SNPs detection in DHPS-WDR83overlapping genes mapping on porcine chromosome 2 in a QTL region for meat pH
© Zambonelli et al.; licensee BioMed Central Ltd. 2013
Received: 19 March 2013
Accepted: 20 September 2013
Published: 8 October 2013
The pH is an important parameter influencing technological quality of pig meat, a trait affected by environmental and genetic factors. Several quantitative trait loci associated to meat pH are described on PigQTL database but only two genes influencing this parameter have been so far detected: Ryanodine receptor 1 and Protein kinase, AMP-activated, gamma 3 non-catalytic subunit. To search for genes influencing meat pH we analyzed genomic regions with quantitative effect on this trait in order to detect SNPs to use for an association study.
The expressed sequences mapping on porcine chromosomes 1, 2, 3 in regions associated to pork pH were searched in silico to find SNPs. 356 out of 617 detected SNPs were used to genotype Italian Large White pigs and to perform an association analysis with meat pH values recorded in semimembranosus muscle at about 1 hour (pH1) and 24 hours (pHu) post mortem.
The results of the analysis showed that 5 markers mapping on chromosomes 1 or 3 were associated with pH1 and 10 markers mapping on chromosomes 1 or 2 were associated with pHu. After False Discovery Rate correction only one SNP mapping on chromosome 2 was confirmed to be associated to pHu. This polymorphism was located in the 3’UTR of two partly overlapping genes, Deoxyhypusine synthase (DHPS) and WD repeat domain 83 (WDR83). The overlapping of the 3’UTRs allows the co-regulation of mRNAs stability by a cis-natural antisense transcript method of regulation. DHPS catalyzes the first step in hypusine formation, a unique amino acid formed by the posttranslational modification of the protein eukaryotic translation initiation factor 5A in a specific lysine residue. WDR83 has an important role in the modulation of a cascade of genes involved in cellular hypoxia defense by intensifying the glycolytic pathway and, theoretically, the meat pH value.
The involvement of the SNP detected in the DHPS/WDR83 genes on meat pH phenotypic variability and their functional role are suggestive of molecular and biological processes related to glycolysis increase during post-mortem phase. This finding, after validation, can be applied to identify new biomarkers to be used to improve pig meat quality.
Meat pH is an important parameter for the quality assessment of fresh and seasoned meat products . The pH is also influenced by slaughter procedure as well as post slaughter carcass management and it is also under genetic control. Phenotypic variation of meat pH is partially regulated by genes as indicated in the review of  who reported 0.16 as the average heritability value for pH scored at about 1 hour post mortem (pH1) and 0.21 as the average value for pH recorded at 24 hours post mortem (pHu). Other researches showed that the heritability of pHu in Large White ranged from 0.29 to 0.45 [3, 4]. Up to now only two major genes related to pig meat pH have been identified: Ryanodine receptor 1 (RYR1) mapped on Sus scrofa chromosome (SSC) 6  and Protein kinase, AMP-activated, gamma 3 non-catalytic subunit (PRKAG3), located on SSC13 . In addition to these evidences showing an effect of single genes, other research reported significant Quantitative Trail Loci (QTL) for meat pH in several porcine chromosomes as indicated in Pig QTL database (PigQTLdb) [7–9].
With the aim of searching genes responsible for QTL effect on pig meat pH, single nucleotide polymorphisms (SNPs) detected in the transcribed sequences of coding genes located on three QTL regions (QTLRs) of SSC1 (60–80 cM), SSC2 (55–66 cM) and SSC3 (42–60 cM), were utilized to perform an association analysis with meat pH values.
