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Article

Variation in Acetyl-CoA Carboxylase Beta Gene and Its Effect on Carcass and Meat Traits in Gannan Yaks

by
Chune Zhu
,
Youpeng Qi
,
Xiangyan Wang
,
Baohong Mi
,
Changze Cui
,
Shaopeng Chen
,
Zhidong Zhao
,
Fangfang Zhao
,
Xiu Liu
,
Jiqing Wang
,
Bingang Shi
* and
Jiang Hu
*
Gansu Key Laboratory of Herbivorous Animal Biotechnology, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(20), 15488; https://doi.org/10.3390/ijms242015488
Submission received: 25 June 2023 / Revised: 12 September 2023 / Accepted: 20 October 2023 / Published: 23 October 2023
(This article belongs to the Section Molecular Biology)
Browse Figures
Versions Notes

Abstract

:
Acetyl-CoA carboxylase beta (ACACB) is a functional candidate gene that impacts fat deposition. In the present study, we sequenced exon 37–intron 37, exon 46–intron 46, and intron 47 of yak ACACB using hybrid pool sequencing to search for variants and genotyped the gene in 593 Gannan yaks via Kompetitive allele-specific polymerase chain (KASP) reaction to determine the effect of ACACB variants on carcass and meat quality traits. Seven single nucleotide polymorphisms were detected in three regions. Eight effective haplotypes and ten diplotypes were constructed. Among them, a missense variation g.50421 A > G was identified in exon 37 of ACACB, resulting in an amino acid shift from serine to glycine. Correlation analysis revealed that this variation was associated with the cooking loss rate and yak carcass weight (p = 0.024 and 0.012, respectively). The presence of haplotypes H5 and H6 decreased Warner–Bratzler shear force (p = 0.049 and 0.006, respectively), whereas that of haplotypes H3 and H4 increased cooking loss rate and eye muscle area (p = 0.004 and 0.034, respectively). Moreover, the presence of haplotype H8 decreased the drip loss rate (p = 0.019). The presence of one and two copies of haplotypes H1 and H8 decreased the drip loss rate (p = 0.028 and 0.004, respectively). However, haplotype H1 did not decrease hot carcass weight (p = 0.011), whereas H3 increased the cooking loss rate (p = 0.007). The presence of one and two copies of haplotype H6 decreased Warner–Bratzler shear force (p = 0.014). The findings of the present study suggest that genetic variations in ACACB can be a preferable biomarker for improving yak meat quality.

1. Introduction

Yaks (Bos grunniens) are unique, large ruminant domestic animals found in the Qinghai–Tibet Plateau and surrounding high altitudes; they can survive in extremely cold, hypoxic, and other harsh climatic conditions [1]. Yaks steadily provide meat, milk, and yak wool fiber to local herders over a long period [2]. Gannan yaks are distributed in highlands (>2800 m above sea level) in the Gannan region of Gansu Province, northwestern China. These yaks grow on natural and organic green pastures and feed on them [3]; therefore, their products are called “green food”.
Yak meat, a special livestock product of the highland region, provides important animal protein for the diet of local herders [4,5]. Compared with common beef, yak meat is richer in calcium, phosphorus, and other trace elements, with high protein content and nutritional value and low fat content [6]. The two important edible sensory qualities of beef that affect consumer acceptance are tenderness and intramuscular fat (IMF) content. However, yak meat has thicker muscle fibers and less IMF deposition. At the consumer level, recently, more consumers have been willing to buy beef of better quality. At present, variation in beef tenderness is an important issue faced by the beef industry and is a critical factor affecting its economy [7].
The development of basic meat composition and quality is affected by various factors, including genetic, nutritional, physiological, and environmental factors [8]; of them, genetic factors are the main determinants [9]. Fat and fatty acid composition is closely associated with the flavor, appearance, texture, and hardness of meat [10,11,12,13]. The degree of marbling is defined as the number and distribution of IMF—the main determinant of beef quality. It has a positive correlation with tenderness, juiciness, and flavor [14]. Furthermore, the main quality traits that should be controlled in beef are tenderness and IMF; these are crucial to the livestock economy [15,16]. However, owing to the special natural environment and traditional concepts, the Chinese yak industry has inefficient production. Therefore, exploring the development of high-quality yak meat is vital. Previous studies have reported that most meat quality traits, particularly meat tenderness, exhibit genetic differences in cattle herds [17,18,19,20]. Many genes related to adipogenesis and metabolism are directly or indirectly associated with IMF content [21].
Acetyl-CoA carboxylase (ACC) was discovered by Salih and coworkers in the late 1950s. It is a biotin-dependent enzyme that catalyzes the conversion of acetyl-CoA to malonyl-CoA during fatty acid biosynthesis and regulates fat deposition by participating in metabolic processes [22,23]. Acetyl-CoA carboxylase is derived from the acyl-CoA superfamily, including ACACA, ACAT1, ACAA2, ACACB, ACADS, and ACADVL. It is a rate-limiting enzyme in fatty acid oxidation that plays an important catalytic role in fatty acid synthesis and β-oxidation. It comprises two isoforms: ACC-α and ACC-β, which are encoded by ACACA and ACACB, respectively [24]. The acetyl-CoA carboxylase beta gene catalyzes the carboxylation of acetyl-CoA to malonyl-CoA, a major precursor of fatty acid synthesis. However, when ACACB expression increases, fatty acid oxidation can be controlled by inhibiting the activity of carnitine palmitoyltransferase 1 (CPT-1) [25]. Among the various major factors contributing to fat deposition, free fatty acid accumulation is extremely critical. The acetyl-CoA carboxylase beta gene is involved in fatty acid metabolism and is mainly expressed in the heart and liver, as well as in skeletal muscle [26]. Acetyl-CoA carboxylase beta gene knockout mice have sustained fatty acid oxidation in the adipocytes, conferring protective effects against obesity and diabetes [27]. Furthermore, in women with kidney disease and postmenopausal women, ACACB variants are associated with obesity and type II diabetes [28,29]. Moreover, in Alentejana bulls, ACACB is associated with the IMF content and fatty acid composition of beef [30]. The variation of 368 C/T in human ACACB genes affects the promoter activity in an allele-specific fashion [31]. Therefore, ACACB is variable and may play a vital role in the regulation of IMF and obesity.
Decreasing fat deposition can increase the economic value of meat and improve feeding efficiency. However, most of the present studies on ACACB have focused on cancer and some obesity-related diseases, and only a few studies have focused on beef quality traits. Therefore, in the present study, we used ACACB as an entry point to screen genetic variants in the yak population and determined the correlation between ACACB variations and the meat quality traits of Gannan yaks. The study’s findings may provide a theoretical basis for molecular genetic studies on the meat quality traits of yaks.

