Skip to main content
Download PDF

SNP variation in the promoter of the PRKAG3gene and association with meat quality traits in pig

BMC Genetics volume 13, Article number: 66 (2012) Cite this article

Abstract

Background

The PRKAG3 gene encodes the γ3 subunit of adenosine monophosphate activated protein kinase (AMPK), a protein that plays a key role in energy metabolism in skeletal muscle. Non-synonymous single nucleotide polymorphisms (SNPs) in this gene such as I199V are associated with important pork quality traits. The objective of this study was to investigate the relationship between gene expression of the PRKAG3 gene, SNP variation in the PRKAG3 promoter and meat quality phenotypes in pork.

Results

PRKAG3 gene expression was found to correlate with a number of traits relating to glycolytic potential (GP) and intramuscular fat (IMF) in three phenotypically diverse F1 crosses comprising of 31 Large White, 23 Duroc and 32 Pietrain sire breeds. The majority of associations were observed in the Large White cross. There was a significant association between genotype at the g.-311A>G locus and PRKAG3 gene expression in the Large White cross. In the same population, ten novel SNPs were identified within a 1.3 kb region spanning the promoter and from this three major haplotypes were inferred. Two tagging SNPs (g.-995A>G and g.-311A>G) characterised the haplotypes within the promoter region being studied. These two SNPs were subsequently genotyped in larger populations consisting of Large White (n = 98), Duroc (n = 99) and Pietrain (n = 98) purebreds. Four major haplotypes including promoter SNP’s g.-995A>G and g.-311A>G and I199V were inferred. In the Large White breed, HAP1 was associated with IMF% in the M. longissmus thoracis et lumborum (LTL) and driploss%. HAP2 was associated with IMFL% GP-influenced traits pH at 24 hr in LTL (pHULT), pH at 45 min in LTL (pH45LT) and pH at 45 min in the M. semimembranosus muscle (pH45SM). HAP3 was associated with driploss%, pHULT pH45LT and b* Minolta. In the Duroc breed, associations were observed between HAP1 and driploss% and pHUSM. No associations were observed with the remaining haplotypes (HAP2, HAP3 and HAP4) in the Duroc breed. The Pietrain breed was monomorphic in the promoter region. The I199V locus was associated with several GP-influenced traits across all three breeds and IMF% in the Large White and Pietrain breed. No significant difference in promoter function was observed for the three main promoter haplotypes when tested in vitro.

Conclusion

Gene expression levels of the porcine PRKAG3 are associated with meat quality phenotypes relating to glycolytic potential and IMF% in the Large White breed, while SNP variation in the promoter region of the gene is associated with PRKAG3 gene expression and meat quality phenotypes.

Background

Adenosine monophosphate activated protein kinase (AMPK) is a heterodimeric serine/threonine protein kinase. This enzyme is a metabolic master regulator of several intracellular pathways, including cellular uptake of glucose, glycogen synthesis and β-oxidation of fatty acids, controlling metabolism through transcription and direct effects on metabolic enzymes [1–3]. AMPK is composed of a catalytic α-subunit and two regulatory non-catalytic β- and γ- subunits. A specific isoform of the regulatory γ subunit of AMPK (AMPK γ3) has a role in the metabolic plasticity of fast-glycolytic muscle [4]. The AMPK γ3 isoform is encoded by the highly conserved PRKAG3 gene and is primarily expressed in white (fast-twitch, type IIb) skeletal muscle fibers [5, 6]. Gain of function mutations in the PRKAG3 gene have been correlated with increased glycogen content in skeletal muscle in pig [7, 8], mice [9] and humans [10]. The metabolic consequences of these mutations extend beyond glycogen metabolism however, and can also influence other characteristics of the muscle, including mitochondrial biogenesis [11], fatty acid uptake [10, 12] oxidative capacity [12] and differential responses to exercise [8].

Five non-synonymous substitutions (T30N, G52S, L53P, I199V and R200Q) have been reported in the porcine PRKAG3 gene [7, 13, 14]. The effects of I199V and R200Q are the most widely studied. Both mutations are located in a highly conserved region of the cystathionine β- synthase domain [13] which is believed to act as a sensor of cellular energy status [15]. The R200Q polymorphism has been linked to a 70% increase in glycogen content in the skeletal muscle of Hampshire pigs. The resultant alteration in GP affects meat quality traits including water holding capacity (drip loss%) and pH, as well as processing yields [13], but the SNP has largely been eliminated from breeding populations [16]. The adjacent polymorphism I199V[7] has also been associated with glycolytic traits including water holding capacity [17] and pH [18]. The minor I allele is widely reported to have positive effect on pH and water holding capacity in diverse pig breed populations [7, 17–19]. The physiological functions of PRKAG3 contribute to a wider range of pork quality characteristics including colour [18, 20, 21], carcass composition [22, 23], seasoning losses [24] and IMF% [25, 26].

Preliminary data from our group has suggested that the PRKAG3 gene is differentially expressed in LTL muscle of pigs displaying extremes in the distribution of drip loss% values in a Large White population [27]. Hence, our first objective was to test for an association between PRKAG3 gene expression and pigmeat quality phenotypes, with a focus on GP-influenced traits and IMF%. This was performed in 3 sire breeds (Large White, Duroc and Pietrain) that are divergent in their muscle and meat quality characteristics [28]. Alterations in gene expression can be caused by genetic variation in the regulatory regions of the gene [29–31]. Hence, the second objective was to identify novel genetic variation in the PRKAG3 gene promoter region. The final objective was to examine if the identified allelic variants differed in promoter function by: a) testing for association between novel SNPs/haplotypes and gene expression and pork quality traits, and b) investigating how haplotype variation affects putative transcription factor binding sites in silico and promoter function in vitro.

Results

Relationship between PRKAG3expression and pork quality phenotypes

A number of correlations were identified between PRKAG3 [GenBank; NM_214077.1] expression levels in the LTL muscle and meat quality phenotypes which are known to be influenced by glycolytic potential in the muscle (termed GP-influenced traits) in the F1 crossbred pig populations (Table 1). The Large White population displayed the greatest number of significant correlations, with a smaller number of correlations observed in the Duroc cross breed, while none were observed in the Pietrain cross breed. In the Large White cross breed a number of traits were positively correlated with PRKAG3 gene expression including water holding capacity measures driploss% and cookloss%. ECULT and colour Minolta L* were also positively correlated with PRKAG3 gene expression while pHULT was negatively correlated. A smaller number of correlations with GP- influenced traits were evident in the Duroc cross breed including pHULT (negative) and Minolta L* on Day 7 (positive). IMF % was also positively correlated with PRKAG3 gene expression in the Large White cross breed.

Table 1 Pearson correlation (r 2 ) between PRKAG3 gene expression and phenotypic measurements in 3 crossbred pig population: Large White, Duroc and Pietrain-sired F1 female offspring with a common Large White x Landrace background
Full size table

SNP Discovery and identification of tagging SNPs

Sequencing of the 1322 bp upstream region revealed 10 novel SNPs (Figure 1) in the crossbred population. Minor Allele Frequencies (MAF) in the F1 population were variable for each cross breed and are outlined in Table 2. Two haplotype blocks were identified in the promoter region in these F1 populations; comprising 4 SNPS in each (Figure 2). Linkage disequilibrium was not significant between any of the SNPs in the promoter region and the non-synonymous SNP I199V in the coding region in the crossbred populations (Figure 2).

Figure 1
figure 1

Overview of SNPs in promoter region of PRKAG3 gene and outline of alternative transcripts.