Results and discussion
SNPs detected in transcribed sequences located within the selected QTL regions
Summary of the steps utilized to identify the genotyped SNPs
No of porcine UniGene clusters whitin QTLR
No of selected porcine UniGene clusters
No of porcine UniGene clusters containing SNPs
No of useful SNPs within QTLR
Average SNPs distance within each of the three analyzed QTLRs
Average distance within each QTLR (Mb)
Identification of genes containing SNPs associated with meat pH
Significant markers detected by association analysis with pH1 values using PLINK
lysine-specific demethylase 3A
EPB42 / LOC100525673
E3 ubiquitin-protein ligase UBR1-like
serine peptidase inhibitor, Kunitz type 1
Significant markers detected by association analysis with pHu values using PLINK
DHPS / LOC100519413
FARSA / LOC100524304
phenylalanyl-tRNA synthetase alpha chain-like
HECT and RLD domain containing E3 ubiquitin protein ligase family member 1
collagen, type V, alpha 3
ACTR10 / LOC100620619
actin-related protein 10 homolog
bromodomain containing 2
tRNA methyltransferase 1 homolog (S. cerevisiae)
MAN2B1 / LOC100518647
Out of the five markers associated with pH1 values, two SNPs detected on the same gene were mapped on SSC3 and three were identified on SSC1. Two of the SNPs matched two different UniGene clusters but they correspond to the same gene. On the whole, the five SNPs associated to pH1 were detected in three genes that are listed here from the most significant to the less significant: KDM3A, EPB42 / LOC100525673, SPINT1. On Table 3 the gene names, their chromosome localization and their position on the genomic sequence are reported. The markers associated with pHu values were mapped on SSC1 and SSC2 and the three most significant located on chromosome 2. The genes corresponding to the markers associated to pHu were (from the most significant to the least significant): DHPS / LOC100519413, Uncharacterized LOC100513647, FARSA / LOC100524304, HERC1, COL5A3, ACTR10 / LOC100620619, BRD2, TRMT1, MAN2B1 / LOC100518647. On Table 4 the gene names, their chromosome localization and their position on the genomic sequence are reported.
After FDR correction for multiple testing only the SNP related to pHu that was found in the DHPS gene remained significant (P = 0.01937).
Association analysis of 5E_003 ( DHPS ) SNP to pHu
LSM ± SE
5.731 ± 0.024a (228)
5.808 ± 0.033b (72)
5.866 ± 0.068b (11)
0.068 ± 0.034
Genomic characterization of the most significant SNP detected
In humans the genes DHPS and WDR83 are included in the group for which was reported a bidirectional regulation of mRNA stability by the natural antisense transcript (NAT) method of regulation. In particular, WDR83 and DHPS are an example of cis-NAT regulation i.e. the two transcripts are partially overlapping in their 3’UTRs, coded in opposite direction by the same DNA stretch . With this system of regulation the mRNA expression and proteins levels are regulated concordantly. The NAT method of regulation was identified in several mammalian genomes [18, 21]. NATs principal functions are related to the regulation of the expression of sense transcripts, the hybridization with them, and to influence mRNA transcription or stability [21, 22]. Other roles proposed for NATs are an involvement in DNA methylation, chromatin modification and mono allelic expression. Researches concerning these two genes are often related to cancer biology [18, 23, 24].
Little is known about the role of these two genes in tissues like skeletal muscle but a possible involvement may be related to the hypoxic conditions occurring in skeletal muscle due to exercise, stress or in post-mortem phase. Hypoxya is a condition that was reported to be present in several conditions: in cancers, when tumor cells grow rapidly their vascular supply become insufficient leading to hypoxia [25, 26] but hypoxic condition occurs also in ischemic cardiac myocytes  and in skeletal muscle under exercise  and in post mortem. The oxygen reduction and the energy deficit of post mortem phase will lead to acidosis due to the anaerobic glycolysis increase that will cause a pH decrease. The complex DHPS/WDR83 is one of the factors modulating EGLN3 and then HIF1A and the polymorphism detected on the common part of the 3’UTR of the two genes may activate this cascade with different efficiency between alleles to cause pH decline in the skeletal muscle cells during post-mortem. In order to validate this hypothesis further researches, aimed to clarify and verify the link of the mutation found in the 3’UTR of DHPS/WDR83 genes with meat pH, are needed before to consider them as functional candidate genes and not only positional markers for the studied trait.
