Skip to main content

Epigenetic regulation of the ELOVL6 gene is associated with a major QTL effect on fatty acid composition in pigs



In previous studies on an Iberian x Landrace cross, we have provided evidence that supported the porcine ELOVL6 gene as the major causative gene of the QTL on pig chromosome 8 for palmitic and palmitoleic acid contents in muscle and backfat. The single nucleotide polymorphism (SNP) ELOVL6:c.-533C > T located in the promoter region of ELOVL6 was found to be highly associated with ELOVL6 expression and, accordingly, with the percentages of palmitic and palmitoleic acids in longissimus dorsi and adipose tissue. The main goal of the current work was to further study the role of ELOVL6 on these traits by analyzing the regulation of the expression of ELOVL6 and the implication of ELOVL6 polymorphisms on meat quality traits in pigs.


High-throughput sequencing of BAC clones that contain the porcine ELOVL6 gene coupled to RNAseq data re-analysis showed that two isoforms of this gene are expressed in liver and adipose tissue and that they differ in number of exons and 3’UTR length. Although several SNPs in the 3’UTR of ELOVL6 were associated with palmitic and palmitoleic acid contents, this association was lower than that previously observed with SNP ELOVL6:c.-533C > T. This SNP is in full linkage disequilibrium with SNP ELOVL6:c.-394G > A that was identified in the binding site for estrogen receptor alpha (ERα). Interestingly, the ELOVL6:c.-394G allele is associated with an increase in methylation levels of the ELOVL6 promoter and with a decrease of ELOVL6 expression. Therefore, ERα is clearly a good candidate to explain the regulation of ELOVL6 expression through dynamic epigenetic changes in the binding site of known regulators of ELOVL6 gene, such as SREBF1 and SP1.


Our results strongly suggest the ELOVL6:c.-394G > A polymorphism as the causal mutation for the QTL on pig chromosome 8 that affects fatty acid composition in pigs.


Elongation of very long-chain fatty acids proteins (ELOVL) are a family of enzymes that catalyze the initial and rate-limiting condensation reaction of fatty acid elongation cycle in mammals [1-3]. To date, seven ELOVL proteins have been identified i.e. ELOVL1, ELOVL3, ELOVL6 and ELOVL7 that act preferentially on saturated fatty acids (SFA) and monounsaturated fatty acids (MUFA) and ELOVL2, ELOVL4 and ELOVL5 that act preferentially on polyunsaturated fatty acids (PUFA) [4-6]. In mammals, the enzyme ELOVL6 catalyzes the elongation of long-chain SFA and MUFA with 12 to 18 carbon atoms and is considered as a key gene in the control of the overall balance of fatty acid composition [2,7]. Expression of the gene coding for ELOVL6 is highly up-regulated, both in liver and adipose tissue in the refed state compared to fasting state, which indicates that this enzyme has a major role in the synthesis of long-chain fatty acids [8].The porcine ELOVL6 gene is located on chromosome 8 (SSC8, SSC for Sus scrofa), in a region where a quantitative trait locus (QTL) that affects palmitic and palmitoleic acid contents was previously detected [2,9]. Moreover, it was recently reported that this gene is differentially expressed in adipose tissue from Iberian x Landrace backcross animals with extreme phenotypic differences in intramuscular fatty acid composition [10].

Expression of ELOVL6 was first identified in the liver of transgenic mice that over-expressed sterol regulatory element binding transcription factors (SREBF) [1]. SREBF are transcription factors that control the expression of genes involved in de novo lipogenesis [11]. In tissues that synthesize fatty acids de novo, expression of SREBF is highly correlated with that of key lipogenic genes involved in this metabolic pathway [12]. Transcriptional regulation of ELOVL6 by SREBF was also confirmed by using DNA microarrays to analyze the expression of ELOVL6 in transgenic mice overexpressing SREBF1. [13], and by analyzing the promoter region of mouse ELOVL6 [14]. Kumadaki et al. [14] demonstrated that in mouse liver, nuclear SREBF1 activates the ELOVL6 promoter by interacting with two sterol response elements (SRE). However, although SREBF1 can bind to E-box motifs, there was no evidence that E-box motifs were involved in ELOVL6 activity [14,15]. Results of our previous analysis on the promoter of pig ELOVL6 [2] showed that: (1) pig and mouse ELOVL6 promoters share SRE and E-box motifs, and in the pig ELOVL6 promoter, SRE elements are present at positions −18, −450 and −524 and an E-box motif at position −331; (2) a single nucleotide polymorphism (SNP) i.e. ELOVL6:c.-533C > T is located close to the most distal SRE element and is highly associated with percentages of palmitic and palmitoleic acids in muscle and backfat and with the expression level of ELOVL6 in backfat; (3) the pig ELOVL6 promoter contains binding sites for other transcription factors i.e. for SP1 transcription factor (SP1) at position −470 with a SNP at position −480 i.e. ELOVL6:c.-480C > T and for MLX interacting protein-like (MLXIPL) at position −322 (also called carbohydrate response element binding protein or ChREBP); (4) the pig ELOVL6 promoter contains five additional SNPs (ELOVL6:c.-574C > T, ELOVL6:c.-534C > T, ELOVL6:c.-492G > A, ELOVL6:c.-394G > A and ELOVL6:c.-313C > T); and (5) expression of ELOVL6 varies between various lipogenic tissues (liver, adipose tissue and muscle), which suggests that the mechanisms that regulate the expression of this gene differ in each tissue. In addition, we performed a whole-genome association study of the expression levels of ELOVL6 (eGWAS) in liver, adipose tissue and muscle and identified several genomic regions that may be involved in the tissue-specific expression of this gene [2]. Epigenetic modifications is another mechanism that can contribute to these tissue-specific differences in the expression of ELOVL6 [16]. DNA methylation is one of the major epigenetic mechanisms that regulates gene transcription and it was shown to be involved in the regulation of genes associated to lipid metabolism, such as fatty acid desaturase 2 (FADS2) [17] or peroxisomal proliferator-activated receptor alpha (PPARα) [18]. Finally, one cannot exclude the possibility that microRNAs, a class of short non-coding RNAs with a key role in gene expression, may affect expression of ELOVL6, since the 3’UTR of porcine ELOVL6 gene has not been fully characterized.

The overall objective of the present study was to investigate the mechanisms that contribute to the control and regulation of ELOVL6 expression and their influence on porcine meat quality traits. Thus, we characterized the 3’UTR of porcine ELOVL6 and identified several polymorphisms. In addition, we performed a methylation study of the ELOVL6 promoter region on DNA extracted from liver, adipose tissue, muscle and spleen, to determine whether epigenetic modifications play a role in the differential expression of ELOVL6 across tissues.