Full size image
Table 2 Novel SNPS in the promoter region, and * I199V non synonymous SNP (previously published), including positions relative to transcription start site, base pair change and minor allele frequency in 3 crossbred pig populations: Large White, Duroc and Pietrain-sired F1 female offspring with a common Large White x Landrace background
Full size table
Figure 2
figure 2

Output from Haploview including linkage disequilibrium (LD) plots indicating r 2 values for SNPs in the regulatory region and inferred haplotypes for entire crossbred populations; Large White X (n = 30), Duroc X (n = 23) Pietrain X (n = 32).

Full size image

The three major haplotypes were identified in the promoter region; AGACAGCA, GAGTGAGG and GAGTAGGG which accounted for 80% of the total variation in the Large White, 90% in the Pietrain and 93% in Duroc (Table 3). Genotypes at the g.-995A>G and g.-311A>G loci permitted resolution of these three major promoter haplotypes in all three F1 populations.

Table 3 Promoter haplotype frequencies for pure bred populations: Large White (n = 98), Duroc (n = 99) and Pietrain (n = 98)
Full size table

Genotypes were tested for association with PRKAG3 gene expression. A significant association (P =0.03) was observed between genotype at the g.-311A>G position and PRKAG3 gene expression in the Large White breed. AA genotypes displayed the lowest gene expression (Mean = 0.31, ± 0.03), heterozygotes intermediate (Mean = 0.43, ± 0.04) and GG the highest (Mean = 0.51, ± 0.06). No association between the g.-995A>G SNP and gene expression was observed in any cross breed.

Genotyping of tagging SNP’s in larger purebred populations

Genotypes were obtained for 2 SNPs in the promoter region (g.-995A>G and g.-311A>G) and the previously reported non-synonymous SNP I199V in three purebred populations. SNPs g.-995A>G and g.-311A>G in the promoter region were polymorphic in the Large White (Minor Allele Frequency (MAF): 0.25 and 0.24 respectively) and Duroc (MAF: 0.49 and 0.29 respectively) breeds. Pietrain animals were almost completely monomorphic for both these SNPs (MAF, 0.051 and 0.053, respectively). All breeds were polymorphic at the I199V locus. Linkage disequilibria plots showed no significant linkage disequilibrium between the two tagging SNPs in the promoter region and the I199V SNP in either the crossbred (Figure 2) or purebred (Figures 3 & 4) populations. Genotype frequencies were in agreement with Hardy Weinberg equilibrium.

Figure 3
figure 3

Output from Haploview including linkage disequilibrium (LD) plots indicating r 2 values for SNPs in the regulatory region and inferred haplotypes for purebred Large White population (n = 98).

Full size image
Figure 4
figure 4

Output from Haploview including linkage disequilibrium (LD) plots indicating r 2 values for SNPs in the regulatory region and inferred haplotypes for purebred Duroc population (n = 99).

Full size image

I199Vand associations with GP traits and IMF%

SNP I199V was associated with a number of GP-influenced traits in all three pure breeds (Table 4). The association with driploss% was observed in all breeds, where the II genotype had the lowest driploss% and the VV genotype had the highest driploss%. The effect was additive in all breeds, a 47%, 54% and 31% decrease in driploss% was observed from VV to II, in Large White, Duroc and Pietrain breeds respectively. In the Duroc breed, there was a small but significant additive association with ultimate pH at 24 hr in the M. semimembranosus muscle (pHUSM), with the II genotype having the highest and VV the lowest pH values. In the Pietrain breed, there were associations with electrical conductivity in the SM muscle (ECUSM), again the effect was additive with a 22% reduction in conductivity from VV to II. Minolta a* was associated with I199V in this breed, the heterozygote AG genotype had a lower colour score (~26%) compared to both homozygotes.

Table 4 Estimated least squares means for meat quality traits in relation to I199V locus in 3 purebred Large White and Duroc and Pietrain animals
Full size table

SNP I199V was associated with IMF% in the LTL muscle in both Large White and Pietrain breeds. The II genotype had the highest IMF% and VV the lowest in both breeds. The effect was additive, with a 37% and 29.4% decrease in IMF% from the II to VV genotype, in Large White and Pietrain, respectively.

SNP g.-995A>G

The major allele (A) was fixed in the Pietrain breed. Association analysis of g.-995A>G and meat quality traits in Large White and Duroc breeds are presented in Table 5. In the Large White breed this SNP was associated with a range of GP-influenced traits. AA animals had a 34.5% decrease in driploss% relative to the alternative homozygote (GG) genotype, with the AG genotype driploss% values intermediate. Samples with alternative AA/GG genotypes thus differed by more than 1.2% in total driploss%. Similarly AA genotype animals had higher pHULT, pH45LT, lower ECULT, and a tendency for lower Minolta L* and b* values. No significant associations were observed in the Duroc population.

Table 5 Estimated least squares means for meat quality traits in relation to g.-995A>G and g.-311A>G SNP genotypes in purebred Large White and Duroc animals
Full size table

SNPg.-311A>G

There was almost complete linkage observed between g.-995A>G and g.-311A>G in the purebred Large White breed population (Figure 3) and to a lesser degree in the purebred Duroc population (Figure 4). All significant associations with g.-311A>G were observed in the Large White breed and mirror those reported for g.-995A>G.

Association analysis between PRKAG3 haplotypes for promoter SNPS and I199Vand pork quality

Four major haplotypes comprising SNP g.-995A>G, SNPg.-311A>G and I199V were inferred by the Arlequin software [32]. Associations for all haplotypes and estimated least square means for meat traits per haplotype copy are outlined for the Large White and Duroc Breeds in Tables 6 and 7 respectively. HAP2 was the most frequent haplotype in the Large White breed: (Freq: 0.47). All haplotypes were relatively evenly represented in the Duroc breed HAP1 (Freq: 0.19), HAP 2 (Freq: 0.28), HAP 3 (Freq: 0.28) and HAP4 (Freq: 0.21). As the promoter SNPs were fixed in the Pietrain breed, haplotype association analysis was not performed for this breed.

Table 6 Estimated least square means for significant meat quality traits in relation to copy number of the most abundant porcine PRKAG3 gene haplotypes as inferred by Arlequin for the promoter SNPs g.-995A>G and SNPg.-311A>G and non-synonymous SNP I199V locus in the Large White breed (n = 98)
Full size table
Table 7 Estimated least square means for significant meat quality traits in relation to copy number of the most abundant porcine PRKAG3 gene haplotypes as inferred by Arlequin for the promoter SNPs g.-995A>G and SNPg.-311A>G and non-synonymous SNP I199V locus in the Duroc breed (n = 98)
Full size table

HAP1 was associated with IMF% in the LTL muscle and driploss% in the Large White breed. In the Duroc breed, HAP1 was associated with driploss% and pHUSM. In both breeds, two copies of this haplotype had the most favourable effects on the associated phenotypes.

HAP2 was associated with IMF% in the LTL muscle, pHULT, pH45LT and pH45SM in the Large White breed. The presence of two copies of this haplotype had the most favourable effect on these associated traits. No associations were observed for HAP2 in the Duroc breed.

HAP3 was associated with driploss%, pHULT, pH45LT and b*Minolta in the Large White breed. The presence of two copies of this haplotype had the least favourable effect on these traits. No associations were observed for HAP3 in the Duroc breed.

HAP4 was present at too low a frequency (Freq: 0.01) in the Large White breed for subsequent analysis. No associations were observed for HAP4 in the Duroc breed.

In-silico analysis of SNPs in relation to transcription factor binding sites in the promoter region

In-silico analysis of the SNP loci using five transcription factor binding site (TFBS) prediction tools (TFSEARCH, TESS, MatInspector, Match™ and ALIBABA2) is presented in Table 8. A CCAAT/enhancer binding protein site, encompassing the g.-995A>G site, was altered to a GATA binding site on substitution to the minor allele. This change was identified using all five tools.

