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Transcriptome shifts triggered by vitamin A and SCD genotype interaction in Duroc pigs

Abstract

Background

The composition of intramuscular fat depends on genetic and environmental factors, including the diet. In pigs, we identified a haplotype of three SNP mutations in the stearoyl-coA desaturase (SCD) gene promoter associated with higher content of monounsaturated fatty acids in intramuscular fat. The second of these three SNPs (rs80912566, C > T) affected a putative retinol response element in the SCD promoter. The effect of dietary vitamin A restriction over intramuscular fat content is controversial as it depends on the pig genetic line and the duration of the restriction. This study aims to investigate changes in the muscle transcriptome in SCD rs80912566 TT and CC pigs fed with and without a vitamin A supplement during the fattening period.

Results

Vitamin A did not affect carcass traits or intramuscular fat content and fatty acid composition, but we observed an interaction between vitamin A and SCD genotype on the desaturation of fatty acids in muscle. As reported before, the SCD-TT pigs had more monounsaturated fat than the SCD-CC animals. The diet lacking the vitamin A supplement enlarged fatty acid compositional differences between SCD genotypes, partly because vitamin A had a bigger effect on fatty acid desaturation in SCD-CC pigs (positive) than in SCD-TT and SCD-TC animals (negative). The interaction between diet and genotype was also evident at the transcriptome level; the highest number of differentially expressed genes were detected between SCD-TT pigs fed with the two diets. The genes modulated by the diet with the vitamin A supplement belonged to metabolic and signalling pathways related to immunity and inflammation, transport through membrane-bounded vesicles, fat metabolism and transport, reflecting the impact of retinol on a wide range of metabolic processes.

Conclusions

Restricting dietary vitamin A during the fattening period did not improve intramuscular fat content despite relevant changes in muscle gene expression, both in coding and non-coding genes. Vitamin A activated general pathways of retinol response in a SCD genotype-dependant manner, which affected the monounsaturated fatty acid content, particularly in SCD-CC pigs.

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Introduction

Intramuscular fat (IMF) content and its composition affect overall pork acceptability by influencing organoleptic attributes such as flavour, texture, and juiciness. Meat quality can be improved by direct husbandry practices, for instance through adapting the diet to meet production aims [1]. In this context, vitamin A restriction during the fattening period can improve IMF content and marbling score in beef cattle (reviewed in [2]). This effect is induced by retinoic acid, one of the bioactive compounds of the vitamin A family, which prompts two waves of effects over preadipocyte differentiation [3, 4]. On the one hand, it stimulates preadipocyte differentiation during the early commitment of embryonic stem cells into the adipocyte lineage but, at later stages, it has an inhibitory role over the terminal differentiation of preadipocytes. Thus, restriction of vitamin A or their precursors (β-carotenes) at early fattening stages (14-22 months of age in beef cattle) had a prominent effect over IMF content through enhanced hyperplasia of adipocytes [2].

In pigs, however, the effect of vitamin A restriction is controversial. The IMF of pigs fed a control feed (7500 IU vitamin A /kg) did not differ from pigs supplemented with > 10 times more vitamin A (100,000 IU/kg) [5]. Other authors have reported increases in IMF content with diets restricted in vitamin A [6,7,8,9,10], although the magnitude (and even the direction) of this effect depends on the genetic type of the pigs [11]. In contrast, the effect of vitamin A on IMF fatty acid (FA) composition is more robust across experiments. Vitamin A restriction promotes deposition of monounsaturated FAs (MUFA) and raises the desaturation index of fat [5, 7].

Intramuscular fat content and its composition are traits of moderate-to-high (range 0.26 to 0.86) heritability [12] and can therefore be improved through selection programs. As a result from a previous genome-wide association study (GWAS) using IMF content and composition data from muscle (gluteus medius and longissimus thoracis) and subcutaneous fat from a line of commercial Duroc pigs, we identified two genomic regions with prominent but distinct effects on IMF content (in SSC6) and on MUFA, notably oleic acid (in SSC14) [13]. These two regions co-located with the position of the leptin receptor (LEPR) and stearoyl-coA desaturase (SCD) genes, respectively. The SCD gene encodes a limiting enzyme in the biosynthesis of MUFA. In a previous work, we identified three linked SNP mutations in the promoter of this gene [14]. The haplotype H1, corresponding to the C-T-A combination of alleles, associated with a higher content of MUFA in IMF and subcutaneous fat regarding the alternative H2 haplotype (T-C-G). The middle SNP (rs80912566, C > T) changes a potential binding site for retinoid X receptors (RXR) [14]. Two families of the retinoic acid receptors (RAR and RXR) mediate signals in multiple physiological processes, including the modulation of genes involved in adipogenesis, mitochondria and lipid metabolism, through their interaction with retinoic acid compounds [15]. However, on the other hand, no relationship has been described between the LEPR activity and vitamin A.

Given all the above, we have investigated here the effect of the interaction between dietary vitamin A content and the SCD genotype on IMF content and FA composition. A global transcriptome sequencing approach was used to characterise the changes in gene expression triggered by this interaction in pigs fed different levels of vitamin A supplement.

Material and methods

Animals and experimental design

All pigs used in the study were raised and slaughtered in commercial units following applicable regulations and good practice guidelines on the protection of animals kept for farming purposes during transport and slaughter. All experimental procedures were approved by the Ethics Committee for Animal Experimentation of the University of Lleida (agreement CEEA 05-04/15). This study is reported in accordance with the ARRIVE guidelines.

In a first experiment to study the effect of the interaction between dietary vitamin A content and the SCD genotype on carcass and IMF content and FA composition, 108 Duroc barrows were reared in two batches and were maintained under the same rearing conditions with ad libitum access to feed (Additional file 1: Table S1). This population was generated by mating 59 sows with 28 boars and randomly choosing, on average, two barrows per litter. At 30 kg of live-weight, pigs were randomly assigned to two dietary treatments (Table 1 and Additional file 1: Table S2): a standard feed supplemented with vitamin A (VA+), or the same diet without supplementation (VA-).

Table 1 Retinyl acetate content in the feed used in this study

Animals were slaughtered at 207 ± 7 days and 130 ± 11 kg of body weight in a commercial slaughterhouse equipped with a carbon dioxide stunning system. Measured body composition traits included carcass yield, loin thickness and backfat thickness between the third and fourth last ribs by using an ultrasound automatic scanner (AutoFOM, SFK-Technology, Denmark). The carcass lean percentage was estimated based on 35 measurements of AutoFOM points by using the official approved equation (Decision 2001/775/CE, 2001). Samples of semimembranosus muscle were collected immediately after slaughter and snap frozen in dry ice. After chilling for about 24 h at 4 °C, samples of the gluteus medius and longissimus thoracis were collected and stored at − 80 °C until required.

In a second experiment to study changes in muscle transcriptome in the pigs from the first experiment, 40 pigs from the same batch were selected using a factorial design balanced for diet (VA+ and VA-) and SCD genotypes (TT and CC) (Additional file 1: Table S2).

Fatty acid analysis

Fatty acid content was analysed in a representative sample from pulverized freeze-dried muscle. For each sample, the IMF content and FA composition were determined in duplicate by gas chromatography [16]. Fatty acid methyl esters were obtained by transesterification using a solution of 20% boron trifluoride in methanol [17]. Methyl esters were determined by gas chromatography using a SP2330 capillary column (30 m by 0.25 mm; Supelco Inc., Bellefonte, PA). The quantification was performed through area normalization after adding into each sample 1,2,3-tripentadecanoylglycerol as an internal standard. The proportion of individual FA, saturated FA (SFA), polyunsaturated FA (PUFA) and MUFA were calculated as percentages relative to total FA content. Intramuscular fat content was predicted as the sum of each individual FA expressed as triglyceride equivalents [18].

Statistical analyses for carcass and fatty acid data

Data on carcass traits and FA composition were analysed using a linear model that included the rearing batch (2 levels), diet (VA+ and VA-), the SCD genotype (TT, TC, CC), and the interaction of diet by SCD genotype. The LEPR genotype was not included in the model as the number of pigs per genotypes was not balanced (there were very few LEPR-TT pigs). The age at slaughter was included as a covariate to analyse carcass traits and IMF content. To analyse FA composition, IMF content was used as a covariate. Multiple pairwise comparisons were performed with a Tukey test setting P < 0.05 as significance threshold. Analyses were performed using the statistical package JMP Pro 15 (SAS Institute Inc., Cary, NC).

DNA isolation and genotyping

Genomic DNA was isolated from muscle samples by incubation with a lysis buffer with proteinase K followed by phenol:chloroform purification using standard protocols [19]. The quantification and purity of DNA was determined in a Nanodrop ND-1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). DNA integrity was tested by electrophoresis in agarose gels.

All samples were genotyped for SCD rs80912566 by real time qPCR (QuantStudio3, Applied Biosystems, Waltham, MA, USA) with High Resolution Melt analysis (Luminaris Color HRM Master Mix, Thermo Scientific, Waltham, MA, USA) as in [13].

RNA isolation and library construction

Total RNA was isolated from m. semimembranosus samples using TRI Reagent (Invitrogen, Thermo Scientific, Waltham, MA, USA) and Direct-zol™ RNA Miniprep Plus Kit (Zymo Research, BioSystems, CA, USA) according to the manufacturer’s protocol. This muscle was selected because it was collected at slaughter and yielded RNA with the integrity needed for RNA-seq analysis. RNA integrity number (RIN) and purity were checked by a Bioanalyzer 2100 (Agilent Technologies, CA, USA). RIN was in the range 8.0-9.0.

The RNA samples were sequenced by Centre Nacional d’Anàlisi Genòmica (CNAG-CRG, Barcelona, Spain, http://www.cnag.crg.eu/). Libraries were prepared using the TruSeq SBS v-3HS kit (Ilumina, San Diego, CA) according to the manufacturer’s protocol. Each library was paired-end sequenced (2 × 100 bp) to 65 M reads with phred quality score 80-90% in a Hi-Seq 2000 platform.

Analysis of RNA-seq transcriptomic data

Quality of the raw sequencing data was assessed with the fastp tool [20]. With this approach, in addition to the default parameters to filtering, reads with quality scores per base lower than 30, shorter than 36 bp or unpaired were removed. Filtered sequences were aligned to the Sus scrofa reference genome (Sscrofa11.1) with the STAR 2.5.4b tool [21]. Mapping statuses were analysed with qualimap and plotted with MultiQC v1.0 [22]. After the alignment, reads were counted with the Feature Counts v1.24.1 software [23] and differential expression (DE) analysis between SCD genotypes, diets, and their combined groups was performed with the approach implemented in DeSeq2 v.1.14.1 software [24]. Adjusted p-values (q-value < 0.10) and fold change (FC) > 1.2 (upregulated) or < 1/1.2 = 0.83 (downregulated) were set as the threshold for significantly different expression. Functional analysis of differentially expressed genes (DEG) was then subjected to gene ontology (GO) functional enrichment analysis, KEGG pathways and targets of transcription factor binding with Enrichr v1.0 software [25]. Visualisation of gene interactions was performed with Cytoscape 3.9.0 and the StringApp connector [26].

In total, an average of 62.4 M (range 48.8-91.5 M) raw reads per sample were generated. After filtering, an average of 58 M of reads were retained for further analysis and 84.8% (range 77.4-86.8%) of the reads were uniquely mapped to the pig reference genome (Sscrofa11.1; Additional file 1: Table S3). Sequencing files are available from NCBI-GEO with access number GSE183909.

Validation of RNA-seq results by quantitative real-time PCR analysis

Quantitative real time PCR (qPCR) was used to validate ten relevant genes in the list of DEGs. Briefly, 2 μg of total RNA from the 40 pigs in the RNA-seq experiment were retrotranscribed using SuperScript IV retrotranscriptase (Invitrogen, Carlsbad, CA) with oligo-dT and random hexamers. Primers (Additional file 1, Table S4) were designed with the Primer Blast tool using the mRNA reference sequences provided in NCBI GENE, so that they will hybridise to all the transcripts described for each gene. Three reference genes (HPRT, B2M and RPL32), were included in this analysis. For each gene, a standard curve was generated by amplifying serial dilutions of a control cDNA to check for linearity between initial template concentration and Ct values. Quantitative real-time PCR assays were carried out in triplicate in a QuantStudio3 device (Applied Biosystems, Waltham, MA, USA) in a final volume of 8 μl containing 1× Maxima SYBR Green/ROX qPCR Master Mix (Thermo Scientific, Waltham, MA, USA), 200 nM of each primer and 3 μl cDNA template diluted 1:30 in water. The following thermal profile was used for all reactions: 10 min at 95 °C, 40 cycles of 15 s at 95 °C and 30 s at 60 °C, followed by a slow ramp from 60 to 95 °C to generate a dissociation curve to control the specificity of the amplified product. In order to quantify and normalise the expression data we used the ΔΔCt method [27] against the geometric mean of the three reference genes. For each gene, expression values between groups were compared with a t-test and differences were considered significant at P < 0.05.

Results

Effect of vitamin A supplementation on carcass traits

Carcasses of pigs fed the VA- diet were on average 3.8 kg heavier than those on the VA+ diet (P < 0.05; Table 2). However, other carcass traits, including IMF content in three muscles, were not affected by the dietary vitamin A content (P > 0.05). On the other hand, pigs with the SCD TT genotype had more subcutaneous fat and less loin thickness than CC pigs, which resulted in a lower carcass lean content (P < 0.05). There was no significant interaction between the SCD genotype and the diet for the tested carcass traits (Table 2).

Table 2 Least square means (±SE) for carcass traits and intramuscular fat content by diet and SCD rs80912566 genotype. Number of pigs per factor is indicated in parenthesis

Effect of vitamin A supplementation on IMF composition

The FA composition in the IMF of the three muscles analysed was not affected by the vitamin A supplement. As described before [14], the rs80912566 TT genotype was associated with an increase in the C16:1, C18:1n9 and MUFA content at the expense of the C16:0, C18:0 and SFA, respectively (Table 3 and Additional file 1; Table S5), which led to increased desaturation indexes (C16:1/C16:0, C18:1n9/C18:0 and MUFA/SFA; Fig. 1 and Additional file 2: Fig. S1). There was a clear interaction between the SCD genotype and the dietary vitamin A content. This interaction was significant for most FAs related to the SCD desaturation pathway in the m. gluteus medius (Table 3 and Fig. 1). The VA- diet tended to enlarge the compositional differences between the TT and CC genotypes. Conversely, in the pigs fed the VA+ diet, the differences between genotypes were smaller and sometimes not significant. Vitamin A restriction triggered differential FA compositional changes in TT and CC pigs (P < 0.05; Fig. 1). The interaction between vitamin A and the SCD genotype was also detected in leaner muscles such as m. longissimus thoracis (C18:0, MUFA, C18:1n7/C18:0, C18:1n9/C18:0 and MUFA/SFA, at P < 0.05) and m. semimembranosus (C18:0 at P < 0.05 and SFA at P < 0.10) (Additional file 1: Table S5 and Additional file 2: Fig. S1). When the dietary vitamin A was restricted, CC pigs had more saturated FAs, while in TT pigs the FAs that result from SCD desaturation were only slightly affected by the diet. This trend was consistent in the three muscles analysed (Fig. 1 and Additional file 2: Fig. S1) and indicates that, overall, CC pigs were more sensitive to dietary vitamin A than TT pigs in increasing FA desaturation associated with SCD activity.

Table 3 Least square means (±SE) for fatty acid composition in the intramuscular fat of m. gluteus medius by diet and SCD rs80912566 genotype. Number of pigs per factor is indicated in parenthesis
Fig. 1
figure 1

Effect of SCD genotype and diet on fatty acid desaturation indexes. Diets with (VA+) and without (VA-) vitamin A supplement in the feed formulation. Within each panel, bars not connected with the same letter differ at P < 0.05. Errors bars are SE

Differentially expressed genes between diet and SCD groups

The number of DEGs between pigs under different diet treatments or with different SCD genotypes was low (43 and 24, respectively), with no overlapping. However, the number of DEGs increased when dietary vitamin A by SCD genotype groups were studied (269 transcripts corresponding to 241 unique genes; Table 4). Only 28 of the 241 genes overlapped across the lists of DEGs in the four diet-by-genotype group comparisons (Fig. 2). Most DEGs were classified as messenger RNA from protein coding genes (79%) (Table 4). The second most abundant RNA type was the long non-coding RNA (lncRNA) class, which accounted for 16% of total DEGs detected in this experiment. The full list of DEGs is shown in Additional file 1: Table S6, which includes 202 unique HGNC gene name identifiers (HUGO Gene Nomenclature Committee database).

Table 4 Number and classification of differentially expressed genes (n. DEG) by diet, SCD genotype and dietary vitamin A by SCD genotype groups
Fig. 2
figure 2

Venn diagram showing the number of overlapping differentially expressed genes between diets (VA+ and VA-) by rs80912566 SCD genotype and between SCD genotypes (SCD-TT and SCD-CC) by diet. Dietary treatment was the same but with (VA+) and without (VA-) vitamin A supplement in the feed formulation

The SCD gene was detected as a DEG when the two genotypes (TT vs CC) were compared (FC = 2.15) with boost effect in the diet with vitamin A supplementation (FC = 2.84) but not under the VA- diet (Additional file 1: Table S6). A group of seven genes (COX15, ABCC2, CWF19L1, ENSSSCG00000048329, ENSSSCG00000049992, SEC31B and FBXW4) located -500 kb/+ 200 kb from the SCD gene were also differentially expressed, which might represent a case of piggyback co-expression due to the close distance between genes [28].

Functional analysis of DEG

The functional analysis of the full list of DEGs was based on GO terms, KEGG pathways and common transcription factors associated to each gene. Functional classification (Additional file 1: Table S7) revealed an overrepresentation of genes encoding proteins located in membrane-bounded vesicles (25 genes; P = 0.007), lipid transport (10 genes; P = 0.003) and connective tissue development (9 genes; P = 0.002), amongst others. This latter group includes several genes with large fold-change values of differential expression, such as TNMD (tenomodulin) and COMP (cartilage oligomeric matrix protein), which were 131- and 52-times more expressed in TT fed the VA- diet respect to those fed the VA+ diet. TNMD encodes for a potent inhibitor of angiogenesis and COMP is a non-collagen extracellular matrix protein that triggers integrin signalling to the cell surface. Their expression has not been related to retinol pathways, so far. Visualisation of protein-to-protein and protein-to-DNA interactions based on the STRING database indicated three main networks of relationships (Additional file 2: Fig. S2). The largest network included 20 genes membrane-bound vesicles (Additional file 2: Fig. S3.A). Additionally, genes involved in the beta-oxidation of FA in peroxisomes also grouped together (MVK, ACCA1, ECHDC2, and peroxins PEX6 and PEX5L) (Additional file 2: Fig. S3.B).

The target transcription factor analysis identified ten transcription factors that can potentially regulate 104 of the 202 unique DEG gene name identifiers (Table 5). These 10 transcription factors, which include proteins activated by retinol (PPARG, PPARA, RARA, RXRA) or by sterols (SREBF1 and SREBF2), are known to interact among themselves in a competitive manner (reviewed in [29]; also [30,31,32]), which agrees with the partial overlap of regulated genes by transcription factor in Table 5. Functional classification within these 104 genes (Additional file 1: Table S8) revealed an overrepresentation of genes encoding proteins located in membrane-bounded vesicles (n = 15; P = 0.003), which had a prominent role in endocytosis (P = 0.002), in the group of 40 genes regulated by SREBF1 and SREBF2 transcription factors. This classification partially overlapped with the genes regulated by the IRF2, HNF1A and HNF4A transcription factors. The genes regulated by the HNF1A and HNF4A transcription factors also included genes with activity related to lipid metabolic process (7 genes; P = 0.02). On the other hand, genes regulated by RARA and RXRA nuclear receptors were involved in a variety of functions, which included immunity and inflammation responses (P = 0.02), lipid binding (P = 0.03) and apoptosis (P = 0.03). Finally, the genes clustered under the regulation of PPARG and PPARA include genes with metal-binding (11 genes, P = 0.03) and long-chain FA-modifying (2 genes, P = 0.05) capacities.

Table 5 Regulators predicted for the set of annotated coding differentially expressed genes

Quantitative PCR validation of RNA-seq results

Ten relevant genes were selected from the DEG list for the validation experiment based on the FC ratios and the functional annotation of the gene. Efficient qPCR assays were established for 8 out of the 10 selected genes. For all genes (Table 6), the fold-change ratios between groups were consistent in both assays. Expression differences between groups were significant for all genes tested (P < 0.05) except for ABCA1 and MTTP. In addition, in most cases the expression ratios were lower in the qPCR experiment than in RNA-seq, which might be due to the higher background noise of the qPCR assay. Altogether, validation of RNA-seq data by qPCR showed a high correspondence between both analyses, confirming differential expression for 8 out of 10 group comparisons (Table 6).

Table 6 Validation of differentially expressed genes (DEG) by quantitative PCR

Discussion

In the present study, carcasses from pigs fed the VA- diet were 3.8 kg heavier than those fed the VA+ diet. Despite of this, dietary vitamin A had no effect on carcass lean content, which agreed with previous studies in different pig lines fed with restricted or elevated vitamin A [5,6,7,8,9,10,11].

Regarding meat quality traits, we observed no differences in IMF between dietary vitamin A treatments in none of the three muscles analysed. The effect of dietary vitamin A on IMF deposition is particularly controversial in pigs. Several authors reported that a dietary reduction of vitamin A impacted the IMF content in m. gluteus medius [8], m. longissimus thoracis [9, 11] and m. semimembranosus [7] in pigs of different genetic lines, although the direction and magnitude of the changes were very variable. For instance, Olivares et al. [11] investigated the effect of supplementing the feed for 11 weeks with 100-fold the vitamin A daily recommendations of the National Research Council. This extra supplementation raised IMF content in Duroc-sired hybrid pigs but not in Large White × Landrace animals. In contrast, removing vitamin A supplements from the formulation also increased IMF content in Iberian [6] and Large White × Landrace [10] pigs. In a previous work with the same Duroc line used in this study, the complete removal of vitamin A from the diet the last 30 days of fattening also resulted in higher IMF content [8].

The experimental conditions, the duration of vitamin A restriction, age and genetic type are several factors that can explain the poor consistency of the results. Retinoic acid, an active metabolite of vitamin A, regulates the adipogenic differentiation of fibroblasts into adipocytes in intramuscular adipose tissue and, for this reason, dietary vitamin A could impact IMF deposition [4]. However, given the opposite effect of retinoic acid in early (positive) and late (negative) differentiation of adipocytes [4], the time and duration of the dietary restriction might be critical to have relevant consequences in the pig.

As opposed to other experiments [5,6,7,8,9, 11], in our study the FA composition was not directly affected by the dietary vitamin A. In general, most studies have detected an increase in the desaturation index (MUFA/SFA) when dietary vitamin A was restricted. We detected this effect as an interaction between the diet and the SCD genotype of the pigs. In this sense, vitamin A supplementation promoted FA desaturation and increased C18:1n9 content at the expense of C18:0 in SCD CC pigs but not in TT and TC pigs (Fig. 1). This trend was consistent on the three muscles analysed but was more evident in gluteus medius, probably due to the higher IMF content of this muscle and the larger number of pigs sampled. The effect of the SCD genotype on MUFA content and FA desaturation indexes has been described before [14, 33, 34]. The TT pigs have more oleic acid (C18:1n9), MUFA and, consequently, C16:1/C16:0, C18:1n7/C18:0, C18:1n9/C18:0 and MUFA/SFA than CC pigs, which we observed in both diets, but the differences were more evident in pigs fed the vitamin A-restricted diet. This interaction reinforces the relationship between vitamin A and SCD activity, with the CC pigs being more sensitive to the effect of vitamin A. In a previous work, we described a genetic interaction between dietary vitamin A and the SCD genotype for liver levels of all-trans-retinol and all-trans-5-6-epoxy retinoic acid, the two most abundant forms of bioactive vitamin A [8]. The effect of the diet on the level of these two compounds was only evidenced in CC pigs, paralleling our current results on FA desaturation indexes. Note that the SCD rs80912566 polymorphism is not present in all pig breeds. The T variant was fixed in the Iberian, Piétrain and Landrace lines tested in [14] but is segregating in Large White lines [14, 35], which might partly explain the inconsistent results from previous studies.

Despite the small impact that the dietary vitamin A had on the phenotype of the pigs, we observed significant changes at the transcriptome level. The highest number of DEGs were detected when diets by SCD or SCD by diets groups were compared. Although most DEG were protein-coding genes, about 15% of DEGs represented transcripts from lncRNA genes, which are still not well annotated in pigs, and little is still know about their functional relevance. In fact, the highest differences in gene expression were mainly lncRNA genes. Other groups have described similar situations where dietary treatments have little effect in the phenotype but profound effects at the transcriptome levels (for instance [36] and [37] for carbohydrate and oleic-enriched diets in pigs, or [38] with pigs on different fasting periods).

The most striking result of our data is the high number of DEGs when the interaction between diets and SCD genotype was considered. On one hand, the vitamin A supplement changed the expression of 162 genes in TT pigs but only 26 genes in CC animals, with little overlap. On the other hand, the VA+ diet induced 73 DEG between pigs of opposite SCD genotypes in contrast with only 8 DEG when the pigs where fed the VA- diet. SCD-TT pigs have more MUFA and less SFA in muscle samples, which include adipocytes, myocytes, and fibroblasts. Oleic acid (C18:1n9) is a potent activator of several transcription cascades, including preadipocyte differentiation [39] and mammary gland FA biosynthesis [40, 41]. Targeted disruption of SCD has significant consequences in the health of knockout mice, which are driven to general hypermetabolism and stimulation of FA beta-oxidation. Consequently, SCD knockout mice are protected from fat diet-induced obesity [42, 43]. In our study, the difference of SCD expression was 2.84-fold in pigs fed the VA+ diet but SCD expression did not differ between genotypes in pigs fed the VA- diet ([14] and Additional file 1: Table S6.A), which agrees with differences in liver SCD expression due to dietary vitamin A levels, reported in a previous experiment [8]. Overall, our animal material allows studying the long-term effect of higher MUFA content in muscle and fat tissues and its interaction with dietary retinoic precursors.

Retinol and retinoic acid, the biologically active compounds of vitamin A, mediate their function normally through specific retinoid receptors, which belong to the ligand-dependent transcription factors superfamily of nuclear receptors, although receptor-independent effects are also known [44]. Upon binding to retinoids, the nuclear receptors RXR, RAR, HNF4A can form heterodimers with ligand-mediated co-regulators PPAR and HNF1A or members of the NR2 nuclear repressors. Most of these transcription factors were identified as main regulators of 104 of the DEGs detected in this experiment. Retinol-mediated gene repression and transactivation have wide effects on cell proliferation, differentiation, cell adhesion, and apoptosis in different cell types, immunity, male and female reproduction, embryonic development, and barrier integrity [45]. This was also reflected in our data (Additional file 1: Tables S7 and S8), as many of these processes were included in the functional analysis of the DEGs.

Among the DEGs, it is interesting to highlight ABCA1, a cholesterol transporter involved in maintaining cholesterol homeostasis and lipid metabolism [46]. In pigs, ABCA1 overexpression is directly associated with an increase in HDL levels [46, 47] and polymorphisms on this gene have been associated with atherosclerosis risk score [48]. ABCA1 has been used as an epigenetic marker for evaluating meat quality in chicken [49] and it has been associated with beef tenderness and FA composition [50] but information in relation to pork quality is still lacking. A gene with a similar function is MVK, a mevalonate kinase that catalyses an early step in cholesterol biosynthesis and is associated with HDL cholesterol [51]. This SREBP2-responsive gene has been appointed as a candidate gene for fat deposition in broilers [52, 53]. Unfortunately, we were not able to validate the differential expression of these two genes by qPCR analysis. On the other hand, a group of four genes encoding extracellular matrix proteins (COMP, CILP2, OTOR and TNMD) were very highly expressed (30 to 130-fold) in CC pigs fed the VA+ diet with respect to the VA- diet. Three of these genes have been individually investigated in relation with adipocyte function (CILP2 [54]) and differentiation (TNMD [55, 56]; COMP [57]) but not together. Several other genes are also well known in relation to adipose tissue differentiation, such as FGF10 and MTTP. In chicken, muscle fibroblast growth factor 10 (FGF10) expression correlates with IMF content [58].. We were able to validate the higher expression of FGF10 in TT pigs than in CC pigs fed the VA+ diet (6.55-fold by RNA-seq analysis and 2.08-fold by qPCR assay) and in the TT pigs fed the VA+ diet vs the VA- diet (5.30 vs 1.95-fold by RNA-seq and qPCR analysis, respectively). Microsomal triglyceride transfer protein (MTTP) is a protein essential to transport triglycerides from the endoplasmic reticulum membrane to lipid droplets [59]. Polymorphisms in this gene have been associated with changes in FA composition in pork [60, 61] but there is so far no report on the effect on the expression of the gene on pork quality.

Another interesting DEG is PPARA, which, as mentioned before, encodes a member of the retinol receptor family whose expression is inhibited by dietary vitamin A intake [62]. In agreement with this, in our study PPARA expression increased by 1.6-fold in TT pigs fed the VA- diet. The PPARα transcription factor promotes de novo lipogenesis, FA storage and improves glycogen synthesis [63]. In pigs, PPARA can be used as a genetic marker for overall adipose tissue accumulation, as polymorphisms in the 3′ untranslated region (UTR) are associated with changes in backfat and IMF deposition [64]. The increase in PPARA expression agrees with the general observation that restricted dietary vitamin A promotes adipogenesis and fat deposition, although we were not able to capture this effect in the phenotype of our pigs.

Conclusions

In our experiment, restricting the dietary vitamin A supplement during the fattening period did not contribute to improving IMF content despite relevant changes in muscle gene expression, both in coding and non-coding genes. However, there was a clear interaction between the SCD genotype and the dietary vitamin A for FA composition, which affected the desaturation rate of IMF, particularly in SCD CC pigs in opposite directions to SCD TT and TC pigs. Taken together, our results indicate that a restriction of dietary vitamin A might be a strategy to enhance monounsaturated FA content only in pigs carrying the SCD-T allele.

Availability of data and materials

Data that support the findings of this study are available within the article and Supplementary Information, or from the authors upon reasonable request. Sequencing files are available from NCBI-GEO with access number GSE183909.

Abbreviations

DEG:

Differentially expressed genes

IMF:

Intramuscular fat

MUFA:

Monounsaturated fatty acids

PUFA:

Polyunsaturated fatty acids

RXR:

Retinoid receptors

RAR:

Retinoic acid receptor

SCD:

Stearoyl-CoA desaturase

SE:

Standard error

SFA:

Saturated fatty acids

SNP:

Single nucleotide polymorphism

References

  1. Wood JD, Nute GR, Richardson RI, Whittington FM, Southwood O, Plastow G, et al. Effects of breed, diet and muscle on fat deposition and eating quality in pigs. Meat Sci. 2004;67(4):651–67. https://doi.org/10.1016/j.meatsci.2004.01.007.

    CAS  Article  PubMed  Google Scholar 

  2. Park SJ, Beak SH, Jung DJS, Kim SY, Jeong IH, Piao MY, et al. Genetic, management, and nutritional factors affecting intramuscular fat deposition in beef cattle - a review. Asian Australas J Anim Sci. 2018;31(7):1043–61. https://doi.org/10.5713/ajas.18.0310.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Bost F, Caron L, Marchetti I, Dani C, Le Marchand-Brustel Y, Binétruy B. Retinoic acid activation of the ERK pathway is required for embryonic stem cell commitment into the adipocyte lineage. Biochem J. 2002;361:621–7. https://doi.org/10.1042/0264-6021:3610621.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. Wang B, Yang Q, Harris CL, Nelson ML, Busboom JR, Zhu MJ, et al. Nutrigenomic regulation of adipose tissue development - role of retinoic acid: a review. Meat Sci. 2016;120:100–6. https://doi.org/10.1016/j.meatsci.2016.04.003.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Olivares A, Daza A, Rey AI, López-Bote CJ. Dietary vitamin a concentration alters fatty acid composition in pigs. Meat Sci. 2009;81(2):295–9. https://doi.org/10.1016/j.meatsci.2008.07.029.

    CAS  Article  PubMed  Google Scholar 

  6. Ayuso M, Fernández A, Isabel B, Rey A, Benítez R, Daza A, et al. Long term vitamin a restriction improves meat quality parameters and modifies gene expression in Iberian pigs. J Anim Sci. 2015;93(6):2730–44. https://doi.org/10.2527/jas.2014-8573.

    CAS  Article  PubMed  Google Scholar 

  7. Ayuso M, Óvilo C, Rodríguez-Bertos A, Rey AI, Daza A, Fenández A, et al. Dietary vitamin a restriction affects adipocyte differentiation and FA composition of intramuscular fat in Iberian pigs. Meat Sci. 2015;108:9–16. https://doi.org/10.1016/j.meatsci.2015.04.017.

    CAS  Article  PubMed  Google Scholar 

  8. Henriquez-Rodriguez E, Pena RN, Seradj AR, Fraile L, Christou P, Tor M, et al. Carotenoid intake and SCD genotype exert complementary effects over fat content and fatty acid composition in Duroc pigs. J Anim Sci. 2017;95(6):2547–57. https://doi.org/10.2527/jas.2016.1350.

    CAS  Article  PubMed  Google Scholar 

  9. Olivares A, Rey AI, Daza A, López-Bote CJ. Low levels of dietary vitamin a increase intramuscular fat content and polyunsaturated fatty acid proportion in liver from lean pigs. Livest Sci. 2011;137:31–6. https://doi.org/10.1016/j.livsci.2010.09.023.

    Article  Google Scholar 

  10. D’Souza DN, Pethick DW, Dunshea FR, Pluske JR, Mullan BP. Nutritional manipulation increases intramuscular fat levels in the Longissimus muscle of female finisher pigs. Aust J Agric Res. 2003;54(8):745–9. https://doi.org/10.1071/AR03009.

    CAS  Article  Google Scholar 

  11. Olivares A, Daza A, Rey AI, Lopez-Bote CJ. Interactions between genotype, dietary fat saturation and vitamin a concentration on intramuscular fat content and fatty acid composition in pigs. Meat Sci. 2009;82(1):6–12. https://doi.org/10.1016/j.meatsci.2008.11.006.

    CAS  Article  PubMed  Google Scholar 

  12. Estany J, Ros-Freixedes R, Tor M, Pena RN. TRIENNIAL GROWTH AND DEVELOPMENT SYMPOSIUM: genetics and breeding for intramuscular fat and oleic acid content in pigs. J Anim Sci. 2017;95(5):2261–71. https://doi.org/10.2527/jas.2016.1108.

    CAS  Article  PubMed  Google Scholar 

  13. Ros-Freixedes R, Gol S, Pena RN, Tor M, Ibáñez-Escriche N, Dekkers JC, et al. Genome-wide association study singles out SCD and LEPR as the two Main loci influencing intramuscular fat content and fatty acid composition in Duroc pigs. PLoS One. 2016;11(3):e0152496. https://doi.org/10.1371/journal.pone.0152496.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Estany J, Ros-Freixedes R, Tor M, Pena RN. A functional variant in the stearoyl-CoA desaturase gene promoter enhances fatty acid desaturation in pork. PLoS One. 2014;9(1):e86177. https://doi.org/10.1371/journal.pone.0086177.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Zhang R, Wang Y, Li R, Chen G. Transcriptional factors mediating retinoic acid signals in the control of energy metabolism. Int J Mol Sci. 2015;16(6):14210–44. https://doi.org/10.3390/ijms160614210.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. Bosch L, Tor M, Reixach J, Estany J. Estimating intramuscular fat content and fatty acid composition in live and post-mortem samples in pigs. Meat Sci. 2009;82(4):432–7. https://doi.org/10.1016/j.meatsci.2009.02.013.

    CAS  Article  PubMed  Google Scholar 

  17. Rule DC. Direct transesterification of total fatty acids of adipose tissue, and of freeze-dried muscle and liver with boron-trifluoride in methanol. Meat Sci. 1997;46(1):23–32. https://doi.org/10.1016/s0309-1740(97)00008-9.

    CAS  Article  PubMed  Google Scholar 

  18. AOAC. Official Method 996.06. Fat (total, saturated, and unsaturated) in foods. Hydrolytic extraction gas chromatographic method. In: Official Methods of Analysis of AOAC International. Gaithersburg: AOAC International; 2012.

    Google Scholar 

  19. Green MR, Sambrook J. Preparation of genomic DNA from mouse tails and other small samples. Cold Spring Harb Protoc. 2017;2017(9):pdb.prot093518. https://doi.org/10.1101/pdb.prot093518.

    Article  PubMed  Google Scholar 

  20. Chen S, Zhou Y, Chen Y, Gu J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–90. https://doi.org/10.1093/bioinformatics/bty560.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635.

    CAS  Article  PubMed  Google Scholar 

  22. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8. https://doi.org/10.1093/bioinformatics/btw354.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Liao Y, Smyth GK, Shi W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30. https://doi.org/10.1093/bioinformatics/btt656.

    CAS  Article  PubMed  Google Scholar 

  24. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. https://doi.org/10.1186/s13059-014-0550-8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128. https://doi.org/10.1186/1471-2105-14-128.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. https://doi.org/10.1101/gr.1239303.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Yuan JS, Reed A, Chen F, Stewart CN Jr. Statistical analysis of real-time PCR data. BMC Bioinformatics. 2006;7:85. https://doi.org/10.1186/1471-2105-7-85.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Michalak P. Coexpression, coregulation, and cofunctionality of neighboring genes in eukaryotic genomes. Genomics. 2008;91(3):243–8. https://doi.org/10.1016/j.ygeno.2007.11.002.

    CAS  Article  PubMed  Google Scholar 

  29. Fadel L, Rehó B, Volkó J, Bojcsuk D, Kolostyák Z, et al. Agonist binding directs dynamic competition among nuclear receptors for heterodimerization with retinoid X receptor. J Biol Chem. 2020;295(29):10045–61. https://doi.org/10.1074/jbc.RA119.011614.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Bloch M, Prock A, Paonessa F, Benz V, et al. High-mobility group A1 protein: a new coregulator of peroxisome proliferator-activated receptor-γ-mediated transrepression in the vasculature. Circ Res. 2012;110(3):394–405. https://doi.org/10.1161/CIRCRESAHA.111.253658.

    CAS  Article  PubMed  Google Scholar 

  31. Ponugoti B, Fang S, Kemper JK. Functional interaction of hepatic nuclear factor-4 and peroxisome proliferator-activated receptor-gamma coactivator 1alpha in CYP7A1 regulation is inhibited by a key lipogenic activator, sterol regulatory element-binding protein-1c. Mol Endocrinol. 2007;21(11):2698–712. https://doi.org/10.1210/me.2007-0196.

    CAS  Article  PubMed  Google Scholar 

  32. Misawa K, Horiba T, Arimura N, Hirano Y, Inoue J, et al. Sterol regulatory element-binding protein-2 interacts with hepatocyte nuclear factor-4 to enhance sterol isomerase gene expression in hepatocytes. J Biol Chem. 2003;278(38):36176–82. https://doi.org/10.1074/jbc.M302387200.

    CAS  Article  PubMed  Google Scholar 

  33. Uemoto Y, Nakano H, Kikuchi T, Sato S, Ishida M, et al. Fine mapping of porcine SSC14 QTL and SCD gene effects on fatty acid composition and melting point of fat in a Duroc purebred population. Anim Genet. 2012;43(2):225–8. https://doi.org/10.1111/j.1365-2052.2011.02236.x.

    CAS  Article  PubMed  Google Scholar 

  34. Henriquez-Rodriguez E, Bosch L, Tor M, Pena RN, Estany J. The effect of SCD and LEPR genetic polymorphisms on fat content and composition is maintained throughout fattening in Duroc pigs. Meat Sci. 2016;121:33–9. https://doi.org/10.1016/j.meatsci.2016.05.012.

    CAS  Article  PubMed  Google Scholar 

  35. Wang B, Li P, Zhou W, et al. Association of Twelve Candidate Gene Polymorphisms with the Intramuscular Fat Content and Average Backfat Thickness of Chinese Suhuai Pigs. Animals. 2019;9(11):858. Published 2019 Oct 23. https://doi.org/10.3390/ani9110858.

    Article  PubMed Central  Google Scholar 

  36. Benítez R, Núñez Y, Fernández A, Isabel B, Rodríguez C, et al. Adipose tissue transcriptional response of lipid metabolism genes in growing Iberian pigs fed oleic acid v. carbohydrate enriched diets. Animal. 2016;10(6):939–46. https://doi.org/10.1017/S1751731115003055.

    CAS  Article  PubMed  Google Scholar 

  37. Benítez R, Trakooljul N, Núñez Y, Isabel B, Murani E, et al. Breed, diet, and interaction effects on adipose tissue Transcriptome in Iberian and Duroc pigs fed different energy sources. Genes (Basel). 2019;10(8):589. https://doi.org/10.3390/genes10080589.

    CAS  Article  Google Scholar 

  38. Cardoso TF, Quintanilla R, Tibau J, Gil M, Mármol-Sánchez E, et al. Nutrient supply affects the mRNA expression profile of the porcine skeletal muscle. BMC Genomics. 2017;18(1):603. https://doi.org/10.1186/s12864-017-3986-x.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. Wang Z, Yin ZT, Zhang F, Li XQ, Chen SR, et al. Dynamics of transcriptome changes during subcutaneous preadipocyte differentiation in ducks. BMC Genomics. 2019;20(1):688. https://doi.org/10.1186/s12864-019-6055-9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. Poudyal H, Brown L. Stearoyl-CoA desaturase: a vital checkpoint in the development and progression of obesity. Endocr Metab Immune Disord Drug Targets. 2011;11(3):217–31. https://doi.org/10.2174/187153011796429826 PMID: 21831035.

    CAS  Article  PubMed  Google Scholar 

  41. Vargas-Bello-Pérez E, Geldsetzer-Mendoza C, Cancino-Padilla N, Morales MS, Leskinen H, et al. Effects of dietary vegetable oils on mammary lipid-related genes in Holstein dairy cows. Animals (Basel). 2019;10(1):57. https://doi.org/10.3390/ani10010057.

    Article  Google Scholar 

  42. Ntambi JM, Miyazaki M, Stoehr JP, Lan H, Kendziorski CM, et al. Loss of stearoyl-CoA desaturase-1 function protects mice against adiposity. Proc Natl Acad Sci U S A. 2002;99(17):11482–6. https://doi.org/10.1073/pnas.132384699.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. Dobrzyń A, Dobrzyń P. Stearoyl-CoA desaturase--a new player in skeletal muscle metabolism regulation. J Physiol Pharmacol. 2006;57(Suppl 10):31–42.

    PubMed  Google Scholar 

  44. Amann PM, Eichmuller SB, Schmidt J, Bazhin AV. Regulation of gene expression by Retinoids. Curr Med Chem. 2011;18(9). https://doi.org/10.2174/092986711795029618.

  45. Falker-Gieske C, Mott A, Franzenburg S, Tetens J. Multi-species transcriptome meta-analysis of the response to retinoic acid in vertebrates and comparative analysis of the effects of retinol and retinoic acid on gene expression in LMH cells. BMC Genomics. 2021;22(1):146. https://doi.org/10.1186/s12864-021-07451-2.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. Attie AD, Kastelein JP, Hayden MR. Pivotal role of ABCA1 in reverse cholesterol transport influencing HDL levels and susceptibility to atherosclerosis. J Lipid Res. 2001;42(11):1717–26.

    CAS  Article  PubMed  Google Scholar 

  47. Geeraert B, De Keyzer D, Davey PC, Crombé F, Benhabilès N, Holvoet P. Oxidized low-density lipoprotein-induced expression of ABCA1 in blood monocytes precedes coronary atherosclerosis and is associated with plaque complexity in hypercholesterolemic pigs. J Thromb Haemost. 2007;5(12):2529–36. https://doi.org/10.1111/j.1538-7836.2007.02786.x.

    CAS  Article  PubMed  Google Scholar 

  48. Dang XY, Chu WW, Shi HC, Yu SG, Han HY, et al. Genetic variants in ABCA1 promoter affect transcription activity and plasma HDL level in pigs. Gene. 2015;555(2):414–20. https://doi.org/10.1016/j.gene.2014.11.041.

    CAS  Article  PubMed  Google Scholar 

  49. Zhang M, Yan FB, Li F, Jiang KR, Li DH, et al. Genome-wide DNA methylation profiles reveal novel candidate genes associated with meat quality at different age stages in hens. Sci Rep. 2017;7:45564. https://doi.org/10.1038/srep45564.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. Zhao C, Ji G, Carrillo JA, Li Y, Tian F, Baldwin RL, et al. The profiling of DNA methylation and its regulation on divergent tenderness in Angus beef cattle. Front Genet. 2020;11:939. https://doi.org/10.3389/fgene.2020.00939.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008;40(2):161–9. https://doi.org/10.1038/ng.76.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. Murphy C, Murray AM, Meaney S, Gåfvels M. Regulation by SREBP-2 defines a potential link between isoprenoid and adenosylcobalamin metabolism. Biochem Biophys Res Commun. 2007;355(2):359–64. https://doi.org/10.1016/j.bbrc.2007.01.155.

    CAS  Article  PubMed  Google Scholar 

  53. Moreira GCM, Boschiero C, Cesar ASM, Reecy JM, Godoy TF, et al. A genome-wide association study reveals novel genomic regions and positional candidate genes for fat deposition in broiler chickens. BMC Genomics. 2018;19(1):374. https://doi.org/10.1186/s12864-018-4779-6.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. Wu T, Zhang Q, Wu S, Hu W, Zhou T, et al. CILP-2 is a novel secreted protein and associated with insulin resistance. J Mol Cell Biol. 2019;11(12):1083–94. https://doi.org/10.1093/jmcb/mjz016.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. Senol-Cosar O, Flach RJ, DiStefano M, Chawla A, Nicoloro S, et al. Tenomodulin promotes human adipocyte differentiation and beneficial visceral adipose tissue expansion. Nat Commun. 2016;7:10686. https://doi.org/10.1038/ncomms10686.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. Aguilera CM, Gomez-Llorente C, Tofe I, Gil-Campos M, Cañete R, Gil Á. Genome-wide expression in visceral adipose tissue from obese prepubertal children. Int J Mol Sci. 2015;16(4):7723–37. https://doi.org/10.3390/ijms16047723.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. Denton N, Pinnick KE, Karpe F. Cartilage oligomeric matrix protein is differentially expressed in human subcutaneous adipose tissue and regulates adipogenesis. Mol Metab. 2018;16:172–9. https://doi.org/10.1016/j.molmet.2018.07.005.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. Zhang R, Lin Y, Zhi L, Liao H, Zuo L, Li Z, et al. Expression profiles and associations of adiponectin and adiponectin receptors with intramuscular fat in Tibetan chicken. Br Poult Sci. 2017;58(2):151–7. https://doi.org/10.1080/00071668.2016.1268252.

    CAS  Article  PubMed  Google Scholar 

  59. Hussain MM, Shi J, Dreizen P. Microsomal triglyceride transfer protein and its role in apoB-lipoprotein assembly. J Lipid Res. 2003;44(1):22–32. https://doi.org/10.1194/jlr.r200014-jlr200.

    CAS  Article  PubMed  Google Scholar 

  60. Estellé J, Fernández AI, Pérez-Enciso M, Fernández A, Rodríguez C, et al. A non-synonymous mutation in a conserved site of the MTTP gene is strongly associated with protein activity and fatty acid profile in pigs. Anim Genet. 2009;40(6):813–20. https://doi.org/10.1111/j.1365-2052.2009.01922.x.

    CAS  Article  PubMed  Google Scholar 

  61. Renaville B, Bacciu N, Lanzoni M, Mossa F, Piasentier E. Association of single nucleotide polymorphisms in fat metabolism candidate genes with fatty acid profiles of muscle and subcutaneous fat in heavy pigs. Meat Sci. 2018;139:220–7. https://doi.org/10.1016/j.meatsci.2018.02.005.

    CAS  Article  PubMed  Google Scholar 

  62. Vega VA, Anzulovich AC, Varas SM, Bonomi MR, Giménez MS, Oliveros LB. Effect of nutritional vitamin a deficiency on lipid metabolism in the rat heart: its relation to PPAR gene expression. Nutrition. 2009;25(7-8):828–38. https://doi.org/10.1016/j.nut.2009.01.008.

    CAS  Article  PubMed  Google Scholar 

  63. Hong F, Pan S, Guo Y, Xu P, Zhai Y. PPARs as nuclear receptors for nutrient and energy metabolism. Molecules. 2019;24(14):2545. https://doi.org/10.3390/molecules24142545.

    CAS  Article  PubMed Central  Google Scholar 

  64. Stachowiak M, Szydlowski M, Flisikowski K, Flisikowska T, Bartz M, Schnieke A, et al. Polymorphism in 3′ untranslated region of the pig PPARA gene influences its transcript level and is associated with adipose tissue accumulation. J Anim Sci. 2014;92(6):2363–71. https://doi.org/10.2527/jas.2013-7509.

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

We acknowledge Josep Reixach and the personnel at Selección Batallé for their cooperation with the farm experiment and on-farm data and sample collection.

Funding

This research was supported by the Spanish Ministry of Science, Innovation & Universities, and the EU Regional Development Funds (grants AGL2015-65846-R and RTI2018-101346-B-I00). ES is recipient of a PhD scholarship from the University of Lleida.

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JE and RNP designed the experiment. ES, MT, YO and RNP conducted the lab experiments. ES, RRF and RGP analysed the data. All the authors contributed to the manuscript. The authors read and approved the final manuscript.

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Correspondence to Ramona N. Pena.

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All pigs used in the study were raised and slaughtered in commercial units following applicable regulations and good practice guidelines on the protection of animals kept for farming purposes during transport and slaughter. All experimental procedures were approved by the Ethics Committee for Animal Experimentation of the University of Lleida (agreement CEEA 05-04/15).

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Solé, E., González-Prendes, R., Oliinychenko, Y. et al. Transcriptome shifts triggered by vitamin A and SCD genotype interaction in Duroc pigs. BMC Genomics 23, 16 (2022). https://doi.org/10.1186/s12864-021-08244-3

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Keywords

  • SCD gene
  • Fatty acid
  • Vitamin A
  • Meat quality
  • RNA-seq
  • Swine