In the present work we studied QTLRs of 10–20 cM and detected some hundreds of SNPs that allowed a more refined analysis of these regions. Applying FDR to correct for multiple test a SNP found in the 3’UTR of DHPS/WDR83 overlapping genes was found to be associated with the ultimate pH of pig meat. This was the unique SNP showing a significant effect on the studied trait out of the 251 markers used. Nevertheless this result was useful to confirm the localization of the QTL for meat pH reported in literature and allowed to identify genes putatively regulating pork ultimate pH mapping on the QTL region of SSC2. The identified association of the detected marker with meat pH could represent, after confirmation, a new biomarker useful to improve pig meat quality.
Animals, phenotypes and DNA extraction
For this study we sampled a pure-bred population of Italian Large White sib-tested pigs provided by the National Association of Pig Breeders (Associazione Nazionale Allevatori Suini, ANAS) . The pigs farmed at the ANAS genetic station are all tested for the RYR1 gene (Halothane locus) in order to have the boars selected for the genetic improvement program free from the recessive allele at this locus. For PRKAG3 gene concerns no genetic test were carried out because in the Italian Large White pig population the negative (dominant) 200Q allele (RN locus) at this locus is not segregating . The animals were reared on the central ANAS Sib-Test station from about 30 kg live weight to about 155 kg live weight. The nutritive level utilised was quasi ad libitum, i.e. about 60% of pigs were able to ingest the entire supplied ration. For the genetic evaluation of a boar, full sib triplets (two females and one castrated male) are performance tested. All pigs were slaughtered after electrical stunning in the same commercial abattoir during the year 2008 in 11 different days. Using the 356 SNPs detected in the three QTLRs an association analysis (see PLINK analysis below) on 251 pigs, 170 females and 81 castrated males (Group 1) was performed. The most significant markers were then tested using the 251 animals of Group 1 plus additional 96 samples obtaining a larger group of 347 samples (231 females and 116 castrated males). We refer to this enlarged sample as Group 2. The sex distribution of the animals with a ratio females: castrated males approximately equal to 2 reflects the sex proportion characteristic of the Italian selection scheme described above.
Statistic describing muscle pH1 values measured in semimembranosus muscle recorded in Italian Large White pigs
Statistic describing muscle pHu values measured in semimembranosus muscle recorded in Italian Large White pigs
QTL selection, SNP detection and genotyping
Genetic crosses and bibliographic references utilized to identify the QTL regions used for this research
Duroc x Berlin Miniature
Duroc x Pietrain
Meishan x Pietrain
Duroc x Landrace
Duroc x Pietrain
Duroc x Pietrain
White Duroc x Erhualian resource population
Commercial crossbred population
Wild Boar x Pietrain
Iberian x Landrace
Duroc x Berlin Miniature
Duroc x Pietrain
Map intervals were defined by searching for the position of the most significant markers reported in each original paper in the USDA-MARC linkage map  that includes all available microsatellite and DNA markers so far analysed. Furthermore, this map is implemented both in PigQTLdb and in NCBI map viewer. In this way it was possible to compare the data contained in both websites. The alignment of porcine and human chromosomes was first performed using pig and human radiation hybrid maps using the tool available within PigQTLdb website, then the aligned regions were visualized using the NCBI map viewer. The obtained output was used to choose in each QTLR all expressed sequences (both mRNAs and expressed sequence tags, ESTs) located in the identified corresponding genomic regions that were grouped according to UniGene clusters. The obtained clusters were filtered to retain only those represented by at least eight sequences, then putative SNPs were searched in silico by a multiple alignment of all members of each cluster using BLASTN  with the algorithm MegaBLAST. We marked as putative SNP a mutation detected in at least three sequences to avoid inconsistencies due to sequencing errors and also to exclude SNPs with a rare allele. Moreover, the obtained multiple alignments were manually scored in order