Table 8 In-silico analysis of transcription factor binding site motifs at SNP sites in the PRKAG3 promoter region
Full size table

Promoter assay

The three major promoter haplotypes outlined in (Table 3) used in the in vitro promoter assay accounted for approximately 90% of the variation within the three breeds. Although no significant difference (P = 0.45) was observed between the haplotypes, transcriptional activity was established in the region cloned when tested in vitro in pre-adipocytes (Figure 5).

Figure 5
figure 5

The firefly luciferase values were normalised for total protein, and data is presented relative to the empty vector control assigned a value of 1. The results are expressed as mean and the SE of three separate experiments performed in quadruplicate. Statistical analysis was performed using one way ANOVA.

Full size image

Discussion

In this study, PRKAG3 gene expression levels in porcine LTL muscle were correlated with a number of meat quality phenotypes including GP-influenced traits and IMF%. As SNPs in the regulatory region of a gene can influence the transcription rate [33], we explored the hypothesis that SNPs/inferred haplotypes in the PRKAG3 promoter would be associated with pork quality. This hypothesis was further validated by identifying an association between g.-311A>G and PRKAG3 gene expression and by the significant associations identified between g.-995A>G, g.-311A>G and quality traits pHULT, pH45LT, ECULT, driploss% and Minolta L* in the Large White pure bred population. The associations with non-synonymous SNP I199V in this study served to validate the studied phenotypic model and provided a useful comparator with which to view the novel promoter SNP associations. Inclusion of this much studied SNP also indicated that the study had sufficient statistical power to detect the influence of polymorphisms of moderate effect size. Ciobanu and colleagues highlighted the complexity of the phenotype/genotype relationship in the PRKAG3 gene, proposing the model of “one gene-several polymorphisms-diverse phenotypes” when describing the phenotypic effects of the non-synonymous SNPs identified in the PRKAG3 gene [7]. Our data supports this model as the novel promoter SNPs were not in significant linkage disequilibrium with the I199V locus, and exert different effects on phenotype which are independent from each other. This adds to the complexity of the phenotype/genotype relationship, and indicates that the additive effects of all these functionally distinct loci need to be considered in future studies on pork quality.

In this study, the associations which were observed between the I199V locus and a number of GP-influenced traits were in concordance with the wider literature in the breeds studied [7, 13, 17, 19, 22]. The association between I199V and IMF%, which was observed in both Large White and Pietrain breeds, is noteworthy because there is a lack of consensus in the wider literature on the relationship between I199V and IMF%. The association identified in our study, i.e. increasing IMF% from the VV to II genotype, is in concordance with two studies that report the same trend in a Korean native breed cross [26] and a Meishan cross [25]. The opposite trend was previously reported for a Landrace population [25] while several studies have reported a lack of association between I199V and IMF% [17, 20, 34]. Given the central role that AMPK has on all forms of energy metabolism, it is not surprising that variation that affects the function of its regulatory component might impact on lipid deposition in muscle. Associations between this gene region and lipid metabolism in muscle appear to be evolutionarily conserved as AMPK/PRKAG3 is implicated as a molecular target to promote marbling in cattle [35] and triglyceride accumulation in mice [9].

The associations between PRKAG3 gene expression and both GP-influenced traits and IMF% suggests that not only are protein structural modifications within the PRKAG3 coding region influencing the meat quality traits studied, but that variability in the abundance of the transcript is also influential. This may be mediated by genetic variation in promoter activity since SNP g.-311A>G was significantly associated with gene expression. This hypothesis is further supported by the associations observed between both g.-995A>G and g.-311A>G and GP-influenced traits i.e. pHULT, driploss% and colour. Generally, the A allele in both promoter SNPs was associated both with improved meat quality based on glycolytic traits and reduced PRKAG3 gene expression. HAP 2 and HAP 3 both have the same genotype V at the I199V locus but differ with respect to their effects on Driploss%, further supporting the effects of the promoter SNPs on phenotype. Drip loss is a significant challenge for the industry [36, 37] and these haplotypes are potentially relevant to selection strategies aimed at improving this trait. Further to this, the promoter SNPs had highly significant associations with pHULT (p = 0.0003) in the Large White breed, an association which was not observed with I199V. Interestingly in this study, the associations with GP-influenced phenotypes are wider ranging and statistically more significant for the promoter SNPs than for I199V in the Large White breed. However, the associations with GP-influenced traits are more consistent across breeds for the I199V SNP and of slightly larger magnitude with respect to driploss%. It is noteworthy that in a recent study, a mouse model developed to over express AMPKγ reported a different phenotype to that expressing the gain of function variant of AMPKγ with respect to mitochondrial biogenesis indicating that the phenotypic effects of transcript abundance and mutations affecting protein function in PRKAG3 are not equivalent in skeletal muscle [38].

One of the modes by which SNPs in the regulatory region of a gene can influence transcription rate is by forming or abolishing a TFBS [39]. In this study, the in-silico analysis suggested that g.-995A>G alters the associated transcription factor-binding site from a putative CCAAT/enhancer binding protein site to a putative GATA site on substitution to the minor allele. Both CCAAT/enhancer binding protein and GATA transcription factors have been implicated as key regulators of adipocyte differentiation and lipid metabolism [40], suggesting a potential role of this SNP in variation in IMF%. While g-311A>G was not associated with a putative TFBS site, it is positioned in a highly conserved 5' UTR, that contains several regulatory motifs characteristic of a core promoter [41]. 5' UTR regions have regulatory significance for translational efficiency and subsequent phenotype [42]. The substitution of SNP at g.-311A>G may affect the core promoter and thus modulate gene expression. This region also forms part of a 5' exon in the porcine transcriptome, referred to as ‘exon zero’ [41]. Exon zero is contained in one of the two known alternative transcripts of the PRKAG3 gene. Hence based on in silico evidence it is possible that both these SNPs could have functional roles in the regulation of PRKAG3, however associations with PRKAG3 gene expression were only observed for SNP g-311A>G and not g.-995A>G.

Taken together, the associations between PRKAG3 promoter haplotypes, gene expression and phenotype gene suggest the possibility of differential promoter activity. While the promoter assay confirmed promoter activity in the region cloned in all the major haplotypes, the assay was unable to discern significant differences in transcriptional activity between the haplotypes in pre-adipocyte cells. It is however worth re-iterating that the transcription of PRKAG3 is mainly ascribed to white skeletal muscle in humans and other species [5]. Furthermore the phenotypic associations observed were highly breed and tissue specific indicating both a highly specific and finely tuned transcriptional apparatus. It is therefore likely that changes in transcription rate mediated by variation in the transcription binding sites may only be realised in vivo as evidenced by the association between genotype at the g-311A>G locus and PRKAG3 gene expression in the Large White cross.

In common with other recent porcine SNP association studies [29, 43, 44], the effects of putative functional SNPs can be breed and muscle specific. The effect of these SNPs was certainly not consistent across breed and muscle type studied. Breed seems to have important consequences for the phenotypic outcome of variation in both the regulatory and coding regions of this gene, and this was particularly evident for the promoter region. Alleles characterising HAP2 and HAP3 were associated with GP-influenced traits in the Large White breed only. Most of the associations observed in this study were seen in the LTL muscle. The relationship between muscle biochemistry and meat quality phenotypes has been shown to differ for the LTL and SM muscle [45]. PRKAG3 gene expression is greater in LTL muscle [46] which displays more glycolytic traits compared to SM muscle [45]. In exercised trained pigs, carriers of the R200Q mutation has been shown to influence the relative fibre composition to varying degrees among functionally different muscles, thereby promoting a more oxidative phenotype [47]. With respect to the breed differences, the Large White has the largest percentage of fast glycolytic fibres in the LT muscle relative to the Pietrain and the Duroc breeds [45]. Hence, breed and muscle fibre type likely play interactive roles in defining the penetrance of the novel alleles in the PRKAG3 promoter in relation to meat quality phenotypes.