The population analyzed was generated by crossing three Iberian (Guadyerbas line) boars with 31 Landrace sows (the so-called IBMAP cross) [19], and contained several generations and backcrosses. The animals used in this study belonged to the backcross (BC1_LD) that was produced by crossing five F1 (Iberian x Landrace) boars with 26 Landrace sows and resulted in 144 backcrossed animals. All pigs were raised and fed under the standard intensive system in Europe and feeding was ad libitum with a cereal-based commercial diet. Pigs were slaughtered at an average age of 179.8 ± 2.6 days following national and institutional guidelines for the Good Experimental Practices and approved by the Ethical Committee of the Institution (IRTA- Institut de Recerca i Tecnologia Agroalimentàries). Samples of liver, muscle (longissimus dorsi) and adipose tissue (backfat) were collected, snap-frozen in liquid nitrogen and stored at −80°C. Genomic DNA was obtained from blood and liver samples from the 144 animals according to the phenol-chloroform method, as described elsewhere. Backfat [20] and intramuscular fatty acid composition [9] was measured with a protocol based on gas chromatography of methyl esters [21].

BAC screening and sequencing

The porcine bacterial artificial chromosome (BAC) INRA library (Centre de Ressources Biologiques Génomique des Animaux Domestiques et d’Intérêt Economique i.e. CRB-GADIE; was used to select BAC clones containing the SSC8 region that carries ELOVL6. This BAC library constructed with the pBeloBAC11 vector comprises 107 520 clones with an average insert size of 135 kb, representing a five-fold coverage of the pig haploid genome [22]. Screening of the library was performed using three sets of primers located respectively in the promoter region, second exon (intermediate gene region) and at the end of the coding region in exon 4, in order to encompass the major part of ELOVL6 gene (See Additional file 1: Table S1). Primers were designed using the software PRIMER3 [23] and validated using the software PRIMER EXPRESSTM (Applied Biosystems). BAC screening was performed by two-step PCR according to CRB-GADIE protocols (PCR of superpools and pools) and positive BAC clones were confirmed by checking the size of their PCR-amplified fragments.

BAC clones were cultured on Luria-Bertani (LB) agar containing 12.5 μg/mL of chloramphenicol overnight at 37 °C and then, isolated clones were grown in 4 ml LB broth with 12.5 μg/mL of chloramphenicol overnight at 37 °C in a shaking incubator. Finally, 4 mL of the overnight starter culture was inoculated in 500 ml LB-medium supplemented with chloramphenicol (12.5 μg/mL) and incubated in the same conditions to an optical density of 2. BAC DNA was isolated using the plasmid DNA purification Nucleobond BAC100 kit (Macherey-Nagel), following the manufacturer’s recommendations of the low-copy plasmid purification (Maxi BAC100) section. DNA was quantified using the Nano-Drop ND-1000 spectrophotometer (NanoDrop products) and checked for purity and integrity by electrophoresis on agarose gels.

For each BAC, bar-coded libraries were generated using the Ion Xpress Plus fragment library kit (Life Technologies) with an insert size of approximately 250 bp. Libraries were sequenced on a Personal Genome Machine (PGM) Ion Torrent instrument (Life Technologies) using an Ion 314R chip. More than 220 000 single-end reads were generated with an average read length of 152 bp for each library.

De novo assembly and characterization of the porcine ELOVL6 gene

Statistics of reads and quality control were determined with FASTQC []. All reads were mapped against the Escherichia coli genome using the Burrows-Wheeler Alignment tool (bwa v.0.6.2) [24], in order to discard all reads corresponding to bacterial DNA. Removal of sequence adapters, quality trimming of reads and de novo assembly were performed using the de novo assembler tool of CLC Genomics Workbench v.6.0.1 []. The 3’UTR sequence of ELOVL6 was identified from de novo assembled reads aligned with the corresponding human (GenBank:NM_001130721) and bovine (GenBank:NM_001102155) genes, using the Basic Local Alignment Search Tool (BLAST v2.2.28) [25]. The resulting pig ELOVL6 3’UTR sequence was used as reference sequence to align reads from liver (12 BC1_LD animals) and adipose tissue (6 BC1_LD animal) transcriptomes that were obtained in previous studies [10,26] by using the software TopHat v2.0.1 [27,28] and including the pig genome (Sscrofa10.2) [] as a combined reference. Finally, SNPs in the ELOVL6 gene were manually identified by comparing the reference sequence with mapped reads with the Integrative Genomic Viewer (IGV v.2.1) [29,30].


SNPs ELOVL6:c.1408C > T and ELOVL6:c.1922C > T were genotyped using the platform KASP SNP genotyping system []. A total of 179 animals including the 144 BC1_LD backcross animals and their corresponding 35 parents from the IBMAP cross (F0 and F1) were genotyped.

For the genome-wide association study (GWAS), the 144 animals of the BC1_LD backcross were genotyped with the Porcine SNP60 Illumina BeadChip using the Infinium HD Assay Ultra protocol (Illumina). Raw data had a high genotyping quality (call rate > 0.99) and were visualized and analyzed with the Illumina GenomeStudio software. For subsequent data analysis, a subset of 54 998 SNPs was selected by removing SNPs with a minor allele frequency less than 5%, SNPs with more than 5% missing genotype data and SNPs that were duplicated in the Sscrofa10.2 assembly.

Chromosome-wide association analyses

Association analyses of whole-genome SNP genotypes, together with the previously identified SNPs ELOVL6:c.-533C > T, ELOVL6:c.-480C > T, ELOVL6:c.416C > T [2] and the newly detected ELOVL6:c.1408A > G and ELOVL6:c.1922C > T SNPs were performed on the following phenotypes: RT-qPCR expression data of ELOVL6 mRNA in backfat and C16:0 and C16:1(n-7) fatty acids composition in backfat and intramuscular fat. The position of the SNPs was based on the Sscrofa10.2 genome assembly []. GWAS were performed with a mixed model [31,32] that accounted for additive effects associated with each marker (see below) by using the Qxpak 5.0 software [33]:

$$ {\mathrm{y}}_{\mathrm{i}\mathrm{jlkm}} = \mathrm{S}\mathrm{e}{\mathrm{x}}_{\mathrm{i}} + \mathrm{Batc}{\mathrm{h}}_{\mathrm{j}}+{\uplambda}_{\mathrm{l}}{\mathrm{a}}_{\mathrm{k}} + {\mathrm{u}}_{\mathrm{l}} + {\mathrm{e}}_{\mathrm{i}\mathrm{jlkm}}, $$