to detect those suitable to be analysed by the high throughput Illumina GoldenGate Genotyping Assay system . When more than one polymorphism was detected within each cluster we discarded those closer more than 80 nucleotides because they were not suitable to design the probes to be used with this genotyping system. These SNPs were finally scored with the specific Illumina software to establish the SNP score of each sequence used to calculate the parameter indicated as designability rank. In this way we obtained a customized array of new SNPs detected in the transcribed region of messenger RNAs. Genotyping of 251 samples of Group 1 was carried out by an outsource company (CBM, Cluster in biomedicine, Trieste, Italy, ). Genotypes of the samples included in Group 2 were obtained by High Resolution Melting (HRM), that is an efficient technique to determine a genotype using the melting profile of small amplicons [48, 49]. For this aim, primers flanking the polymorphism were designed, to amplify a 181 bp fragment (For: 5’- GCCCGAAAAGAACGAGGA -3’, Rev: 5’- ACCCACTACCAAGGACACAGA -3’). Amplifications were performed with Rotor-Gene TM 6000 (Corbett Research, Mortlake, New South Wales, Australia), in a total volume of 20 μl containing 2 μl of 10× standard buffer, 3 mM MgCl2, 0.3 μM of each primer, 160 μM dNTP, 1 U EuroTaq polymerase, 1 U EvaGreen TM (Biotium Inc., Hayward, CA, USA) and 50–100 ng of template DNA. Cycling conditions were: initial denaturation at 95°C for 5 min, 40 cycles of 95°C for 30 s, 56°C for 15 s and 72°C for 2 min, followed by a final extension step of 72°C for 2 min. Fluorescence was acquired at the end of each extension step to ensure that all reactions reached the plateau stage. After a holding step at 50°C for 60 s, a HRM analysis was performed heating the samples from 83 to 88°C, at a rate of 0.1°C each 4 s, with continuous fluorescence acquisition. The HRM data were analysed by Rotor-Gene TM 6000 software. Fluorescence vs. temperature plots were normalized by selecting linear regions before and after the melting transition. Genotypes were determined setting known genotypes samples as reference and using a reliability threshold of 0.90 for the genotype assignment.
The association study including the markers detected within the analysed QTLRs was performed with PLINK whole genome association analysis toolset [50, 51]. The genotypes of animals belonging to Group 1 were filtered before the association analysis. All markers having a minor allele frequency below 0.01 (N = 162) were discarded. Furthermore Hardy-Weinberg equilibrium was tested and four SNPs were discarded because not in equilibrium (p < =0.01). After filtering, 218 markers and all 251 individuals (Group 1) were retained. To correct for stratification of the considered population a clustering method based on an identical by state (IBS) approach included in PLINK was used. Furthermore, a stratified association analysis was performed using the Cochran-Mantel-Haenszel test implemented in PLINK. The significant markers were further assayed for multiple testing using the False Discovery Rate (FDR) correction using as significance threshold P < 0.05.
where: pHu = ultimate pH; μ = overall mean; SNP = fixed effect of each genotype (i = 1–3); Sex = fixed effect of sex (j = 1,2); Day = fixed effect of day of slaughter (k = 1–11); Sire = random effect of the sire; ϵ = residual error.
Finally, the GLM procedure of SAS release 9.2 (SAS, Institute Inc., Cary, NC) was used to calculate the part of total variance explained by the SNP (R2). In order to estimate the proportion of the genetic variance explained by the analysed SNP we compared the R2 of a fixed model including genotypes of the marker, sex, and day of slaughter with a reduced model without genotypes including as fixed effects sex, and day of slaughter. The difference between the two R2 indicates the total variance of pork ultimate pH explained only by the SNP.
where: pHu = ultimate pH; μ = overall mean; SNP = fixed effect of each genotype (i = 1–3); Sex = fixed effect of sex (j = 1,2); Day = fixed effect of day of slaughter (k = 1–11); ϵ = residual error.
where: pHu = ultimate pH; μ = overall mean; Sex = fixed effect of sex (i = 1,2); Day = fixed effect of day of slaughter (j = 1–11); ϵ = residual error.
This research was supported by project AGER-HEPIGET (grant N. 2011–0279).
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