While the overall goal of SNP/meat quality association studies is to provide markers to improve quality parameters in breeding populations, it is becoming increasingly apparent that there is significant breed variation in the potential for genetic improvement based on fixation of particular haplotypes in certain breeds. It is of interest that the I199V substitution effect on GP-influenced traits was conserved across all breeds, despite their divergent muscle characteristics. In contrast the promoter allele effect was apparent in the Large White breed, but not in the Duroc breed. This was also the case for the associations between gene expression and phenotype. It is noteworthy that the Duroc breed had superior meat quality characteristics as evidenced in earlier work [45, 48]. This is supported by previous studies, where substantial genetic variation has been observed in IGF2 gene and the ANK1 gene regions in the Large White breed, but dramatically less in the Pietrain and Duroc breeds [29, 43]. While Duroc and Pietrain are terminal breeds, in which carcass characteristics are the main selection criteria, the Large White breed is a maternal breed and therefore may retain a higher degree of genotypic and phenotypic diversity for meat quality traits. It is important to note however that there may have been a degree of ascertainment bias in SNP discovery in this study, because the genetic contribution of the Large White breed was approximately 33% in the Pietrain and Duroc cross-bred populations which were used for SNP discovery.

Conclusions

To conclude, variation in the promoter region of the porcine PRKAG3 gene has associations with meat quality phenotypes, including traits which are influenced by glycolytic potential and muscle metabolism in a breed-dependent manner. The novel SNPs presented here, combined with the I199V SNP, represent a new opportunity to select for reduced drip loss in terminal Large White sires based on combined novel genetic variation in the promoter, and known coding SNP in the PRKAG3 gene. The lack of linkage disequilibrium observed between the promoter SNPs and the I199V locus infer that their effects on phenotype are independent hence the additive effects of all these loci should be considered in future studies on pork quality.

Methods

Animal resources

M. longissimus thoracis et lumborum (LTL) and M. semimembranosus (SM) tissues and blood were collected from two animal resources (86 crossbred and 295 purebred pigs) in which meat quality phenotypic data was available for a number of traits as previously described [43, 45, 46, 49]. The cross-bred resource comprised of 31 Large White-sired, 23 Duroc-sired and 32 Pietrain-sired F1 female offspring with a common Large White × Landrace background. The purebred animals were females sampled from each of three closed populations (breeding lines) based on Large White (n = 98), Duroc (n = 99) and Pietrain (n = 98) and all reared in the same production system The purebred animals were reared in the same conditions and fed the same diet and were slaughtered at 140 days with a live weight of 109.56 ± 7.81 kg. [45]. The Pietrain line was homozygous dominant, NN (“Normal”) for the RYR1 [45, 46].

PRKAG3gene expression quantification

PRKAG3 gene expression was quantified in the LTL tissue samples which were collected from the crossbred F1 animal resource for transcriptomic analysis. All tissue was taken from approximately the same posterior location, in RNAse free conditions and preserved in RNALater® (Ambion Ltd., Cambridge, UK) within 10 min post-exsanguination, snap-frozen in dry ice, kept overnight at 4°C, and then stored at −20°C. RNA was extracted using the Qiagen RNeasy® Fibrous Tissue Mini Kit (Qiagen, Hilden, Germany Ltd, West Sussex, UK) according to the manufacturer’s instructions, together with a DNase treatment. RNA integrity was assessed using the Bioanalyser 2100 RNA nano chip (Agilent Technologies, Santa Clara, California, USA) and quantified with the NanoDrop 1000 Spectrophotometer (Thermo Scientific, Waltham, MA).

Reverse transcription was carried out using total RNA to generate a cDNA template for use with the QuantiTect SYBR Green PCR Kit (Qiagen, Hilden, Germany). For the reverse transcription 2.5 μg of total RNA, 1 μl oligo (dT)12-18 (500 μg/ml), 1 μl of a 10 mM dNTP mix were combined together in a final volume of 12 μl, heated at 65°C for 5 min and then placed immediately on ice. The contents were collected by a brief centrifugation before adding 4 μl 5X first strand buffer, 2 μl 0.1 M DTT, 1 μl SUPERase-In (Ambion, Foster City) and 1 μl of Superscript III RNase H reverse transcriptase (200 u/μl) (Invitrogen, Carlsbad, CA). The reverse transcription was carried out at 50°C for 1 h followed by an enzyme inactivation step of 70°C for 15 min. The cDNA was diluted to 10 ng/μl for use as a template for quantitative PCR (qPCR).

All qPCR was performed on the Mx3000P™ Real-Time PCR System (Stratagene, La Jolla, California, USA) using the QuantiTect™ SYBR Green Kit (Qiagen, Hilden, Germany). qPCR reactions were carried out using 2X QuantiTect SYBR Green PCR Master mix, 1 μl of 10 mM sense and anti-sense gene specific primers, (final conc. 0.5 mM), 3 μl dH2O and 5 μl of total cDNA template (10 ng/ml) in a total volume of 20 μl. The thermal profile was 95°C for 15 min, followed by 40 cycles of 94°C for 15 s, 60°C for 30 s and 72°C for 30 s.

PRKAG3 mRNA expression levels were determined for both splice variants using gene specific primers [Genbank:NM_2140777]:

Forward: 5' CTCCGACTCCAACACAGACCATCT 3',

Reverse: 5' TTCTGCAGCTCATCATCCCAGC 3'. PRKAG3 gene expression was normalised using the geometric mean of reference genes: Ribosomal protein L4 (RPL4): [Genbank: DQ845176]:

Forward: 5' AGAGATCCAAAGAGCCCTCCGC 3' and

Reverse 5' GCCTGGCGAAGAATGGTGTTTC 3' and TATA box binding protein (TBP): [Genbank: DQ845178]: Forward: 5' TTAATGGTGGTGTTGTGGACGGC 3', Reverse: 5'CCAAATAGCAGCACAGTACGAGCAA 3'. These reference genes were previously found to be stable for gene expression analysis in LTL muscle [50]. Stability was re-confirmed on these samples using Genorm [51]. PCR efficiencies (E) was calculated for each target PRKAG3 (99%), RPL4 (102%) and TBP (99.9%) and hence were suitable for comparison. The relative expression was calculated according to an established protocol [52, 53].

DNA preparation, promoter SNP discovery and SNP genotyping

Genomic DNA was extracted from LTL muscle tissue samples using DNeasy kit from Qiagen (Qiagen, Hilden, Germany) or from whole blood using the Wizard Genomic DNA Purification Kit (Promega, Madison, USA). DNA quantity and purity (A260/A280 ratio) for each sample was assessed using the NanoDrop™ 1000 Spectrometer (Thermo Scientific, Waltam, MA, USA).

The promoter region of the PRKAG3 gene (1322 bp long) was sequenced in the crossbred F1 population.

Primers were designed from draft sequence of a BAC clone (GenBank Ref. AY263454) which contained sequence flanking the PRKAG3 gene [41]. Primers were designed using the web based application Primer 3 [54].