in which yijlkm is the l-th individual’s record, sex (two levels) and batch (five levels) are fixed effects, λl is a −1, 0, +1 indicator variable depending on the l-th individual’s genotype for the k-th SNP, ak represents the additive effect associated with the k-th SNP, ul represents the polygenic effect for individual l, treated as random and distributed as N(0, Aσu), where A is the numerator of the kinship matrix, and eijlkm is the residual. The polygenic effect allows us to account for family relationships. In this analysis, each SNP was tested individually for association with phenotype. The R package q-value [34] was used to calculate the FDR-based q-value to measure the statistical significance at the genome-wide level for association studies. The cut-off for a significant association at the chromosome level was set at a q-value ≤ 0.05. In addition, carcass weight (βcl) was added to the model as a covariate for fatty acid composition:

$$ {\mathrm{y}}_{\mathrm{i}\mathrm{jlkm}} = \mathrm{S}\mathrm{e}{\mathrm{x}}_{\mathrm{i}} + \mathrm{Batc}{\mathrm{h}}_{\mathrm{j}}+\upbeta {\mathrm{c}}_{\mathrm{l}}+{\uplambda}_{\mathrm{l}}{\mathrm{a}}_{\mathrm{k}} + {\mathrm{u}}_{\mathrm{l}} + {\mathrm{e}}_{\mathrm{i}\mathrm{jlkm}}. $$

The same model was used to determine the effect of haplotypes on the traits of interest. The only difference is that haplotypes were treated as random additive effects, in contrast to the individual SNPs, which were considered as fixed additive effects.

DNA methylation analyses

DNA methylation analyses of liver and backfat were performed on 43 animals, while muscle and spleen that were chosen as control tissues expressing low levels of ELOVL6 were analyzed on six animals. DNA was extracted using the phenol-chloroform method, as described elsewhere. Methylation studies were performed using the bisulfite methodology [35] and the pyrosequencing technique [36,37]. The bisulfite gDNA conversion was performed on 500 ng of genomic DNA from each sample with the EZ DNA Methylation kit (Zymo Research). The regions of interest were amplified using primers (See Additional file 1: Table S1) that were designed from the resulting methylated sequence using the allele quantification assay type of the PSQ assay design software (Biotage). PCR were performed in 25 μL samples containing 0.6 units of AmpliTaq Gold (Applied Biosystems), 1.5 to 2.5 mM MgCl2 (depending on the primers; (See Additional file 1: Table S1)), 0.2 mM of each dNTP, 0.5 μM of each primer and 25 ng of treated genomic DNA. PCR were carried out under the following conditions: 94°C for 10 min, 40 cycles of 94°C for 1 min, 60°C for 1 min and 72°C for 1 min, with a final extension at 72°C for 7 min. Pyrosequencing analysis was carried out on a PSQ HS 96A system with the Pyro Gold sequence analysis (SQA) reagent (Biotage) using specific pyrosequencing primers for each region (See Additional file 1: Table S1). Statistical comparison of methylation values between tissues, genotypes and gene expression data was made using an ANOVA test in R considering sex and batch. RNA extraction, cDNA synthesis and gene expression analysis by real time quantitative PCR (RT-qPCR) were performed following the procedure described in [2].

Results and discussion

Pig ELOVL6 gene structure and identification of a new isoform

Despite the important role played by ELOVL6 in lipid metabolism [2,3,7], a comparison of human, bovine and porcine ELOVL6 mRNA sequences revealed that the current sequence of the porcine ELOVL6 gene is incomplete (data not shown). In order to characterize pig ELOVL6, we screened a pig BAC library for its promoter region, exon 2 (intermediate region) and exon 4 (terminal region). Six BAC clones that contain at least one of these three regions were identified by PCR: BAC 651E12, 650D01 and 385A04 were positive for the promoter region, BAC 201D05, 95C02, 754E02 and 385A04 were positive for exon 2 and BAC 754E02 was positive for exon 4. Of these six BAC clones, only BAC 385A04 and 754E02 were positive for two different sequences, i.e. BAC 385A04 contained the promoter and exon 2 regions and BAC 754E02 contained exons 2 and 4, which suggests that these two clones cover most of the porcine ELOVL6 gene. DNA of both BAC clones was sequenced with the PGM of Ion Torrent and around 265 000 single-end reads were generated for each BAC with an average read length of 152 bp. The data generated was used to perform a de novo assembly of porcine ELOVL6 with the CLC Genomics Workbench v.6.0.1. The 129 672 bp long sequence contained 1942 bp of the upstream region, four introns and five exons of porcine ELOVL6 (Figure 1). The protein coding region starts at position 2201 bp and ends at position 123 132 bp of this sequence (Figure 1). To validate the new gene annotation, RNA-Seq data from adipose tissue and liver transcriptomes [10,26] were used to map the reads against the new ELOVL6 sequence. The alignments obtained were concordant with the proposed ELOVL6 gene structure, but some reads were located between the first and second exons. In addition, the number of mapped reads was clearly reduced in the middle of the fifth exon. Interestingly, a poly-A signal was identified in this region (at position 124 578 bp), which indicates the end of an alternative isoform (Figure 1). Therefore, as for the human ELOVL6 gene, two different isoforms of porcine ELOVL6 are expressed in both liver and adipose tissue. These isoforms differ in: (i) total number of exons, i.e. four in variant 2 (the first and second exons are combined into a single exon) against five in variant 1, and (ii) length of the 3’UTR, which is shorter in variant 2 (1455 bp) than in variant 1 (5117 bp) (Figure 1).

Figure 1
figure 1

Genetic architecture of the porcine ELOVL6 gene, with the two transcribed variants identified by BAC sequencing and RNAseq analysis. Exons are indicated by blue boxes with dark blue corresponding to sequences that code for ELOVL6 protein and pale blue to 5’UTR and 3’UTR regions.