2. Results

2.1. Subsection

2.1.1. Identification of Sequence Variation in Yak ACACB

The genomic DNA of 20 Gannan yaks was used to amplify the exon 37–intron 37, exon 46–intron 46, and intron 47 regions of ACACB, followed by the sequencing of all amplicons. Seven novel SNPs were identified at g.50421 A > G, g.50592 C > A, g.50648 C > G, g.64548 C > T, g.64617 C > T, g.67836 G > A, and g.68017 G > A (Figure 1). The locations of SNPs on the chromosomes are chr17:1393930 A > G, chr17:1393764 C > A, chr17:1393706 C > G, chr17:1379806 C > T, chr17:1379741 C > T, chr17:1376519 G > A, and chr17:1376338 G > A, respectively. The SNPs were genotyped using KASP; all SNPs had three genotypes (Figure 2). The genotype frequency of GG and allele frequency of G were the highest at position g.50421, and nucleotide transition from A to G led to an amino acid change from serine to glycine. Moreover, the genotype frequency of CC and allele frequency of C were the highest at position g.50592, genotype frequency of GC and allele frequency of G were the highest at position g.50648, genotype frequency of CC and allele frequency of C were the highest at positions g.64548 and g.64617, genotype frequency of GG and allele frequency of G were the highest at position g.67836, and genotype frequency of AG and allele frequency of A were the highest at position g.68017.
The population genetic analysis of the seven positions in Gannan yaks revealed that g.50648 and g.68017 were moderately polymorphic (0.25 < PIC < 0.5) and g.50421, g.50592, g.64548, g.64617, and g.67836 were low polymorphic (PIC < 0.25). The seven positions were in HWE in yak population (p > 0.05) (Table 1).
Next, linkage disequilibrium and haplotype analyses revealed that the SNPs were in a weak linkage state (r2 < 0.33), except for a strong linkage state between g.50421 and g.50592 (r2 = 0.92) (Table 2). D’ and r2 are two common parameters that represent linkage disequilibrium. The D’ value reflects the probability of recombination events in the linkage disequilibrium region, whereas the r2 value is associated with the effectiveness of linkage analysis. r2 considers the effects of recombination and mutation rates, which can more objectively reflect the linkage disequilibrium between different positions. Therefore, in the present study, r2 was used for linkage disequilibrium. Haplotypes were inferred from the genotype data according to the principle of combining alleles at multiple loci that are co-inherited on the same chromosome using the online software SHEsis (SHEsisPlus Online Version—Beta: http://shesisplus.bio-x.cn/SHEsis.html, accessed on 27 December 2022) [32,33,34,35]. Eight haplotypes with frequencies greater than 0.03 were constructed in the tested Gannan yak population. These haplotypes formed 10 diplotypes with frequencies greater than 0.03 (Table 3).

2.1.2. Association between Yak ACACB Genotype and Carcass and Meat Quality Traits

Individuals with the GA genotype at position g.50421 had a higher cooking loss rate (CLR; %) than those with the GG genotype (p < 0.05). On the other hand, they had a lower hot carcass weight (HCW; kg) than those with the AA genotype (p < 0.05). Furthermore, individuals with the AA genotype at position g.50592 had a higher CLR than those with the AC and CC genotypes (p < 0.05). In addition, individuals with the GC genotype at position g.50648 had a higher drip loss rate (DLR; %) than those with the CC and GG genotypes (p < 0.05). Individuals with the CT genotype at position g.64548 had a higher Warner–Bratzler shear force (WBSF; kg) than those with the CC and TT genotypes; in contrast, they had a lower DLR than those with the TT genotype (p < 0.05). Individuals with the AA genotype at position g.67836 had a significantly higher DLR than those with the GA and GG genotypes (p < 0.01) (Table 4).

2.1.3. Association between Yak ACACB Haplotype and Carcass and Meat Quality Traits

Table 5 presents the associations between the ACACB haplotypes and carcass and meat quality traits. In the single-haplotype (presence or absence) models, the presence of haplotypes H5 and H6 was associated with decreased WBSF (p = 0.049 and 0.006, respectively), whereas the presence of haplotype H3 and H4 was associated with an increased CLR (p = 0.004) and rib eye area (REA; cm2) (p = 0.034), respectively. Furthermore, the presence of haplotype H8 was associated with decreased DLR (p = 0.019). When other haplotypes (p < 0.2) were included in the models, their associations were significant (p < 0.05). No haplotypes had an association with HCW in Gannan yak ACACB (p > 0.05).
A second set of analyses was performed using the copy number of the haplotype present (presence/absence, Table 6). The presence of one and two copies of haplotypes H1 and H8 was associated with decreased DLR (p = 0.028 and 0.004, respectively). Furthermore, compared with noncarriers in the single-haplotype and multi-haplotype models, H1 was associated with decreased HCW (p = 0.011), whereas H3 was associated with increased CLR (p = 0.007). For WBSF, the presence of two copies of H6 was associated with decreased WBSF (p = 0.014), and the presence of H5 was associated with decreased WBSF (p = 0.075). The association of these haplotypes remained significant (p < 0.2) when other haplotypes were included in the models. The presence of H5 was significantly associated with decreased WBSF (p = 0.046). Nevertheless, no associations were observed between the copy numbers of these haplotypes and REA in Gannan yak ACACB.
Furthermore, we did not observe the significant effect of any diplotypes on carcass and meat quality traits. However, H5H6 had a significant tendency to increase HCW compared with other diplotypes (p = 0.095) (Table 7).

