from Brahman, Angus, Hereford, Shorthorn, Holstein, Jersey, Belmont Red, Santa Gertrudis composite, crossbred unknown breed. | 1701 | 709,068 Imputed SNPs from the Illumina HD array | Tenderness: CAPN1, CAST genes; juiciness and flavor: MOXD1 APOB, KIF13A | [27] |
Shear force, marbling score, and intramuscular fat | Nellore cattle | 6910 young bulls with phenotypic information and 23,859 genotyped animals | 435,447 Imputed SNPs from multiple Bead chip assay densities | Several candidate genes located on chromosomes BTA1, 2, 5, 7, 9, 10, 19, and 25 for Shear force, on BTA4, 7, 10, 11, 12, 13, 15, and 20 for marbling score, and BTA8, 9, 10, 12, 13, and 28 for intramuscular fat | [29] |
Trait | Accuracy N | Reference | |
---|---|---|---|
Meat tenderness | 0.52 to 0.59 | 427 | [53] |
Meat tenderness | 0.57 to 0.60 | 5062 | [54] |
Lipids | 0.23 | 3812 | [54] |
Marbling | 0.32 | 5039 | [54] |
Carcass intramuscular fat % | 0.20 | 1031 | [55] |
Marbling score | 0.08 to 0.56 | 4228 | [56] |
a * color | 0.40 | 5052 | [54] |
b * color | 0.49 to 0.53 | 5046 | [54] |
L * color | 0.68 to 0.73 | 5071 | [54] |
Sum of SFA | 0.04 to 0.24 | 868 | [57] |
Sum of MUFA | 0.05 to 0.13 | 868 | [57] |
Sum of PUFA | 0.15 to 0.56 | 868 | [57] |
Beef Attribute | Animal and Age at Slaughter | Sample Size | Protein Extracts | Proteomics Platform | N. of Identified Proteins | Reference |
---|---|---|---|---|---|---|
Sensory attributes (tenderness, chewiness, stringiness, and flavor) | Limousin-sired bulls, 16 months | 34 | Total LD muscle proteins | LC-MS/MS | 84 | [80] |
pH, instrumental color, cooking loss, and WBSF | Immunocastrated F1 Montana-Nellore, heifers + steers, 15 months | 16 | Myofibrillar and sarcoplasmic proteins | 2D-PAGE, MS (ESI-MS/MS) | 23 | [81] |
Tenderness (WBSF) | Nellore cattle, steers, and bulls, 27.7 months | 155 | Whole LD muscle proteins | 2DE and mass spectrometry, MALDI-TOF/TOF MS/MS | 40 | [82] |
Tenderness (WBSF) | Nellore bulls, 27 months | Cytoplasmatic proteins | 2D-PAGE, MS (ESI-MS/MS) | 29 | [83] | |
pH, WBSF, and WHC | Angus × Simmental beef cattle (USDA Select; A maturity) | 8 | Whole muscle protein | Western blots, SDS-PAGE | 14 | [84] |
Beef tenderness | Angus Steers, 18 months | 6 | Myofibrillar proteins | 1DE + nano-LC-MS/MS | 19 | [79] |
Beef tenderness | Angus Steers, 12 months | 19 | High salt and low salt soluble proteins | 1DE + LC-MS/MS | 8 | [85] |
Beef tenderness | Angus Steers | 15 | Myofibrillar and sarcoplasmic proteins | 2D-DiGE + Linear Ion Trap MS | 28 | [86] |
Beef tenderness and intramuscular fat | Nellore Bulls + Steers | 12 | Myofibrillar and sarcoplasmic proteins | 2DE + MALDI-TOF/TOF | 9 | [87] |
Beef tenderness | Charolais × Aubrac Heifers, 33 ± 3 months | 10 | Myofibrillar and sarcoplasmic proteins | Label-free + Nano-LC-MS/MS | 40 | [88] |
Beef tenderness | Charolais Bulls, 17 months | 8 | Myofibrillar and sarcoplasmic proteins | 2DE + MALDI-TOF/TOF | 23 | [89] |
Beef tenderness and marbling | PDO Maine Anjou Cows, 67.4 months | 188 | Myofibrillar and sarcoplasmic proteins | RPPA | 10 | [90] |
Tenderness (shear force) | Piedmontese bulls, 7 months | 10 | Cytoplasmatic proteins | SWATH-MS | 43 | [91] |
Dark-cutting | 6 dark-cutters and 6 normal-pH beef, other information not visible | 12 | Label-free quantitative proteomics using LC-MS/MS | Total protein extract | 57 | [92] |
Dark-cutting | Beef cattle, other information not visible | 22 | LD muscle mitochondrial proteins | LC-MS/MS | 12 | [93] |
Beef Attribute | Analytical Techniques | Multivariate Analysis Techniques | Metabolites | Reference |
---|---|---|---|---|
Sensory evaluation of beef taste | GC/MS | PCA | Cold storage led to increased free fatty acids and Glutamic acid and decreased creatinine and inosinic acid | [97] |
Meat color, pH, water holding capacity, shear force, and texture | NMR | PCA, OPLS-DA | Beef quality differences related to acetylcholine, valine, adenine, leucine, phosphocreatine, β-hydroxypyruvate, ethanol, adenosine diphosphate, creatine, acetylcholine, and lactate | [98] |
Beef fat color | LC-MS | PCA, PLS-DA | 3-hydroxyoctanoic acid, anethofuran, 9,10-DiHODE, furanoeremophilane, pregeijerene, N-glycolylneuraminic acid, and glycocholic acid were identified as potential biomarkers for differentiating fat color | [99] |
Marbling | NMR | PLS-DA | Carnosine, creatine, glucose, and lactate were associated with higher marbling | [100] |
Marbling | Mass spectrometry-based untargeted and targeted metabolomics | ASCA | Unconjugated-BA and Glucocorticoids were associated with marbling | [101] |
Aroma of cooked beef | SPME and GC–MS | Linear and logarithmic regression model | Benzeneacetaldehyde and Heterocyclic compounds | [102] |
Meat freshness | NMR | PCA, PLS | 60 identified metabolites, metabolomics classified meat samples according to their storage time | [103] |
Intramuscular fat | NMR | PCA, OPLS-DA | The unsaturation degree of triacylglycerol was estimated by the 1H NMR spectra and was correlated with the content ratio of unsaturated fatty acids and the melting point of IMF. Leucine and creatine were found as biomarkers, positively and negatively correlated with aging duration, respectively. | [104] |
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Tian, R.; Mahmoodi, M.; Tian, J.; Esmailizadeh Koshkoiyeh, S.; Zhao, M.; Saminzadeh, M.; Li, H.; Wang, X.; Li, Y.; Esmailizadeh, A. Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality. Genes 2024, 15, 1104. https://doi.org/10.3390/genes15081104
Tian R, Mahmoodi M, Tian J, Esmailizadeh Koshkoiyeh S, Zhao M, Saminzadeh M, Li H, Wang X, Li Y, Esmailizadeh A. Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality. Genes. 2024; 15(8):1104. https://doi.org/10.3390/genes15081104
Chicago/Turabian StyleTian, Rugang, Maryam Mahmoodi, Jing Tian, Sina Esmailizadeh Koshkoiyeh, Meng Zhao, Mahla Saminzadeh, Hui Li, Xiao Wang, Yuan Li, and Ali Esmailizadeh. 2024. "Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality" Genes 15, no. 8: 1104. https://doi.org/10.3390/genes15081104