Identification of polymorphisms in the 3’UTR of porcine ELOVL6

Alignment and analysis of all mapped reads from RNA-Seq data [10,26] allowed us to identify 11 SNPs (Table 1) in the 3’UTR of porcine ELOVL6, among which five were found in both variants and six were found only in variant 2. All SNPs were arranged in three haplotypes, which can be distinguished by genotyping ELOVL6:c.1408A > G and ELOVL6:c.1922A > G SNPs. Hence, these two tag SNPs were genotyped in IBMAP founders, parental BC1_LD animals and the BC1_LD population. Regarding the IBMAP founders, alleles ELOVL6:c.1408G and ELOVL6:c.1922G were fixed in Iberian boars. The ELOVL6:c.1408A allele was fixed in the founder Landrace sows, whereas the allele ELOVL6:c.1922A had a frequency of 0.7 in these sows. In BC1_LD Landrace sows, allelic frequencies of ELOVL6:c.1408A and ELOVL6:c.1922A were 0.94 and 0.38, respectively. Both ELOVL6:c.1408A > G and ELOVL6:c.1922A > G SNPs segregated in the BC1_LD animals with frequencies for allele A equal to 0.72 and 0.46, respectively.

Table 1 Polymorphisms identified in the 3’UTR of the ELOVL6 gene

It is well known that the presence of polymorphisms in the 3’UTR of genes may affect the binding of microRNAs. This interaction is important for the regulation of gene expression, since microRNAs mediate translational repression and mRNA destabilization [38]. For instance, microRNA miR-33a/b has been described as a potential regulator of lipid metabolism by repressing the translation of genes coding for key enzymes that are involved in cholesterol efflux (ABCA1 and NPC1), fatty acid metabolism (CROT and CPT1a) and insulin signaling (IRS2) [39,40]. To assess if the polymorphisms present in the 3’UTR of ELOVL6 affect the disruption or creation of microRNA binding sites, a computer-assisted identification of potential microRNA binding elements was performed using the finder tool of patrocles programme [41]. Twelve putative microRNA binding sites were found to be modified by the 11 detected SNPs. MicroRNAs miR-524-3p, miR-525-3p, miR-18a/b, miR-204 and miR-211 were predicted to bind to both mRNA isoforms, whereas miR-584, miR-452, miR-603, miR-1262, miR-490-5p, miR-30a/d/e and miR-335 were predicted to bind only to variant 2. These predictions suggest that microRNAs may be involved in the regulation of porcine ELOVL6. However, further studies are needed to elucidate their role in the differential expression of ELOVL6 in pig adipose tissue, liver and muscle.

Association studies support the major role of the ELOVL6:c.-533C > T polymorphism

Previously, we found that SNP ELOVL6:c.-533C > T in the promoter region of ELOVL6 was associated with a QTL on SSC8 that affects palmitic and palmitoleic acid contents in muscle and backfat [2]. This SNP explained a large part of the phenotypic variance of each of these traits in both BF and IMF tissues: 18% for C16IMF, 32% for C16BF, 20% for C16:1(n-7)IMF and 19% for C16:1(n-7)GD. Nevertheless, we cannot exclude that other SNPs may also have a major association with these traits. The two newly genotyped 3’UTR SNPs and the three SNPs previously described in [2] were added to the 2565 SNPs on SSC8 that are included in the Porcine Illumina SNP60 BeadChip, in order to perform association analyses on 136 BC1_LD animals for FA composition in muscle and backfat. In this analysis, SNP ELOVL6:c.1922A > G showed a significant association with the percentages of palmitic acid in muscle (p-value = 3.38×10−04) and backfat (p-value = 1.23x10−11) (Figure 2A and C). In contrast, SNP ELOVL6:c.1408A > G showed significant associations only with percentage of palmitic acid in backfat (p-value = 1.73x10−06) (Figure 2C). In addition, percentage of palmitoleic acid was significantly associated with SNP ELOVL6:c.1922A > G in both muscle (p-value = 1.51×10−07) and backfat (p-value = 1.22x10−06) (Figure 2B and D). Significant associations were also found between palmitoleic acid and SNP ELOVL6:c.1408A > G in muscle (p-value = 4.86×10−05) and backfat (p-value = 4.24×10−04) (Figure 2B and D). The effect of the haplotypes formed by the combination of these two SNPs (ELOVL6:c.1408A > G and ELOVL6:c.1922A > G) on the different traits was tested, but no significant associations were observed (data not shown). Nevertheless, for both FA, SNP ELOVL6:c.-533C > T always showed a greater association than the 3’UTR SNPs (Figure 2), which further supports its role in the determination of the SSC8 QTL. No significant associations were observed between the SNPs in the 3’UTR sequence and ELOVL6 expression levels in backfat, liver and muscle (data not shown).

Figure 2
figure 2

Association analysis between SNP genotypes for SSC8 and the percentages of: palmitic (A) and palmitoleic (B) acid in muscle and palmitic (C) and palmitoleic (D) acid in backfat. ELOVL6 polymorphisms are included and labeled with a red circle. Positions in Mb are relative to the Sscrofa 10.2 assembly of the pig genome. The vertical dashed line indicates the position of ELOVL6 gene and the horizontal dashed line marks the chromosome-wide significance level (FDR-based q-value ≤ 0.05).

Taken together, these results indicate that SNP ELOVL6:c.-533C > T in the promoter of ELOVL6 is the most promising candidate among the genotyped SNPs on SSC8. However, to further support the main role of this SNP, an association study was performed to compare the individual effect of SNPs ELOVL6:c.-533C > T and ELOVL6:c.1922A > G against the effect of the haplotypes formed by the combination of these two SNPs. These analyses were performed with a reduced number of animals (n = 94), for which the allele origin (Iberian or Landrace) could be unambiguously determined based on pedigree information. As expected, the effect of SNP ELOVL6:c.-533C > T was greater than that of the haplotypes for all analyzed traits (p-value backfat gene expression = 3.68×10−03, p-value IMF_C16:0 = 1.33×10−03, p-value IMF_C16:1(n-7) = 3.72×10−04, p-value BF_C16:0 = 6.15×10−10 and p-value BF_C16:1(n-7) = 9.15×10−04) (See Additional file 2: Table S2). In conclusion, our results confirm that SNP ELOVL6:c.-533C > T plays a key role in explaining the SSC8 QTL that affects palmitic and palmitoleic acid contents in pig. However, we cannot exclude the possibility that the 3’UTR may have a secondary role on the regulation of ELOVL6 expression or that other variants located in intronic regions may be involved.