3. Discussion

This is the first study to determine the relationship between sequence variation in ACACB and the carcass and meat quality traits of yaks. The results suggest the presence of genetic variations in yak ACACB that have a significant effect on the tenderness of Gannan yaks. This confirms the variability of ACACB in yaks and suggests that further research on ACACB gene variation in yak breeds is of interest.
Because ACACB synthesizes malonyl-CoA for CPT-1 inhibition, it can be an attractive candidate gene for disorders of energy metabolism, including obesity and diabetes, that can primarily regulate impaired fatty acid oxidation. Studies on fatty acid oxidation have reported that the allelic variants of human ACACB may be associated with metabolic syndrome [36]. Furthermore, ACACB regulates fatty acid oxidation in the skeletal muscle, liver, and heart [37], and its variants are associated with lipid metabolism [29,38]. Acetyl-CoA carboxylase beta gene polymorphism (rs2268388, G > A) is associated with diabetes, diabetic nephropathy, insulin resistance, and obesity in some human populations [29,39,40,41]. Acetyl-CoA carboxylase beta gene SNPs and their haplotypes, which are associated with milk fat traits in Chinese Holstein cows, were identified, and it was concluded that ACACB variation impacts fatty acid metabolism and regulates fat mobilization, which ultimately affects milk quality [42]. Taken together, these studies suggest that the variability of ACACB regulates obesity and milk quality via fatty acid oxidation. Nevertheless, further studies are warranted to elucidate the relevance of ACACB as it relates to fat. At present, the content and composition of fatty acids in meat products are being extensively evaluated both at the national and international levels with the primary aim of producing better-quality meat products.
In the present study, seven SNPs were detected in the three fragments of Gannan yak ACACB examined. Among them, the variant sites g.50592, g.50648, g.64617, g.67836, and g.68017 were located in introns. Genotype association analysis revealed that mutations in the intron regions were significantly correlated with Gannan yak meat quality in terms of tenderness, leanness, water loss, eye muscle area, and carcass weight. Some previous studies have reported that genetic variation in the intron regions plays an important role in regulating their transcription, translation, and biological functions [43,44]. This genetic variation is most likely in LD with causative variation. It has been reported that the rs2075786 SNP in TERT is associated with a differential risk of developing cancer for MSH2 pathogenic variant carriers [45]. McNamara et al. have reported that the TNNT2 intronic mutation is the most likely cause of this case of feline cardiomyopathy [46]. In many eukaryotes, SNPs in introns affect gene expression by regulating transcription and translation. Furthermore, the presence of one or more introns is needed for the optimal expression of several genes [47]. Although intronic variations may not directly affect the structure of a gene [48,49], they can affect transcriptional efficiency by affecting regulatory elements such as enhancers, silencers, or other DNA structures [49]. Gao et al. have reported that variants in introns 1 and 15 of yak DGAT1 were positively correlated with yak meat tenderness [50]. Furthermore, Angiolillo et al. have reported that variants in intron 16 of DGAT1 are correlated with milk fat content in goats [51]. Wang et al. have reported that the g.112558859 A > G motif in intron 1 of LCORL can be used as a potential candidate marker, affecting body size and rawhide weight [52]. In addition, Wang et al. have reported that polymorphisms in intron 8 of OBR are associated with obesity traits such as abdominal fat in chickens [53]. Grochowska et al. have reported that polymorphisms in intron 1 of MSTN exert a significant effect on body weight and loin and anterior calf weight in colored Polish Merino sheep [54]. The results of the present study are generally consistent with those of previous studies. This suggests that the variation of the intronic region may affect gene expression and some economic traits in yaks. Nevertheless, all coding region polymorphisms within the ACACB have not been identified in this study, and it is possible that these non-coding region polymorphisms are in LD with a causative variant.
In the present study, a missense mutation, g.50421 A > G, was identified in exon 37 of ACACB in Gannan yaks. Correlation analysis revealed that this variant was significantly associated with the CLR and HCW of yaks. The codon serine is mutated to glycine. Glycine is not only a protein component but also a bioactive amino acid involved in gene expression regulation [55]. The addition of glycine to the diet can regulate the carcass trait and meat quality of Huanjiang mini-pigs [56]. This nonsynonymous variation may affect protein structure, thereby affecting phenotypic function; this is consistent with our findings. Nevertheless, the relationship between genotype and economic yak traits should be further explored and verified.
Haplotype information comprises multiple markers and plays an important role in many cases, including linkage analysis, association studies, and population genetics [57]. Genomic selection enhanced using QTL information facilitates faster genetic gain in low heritability traits [58]. In general, association analysis between haplotypes and SNPs can help accurately identify molecular marker results [59]. Haplotype analysis provides richer information and more accurate statistical results and is more effective than single SNP analysis in transmitting haplotypes [60]. Hu et al. studied Gannan yaks and reported that the presence or absence of ANK1 haplotypes and haplotype copy number affect carcass weight, muscle water loss, and shear force [6]. In addition, An et al. reported that the absence of haplotype A2-B5 in UCP1 in New Zealand Romney lambs is associated with an increase in HCW and loin lean meat yield [61]. Han et al. studied ACACB in Chinese Holstein cows and reported that haplotypes were significantly associated with milk yield and composition [42]. In the present study, the presence of haplotypes H1 and H8 was associated with decreased DLR, that of haplotype H1 was associated with decreased HCW, that of haplotype H3 was associated with increased CLR, and that of haplotype H6 was associated with decreased WBSF. We hypothesize that yak ACACB variation affects the drip loss rate and CLR of yak meat. We constructed ten diplotypes with frequencies greater than 0.03. Association analysis with carcass and meat quality traits revealed that the diplotype H5H6 was associated with increased HCW (p = 0.095); this was a significant trend compared with other diplotypes. Haplotypes are sequences of genetic variation that occur together along a single chromosome. The results of this study suggest that yak ACACB variations affect the WBSF, CLR, DLR, and HCW of meat. However, a limitation of this study is that only some Gannan yaks were investigated. Therefore, it remains unclear whether these results can be determined and extended to other cattle breeds. Yaks from different farms, a higher number of yak samples, and yak breeds will be investigated in future studies to further confirm the results of the present study as well as to discover more valuable and new variants. These newly identified variants have significant effects on the meat quality and carcass weight of yaks, suggesting the importance of studying yak ACACB variations.