A 1322 bp fragment located in the promoter region was amplified using 20 pmol of primer: Forward: 5′ AGGGATGCTGCAGAAGAAGA '3 and Reverse: 5′ CACACAGAACCGCACAGACT '3, 20 ng genomic DNA using Qiagen PCR Master Mix (Qiagen, Hilden, Germany) in a 50 μl volume. The PCR conditions for the touchdown PCR reaction are as follows: 95°C for 2 min, and for 14 cycles; 95°C for 30 s, 62.3°C (decrease 0.5°C per cycle) for 30 s, 72°C for 2 min 20 s. Followed by 19 cycles; 95°C for 30 s, 53.3°C for 30 s and 72°C for 2 min, 20 s.

PCR products were analysed by agarose gel electrophoresis (2%) and ethidium bromide staining and visualised on MultiDoc Imaging System (UVP, Upland, CA, USA). Products were purified prior to sequencing using GenElute™ Mammalian Genomic DNA Miniprep Kit (Sigma-Aldrich Corp., St. Louis, MO, USA) and quantified on a NanoDrop™ 1000 Spectrometer (Thermo Scientific, Waltam, MA, USA). Sequencing of the purified PCR product was carried out in both directions by Eurofins MWG-Biotech (Ebersberg, Germany). Sequences were aligned and data analysed using MEGA® (Molecular Evolutionary Genetic Analysis) v 4.0 software [55]. The I199V locus was genotyped using nested PCR followed by restriction digest with 10 U of Hga1 (Fermentas, Vilnius, Lithuania) at 37°C for 6 hrs [18].

Tagging SNP analysis

Based on patterns of linkage disequilibrium in crossbred animals, minor allele frequencies and the ability to characterise haplotype blocks, two SNPs (g.-995A>G, g. SNP -311A>G) in the promoter region and one in the coding region I199V were selected for association analysis in the three purebred populations. Genotyping of SNPs g.-311A>G and I199V was performed using the Sequenom iPLEX assay (Sequenom, Hamburg, Germany). SNP g.-995A>G was genotyped using a custom TaqMan assay, Assay ID: AHN1HPD (Applied Biosystems, Warrington, UK).

Phenotypic analysis

Meat quality phenotypic information for LTL and SM were available for cross-bred samples [49] and purebred samples [46] as previously described. pH and temperature at 45 min and 24 hr were measured in cross-bred samples as detailed in [49] and in pure-bred animals according to [46]. For both sets of LTL samples, drip loss (driploss %) was determined after 3 days according to the method of Honikel (1998) and expressed as a percentage of the initial weight [56]. Percentage cooking loss (cookloss %) was measured in cross-bred samples only and was determined by weighing transverse sections of the LTL before and after they were heated to a core temperature of 75°C in a circulating water bath held at 77°C. Electrical conductivity for both sets of samples was measured at a frequency of 1 KHz using a Pork Quality Meter (PQM, INTEK, Aichach, Germany) in accordance with manufacturer’s instructions. Bloomed CIE L* (lightness), a* (redness) and b* (yellowness) values were determined in the LTL at 7 days post mortem in the cross-bred animals as detailed in [49] and for pure-bred pigs at the last rib at 1 day post mortem using a Minolta C2002 Spectrophotometer (Minolta, Japan) as described in [46]. For cross-bred animals, intramuscular fat (IMF%) concentrations were determined in thawed minced LTL samples using the Smart System 5 microwave moisture drying oven and NMR Smart Trac Rapid Fat Analyser (CEM Corporation USA) using AOAC Official Methods 985.14 & 985.26, 1990. In purebred samples, IMF% levels in LTL and SM were assessed using a Near Infrared Spectroscopy apparatus [46].

Promoter assay

The primers used to amplify the promoter region were modified (modification highlighted in bold) at the 5' end to contain HIND III and BGL II restriction sites in the Forward:

5' CCTTAGATCTGGGATGCTGCAGAAGAAGAG 3’ and Reverse: 5' GGATAAGCTTAGGAGTGCGCAACACTGTATC 3’ primers, respectively.

The promoter region was amplified using 80 ng DNA, 45 μl Platinum High Fidelity Master Mix (Invitrogen Carlsbad, California, USA) and 20 ng primer in a final volume of 50 μl. The PCR conditions were as follows 95°C, 5 min, and for 30 cycles; 95°C for 1 min, 60°C for 1 min and 72°C for 2 min. This was followed by a single step of 72°C for 5 min. Samples were purified and quantified.

The PCR products for each haplotype (1 μg) and (4 μg) reporter vector pGL 4.17 (Promega, Corp., Madison, WI, USA) were then digested using HIND III (Promega, Corp., Madison, WI, USA) at 37°C for 3 hr and purified using GenElute PCR Purification Kit (Sigma-Aldrich Corp., St. Louis, MO, USA) and then digested with BGL II (Promega, Corp., Madison, WI, USA) at 37°C for 3 hr. 100 ng of vector and 70 ng of digested product were then ligated using T4 DNA Ligase (Promega, Corp., Madison, WI, USA) at 4°C.

Chemically competent E. Coli XL1 Blue host cells were transformed with the ligated product 5 μl and selected on LB medium containing ampicillin (100 μg/ml). The genotypes of the positive clones were verified with DNA sequencing (Eurofins MWG-Biotech, Ebersberg, Germany).

Cell culture and transient transfection assay

Mouse 3T3-L1 pre-adipocytes were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). Cells were cultured in a dulbecco modified eagle’s medium (DMEM, Gibco, Invitrogen Corp., San Diego, CA, USA) containing 10% fetal calf serum (Gibco, Invitrogen Corp., San Diego, CA, USA) and 1% penicillin-streptomycin (Sigma-Aldrich Corp., St. Louis, MO, USA) in a 37°C humidified incubator with 5% CO2. Medium was replaced every alternate day.

The day prior to transfection, 3T3-L1 cells were cultured 3 ×104 per ml cells in DMEM containing 10% fetal calf serum in a 24 well cell culture plate (Greiner Bio-One, Gmbh, Germany) in a 37°C humidified incubator with 5% CO2. The transfection cocktail (for each well) contained 25 μl DMEM basal media, 0.8 μl FuGENE HD Transfection Reagent (Roche Diagnostics GmbH, Mannheim, Germany) and 200 ng of endotoxin free PRKAG3 promoter construct. Following incubation at room temperature for 15 min, this cocktail was introduced drop wise onto the cells. Cells were then grown on a DMEM containing 10% fetal calf serum for 24 hr.

After 24 hr the media was removed and the cells washed with 500 μl of sterile phosphate buffer saline. Lysis of the cells was performed by adding 200 μl of passive lysis buffer (Promega Corp., Madison, WI, USA) followed by incubation at 37°C in a shaking incubator for 30 min at 800 rpm. Luciferase activity was measured in 20 μl of cell lysate after adding 100 μl luciferase assay reagent in a luminometer GLOMAX™, (Promega Corp., Madison, WI, USA). All measurements were normalised to the total protein.

Identification of putative regulatory SNP’s in the PRKAG3promoter region

To identify SNPs that putatively affect promoter elements, the upstream sequence containing the SNPs was screened for the presence of putative selective transcription factor binding sites in silico using five prediction tools including;TFSEARCH [57], TESS [58], MatInspector [59], AliBaba2 [60] and MATCH [60].

Particular attention was paid to binding sites which changed on substitution to the minor allele as well as sites known to be of relevance to mammalian muscle with high similarity scores and matrix similarities.

Statistical methods

Haplotype analysis

Estimation of SNP frequencies and tests for departure from Hardy Weinberg equilibrium were carried out using the Excel Microsatellite Toolkit [61] at each locus for each breed. Linkage disequilibrium and haplotype blocks were identified using Haploview [62]. Following haplotype inference the ELB algorithm was used to assign the most likely haplotype combination to each individual animal as implemented using Arlequin [32].

Association analysis

The relationship between normalised expression of PRKAG3 and meat quality traits was calculated using Pearson correlation in PASW Statistics 18.0 software (SPSS, Inc., Somers, NY, USA). Association analysis was carried between genotyped SNPs/haplotypes and values of meat and carcass quality traits in each breed using the least square means method of GLM (General Linear Model) procedure in SAS (version 9.1; SAS Institute, Cary, NC, USA). Slaughter date was included in the model as a covariate. Each analysis tested for a difference in least square means of meat and carcass quality traits where 0, 1, or 2 copies of each haplotype were present.

The significance of the in vitro reporter assay across the three haplotypes and the association between genotypes and PRKAG3 expression were established using One-Way ANOVA, in PASW Statistics 18.0 software (SPSS, Inc., Somers, NY, USA).

Abbreviations

AMPK:

Adenosine monophosphate activated protein kinase

SNPs:

Single nucleotide polymorphisms

GP:

Glycolytic potential

IMF:

Intramuscular fat

LTL:

M. longissmus thoracis et lumborum muscle

SM:

M. semimembranosus muscle

pHULT:

pH at 24 hr in LTL

pHUSM:

pH at 24 hr in SM

pH45LT:

pH at 45 min in LTL

pH45SM:

pH at 45 min in SM

ECULT:

Electrical conductivity in LTL

ECUSM:

Electrical conductivity in SM

MAF:

Minor allele frequency

TFBS:

Transcription factor binding sites

UTR:

Un-translated region

qPCR:

Quantitative polymerase chain reaction.

References

  1. Mihaylova MM, Shaw RJ: The AMPK signalling pathway coordinates cell growth, autophagy and metabolism. Nat Cell Biol. 2011, 13 (9): 1016-1023. 10.1038/ncb2329.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  2. Hardie DG, Sakamoto K: AMPK: A Key Sensor of Fuel and Energy Status in Skeletal Muscle. Physiol. 2006, 21 (1): 48-60. 10.1152/physiol.00044.2005.

    Article  CAS  Google Scholar 

  3. Winder WW: Energy-sensing and signaling by AMP-activated protein kinase in skeletal muscle. J Appl Physiol. 2001, 91 (3): 1017-1028.

    CAS  PubMed  Google Scholar 

  4. Essén-Gustavsson B, Granlund A, Benziane B, Jensen-Waern M, Chibalin AV: Muscle glycogen resynthesis, signalling and metabolic responses following acute exercise in exercise-trained pigs carrying the PRKAG3 mutation. Exp Physiol. 2011, 96 (9): 927-937.

    Article  PubMed  Google Scholar 

  5. Mahlapuu M, Johansson C, Lindgren K, Hjälm G, Barnes BR, Krook A, Zierath JR, Andersson L, Marklund S: Expression profiling of the γ3-subunit isoforms of AMP-activated protein kinase suggests a major role for γ3 in white skeletal muscle. Am J Physiol-Endoc M. 2004, 286 (2): E194-E200.

    CAS  Google Scholar 

  6. Yu H, Fujii N, Hirshman MF, Pomerleau JM, Goodyear LJ: Cloning and characterization of mouse 5'-AMP-activated protein kinase γ3 subunit. Am J Physiol-Cell Ph. 2004, 286 (2): C283-C292. 10.1152/ajpcell.00319.2003.

    Article  CAS  Google Scholar 

  7. Ciobanu D, Bastiaansen J, Malek M, Helm J, Woollard J, Plastow G, Rothschild M: Evidence for new alleles in the protein kinase adenosine monophosphate-activated gamma(3)-subunit gene associated with low glycogen content in pig skeletal muscle and improved meat quality. Genetics. 2001, 159 (3): 1151-1162.

    PubMed Central  CAS  PubMed  Google Scholar 

  8. Granlund A, Jensen-Waern M, Essen-Gustavsson B: The influence of the PRKAG3 mutation on glycogen, enzyme activities and fibre types in different skeletal muscles of exercise trained pigs. Acta Vet Scand. 2011, 53 (1): 20-10.1186/1751-0147-53-20.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Barnes B, Marklund S, Steiler T, Walter M, Hjalm G, Amarger V, Mahlapuu M, Leng Y, Johansson C, Galuska D, et al: The 5'-AMP-activated protein kinase gamma 3 isoform has a key role in carbohydrate and lipid metabolism in glycolytic skeletal muscle. J Biol Chem. 2004, 37: 38441-38447.

    Article  Google Scholar 

  10. Costford S, Kavaslar N, Ahituv N, Chaudhry S, Schackwitz W, Dent R, Pennacchio L, McPherson R, Harper M: Gain-of-function R225W mutation in human AMPK gamma(3) causing increased glycogen and decreased triglyceride in skeletal muscle. PLoS One. 2007, 2 (9): e903-10.1371/journal.pone.0000903.

    Article  PubMed Central  PubMed  Google Scholar 

  11. Crawford S, Costford S, Aguer C, Thomas S: deKemp R, DaSilva J, Lafontaine D, Kendall M, Dent R, Beanlands R et al: Naturally occurring R225W mutation of the gene encoding AMP-activated protein kinase (AMPK)γ subunit results in increased oxidative capacity and glucose uptake in human primary myotubes. Diabetologia. 2010, 53 (9): 1986-1997. 10.1007/s00125-010-1788-7.

    Article  CAS  PubMed  Google Scholar 

  12. Nilsson E, Long Y, Martinsson S, Glund S, Garcia-Roves P, Svensson L, Andersson L, Zierath J, Mahlapuu M: Opposite transcriptional regulation in skeletal muscle of AMP-activated protein kinase gamma3 R225Q transgenic versus knock-out mice. J Biol Chem. 2006, 281 (11): 244-252.

    Article  Google Scholar 

  13. Milan D, Jeon J, Looft C, Amarger V, Robic A, Thelander M, Rogel-Gaillard C, Paul S, Iannuccelli N, Rask L, et al: A mutation in PRKAG3 associated with excess glycogen content in pig skeletal muscle. Science. 2000, 288: 1248-1251. 10.1126/science.288.5469.1248.

    Article  CAS  PubMed  Google Scholar 

  14. Rothchild MF, Ciobanu DC, Malek M, Plastow G: Novel PRKAG3 alleles and use of the same as genetic markers for reproductive and meat quality traits. [http://patentscope.wipo.int]. Patent publication 2002, WO/2002/020850

  15. Scott JW, Hawley SA, Green KA, Anis M, Stewart G, Scullion GA, Norman DG, Hardie DG: CBS domains form energy-sensing modules whose binding of adenosine ligands is disrupted by disease mutations. J Clin Investig. 2004, 113 (2): 274-284.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Closter AM, Guldbrandtsen B, Henryon M, Nielsen B, Berg P: Consequences of elimination of the Rendement Napole allele from Danish Hampshire. J Anim Breed Genet. 2011, 128 (3): 192-200. 10.1111/j.1439-0388.2010.00900.x.

    Article  CAS  PubMed  Google Scholar 

  17. Otto G, Roehe R, Looft H, Thoelking L, Knap PW, Rothschild MF, Plastow GS, Kalm E: Associations of DNA markers with meat quality traits in pigs with emphasis on drip loss. Meat Sci. 2007, 75 (2): 185-195. 10.1016/j.meatsci.2006.03.022.

    Article  CAS  PubMed  Google Scholar 

  18. Yoo JY, Lee J, Kim GW, Yoon JM, Park HY, Kim YB: Effect of single nucleotide polymorphisms of PRKAG3 to carcass quality in swine. Korean J Genet. 2007, 29 (1): 73-80.

    CAS  Google Scholar 

  19. Fontanesi L, Davoli R, Nanni Costa L, Beretti F, Scotti E, Tazzoli M, Tassone F, Colombo M, Buttazzoni L, Russo V: Investigation of candidate genes for glycolytic potential of porcine skeletal muscle: Association with meat quality and production traits in Italian Large White pigs. Meat Sci. 80 (3): 780-787.

  20. Lindahl G, Enfält A-C, Seth G, Joseli A, Hedebro-Velander I, Andersen HJ, Braunschweig M, Andersson L, Lundström K: A second mutant allele (V199I) at the PRKAG3 (RN) locus--II. Effect on colour characteristics of pork loin. Meat Sci. 2004, 66 (3): 621-627. 10.1016/S0309-1740(03)00180-3.

    Article  CAS  PubMed  Google Scholar 

  21. Lindahl G, Enfält A-C, Andersen HJ, Lundström K: Impact of RN genotype and ageing time on colour characteristics of the pork muscles longissimus dorsi and semimembranosus. Meat Sci. 2006, 74 (4): 746-755. 10.1016/j.meatsci.2006.06.006.

    Article  CAS  PubMed  Google Scholar 

  22. Škrlep M, Kavar T, Čandek-Potokar M: Comparison of PRKAG3 and RYR1 gene effect on carcass traits and meat quality in Slovenian commercial pigs. Czech J Anim Sci. 2010, 55 (4): 149-159.

    Google Scholar 

  23. Enfält A-C, von Seth G, Josell Å, Lindahl G, Hedebro-Velander I, Braunschweig M, Andersson L, Lundström K: Effects of a second mutant allele (V199I) at the PRKAG3 (RN) locus on carcass composition in pigs. Livest Sci. 2006, 99 (2–3): 131-139.

    Article  Google Scholar 

  24. Škrlep M, Čandek-Potokar M, Mandelc S, Javornik B, Gou P, Chambon C, Santé-Lhoutellier V: Proteomic profile of dry-cured ham relative to PRKAG3 or CAST genotype, level of salt and pastiness. Meat Sci. 2011, 88 (4): 657-667. 10.1016/j.meatsci.2011.02.025.

    Article  PubMed  Google Scholar 

  25. Chen JF, Dai LH, Peng J, Li JL, Zheng R, Zuo B, Li FE, Liu M, Yue K, Lei MG, et al: New evidence of alleles (V199I and G52S) at the PRKAG3 (RN) locus affecting pork meat quality. Asian Australas J Anim Sci. 2008, 21 (4): 471-477.

    Article  CAS  Google Scholar 

  26. Kim S, Roh JG, Cho YI, Choi BH, Kim TH, Kim JJKS: K: development of optimal breeding pigs using DNA marker information. Genomics Inform. 2010, 8 (2): 81-85. 10.5808/GI.2010.8.2.081.

    Article  Google Scholar 

  27. Davey G, McGee C, Luca AD, Talbot A, Hamill R, Sweeney T, Mullen A, Cairns M: Gene expression profiling of animals from an Irish pig herd divergent in meat quality attributes. 2008, In: Second European Conference on Pig Genomics: 2008; Ljubljana, Slovenia

    Google Scholar 

  28. Amaral AJ, Ferretti L, Megens H-J, Crooijmans RPMA, Nie H, Ramos-Onsins SE, Perez-Enciso M, Schook LB, Groenen MAM: Genome-wide footprints of pig domestication and selection revealed through massive parallel sequencing of pooled DNA. PLoS One. 2011, 6 (4): e14782-10.1371/journal.pone.0014782.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  29. Aslan O, Sweeney T, Mullen A, Hamill R: Regulatory polymorphisms in the bovine Ankyrin 1 gene promoter are associated with tenderness and intramuscular fat content. BMC Genet. 2010, 11 (1): 111-

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  30. Chen K, van Nimwegen E, Rajewsky N, Siegal ML: Correlating gene expression variation with cis-regulatory polymorphism in saccharomyces cerevisiae. Genome Biol Evol. 2010, 2: 697-707.

    PubMed Central  PubMed  Google Scholar 

  31. Bahar B, O’Halloran F, Callanan MJ, McParland S, Giblin L, Sweeney T: Bovine lactoferrin (LTF) gene promoter haplotypes have different basal transcriptional activities. Anim Genet. 2011, 42 (3): 270-279. 10.1111/j.1365-2052.2010.02151.x.

    Article  CAS  PubMed  Google Scholar 

  32. Excoffier L, Laval G, Laval S: Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol Bioinform Online. 2005, 1: 47-50.

    PubMed Central  CAS  Google Scholar 

  33. Chorley B, Wang X, Campbell M, Pittman G, Noureddine M, Bell D: Discovery and verification of functional single nucleotide polymorphisms in regulatory genomic regions: current and developing technologies. Mutat Res Rev Mutat Res. 2008, 659: 147-157.

    Article  CAS  Google Scholar 

  34. ŠKrlep M, Kavar T, SantÉ-Lhoutellier V, ČAndek-Potokar M: Effect of I199V polymorphism on PRKAG3 gene on carcass and meat quality traits in Slovenian commercial pigs. J Muscle Foods. 2009, 20 (3): 367-376. 10.1111/j.1745-4573.2009.00158.x.

    Article  Google Scholar 

  35. Underwood KR, Means WJ, Zhu MJ, Ford SP, Hess BW, Du M: AMP-activated protein kinase is negatively associated with intramuscular fat content in longissimus dorsi muscle of beef cattle. Meat Sci. 2008, 79 (2): 394-402. 10.1016/j.meatsci.2007.10.025.

    Article  CAS  PubMed  Google Scholar 

  36. Huff-Lonergan E, Lonergan SM: Mechanisms of water-holding capacity of meat: The role of postmortem biochemical and structural changes. Meat Sci. 2005, 71 (1): 194-204. 10.1016/j.meatsci.2005.04.022.

    Article  CAS  PubMed  Google Scholar 

  37. Pearce KL, Rosenvold K, Andersen HJ, Hopkins DL: Water distribution and mobility in meat during the conversion of muscle to meat and ageing and the impacts on fresh meat quality attributes — A review. Meat Sci. 2011, 89 (2): 111-124. 10.1016/j.meatsci.2011.04.007.

    Article  PubMed  Google Scholar 

  38. Garcia-Roves PM, Osler ME, Holmström MH, Zierath JR: Gain-of-function R225Q Mutation in AMP-activated Protein Kinase γ3 Subunit Increases Mitochondrial Biogenesis in Glycolytic Skeletal Muscle. J Biol Chem. 2008, 283 (51): 35724-35734. 10.1074/jbc.M805078200.

    Article  CAS  PubMed  Google Scholar 

  39. Raijman D, Shamir R, Tanay A: Evolution and selection in yeast promoters: analyzing the combined effect of diverse transcription factor binding sites. PLoS Comput Biol. 2008, 4 (1): e7-10.1371/journal.pcbi.0040007.

    Article  PubMed Central  PubMed  Google Scholar 

  40. Lefterova MI, Lazar MA: New developments in adipogenesis. Trends Endocrin Met. 2009, 20 (3): 107-114. 10.1016/j.tem.2008.11.005.

    Article  CAS  Google Scholar 

  41. Amarger V, Erlandsson R, Pielberg G, Jeon JT, Andersson L: Comparative sequence analysis of the PRKAG3 region between human and pig: evolution of repetitive sequences and potential new exons. In: Cytogenet Genome Res. vol. 2003, 102: 163-172. 10.1159/000075743.