Promoter methylation is an additional level in the regulation of porcine ELOVL6 expression

DNA bisulfite conversion was used to compare the methylation patterns of the promoter region of pig ELOVL6 between liver, adipose tissue (backfat), muscle and spleen (tissues with low levels of ELOVL6 expression). The methylation study was focused on CpG-sites whose methylated states may affect the binding of SREBF1, the most relevant transcription factor of ELOVL6. All individual CpG-sites identified in the SRE and E-box motifs of ELOVL6 promoter [2] were included in the study. In addition, it was observed that SP1 is required as an additional regulator for SREBF1 activity in several lipogenic genes [1,42]. Interestingly, a CpG-site was identified in the SP1 binding element, in which SNP ELOVL6:c-416C > T [2] is located (Figure 3A), making this CpG a clear candidate. Finally, six CpG sites, that covered the major part of the described promoter, were analyzed (Figure 3A). Methylation analysis of these CpG sites showed that methylation levels were higher in muscle and spleen than in liver and adipose tissue (Figure 3B). These results are in agreement with the lower level of ELOVL6 expression observed in the former tissues (Figure 4), which suggests that an epigenetic mechanism may be involved in the regulation of ELOVL6 mRNA abundance.

Figure 3
figure 3

Characterization of the methylation patterns on the ELOVL6 gene promoter. (A) Schematic representation of the transcription factor binding elements studied, together with the CpG-sites analyzed in the methylation study. (B) Plot showing the percentages of methylation observed for each CpG-site in four porcine tissues: liver, backfat, muscle and spleen. Data represent means ± SEM. Values with different superscript letters (a, b, c and d) indicate significant differences between groups (p-value < 0.05), as determined by a linear model in R.

Figure 4
figure 4

Tissue-specific differences in ELOVL6 gene expression among liver, adipose tissue, muscle and spleen. Gene expression quantification was performed by real-time quantitative PCR (RT-qPCR), according to the procedure described in [2]. Gene expression was compared using the 2ΔCt data obtained from quantitative PCR analyses. Data represent mean ± SEM.

The six selected CpG sites were distributed in two clear regions with opposite levels of methylation i.e. (i) lower methylation levels in the proximal region (−349 bp to −1 bp) and (ii) higher methylation levels in the distal region (−529 bp to -350 bp) (Figure 3B). On the one hand, the low methylation levels in the proximal region (CpG1, CpG2 and CpG3) in all tissues, suggest that this region may be important for maintaining a basal gene expression. On the other hand, the higher methylation levels in the distal promoter regions (CpG4, CpG5 and CpG6), suggest that methylation of these motifs may be relevant for the regulation of ELOVL6 expression among tissues. Statistical analyses showed significant lower methylation levels of these sites in liver than in backfat (p-value CpG4 = 7.27×10−09, p-value CpG5 = 1.18×10−05 and p-value CpG6 = 1.04×10−07), muscle (p-value CpG4 = 2.13×10−14, p-value CpG5 = 1.52×10−10 and p-value CpG6 = 1.82×10−14) and spleen (p-value CpG4 = 4.42×10−11, p-value CpG5 = 5.24×10−07 and p-value CpG6 = 1.47x10−10) (Figure 3B). Methylation levels in adipose tissue were clearly higher than in liver, although expression of ELOVL6 is higher in adipose tissue than in liver [2]. The higher levels of ELOVL6 expression in adipose tissue are explained by the major role of this tissue in lipogenic pathways in pigs [43,44] and, consequently, lipogenic genes are upregulated by SREBF1 in this tissue [43]. In agreement, gene expression correlation analysis performed in our animal material, showed a high correlation between SREBF1 and ELOVL6 (r = 0.77) in adipose tissue, but not in liver (Ballester et al., unpublished). Thus, these results allowed us to hypothesize that methylation of the ELOVL6 promoter region between −529 bp and −350 bp may be one possible mechanism responsible for the differential expression of this gene among the tissues analyzed. This region contains one methylated SRE element and one SP1 binding site, which are well-known transcription factors involved in the regulation of lipogenic genes such as ELOVL6 [1,2,14,42]. Nevertheless, since several SRE elements have been identified in the promoter region of ELOVL6, a site-specific chromatin immunoprecipitation (ChIP) approach is required to investigate the capacity of SREBF1 to bind the specific methylated SRE [45,46], as well as the effect of the methylation level on SREBF1 binding. Regarding SP1 elements, some studies have reported that variations in the SP1 binding site can reduce the methylation level of CpG sites [47,48]. In our case, we found that the SP1 element contains SNP ELOVL6:c-416C > T that was reported in [2], which suggests a putative protective role against methylation. In our data, this putative protective role was also observed in the methylation levels of CpG5 (p-value = 1.13×10−03) and CpG4 motifs (p-value = 5.43×10−04) and a suggestive significant effect in the CpG6 motif (p-value = 8×10−02) in adipose tissue. Nevertheless, functional studies are needed to investigate the binding of SP1 to the ELOVL6 promoter and its role in the control of the expression of ELOVL6.

Moreover, it has been shown that some transcription factors are capable of regulating dynamic methylation cycles that lead to rapid changes in the methylation levels of the promoter of the regulated gene [49]. This additional level of regulation may affect the regulation of ELOVL6 expression in response to physiological changes.

Role of the ELOVL6 promoter in the regulation of its expression

Previous studies have shown that estrogen receptor alpha (ERα) can cause rapid epigenetic modifications that influence the regulation of gene expression [49,50]. Activation of ERα, by estradiol binding or phosphorylation of serine 118, modulates its three-dimensional surface, causing a recruitment of coactivator complexes, including DNA methyltransferases (DNMT) [49]. Hence, to assess if ERα binding may be related to the different methylation patterns detected in the promoter of ELOVL6 and, consequently to the expression of ELOVL6, a computer-assisted identification of putative ERα binding sites was performed using the LASAGNA-Search software [51]. Interestingly, an estrogen response element (ERE) was predicted at position −397 to −382, between the two regions with different methylation patterns (Figure 3A). The minimal consensus ERE sequence is a 13 bp palindromic inverted repeat: 5'-GGTCAnnnTGACC-3' [52,53]. However, most estrogen-regulated genes contain imperfect ERE with variations of one or more nucleotides from the consensus sequence. It has been reported that up to two changes in the ERE consensus sequence may still allow the binding of ER with the appropriate flanking sequences adjacent to the core [52]. In this way, although the ERE sequence in the ELOVL6 promoter presents two base changes from the consensus: 5’-GGGCTnnnTGACC-3’, the immediate flanking sequences 5’-CAGGGCTnnnTGACCTG-3’ may be sufficient to retain ERE function [52,53]. However, it should be noted that the consensus ERE sequence of the porcine ELOVL6 promoter, contains SNP ELOVL6:c.-394G > A, which constitutes a third mutation in the half-site of the ERE palindrome that may prevent the ER-ERE binding [52]. Interestingly, genotyping data showed that ELOVL6:c.-394G > A and ELOVL6:c.-533C > T SNPs are in complete linkage disequilibrium. Statistical analyses showed a significant association between SNP ELOVL6:c.-394G > A and CpG6 (p-value = 1.29×10−03), CpG5 (p-value = 6.92×10−04) and CpG4 (p-value = 7.41×10−04) in adipose tissue. Animals homozygous for the ELOVL6:c.-394G allele showed a higher methylation rate (Figure 5A). Accordingly, a significant association was also observed between ELOVL6 expression levels and SNP ELOVL6:c.-394G > A (p-value = 3.25×10−02) (Figure 5B), as previously described [2]. Based on RNA-Seq data [10], the ratio of differential allelic expression between ELOVL6:c.-394G > A alleles was found to be greater than 1.5 (A:G). Finally, a significant association was observed between ELOVL6 expression and CpG5 (p-value = 3.47x10−02) and a suggestive effect was found between ELOVL6 expression and CpG6 (p-value = 6.79×10−02).