4. Materials and Methods

4.1. Animals and Sample Collection

All animal experiments were carried out in accordance with the guidelines from the Gansu Agricultural University Animal Care Committee (2006-398).
During the study, at slaughter, blood samples were collected from the jugular vein of 593 Gannan yaks, stored in (Acid-Citrate-Dextrose, ACD) anticoagulant tubes, and stored at −70 °C. These yaks were raised in the same feeding environment and management conditions in the Gannan Tibetan Autonomous Prefecture, Gansu Province, China. We recorded the sex, age, and population of each yak. Genomic DNA for PCR amplification was isolated using a TIANamp Blood DNA Kit (TIANGEN) according to the manufacturer’s instructions [42].

4.2. Measurement of Carcass and Meat Quality Traits

After slaughter, the HCW of 593 Gannan yaks was determined and blood samples were collected. Forty-eight hours after slaughter, the REA of each animal was measured using sulfate paper and estimated using a grid. Then, a portion of the Longissimus muscle from the 12th to 13th ribs of the right carcass side was packaged, quickly frozen, and stored at −18 °C for quality assessment. The WBSF, which represents meat tenderness, was measured using a digital muscle tenderometer (C-LM3, Northeast Agricultural University, Harbin, China). DLR and CLR were determined using the methods described by Liu et al. [62] and Honikel [63], respectively.

4.3. Polymerase Chain Reaction (PCR) Amplification and Genotyping

Three PCR primer pairs were designed using Primer 5.0 (Table 8) to amplify three regions (exon 37–intron 37, exon 46–intron 46, and intron 47) of ACACB using a wild yak ACACB sequence (GenBank No. NW_005393292.1). The primers were synthesized by TsingKe Biotechnology Co., Ltd. (Xi’an, China). The total PCR amplification reaction volume was 20 µL: 0.8 µL of genomic DNA, 0.8 µL of each primer, 10.0 µL of Taq DNA polymerase, and 7.6 µL of ddH2O. The cycling conditions were as follows: 2 min at 94 °C, followed by 35 cycles of 30 s at 94 °C, 30 s at the annealing temperatures (Table 1), and 30 s at 72 °C, followed by a final extension of 5 min at 72 °C. PCR amplification products were sequenced by Sangon Biotech Co., Ltd. (Shanghai, China). The sequencing results were detected using DNAMAN (version 5.2.10, Lynnon BioSoft, Vaudreuil, QC, Canada) to detect SNPs. Kompetitive allele-specific PCR (KASP) genotyping assays were performed by Gentides Biotech Co., Ltd. (Wuhan, China). The fluorescence data were collected by employing a microplate reader with a fluorescence resonance energy transfer (FRET) probe. Genotyping maps were created using the online software (http://www.snpway.com/snpdecoder/, accessed on 24 August 2022). LGC-OMEGA software.

4.4. Statistical Analyses

Genotype frequencies, allele frequencies, Hardy–Weinberg equilibrium (HWE), polymorphism information content (PIC), homozygosity, heterozygosity, and the effective allele numbers of the SNPs of ACACB were calculated using Microsoft Excel 2016. Linkage disequilibrium (LD) and haplotype analysis of SNPs were performed using the online software SHEsis (http://shesisplus.bio-x.cn/SHEsis.html, accessed on 27 December 2022).
The associations between the different genotypes and haplotypes (frequencies of >3%) and values of carcass and meat quality traits of yak were determined using the general linear mixed models (GLMMs) of IBM SPSS 26.0 software (IBM Corp., Armonk, NY, USA). The model was calculated as follows: Yijkl = µ + Gi (Hi) + Pj + Sk + Al + eijkl, where Yijkl represents the phenotypic observation, µ represents the population mean, Gi or Hi represent the fixed effect of the genotype or the fixed effect of the ith haplotype (i = 0 or 1) or the fixed effect of the ith number of copies of the haplotype (i = 0, 1, 2), Pj represents the fixed effect of the population, Sk represents the effects of sex, Al represents the effects of age, and eijkl represents random error. Population, age, and sex were included in the statistical mode as fixed factors. Unless otherwise mentioned, all p values less than 0.05 were considered to be significantly different.
First, single-haplotype models were used to determine the presence of a relationship. All haplotypes with p > 0.2 potentially affected carcass and meat quality traits and were included in the multivariate model. Therefore, if the haplotypes that possibly affected traits were considered in the model, we could determine the effects of independent haplotypes.
A second set of models, similar to GLMMs, which was used to test the single haplotype (presence or absence), was constructed using the number of haplotype copies present (presence or absence).
In addition, for combined haplotypes with frequencies of >3% (providing sufficient sample size), a second set of GLMMs was used to determine the effect of combined haplotypes on carcass and meat quality traits. The Bonferroni procedure was performed for multiple comparisons if significant associations were identified in these models.

5. Conclusions

Seven SNPs were identified in the detection regions of yak ACACB. Eight haplotypes and ten combined haplotypes were constructed. Among them, different genotypes at g.50421 A > G, g.50592 C > A, g.50648 C > G, g.64548 C > T, and g.67836 G > A loci affect carcass and meat quality traits, and the presence of haplotypes H5 and H6 contributed to the improvement of tenderness, which can be used for the genetic improvement of meat quality traits through marker-assisted selection, and may be used in selection along with other traits to improve the economic value of yak. In the later studies, it is necessary to further expand the research scope of this gene and actively carry out functional validation so that it can be more accurately applied to the genetic improvement of yak populations.

Author Contributions

Conceptualization, Y.Q. methodology, X.W.; software, B.M.; validation, C.C.; formal analysis, S.C.; investigation, Y.Q. and C.Z.; resources, Z.Z.; data curation, F.Z.; writing—original draft preparation, C.Z.; writing—review and editing, X.L.; visualization, J.W.; project administration, B.S.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the “Gansu Agricultural University Public Recruitment Doctoral Research Start-up Fund (GAU-KYQD-2020-20)”, “Gansu Provincial Department of Education: Young PhD Support Program (2023QB-128)”, “National Natural Science Foundation of China (NSFC), grant Number 32360821”, “Development and demonstration of high-efficiency production technology of yaks and cattle farming and animal husbandry cycle in pastoral areas of Qilian Mountains (2022CYZC-43)”, “Discipline Team Project of Gansu Agricultural University (GAU-XKTD-2022-22)”, “Graduate student star of Innovation (2023 CXZC-630)”.

Institutional Review Board Statement

The study involving animals was approved by the Animal Ethics Committee of Gansu Agricultural University (Approval number 2006-398).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We would like to express our sincere gratitude to Zhoume Ruo for her help and contribution in the preparation of the experimental materials.

Conflicts of Interest

The funders had no participation in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Ijms 24 15488 g001
Figure 1. Polymerase chain reaction (PCR) amplification and sequencing results of ACACB in Gannan yaks. The overlapping peak indicates the single nucleotide polymorphisms (SNPs).
Figure 1. Polymerase chain reaction (PCR) amplification and sequencing results of ACACB in Gannan yaks. The overlapping peak indicates the single nucleotide polymorphisms (SNPs).
Ijms 24 15488 g001
Ijms 24 15488 g002
Figure 2. Kompetitive allele-specific PCR (KASP) genotyping assay results of seven positions of ACACB in Gannan yaks. The red, blue, and green dots in (A,F) and (G) indicate the GA, GG, and AA genotypes, respectively; those in (B) indicate the AC, CC, and AA genotypes, respectively; those in (C) indicate the GC, GG, and CC genotypes, respectively; those in (D,E) indicate the CT, CC, and TT genotypes, respectively.
Figure 2. Kompetitive allele-specific PCR (KASP) genotyping assay results of seven positions of ACACB in Gannan yaks. The red, blue, and green dots in (A,F) and (G) indicate the GA, GG, and AA genotypes, respectively; those in (B) indicate the AC, CC, and AA genotypes, respectively; those in (C) indicate the GC, GG, and CC genotypes, respectively; those in (D,E) indicate the CT, CC, and TT genotypes, respectively.
Ijms 24 15488 g002
Table 1. Population genetics of the seven positions of yak ACACB.
Table 1. Population genetics of the seven positions of yak ACACB.
LocusGenotype Frequency/%Allele Frequency/%PIC 1He 2Ho 3Ne 4HWE 5
g.50421 A > GAA(19)
3.22		GA(109)
18.47		GG(462)
78.31		A
12.46		G
87.54		0.19430.21810.78191.2790p > 0.05
g.50592 C > AAA(11)
1.86		AC(119)
20.17		CC(460)
77.97		A
11.95		C
88.05		0.18830.21040.78961.2665p > 0.05
g.50648 C > GCC(131)
22.24		GC(312)
52.97		GG(146)
24.79		C
48.73		G
51.28		0.37480.49970.50031.9987p > 0.05
g.64548 C > TCC(443)
75.08		CT(132)
22.37		TT(15)
2.54		C
86.27		T
13.73		0.20880.23680.76311.3104p > 0.05
g.64617 C > TCC(401)
68.20		CT(166)
28.23		TT(21)
3.57		C
82.32		T
17.69		0.24880.29120.70881.4108p > 0.05
g.67836 G > AAA(12)
2.04		GA(158)
26.87		GG(418)
71.09		A
15.48		G
84.53		0.22740.26160.73841.3543p > 0.05
g.68017 G > AAA(177)
29.95		GA(269)
45.52		GG(145)
24.53		A
52.71		G
47.29		0.37430.49850.50151.9942p > 0.05
1 Polymorphism information content; 2 heterozygosity; 3 homozygosity; 4 effective allele numbers; 5 Hardy–Weinberg equilibrium.
Table 2. Linkage disequilibrium analysis of the seven single nucleotide polymorphisms (SNPs) of ACACB.
Table 2. Linkage disequilibrium analysis of the seven single nucleotide polymorphisms (SNPs) of ACACB.
Locusg.50421 A > Gg.50592 C > Ag.50648 C > Gg.64548 C > Tg.64617 C > Tg.67836 G > Ag.68017 G > A
D’r2D’r2D’r2D’r2D’r2D´r2D’r2
g.50421 A > G--0.990.920.980.130.950.020.690.010.990.020.980.12
g.50592 C > A----0.980.121.000.021.000.021.000.020.980.11
g.50648 C > G------1.000.160.620.070.910.150.310.09
g.64548 C > T--------1.000.030.910.020.970.13
g.64617 C > T----------0.990.030.790.12
g.67836 G > A------------1.000.20
g.68017 G > A--------------
Table 3. Haplotypes and diplotypes of the seven single nucleotide polymorphisms (SNPs) of ACACB.
Table 3. Haplotypes and diplotypes of the seven single nucleotide polymorphisms (SNPs) of ACACB.
Haplotypeg.50421 A > Gg.50592 C > Ag.50648 C > Gg.64548 C > Tg.64617 C > Tg.67836 G > Ag.68017 G > AFrequencyDiplotypesFrequency
H1GCCCCGG0.271H1H10.069
H2GCGCTGA0.093H1H20.054
H3AAGCCGA0.115H1H30.073
H4GCGCCAG0.138H1H40.076
H5GCCTCGA0.136H1H50.057
H6GCGCCGA0.088H1H60.039
H7GCGCCGG0.035H4H50.040
H8GCCCTGA0.045H4H60.030
H5H60.039
H7H80.054
Table 4. Association between genotype and carcass and meat quality traits in yak.
Table 4. Association between genotype and carcass and meat quality traits in yak.
LocusGenotypeMeat QualityCarcass Quality
nWBSF (kg)CLR (%)DLR (%)REA (cm2)nHCW (kg)
g.50421 A > GAA195.18 ± 0.3567.44 ± 1.25 ab23.05 ± 1.3132.61 ± 1.936157.25 ± 13.56 a
GA1095.45 ± 0.1667.57 ± 0.56 a21.28 ± 0.5931.84 ± 0.8639113.78 ± 6.18 b
GG4625.46 ± 0.1066.00 ± 0.37 b21.53 ± 0.3932.36 ± 0.58153111.25 ± 3.83 b
p-value 0.8360.0240.6140.827 0.012
g.50592 C > AAA115.25 ± 0.4568.94 ± 1.63 a20.44 ± 1.7128.58 ± 2.520/
AC1195.45 ± 0.1567.31 ± 0.54 ab21.55 ± 0.5632.22 ± 0.8346122.16 ± 5.86
CC4605.46 ± 0.1066.03 ± 0.37 b21.42 ± 0.3932.28 ± 0.58151112.96 ± 3.89
p-value 0.9600.0400.2420.400 0.101
g.50648 C > GCC1315.49 ± 0.1566.34 ± 0.5320.51 ± 0.55 b31.79 ± 0.8147113.30 ± 5.71
GC3125.45 ± 0.1166.28 ± 0.4121.81 ± 0.42 a32.57 ± 0.62114113.97 ± 4.30
GG1465.48 ± 0.1566.74 ± 0.5421.73 ± 0.56 ab31.98 ± 0.8336117.80 ± 6.24
p-value 0.2380.7920.0380.782 0.842
g.64548 C > TCC4435.44 ± 0.10 ab66.29 ± 0.3721.33 ± 0.38 b32.54 ± 0.57148112.34 ± 3.89
CT1325.60 ± 0.15 a66.54 ± 0.5421.74 ± 0.56 b31.52 ± 0.8345112.46 ± 5.96
TT154.94 ± 0.39 b67.61 ± 1.4023.39 ± 1.46 a29.92 ± 2.155124.18 ± 15.04
p-value 0.0430.7580.0150.405 0.160
g.64617 C > TCC4015.41 ± 0.1066.31 ± 0.3821.42 ± 0.3932.52 ± 0.58136113.74 ± 4.12
CT1665.51 ± 0.1466.71 ± 0.5021.83 ± 0.5331.92 ± 0.7754115.05 ± 5.42
TT215.89 ± 0.3366.47 ± 1.2019.71 ± 1.2529.70 ± 1.846114.24 ± 13.97
p-value 0.4740.5210.2220.385 0.995
g.67836 G > AAA125.46 ± 0.4366.65 ± 1.5724.59 ± 1.61 a32.16 ± 2.39390.46 ± 20.00
GA1585.42 ± 0.1465.91 ± 0.5221.40 ± 0.53 b33.36 ± 0.7940115.51 ± 6.06
GG4185.48 ± 0.1066.61 ± 0.3821.23 ± 0.39 b31.77 ± 0.57153114.25 ± 3.97
p-value 0.7730.3480.0000.096 0.551
g.68017 G > AAA1775.50 ± 0.1467.03 ± 0.4921.62 ± 0.5331.58 ± 0.7564121.10 ± 5.18
GA2695.46 ± 0.1266.37 ± 0.4221.52 ± 0.4432.32 ± 0.6589115.82 ± 473
GG1455.43 ± 0.1465.65 ± 0.5121.10 ± 0.5132.93 ± 0.7945106.82 ± 5.38
p-value 0.2770.1340.6490.514 0.098
Bold values indicate p < 0.05; data in the same column with different lowercase letters on the shoulders indicate significant differences (p < 0.05). p is derived from the general linear mixed models (GLMMs). HCW: hot carcass weight; REA: rib eye area; WBSF: Warner–Bratzler shear force; DLR: drop loss rate; CLR: cook loss rate.
Table 5. Association between the presence and absence of ACACB haplotypes and carcass and meat quality traits (mean ± SE) a in yak.
Table 5. Association between the presence and absence of ACACB haplotypes and carcass and meat quality traits (mean ± SE) a in yak.
Trait (Unit)2HaplotypenSingle-Haplotype ModelpMulti-Haplotype Model p
PresentAbsentPresentAbsentOther Haplotypes in ModelPresentAbsent
WBSF (kg)H12452775.33 ± 0.115.43 ± 0.120.420H2, H5, H65.30 ± 0.165.43 ± 0.140.358
H2894335.56 ± 0.175.34 ± 0.100.186H5, H65.49 ± 0.195.30 ± 0.120.220
H31124105.34 ± 0.155.38 ± 0.100.797H2, H5, H65.37 ± 0.205.39 ± 0.130.896
H41563665.33 ± 0.145.39 ± 0.100.639H2, H5, H65.35 ± 0.185.40 ± 0.130.743
H51373855.31 ± 0.145.58 ± 0.100.049H2, H65.25 ± 0.175.35 ± 0.130.051
H61044185.03 ± 0.165.45 ± 0.100.006H2, H55.20 ± 0.185.59 ± 0.120.011
H7404825.28 ± 0.245.41 ± 0.100.576H2, H5, H65.17 ± 0.275.39 ± 0.130.336
H8724505.55 ± 0.185.38 ± 0.100.322H2, H5, H65.64 ± 0.215.37 ± 0.130.127
CLR (%)H124527766.08 ± 0.4366.72 ± 0.460.171H3, H4, H765.60 ± 0.6566.36 ± 0.600.120
H28943366.75 ± 0.6566.30 ± 0.380.468H1, H3, H4, H766.50 ± 0.9066.01 ± 0.580.449
H311241067.60 ± 0.5665.96 ± 0.390.004H1, H4, H766.54 ± 0.7265.31 ± 0.550.020
H415636665.80 ± 0.5266.58 ± 0.400.129H1, H3, H765.64 ± 0.6966.32 ± 0.570.200
H513738566.50 ± 0.5566.32 ± 0.390.737H1, H3, H4, H765.88 ± 0.8265.98 ± 0.570.868
H610441866.23 ± 0.6066.39 ± 0.390.777H1, H3, H4, H765.57 ± 0.8565.99 ± 0.570.505
H74048265.00 ± 0.9066.44 ± 0.370.097H1, H3, H465.28 ± 0.9266.68 ± 0.420.107
H87245066.11 ± 0.6866.41 ± 0.390.663H1, H3, H4, H765.22 ± 0.8866.01 ± 0.570.259
DLR (%)H124527721.45 ± 0.4221.11 ± 0.450.476
H28943321.79 ± 0.6521.22 ± 0.380.356
H311241021.25 ± 0.5621.32 ± 0.400.909
H415636621.55 ± 0.5221.20 ± 0.400.489
H513738521.65 ± 0.5521.20 ± 0.390.385
H610441821.16 ± 0.6021.33 ± 0.390.762
H74048222.15 ± 0.9021.25 ± 0.370.298
H87245019.96 ± 0.6821.53 ± 0.380.019
REA (cm2)H124527732.48 ± 0.6531.79 ± 0.700.336
H28943331.92 ± 0.9932.22 ± 0.580.754
H311241031.55 ± 0.8732.38 ± 0.610.339
H415636633.37 ± 0.8031.72 ± 0.610.034
H513738531.57 ± 0.8432.35 ± 0.600.332
H610441832.04 ± 0.9232.21 ± 0.590.842
H74048232.62 ± 1.3832.15 ± 0.570.725
H87245031.63 ± 1.0432.27 ± 0.590.536
HCW (kg)H19386102.43 ± 3.85110.05 ± 4.150.094
H230149105.92 ± 6.38105.79 ± 3.390.983
H336143109.55 ± 5.45104.70 ± 3.550.387
H441138109.94 ± 5.20104.42 ± 3.570.303
H543136111.64 ± 5.26104.03 ± 3.520.155
H641138106.97 ± 5.35105.47 ± 5.320.781
H712167108.63 ± 9.03105.58 ± 3.380.736
H827152106.36 ± 6.38105.70 ± 3.450.918
Bold values indicate p < 0.05. a Estimated marginal means and standard errors (SE); p is derived from the general linear mixed models (GLMMs). HCW: hot carcass weight; REA: rib eye area; WBSF: Warner–Bratzler shear force; DLR: drop loss rate; CLR: cook loss rate.
Table 6. Association between ACACB haplotype copy numbers and carcass and meat quality traits (mean ± SE) a in yak.
Table 6. Association between ACACB haplotype copy numbers and carcass and meat quality traits (mean ± SE) a in yak.
Trait (Unit)2HaplotypenSingle-Haplotype ModelpMulti-Haplotype Modelp
AbsentOne Copy
Present		Two Copy
Present		AbsentOne Copy
Present		Two Copy
Present		Other Haplotypes in ModelAbsentOne Copy
Present		Two Copy
Present
WBSF (kg)H1277204415.43 ± 0.125.30 ± 0.125.46 ± 0.230.570H2, H5, H65.75 ± 0.325.62 ± 0.365.80 ± 0.420.539
H24338365.34 ± 0.105.47 ± 0.186.56 ± 0.570.077H5, H65.31 ± 0.245.43 ± 0.306.53 ± 0.620.085
H341010395.37 ± 0.105.37 ± 0.154.86 ± 0.480.549H2, H5, H65.74 ± 0.325.76 ± 0.375.27 ± 0.580.587
H4366145115.39 ± 0.105.28 ± 0.145.90 ± 0.430.322H2, H5, H65.77 ± 0.325.69 ± 0.366.33 ± 0.550.316
H5385123145.30 ± 0.105.63 ± 0.155.18 ± 0.380.075H2, H65.71 ± 0.28 b6.04 ± 0.31 a5.53 ± 0.47 b0.046
H64189865.52 ± 0.10 a5.44 ± 0.16 b4.99 ± 0.57 b0.014H2, H56.02 ± 0.25 a5.41 ± 0.29 b5.84 ± 0.62 b0.021
H84507025.34 ± 0.105.54 ± 0.185.93 ± 0.980.450H2, H5, H65.78 ± 0.326.06 ± 0.376.42 ± 1.030.249
CLR (%)H12772044166.72 ± 0.4666.92 ± 0.4566.85 ± 0.860.232
H243383666.30 ± 0.3866.84 ± 0.6765.58 ± 2.190.658
H3410103966.00 ± 0.39 bc67.45 ± 0.58 b69.20 ± 1.80 a0.007
H43661451166.58 ± 0.4065.81 ± 0.5365.78 ± 1.630.316
H53851231466.32 ± 0.3966.36 ± 0.5767.75 ± 1.450.611
H641898666.39 ± 0.3966.25 ± 0.6265.87 ± 2.180.947
H845070266.41 ± 0.3966.06 ± 0.6967.88 ± 3.760.811
DLR (%)H12772044121.12 ± 0.45 ab21.84 ± 0.45 a19.52 ± 0.86 b0.028H817.70 ± 1.29 b18.17 ± 1.32 a15.71 ± 1.52 ab0.023
H243383621.23 ± 0.3821.95 ± 0.6719.74 ± 2.180.402H1, H817.19 ± 1.3117.60 ± 1.4615.45 ± 2.540.575
H3410103921.31 ± 0.4021.27 ± 0.5821.03 ± 1.810.985H1, H817.22 ± 1.3116.91 ± 1.4116.71 ± 2.220.842
H43661451121.21 ± 0.4021.47 ± 0.5322.70 ± 1.620.596H1, H817.21 ± 1.3117.19 ± 1.4118.41 ± 2.100.752
H53851231421.20 ± 0.3921.64 ± 0.5721.74 ± 1.450.684H1, H817.20 ± 1.3117.35 ± 1.4317.55 ± 1.970.947
H641898621.33 ± 0.3921.16 ± 0.6221.03 ± 2.170.954H1, H817.19 ± 1.3116.82 ± 1.4416.47 ± 2.540.797
H845070221.52 ± 0.38 a20.18 ± 0.68 b11.55 ± 3.71 c0.004H121.00 ± 0.42 a19.58 ± 0.74 b11.00 ± 3.70 c0.004
REA (cm2)H12772044131.79 ± 0.7032.51 ± 0.7032.34 ± 1.320.625
H243383632.22 ± 0.5832.03 ± 1.0230.54 ± 3.350.867
H3410103932.30 ± 0.6131.91 ± 0.8826.60 ± 2.760.107
H43661451131.72 ± 0.6133.36 ± 0.8133.50 ± 2.480.105
H53851231432.35 ± 0.6031.66 ± 0.8830.78 ± 2.220.580
H641898632.21 ± 0.5932.05 ± 0.9531.81 ± 3.340.978
H845070232.27 ± 0.5931.60 ± 1.0632.83 ± 0.750.807
HCW (kg)H1837719109.40 ± 4.10 a105.99 ± 4.05 ab86.68 ± 7.32 c0.011H4101.72 ± 6.56 a97.79 ± 7.05 ab79.17 ± 9.54 c0.014
H2149282105.79 ± 3.40106.24 ± 6.56101.36 ± 21.510.975H1, H492.72 ± 6.7988.84 ± 9.3882.63 ± 22.380.755
H4138383104.44 ± 3.55112.08 ± 5.3483.02 ± 17.390.160H1100.27 ± 3.82104.73 ± 5.8173.69 ± 17.380.197
H5138383103.92 ± 3.53110.40 ± 5.62119.53 ± 13.630.300H1, H492.78 ± 6.7895.27 ± 9.03105.99 ± 15.570.625
H613833167105.44 ± 3.53106.27 ± 5.57115.20 ± 18.260.861H1, H492.91 ± 6.7990.21 ± 9.1496.25 ± 19.650.880
H8153251105.64 ± 3.46107.63 ± 6.6097.84 ± 30.520.945H1, H492.75 ± 6.7989.13 ± 9.8977.77 ± 30.820.784
Bold values indicate p < 0.05; data in the same column with different lowercase letters on the shoulders indicate significant differences (p < 0.05). a Estimated marginal means and standard errors (SE); p is derived from the general linear mixed models (GLMMs). HCW: hot carcass weight; REA: rib eye area; WBSF: Warner–Bratzler shear force; DLR: drop loss rate; CLR: cook loss rate.
Table 7. Association between ACACB diplotypes and carcass and meat quality traits (Mean ± SE) a in yak.
Table 7. Association between ACACB diplotypes and carcass and meat quality traits (Mean ± SE) a in yak.
DiplotypesMeat QualityCarcass Quality
nWBSF (kg)CLR (%)DLR (%)REA (cm2)nHCW (kg)
H1H1415.45 ± 0.2266.59 ± 0.9219.65 ± 0.9132.15 ± 1.471987.99 ± 8.21
H1H2325.16 ± 0.2567.17 ± 1.0522.29 ± 1.0430.76 ± 1.671294.52 ± 10.07
H1H3435.29 ± 0.2166.91 ± 0.8823.09 ± 0.8733.39 ± 1.4120114.38 ± 7.44
H1H4455.12 ± 0.2165.12 ± 0.8922.19 ± 0.8733.50 ± 1.4115110.03 ± 8.76
H1H5345.77 ± 0.2465.12 ± 1.0120.98 ± 1.0031.01 ± 1.6211111.14 ± 10.32
H1H6235.05 ± 0.2966.22 ± 1.2221.16 ± 1.2132.46 ± 1.951197.36 ± 9.97
H4H5245.65 ± 0.2965.82 ± 1.2221.70 ± 1.2032.31 ± 1.946112.55 ± 13.42
H4H6184.72 ± 0.3365.31 ± 1.3820.57 ± 1.3633.29 ± 2.20283.57 ± 22.65
H5H6235.30 ± 0.3066.17 ± 1.2421.66 ± 1.2330.74 ± 1.9910122.13 ± 10.60
H7H8325.16 ± 0.2567.17 ± 1.0522.29 ± 1.0430.76 ± 1.671294.52 ± 10.07
p-value 0.1460.7160.2200.839 0.095
Table 8. Primer sequence information for the three regions of yak ACACB.
Table 8. Primer sequence information for the three regions of yak ACACB.
GeneRegionPrimer Sequence (5′–3′)Amplicon Size (bp)Annealing Temperature (°C)
ACACBExon 37–intron 37F: AAAATCTTCTTCCTCCCTG
R: CGTGTATCTGTGCCGTCTA		45560
ACACBExon 46–intron 46F: ACGGTGGCTGCCTTGCTTT
R: ATGCTGGACGCTGGTTTCA		36260
ACACBIntron 47F: TCCCAGAGCACTTTACTT
R: ATACCCGTCATCACCAT		79060
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Zhu, C.; Qi, Y.; Wang, X.; Mi, B.; Cui, C.; Chen, S.; Zhao, Z.; Zhao, F.; Liu, X.; Wang, J.; et al. Variation in Acetyl-CoA Carboxylase Beta Gene and Its Effect on Carcass and Meat Traits in Gannan Yaks. Int. J. Mol. Sci. 2023, 24, 15488. https://doi.org/10.3390/ijms242015488

AMA Style

Zhu C, Qi Y, Wang X, Mi B, Cui C, Chen S, Zhao Z, Zhao F, Liu X, Wang J, et al. Variation in Acetyl-CoA Carboxylase Beta Gene and Its Effect on Carcass and Meat Traits in Gannan Yaks. International Journal of Molecular Sciences. 2023; 24(20):15488. https://doi.org/10.3390/ijms242015488

Chicago/Turabian Style

Zhu, Chune, Youpeng Qi, Xiangyan Wang, Baohong Mi, Changze Cui, Shaopeng Chen, Zhidong Zhao, Fangfang Zhao, Xiu Liu, Jiqing Wang, and et al. 2023. "Variation in Acetyl-CoA Carboxylase Beta Gene and Its Effect on Carcass and Meat Traits in Gannan Yaks" International Journal of Molecular Sciences 24, no. 20: 15488. https://doi.org/10.3390/ijms242015488

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