    CAS  Google Scholar 

  42. Chatterjee S, Pal JK: Role of 5′- and 3′-untranslated regions of mRNAs in human diseases. Biol Cell. 2009, 101 (5): 251-262. 10.1042/BC20080104.

    Article  CAS  PubMed  Google Scholar 

  43. Aslan O, Hamill R, Davey G, McBryan J, Mullen A, Gispert M, Sweeney T: Variation in the IGF2 gene promoter region is associated with intramuscular fat content in porcine skeletal muscle. Mol Biol Rep. 2012, 39 (4): 4101-4110. 10.1007/s11033-011-1192-5.

    Article  CAS  PubMed  Google Scholar 

  44. Hocquette JF, Gondret F, Baéza E, Médale F, Jurie C, Pethick DW: Intramuscular fat content in meat-producing animals: development, genetic and nutritional control, and identification of putative markers. Animal. 2010, 4 (02): 303-319. 10.1017/S1751731109991091.

    Article  CAS  PubMed  Google Scholar 

  45. Gil M, Delday MI, Gispert M, Furnols MF I, Maltin CM, Plastow GS, Klont R, Sosnicki AA, Carrión D: Relationships between biochemical characteristics and meat quality of Longissimus thoracis and Semimembranosus muscles in five porcine lines. Meat Sci. 2008, 80 (3): 927-933. 10.1016/j.meatsci.2008.04.016.

    Article  CAS  PubMed  Google Scholar 

  46. Plastow GS, Carrión D, Gil M, GarcIa-Regueiro JA, Furnols MF I, Gispert M, Oliver MA, Velarde A, Guàrdia MD, Hortós M, et al: Quality pork genes and meat production. Meat Sci. 2005, 70 (3): 409-10.1016/j.meatsci.2004.06.025.

    Article  CAS  PubMed  Google Scholar 

  47. Granlund A, Kotova O, Benziane B, Galuska D, Jensen-Waern M, Chibalin A, Essen-Gustavsson B: Effects of exercise on muscle glycogen synthesis signalling and enzyme activities in pigs carrying the PRKAG3 mutation. Exp Physiol. 2011, 95: 541-549.

    Article  Google Scholar 

  48. Cagnazzo M, te Pas MFW, Priem J, de Wit AAC, Pool MH, Davoli R, Russo V: Comparison of prenatal muscle tissue expression profiles of two pig breeds differing in muscle characteristics. J Anim Sci. 2006, 84 (1): 1-10.

    CAS  PubMed  Google Scholar 

  49. Di Luca A, Mullen AM, Elia G, Davey G, Hamill RM: Centrifugal drip is an accessible source for protein indicators of pork ageing and water-holding capacity. Meat Sci. 2011, 88 (2): 261-270. 10.1016/j.meatsci.2010.12.033.

    Article  CAS  PubMed  Google Scholar 

  50. McBryan J, Hamill RM, Davey G, Lawlor P, Mullen AM: Identification of suitable reference genes for gene expression analysis of pork meat quality and analysis of candidate genes associated with the trait drip loss. Meat Sci. 2010, 86 (2): 436-439. 10.1016/j.meatsci.2010.05.030.

    Article  CAS  PubMed  Google Scholar 

  51. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3 (7): research0034.0031-research0034.0011.

    Article  Google Scholar 

  52. Pfaffl M: A new mathematical model for relative quantification in real-time RT-PCR. Nucl Acids Res. 2001, 29 (9): e45-10.1093/nar/29.9.e45.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  53. Ryan MT, Collins CB, O'Doherty JV, Sweeney T: Selection of stable reference genes for quantitative real-time PCR in porcine gastrointestinal tissues. Livest Sci. 2010, 133 (1): 42-44. 10.1016/j.livsci.2010.06.020.

    Article  Google Scholar 

  54. Primer3. http://frodo.wi.mit.edu/primer3,

  55. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5: Molecular Evolutionary Genetics Analysis using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods. Mol Biol Evol. 2011, 28 (10): 2731-2739. 10.1093/molbev/msr121.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  56. Honikel KO: Reference methods for the assessment of physical characteristics of meat. Meat Sci. 1998, 49 (4): 447-457. 10.1016/S0309-1740(98)00034-5.

    Article  CAS  PubMed  Google Scholar 

  57. TFSEARCH: Searching Transcription Factor Binding Sites (1.3). http://www.cbrc.jp/research/db/TFSEARCH.html,

  58. TESS: Transcription Element Search System. www.cbil.upenn.edu/cgi-bin/tess/tess,

  59. MatInspector. www.genomatix.de,

  60. GeneRegulation.com. [www.gene-regulation.com/pub/programs.html]

  61. Park SDE: Trypanotolerance in West African Cattle and the Population Genetic Effects of Selection. 2001, Dublin: University College Dublin

    Google Scholar 

  62. Barrett J, Fry B, Maller J, Daly M: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21: 263-265. 10.1093/bioinformatics/bth457.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors acknowledge Genus Plc/PIC US for provision of gDNA samples from purebred Large White, Pietrain and Duroc pigs. The authors would also like to acknowledge Ms Paula Reid (Teagasc, Ashtown) for help with data analysis, Mr Peadar Lawlor (Teagasc, Moorepark) for supply of animals and Dr Bojlul Bahar for his help with promoter assay development and optimisation. Funding for this research was provided under the National Development Plan, through the Food Institutional Research Measure, administered by the Department of Agriculture, Fisheries & Food, Ireland.

Author information

Authors and Affiliations

  1. School of Veterinary Medicine, University College Dublin Belfield, Dublin, 4, Ireland

    Marion T Ryan, Aisling M O’Halloran & Torres Sweeney

  2. Functional Genomics & Glycomics Group, Martin Ryan Institute, National University of Ireland Galway, Galway, Ireland

    Grace C Davey & Chris McGee

  3. Teagasc, Ashtown Food Research Centre, Ashtown, Dublin, 15, Ireland

    Ruth M Hamill, Jean McBryan & Anne M Mullen

  4. IRTA, 17121, Monells, Girona, Spain

    Marina Gispert

  5. Genus PLC/PIC, Alpha Building, London Road, Nantwich, CW5 7JW, UK

    Olwen I Southwood

Authors
  1. Marion T Ryan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruth M Hamill
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aisling M O’Halloran
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Grace C Davey
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jean McBryan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anne M Mullen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chris McGee
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Marina Gispert
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Olwen I Southwood
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Torres Sweeney
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruth M Hamill.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

MR carried out laboratory work, collation of data analysis, bioinformatic and population data analysis and prepared the first draft of the manuscript. AH, CM and RMH carried out laboratory work relating to DNA purification, qPCR and TaqMan genotyping respectively. JMB and MG were involved in determination of meat phenotypes for gene expression study and association analysis respectively and OS contributed to determination of phenotypes and DNA extraction. TS, RMH, AMM and GD designed and coordinated the study. TS, RMH and MR edited the manuscript and participated in the development of the final draft. All authors agreed with the final manuscript.

Authors’ original submitted files for images

Rights and permissions

Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Ryan, M.T., Hamill, R.M., O’Halloran, A.M. et al. SNP variation in the promoter of the PRKAG3gene and association with meat quality traits in pig. BMC Genet 13, 66 (2012). https://doi.org/10.1186/1471-2156-13-66

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/1471-2156-13-66

Keywords

  • Promoter activity
  • Gene expression
  • Transcription factor binding site
  • Gene expression
  • Single nucleotide polymorphisms

BMC Genomic Data

ISSN: 2730-6844

Contact us