Figure 5
figure 5

Effect of the ELOVL6:c.-394G > A polymorphism on ELOVL6 gene expression regulation. (A) Plot showing the percentage of methylation observed for each CpG-site according to ELOVL6:c.-394G > A genotypes. (B) Plot showing ELOVL6 expression levels in backfat according to ELOVL6:c.-394G > A genotypes. Data represents means ± SEM. Values with different superscript letter (a, b and c) indicate significant differences between groups (p-value < 0.05) as determined by a linear model in R.

Taken together, the results suggest a new mechanism for the regulation of the expression of ELOVL6. The hypothetical alteration of ERα binding by SNP ELOVL6:c.-394G > A may be the main factor responsible for the differential expression of ELOVL6 in adipose tissue. In the methylated region that contains two SRE elements, the SRE element between positions −460 and −449 constitutes a clear candidate for methylation changes. Its core binding site contains two CpG sites (CpG4 and CpG5) (Figure 3A), whose methylation levels are associated with the ELOVL6:c.-394G > A genotype and ELOVL6 expression. In addition, previous studies in mouse have shown that this SRE element has a relevant effect on ELOVL6 expression [14]. Hence, inhibition of SREBF1 binding caused by the methylation of SRE elements in the promoter of ELOVL6, in particular the SRE element located between positions −460 and −449, is the most likely mechanism responsible for the reduction of ELOVL6 expression. Previously, we showed that a reduced ELOVL6 expression is associated with an accumulation of palmitic and palmitoleic acids in muscle and adipose tissue [2]. Thus, variation in ELOVL6 expression can modulate fatty acid composition in muscle and backfat and have important sensorial and technological consequences on meat quality [54] and insulin sensitivity [7]. Our results provide genetic evidence to support SNP ELOVL6:c.-394G > A as the causal mutation of the QTL on SSC8 but additional investigations are necessary to validate its effect on ERα binding. Furthermore, to our knowledge, this is the first study that suggests a mechanism for the regulation of ELOVL6 expression in pigs. Therefore, based on the metabolic similarities between pigs and humans [43], the regulatory mechanism described here may be useful to improve knowledge on human lipid-related diseases, such as obesity, diabetes or metabolic syndrome.


In this paper, we describe the complete genetic structure of porcine ELOVL6 gene and show that two different isoforms are expressed in both liver and adipose tissue. SNP ELOVL6:c.-533C > T was found to be more strongly associated with the expression of ELOVL6 and with the percentages of palmitic and palmitoleic acids in longissimus dorsi and adipose tissue than the two genotyped SNPs of the 3’UTR region. These results indicate that the promoter region of ELOVL6 may be the main regulatory region involved in the variation of ELOVL6 expression in pigs. Interestingly, SNP ELOVL6:c.-394G > A, which is in linkage disequilibrium with SNP ELOVL6:c.-533C > T and is located in the only ERα binding site predicted in the promoter of ELOVL6, was found to be associated with variations in methylation patterns of the region between −529 bp and −350 bp and with ELOVL6 expression. This region contains binding motifs for several regulators of the ELOVL6 gene, which suggests that epigenetic changes may have a central role in the regulation of ELOVL6 expression. Hence, we suggest that SNP ELOVL6:c.-394G > A is most likely responsible for the differential expression of ELOVL6 and, consequently, for the palmitic and palmitoleic acid contents in muscle and backfat.


  1. Moon YA, Shah NA, Mohapatra S, Warrington JA, Horton JD. Identification of a mammalian long chain fatty acyl elongase regulated by sterol regulatory element-binding proteins. J Biol Chem. 2001;276:45358–66.

    Article  CAS  PubMed  Google Scholar 

  2. Corominas J, Ramayo-Caldas Y, Puig-Oliveras A, Pérez-Montarelo D, Noguera JL, Folch JM, et al. Polymorphism in the ELOVL6 gene is associated with a major QTL effect on fatty acid composition in pigs. PLoS ONE. 2013;8:e53687.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  3. Jakobsson A, Westerberg R, Jacobsson A. Fatty acid elongases in mammals: their regulation and roles in metabolism. Prog Lipid Res. 2006;45:237–49.

    Article  CAS  PubMed  Google Scholar 

  4. Guillou H, Zadravec D, Martin PGP, Jacobsson A. The key roles of elongases and desaturases in mammalian fatty acid metabolism: Insights from transgenic mice. Prog Lipid Res. 2010;49:186–99.

    Article  CAS  PubMed  Google Scholar 

  5. Leonard AE, Bobik EG, Dorado J, Kroeger PE, Chuang LT, Thurmond JM, et al. Cloning of a human cDNA encoding a novel enzyme involved in the elongation of long-chain polyunsaturated fatty acids. Biochem J. 2000;350:765–70.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  6. Kitazawa H, Miyamoto Y, Shimamura K, Nagumo A, Tokita S. Development of a high-density assay for long-chain fatty acyl-CoA elongases. Lipids. 2009;44:765–73.

    Article  CAS  PubMed  Google Scholar 

  7. Matsuzaka T, Shimano H, Yahagi N, Kato T, Atsumi A, Yamamoto T, et al. Crucial role of a long-chain fatty acid elongase, Elovl6, in obesity-induced insulin resistance. Nat Med. 2007;13:1193–202.

    Article  CAS  PubMed  Google Scholar 

  8. Turyn J, Stojek M, Swierczynski J. Up-regulation of stearoyl-CoA desaturase 1 and elongase 6 genes expression in rat lipogenic tissues by chronic food restriction and chronic food restriction/refeeding. Mol Cell Biochem. 2010;345:181–8.

    Article  CAS  PubMed  Google Scholar 

  9. Ramayo-Caldas Y, Mercadé A, Castelló A, Yang B, Rodríguez C, Alves E, et al. Genome-wide association study for intramuscular fatty acid composition in an Iberian x Landrace cross. J Anim Sci. 2012;90:2883–93.

    Article  CAS  PubMed  Google Scholar 

  10. Corominas J, Ramayo-Caldas Y, Puig-Oliveras A, Estellé J, Castelló A, Alves E, et al. Analysis of porcine adipose tissue transcriptome reveals differences in de novo fatty acid synthesis in pigs with divergent muscle fatty acid composition. BMC Genomics. 2013;14:843.

    Article  PubMed Central  PubMed  Google Scholar 

  11. Knebel B, Haas J, Hartwig S, Jacob S, Köllmer C, Nitzgen U, et al. Liver-specific expression of transcriptionally active SREBP-1c is associated with fatty liver and increased visceral fat mass. PLoS ONE. 2012;7:e31812.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Gondret F, Ferré P, Dugail I. ADD-1/SREBP-1 is a major determinant of tissue differential lipogenic capacity in mammalian and avian species. J Lipid Res. 2001;42:106–13.

    CAS  PubMed  Google Scholar 

  13. Matsuzaka T, Shimano H, Yahagi N, Yoshikawa T, Amemiya-Kudo M, Hasty AH, et al. Cloning and characterization of a mammalian fatty acyl-CoA elongase as a lipogenic enzyme regulated by SREBPs. J Lipid Res. 2002;43:911–20.

    CAS  PubMed  Google Scholar 

  14. Kumadaki S, Matsuzaka T, Kato T, Yahagi N, Yamamoto T, Okada S, et al. Mouse Elovl-6 promoter is an SREBP target. Biochem Biophys Res Commun. 2008;368:261–6.

    Article  CAS  PubMed  Google Scholar 

  15. Amemiya-Kudo M, Shimano H, Hasty AH, Yahagi N, Yoshikawa T, Matsuzaka T, et al. Transcriptional activities of nuclear SREBP-1a, −1c, and −2 to different target promoters of lipogenic and cholesterogenic genes. J Lipid Res. 2002;43:1220–35.

    CAS  PubMed  Google Scholar 

  16. Ferrari A, Fiorino E, Giudici M, Gilardi F, Galmozzi A, Mitro N, et al. Linking epigenetics to lipid metabolism: focus on histone deacetylases. Mol Membrane Biol. 2012;29:257–66.

    Article  CAS  Google Scholar 

  17. Devlin AM, Singh R, Wade RE, Innis SM, Bottiglieri T, Lentz SR. Hypermethylation of Fads2 and altered hepatic fatty acid and phospholipid metabolism in mice with hyperhomocysteinemia. J Biol Chem. 2007;282:37082–90.

    Article  CAS  PubMed  Google Scholar 

  18. Wang L, Chen L, Tan Y, Wei J, Chang Y, Jin T, et al. Betaine supplement alleviates hepatic triglyceride accumulation of apolipoprotein E deficient mice via reducing methylation of peroxisomal proliferator-activated receptor alpha promoter. Lipids Health Dis. 2013;12:34.

    Article  PubMed Central  PubMed  Google Scholar 

  19. Perez-Enciso M, Clop A, Noguera JL, Ovilo C, Coll A, Folch JM, et al. A QTL on pig chromosome 4 affects fatty acid metabolism: evidence from an Iberian by Landrace intercross. J Anim Sci. 2000;78:2525–31.

    CAS  PubMed  Google Scholar 

  20. Muñoz M, Rodríguez MC, Alves E, Folch JM, Ibañez-Escriche N, Silió L, et al. Genome-wide analysis of porcine backfat and intramuscular fat fatty acid composition using high-density genotyping and expression data. BMC Genomics. 2013;14:845.

    Article  PubMed Central  PubMed  Google Scholar 

  21. Mach N, Devant M, Díaz I, Font-Furnols M, Oliver MA, García JA, et al. Increasing the amount of n-3 fatty acid in meat from young Holstein bulls through nutrition. J Anim Sci. 2006;84:3039–48.

    Article  CAS  PubMed  Google Scholar 

  22. Rogel-Gaillard C, Bourgeaux N, Billault A, Vaiman M, Chardon P. Construction of a swine BAC library: application to the characterization and mapping of porcine type C endoviral elements. Cytogenet Cell Genet. 1999;85:205–11.

    Article  CAS  PubMed  Google Scholar 

  23. Rozen S, Skaletsky H. Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol. 2000;132:365–86.

    CAS  PubMed  Google Scholar 

  24. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.

    Article  CAS  PubMed  Google Scholar 

  26. Ramayo-Caldas Y, Mach N, Esteve-Codina A, Corominas J, Castello A, Ballester M, et al. Liver transcriptome profile in pigs with extreme phenotypes of intramuscular fatty acid composition. BMC Genomics. 2012;13:547.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  27. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25:1105–11.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7:562–78.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  29. Thorvaldsdóttir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform. 2013;14:178–92.

    Article  PubMed Central  PubMed  Google Scholar 

  30. Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat Biotechnol. 2011;29:24–6.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  31. Henderson CR. Best linear unbiased estimation and prediction under a selection model. Biometrics. 1975;31:423–47.

    Article  CAS  PubMed  Google Scholar 

  32. Henderson CR. Applications of linear models in animal breeding. 3rd ed. University of Guelph: CGIL publications; 1984.

  33. Perez-Enciso M, Misztal I. Qxpak.5: Old mixed model solutions for new genomics problems. BMC Bioinformatics. 2011;12:202.

    Article  PubMed Central  PubMed  Google Scholar 

  34. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Nat Acad Sci USA. 2003;100:9440–5.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Frommer M, McDonald LE, Millar DS, Collis CM, Watt F, Grigg GW, et al. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Nat Acad Sci USA. 1992;89:1827–31.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Colella S, Shen L, Baggerly K, Issa J, Krahe R. Sensitive and quantitative universal pyrosequencing methylation analysis of CpG sites. Biotechniques. 2003;35:146–50.

    CAS  PubMed  Google Scholar 

  37. Tost J, Dunker J, Gut I. Analysis and quantification of multiple methylation variable positions in CpG islands by pyrosequencing. Biotechniques. 2003;35:152–6.

    CAS  PubMed  Google Scholar 

  38. Bartel DP. MicroRNAs: Target recognition and regulatory functions. Cell. 2009;136:215–33.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  39. Dávalos A, Goedeke L, Smibert P, Ramírez CM, Warrier NP, Andreo U, et al. miR-33a/b contribute to the regulation of fatty acid metabolism and insulin signaling. Proc Natl Acad Sci USA. 2011;108:9232–7.

    Article  PubMed Central  PubMed  Google Scholar 

  40. Goedeke L, Vales-Lara FM, Fenstermaker M, Cirera-Salinas D, Chamorro-Jorganes A, Ramírez CM, et al. A regulatory role for microRNA 33* in controlling lipid metabolism gene expression. Mol Cell Biol. 2013;33:2339–52.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Hiard S, Charlier C, Coppierters W, George M, Baurain D. Patrocles: a database of polymorphic miRNA-mediated gene regulation in vertebrates. Nucleic Acids Res. 2010;38:D640–51.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  42. Reed BD, Charos AE, Szekely AM, Weissman SM, Snyder M. Genome-wide occupancy of SREBP1 and its partners NFY and SP1 reveals novel functional roles and combinatorial regulation of distinct classes of genes. PLoS Genet. 2008;4:e1000133.

    Article  PubMed Central  PubMed  Google Scholar 

  43. Bergen WG, Mersmann HJ. Comparative aspects of lipid metabolism: Impact on contemporary research and use of animal models. J Nutr. 2005;135:2499–502.

    CAS  PubMed  Google Scholar 

  44. O'Hea EK, Leveille GA. Significance of adipose tissue and liver as sites of fatty acid synthesis in the pig and the efficiency of utilization of various substrates for lipogenesis. J Nutr. 1969;99:338–44.

    PubMed  Google Scholar 

  45. Schuch R, Agelopoulos K, Neumann A, Brandt B, Burger H, Korsching E. Site-specific chromatin immunoprecipitation: A selective method to individually analyze neighboring transcription factor binding sites in vivo. BMC Res Notes. 2012;5:109.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  46. Kang SH, Vieira K, Bungert J. Combining chromatin immunoprecipitation and DNA footprinting: A novel method to analyze protein-DNA interactions in vivo. Nucleic Acids Res. 2002;30:e44.

    Article  PubMed  Google Scholar 

  47. Brandeis M, Frank D, Keshet I, Siegfried Z, Mendelsohn M, Names A, et al. Spl elements protect a CpG island from de novo methylation. Nature. 1994;371:435–8.

    Article  CAS  PubMed  Google Scholar 

  48. Boumber YA, Kondo Y, Chen X, Shen L, Guo Y, Tellez C, et al. An Sp1/Sp3 binding polymorphism confers methylation protection. PLoS Genet. 2008;4:e1000162.

    Article  PubMed Central  PubMed  Google Scholar 

  49. Metivier R, Gallais R, Tiffoche C, Le Péron C, Jurkowska RZ, Carmouche RP, et al. Cyclical DNA methylation of a transcriptionally active promoter. Nature. 2008;452:45–50.

    Article  CAS  PubMed  Google Scholar 

  50. Fritah A. Redeuilh Gr, Sabbah M. Molecular cloning and characterization of the human WISP-2/CCN5 gene promoter reveal its upregulation by oestrogens. J Endocrinol. 2006;191:613–24.

    Article  CAS  PubMed  Google Scholar 

  51. Lee C, Huang C-H. LASAGNA: A novel algorithm for transcription factor binding site alignment. BMC Bioinformatics. 2013;14:108.

    Article  PubMed Central  PubMed  Google Scholar 

  52. Driscoll MD, Sathya G, Muyan M, Klinge CM, Hilf R, Bambara RA: Sequence requirements for estrogen receptor binding to estrogen response elements. The Journal of Biological Chemistry 1998, 273(45):29321–30.

    Article  CAS  PubMed  Google Scholar 

  53. Klinge CM: Estrogen receptor interaction with estrogen response elements. Nucleic Acids Research 2001, 29(14):2905–19.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  54. Wood JD, Richardson RI, Nute GR, Fisher AV, Campo MM, Kasapidou E, et al. Effects of fatty acids on meat quality: a review. Meat Science 2004, 66(1):21–32.

    Article  CAS  PubMed  Google Scholar 

Download references


We wish to thank the staff of CRB-GADIE Resource Center of the Animal Genetics Department at INRA, especially D Jardet and M Moroldo, for the BAC screening. In addition, we are indebted to the Centre de Recerca En Sanitat Animal (CRESA) for its help in the BAC DNA isolation, especially to M Pérez. Furthermore, we would like to thank JL Noguera (IRTA) for the animal material. This work was funded by the Ministerio de Economía y Competitividad project AGL2011-29821-C02. J Corominas was funded by a FPI PhD grant from the Spanish Ministerio de Educación (BES-2009-081223), M Revilla was funded by a Formació i Contractació de Personal Investigador Novell (FI-DGR) PhD grant from Generalitat de Catalunya (ECO/1639/2013), A Puig-Oliveras was funded by a PIF PhD grant from the Universitat Autónoma de Barcelona (458-01-1/2011) and JAP Marchesi was funded by a CNPq scholarship from the Brazilian Science Without Borders Program.

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Josep M Folch or Maria Ballester.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

JC, JMF and MB conceived and designed the experiment. JMF was the principal investigator of the project. JC and MB carried out the BAC screening and sequencing. JC and JE performed de novo assembly analysis. JC and APO performed the chromosome-wide association analysis. JC, MB, JAPM and MR carried out the DNA methylation analyses. EA, JE and JMF collected the samples. JC, JMF and MB drafted the manuscript. All authors read and approved the final manuscript.

Additional files

Additional file 1: Table S1.

Title: Primers for the BAC screening (S) and the methylation study (M). Description: Table S1 shows all primers used for this work with the corresponding values of amplicon length (bp), melting temperature (Tm) and MgCl2 concentration. Primers used for the BAC screening are indicated with the letter “S” and primers for the methylation study with the letter “M”.

Additional file 2: Table S2.

Title: Association analyses for phenotypic data and the selected polymorphisms (ELOVL6:c.-533C > T and ELOVL6:c.1922A > G) and the haplotypes formed by the two SNPs. Description: Table S2 shows associations (shown with p-values) between ELOVL6:c.-533C > T (promoter) and ELOVL6:c.1922A > G (3’UTR); and between the haplotype formed by the two SNPs and backfat ELOVL6 expression and C16:0 and C16:1(n-7) contents in IMF and backfat.

Rights and permissions

Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit

The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Corominas, J., Marchesi, J.A., Puig-Oliveras, A. et al. Epigenetic regulation of the ELOVL6 gene is associated with a major QTL effect on fatty acid composition in pigs. Genet Sel Evol 47, 20 (2015).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: