Analysis of porcine adipose tissue transcriptome reveals differences in de novo fatty acid synthesis in pigs with divergent muscle fatty acid composition
© Corominas et al.; licensee BioMed Central Ltd. 2013
Received: 7 June 2013
Accepted: 25 November 2013
Published: 1 December 2013
In pigs, adipose tissue is one of the principal organs involved in the regulation of lipid metabolism. It is particularly involved in the overall fatty acid synthesis with consequences in other lipid-target organs such as muscles and the liver. With this in mind, we have used massive, parallel high-throughput sequencing technologies to characterize the porcine adipose tissue transcriptome architecture in six Iberian x Landrace crossbred pigs showing extreme phenotypes for intramuscular fatty acid composition (three per group).
High-throughput RNA sequencing was used to generate a whole characterization of adipose tissue (backfat) transcriptome. A total of 4,130 putative unannotated protein-coding sequences were identified in the 20% of reads which mapped in intergenic regions. Furthermore, 36% of the unmapped reads were represented by interspersed repeats, SINEs being the most abundant elements. Differential expression analyses identified 396 candidate genes among divergent animals for intramuscular fatty acid composition. Sixty-two percent of these genes (247/396) presented higher expression in the group of pigs with higher content of intramuscular SFA and MUFA, while the remaining 149 showed higher expression in the group with higher content of PUFA. Pathway analysis related these genes to biological functions and canonical pathways controlling lipid and fatty acid metabolisms. In concordance with the phenotypic classification of animals, the major metabolic pathway differentially modulated between groups was de novo lipogenesis, the group with more PUFA being the one that showed lower expression of lipogenic genes.
These results will help in the identification of genetic variants at loci that affect fatty acid composition traits. The implications of these results range from the improvement of porcine meat quality traits to the application of the pig as an animal model of human metabolic diseases.
The pig (Sus scrofa) is one of the most important livestock animals due to its economic importance in the alimentary industry, but it is also an interesting biomedical model for human diseases . Over the last few decades, genetic selection in commercial pig breeds has greatly improved meat-production efficiency at the expense of reducing the sensorial and technological properties of the meat. These changes are mainly caused by the reduction in intramuscular fat (IMF) content and alterations in fatty acid (FA) composition, both critical for various meat quality attributes such as muscle color, firmness, water-holding capacity and also important nutritional aspects . In this regard, FA composition of food has also become a critical aspect in human nutrition: a high consumption of SFA has been associated with obesity, high plasma cholesterol and cardiovascular diseases [3, 4], while replacing SFA with MUFA or PUFA decreases serum LDL cholesterol and total cholesterol, reducing the risk of coronary heart disease [5, 6].
Factors other than dietary intake have been less characterized in relation with tissue lipid composition. These include the role of candidate-gene genotypes in lipid and FA metabolism [7–12]. In this context, studies in pigs can have a dual purpose: first, to study the genetics of food, i.e., how the genotype of the animal influences the FA content and profile of meat and, secondly, as an animal model for nutrigenomic studies or for human metabolic diseases.
The liver, adipose tissue and skeletal muscle are the principal organs involved in the regulation of lipid metabolism. The adipose tissue is an organ which is responsible for energy storage in the form of lipids and, in pigs, is the major source of circulating free FAs (FFA) . It also acts as a major endocrine organ, producing adipocytokines like TNFα, peptide hormones such as leptin, adiponectin, estrogen and resistin and lipid hormones (lipokines) such as palmitoleate, all of which are involved in the maintenance of metabolic homeostasis [14, 15]. Furthermore, pig adipose tissue has a greater contribution to overall FA synthesis than does the liver . Thus, the characterization of the transcriptome landscape of this organ may be relevant for the improvement of pork nutritional quality.
The development of next-generation sequencing (NGS) methods has provided new tools for both transcriptome characterization and gene-expression profiling. The RNA-Seq technique is based on sequencing the poly-A RNA fraction and allows for characterizing isoforms from known genes or discovering novel, predicted coding genes . To-date, the number of RNA-Seq analyses in livestock is still scarce, some recent reports have focused on the study of organs [17, 18], animal products such as milk [19, 20] or embryos . Thus, in 2011, Esteve-Codina et al. compared the pig gonads of two individuals from two breeds (Iberian and Large White). The same year, Chen et al. (2011) analyzed the transcriptome of three pig tissues (liver, longissimus dorsi muscle and abdominal fat) in two full-sib F2 females with extreme phenotypes in growth and fatness (White Duroc × Erhualian). Furthermore, a liver RNA-Seq study was performed using four animals from genetically different porcine breeds (Berkshire, Duroc, Landrace and Yorkshire) .
In a previous work of our group, the livers of ten Iberian × Landrace backcrossed pigs classified in two phenotypically-extreme groups for intramuscular FA composition (five per group), were analyzed using RNA-Seq . This study identified 55 genes differentially expressed in the liver that may play a crucial role in muscle lipid composition. Nevertheless, muscle lipids are derived not only from the liver (mostly dietary lipids), but also from adipose tissue (mostly de novo lipogenesis) [15, 23]. Therefore, the aim of the present study is to investigate the contribution of backfat transcriptome to the FA content and profile of intramuscular fat in pigs. The two main goals of our study are: (i) the identification of genes and pathways differentially expressed in the backfat of Iberian × Landrace crossbred pigs (BC1_LD) showing extreme phenotypes for intramuscular FA composition, and (ii) to describe transposable elements and new putative protein-coding genes in the transcriptome of pig backfat. Combining the new adipose tissue transcriptome data with the already available liver transcriptome information will allow us to study the expression of genes regulating the overall lipid metabolism in pigs.
Results and discussion
Phenotypic variations between extreme groups
In a previous work by our group, animals from an Iberian × Landrace backcross (BC1_LD) were analyzed with a Principal Component Analysis (PCA) to describe the phenotypic variation of traits related to carcass quality and intramuscular FA composition . The score information of the first principal component in PCA was used to classify the BC1_LD animals into two groups High (H) and Low (L) and the hepatic transcriptome from the most extreme females (five per group) was evaluated using an RNA-Seq approach . In the present study, a total of six females (three per group) were selected for RNA sequencing of their backfat tissue. Pedigree information was used to avoid the selection of sibs in the same group. When phenotypic means between groups were compared, 50% of the traits showed significant statistical differences (14/28) (Additional file 1: Table S1). In summary, the H group showed higher levels of intramuscular polyunsaturated fatty acids (PUFA), and group L showed higher levels of saturated fatty acids (SFA) and monounsaturated fatty acids (MUFA) (Additional file 1: Table S1). The lack of statistical significance of palmitoleic (C16:1 n-7) and heptadecenoic (C17:1) acids content, in comparison with Ramayo-Caldas et al. (2012), may be explained by the lower sample size. Results obtained are in agreement with the breed effect on backfat FA composition, showing similarities between L-group animals and Iberian pigs, which had higher percentages of C16:0 and MUFA (particularly C18:1) and a lower content of PUFA than did commercial breeds [24–26]. Meanwhile, animals of the H group had higher percentages of PUFA, as observed in Landrace animals . Most importantly, the two groups of pigs did not differ in either IMF content or backfat thickness. This is relevant, as the two groups include pigs with similar abilities to deposit fat in muscle and backfat, but they incorporate FA with different elongation and desaturation indexes.
Characterization of pig adipose tissue transcriptome
Summary of mapped reads
Number of reads
Exploring for novel coding transcripts and transposable elements in the adipose tissue transcriptome
Transcript annotation performed with cufflinks showed a mean value of 10,862 total unknown intergenic transcripts (Additional file 1: Table S2). This value doubles the number of intergenic transcripts detected in previous studies [22, 28]. To determine which of these transcripts encoded a protein, Augustus software  was used and, as was expected, the total amount of predicted proteins was also higher, as compared to previous studies: 4,130 predicted proteins against 326  and 714 . Our analysis showed an improvement in the sequence length and sequence coverage. Furthermore, the conservative approach used in , in which only those transcripts expressed in at least four of the five animals of each group were considered, could aid to explain the differences obtained (4,130 vs. 326). BLASTP analysis was performed to compare the putative proteins predicted by Augustus against the predicted proteins reported by Ramayo-Caldas et al. (2012) in the liver and Esteve-Codina et al. (2011) in gonads. A total of 269 new putative proteins fitted in with those previously described: 93 putative proteins were expressed in liver transcriptome (34%) and 211 were expressed in gonad transcriptome (78%). Additionally, a functional annotation was performed using BLAST2GO software. BLASTP analysis of the 4,130 predicted proteins revealed that 2,100 proteins (50.8%) displayed significant similarity with existing protein sequences (the top hit species was Sus scrofa, 59.1%). These proteins corresponded to: 16 novel computationally predicted proteins and 1,108 known human proteins, 361 novel and 598 known bovine proteins and 997 novel and 599 known porcine proteins. Hence, the number of novel predicted proteins was lower in better-described genomes (human and bovine). From the 2,100 predicted proteins, only 1,226 were functionally annotated with at least one gene ontology (GO) term. At the third GO level, the most represented biological terms were ‘primary metabolic process’ (8% of predicted proteins), ‘cellular metabolic process’ (8%) and ‘regulation of biological process’ (7%). According to the molecular function, ‘protein binding’ (25%), ‘ion binding’ (17%) and ‘nucleic acid binding’ (16%) were the most represented categories. In the cellular compartments category, ‘cell part’ (29%) was the most represented term, followed by ‘membrane-bounded organelle’ (20%) and ‘organelle part’ (16%) (data not shown). Furthermore, the main metabolic pathways represented were purine metabolism (49 sequences), pyrimidine metabolism (11), phosphatidylinositol signaling system (11) and inositol phosphate metabolism (10); these and other pathways observed are shown in Additional file 2: Table S3.
Repetitive elements (RE) were identified in the adipose tissue transcriptome using the RepeatMasker software. The total interspersed repeats represented 36% of the intergenic transcripts (Additional file 3: Table S4), a percentage higher than those observed in previous porcine transcriptome analyses: 5.8%-7.3%  and 7.3% . As described above, the differences obtained could be explained by the assembly used for each analysis. The description of these regions has been improved in the current assembly (Sscrofa10.2), as demonstrated in the results observed. The major part of the RE were classified as SINEs (n = 92,007), representing 13.96% of the transcriptome sequenced. The second group was LINEs (n = 43,428), but these RE constitute a larger part of the transcriptome than do SINEs due to the bigger size of LINEs (15.88% of adipose tissue transcriptome). The remaining RE were classified as: LTR elements (4.04%), DNA elements (2.06%) and unclassified RE (0.02%).
Gene expression analysis
Counting the reads mapped in each gene, around 15,747 annotated genes were expressed in adipose tissue with similar amounts between groups (L = 15,608-16,001, H = 15,433-15,834). Taking into account only those genes with a minimum mean of 20 reads per gene in at least one of the extreme groups, 13,086 expressed genes were selected. Gene-expression distribution was similar in both groups, classifying 1% of the selected genes between 0–20 mapped reads; 27%-28% among 20–200 mapped reads; most genes (46%-47%) had between 200–2,000 mapped reads; 23% among 2,000-20,000 mapped reads, and the remaining genes (2%) more than 20,000 mapped reads (Additional file 4: Figure S1). Mean gene-expression levels were highly correlated between groups (r = 0.98 between H and L groups), indicating that most genes were similarly expressed in both groups. Five of the six individuals were also assayed with the Gene-Chip® Porcine microarray (Affymetrix; Santa Clara, CA) to analyze the gene expression of 20,201 Sus scrofa genes. After signal normalization, correlation between the expression data obtained by microarrays and RNA-Seq was calculated. All animals showed a high Spearman correlation (r = 0.65-0.68) (Additional file 5: Figure S2) in accordance with previous studies of the porcine transcriptome [22, 28, 30], confirming the reproducibility of the data. Genes with intermediate expression values had a higher correlation between technologies than did genes with low or high expression values. This same pattern had already been observed in previous studies, and it is explained by the higher dynamic range of RNA-Seq analyses [30, 31]. Finally, the top 100 expressed genes showed an over-representation in biological gene ontologies related to hormone-sensitive lipase-mediated triacylglycerol hydrolysis, lipid digestion, mobilization and transport, pyruvate metabolism and biosynthesis of unsaturated FAs. Additionally, key regulatory pathways were also detected: the PPAR signaling pathway, which is important for the induction of pre-adipocyte differentiation and FA storage , or the ChREBP transcription factor, which has emerged as a major mediator of glucose action on lipogenic gene expression and as a key regulator of lipid synthesis .
Differential gene expression among animals with extreme phenotypes of intramuscular FA composition
Differentially expressed genes associated with intramuscular FA composition in a genome-wide association study in the same population
Ensembl Gene ID
Counts L group
Counts H group
Top five biological functions significantly modulated in backfat adipose tissue when comparing H vs. L animals
ACADL, ACAN, ACBD4, ACE2, ACLY, ACTG2, ACVR1C, ADH1A, AFAP1L1, AHSG, ALB, ALDH1A1, ALDOC, ANXA4, AP3M2, APAF1, APOB, AQPEP, ATP5J2, AZGP1, BCHE, BCL10, BMPER, BNIP2, C10orf116, C19orf53, C2orf40, C8A, CA3, CAPN6, CAPZA2, CCBP2, CCL21, CCT6A, CD1E, CD300LG, CENPF, CES1, CHST13, CLCA2, CLEC2D, CLIC5, CLK1, CMPK2, CNN1, COL11A1, COL15A1, COL5A1, COL8A2, COMT, CPXM2, CRABP1, CRABP2, CTCFL, CTNNAL1, CTSF, CXCL1…
Concentration of lipids
ACACA, ACADL, ACLY, AHSG, ALB, ALDH1A1, APAF1, APOB, APOC3, CD4, CES1, CIDEC, COMT, CTDNEP1, CYP2E1, DHCR24, FASN, FFAR4, GC, GNAT1, HIF1AN, HP, MOGAT2, PHGDH, PLP1, PON1, PON2, PON3, RBP1, RDH16, RGS4, SCD, SNCA, STEAP4, TGFBR2, THEM5, THRSP, UGT8, VAV3
Synthesis of lipids
ACACA, ACADL, ACLY, ACSS2, ALB, ALDH1A1, APOB, APOC3, C1QTNF3, CD4, CXCL1, CYP2E1, DHCR24, ESRRG, FASN, G6PD, KDR, KIT, LSS, MOGAT2, NFATC2, PLP1, PMVK, PON1, PON2, PRKG2, RBP1, RDH16, RDH5, SCD, SLC6A6, SNCA, THRSP, UGT8
Homeostasis of blood
AHSG, APOC3, APOH, CIDEC, COMT, CYP2E1, ESRRG, MUT, VAV3
Fatty acid metabolism
AACS, ACACA, ACADL, ACADSB, ACLY, ACSS2, ALB, APOB, APOC3, APOH, CD4, CXCL1, CYP2E1, EPHX1, FASN, GC, GM2A, KIT, ME1, NFATC2, PHGDH, PLP1, SCD, SLC36A2, SLC38A2, SLCO1A2, SNCA, UGT8
Top five canonical pathways significantly modulated in backfat adipose tissue when comparing H vs. L animals
Ingenuity canonical pathway
SCD, APOB, APOH, AHSG, PCYOX1, PON1, ALB, LYZ, APOC3, FASN, ACACA, S100A8, FGA, GC, PON3, TNFRSF11B
Ethanol Degradation II
ALDH4A1, ADH1A, ALDH1A1, ACSS2, PECR, ADHFE1
Noradrenaline and adrenaline degradation
ALDH4A1, ADH1A, ALDH1A1, COMT, PECR, ADHFE1
Acute phase response signaling
ALB, HP, RBP7, APOH, AHSG, CRABP2, FGB, FGA, RBP1, FGG, CRABP1, TNFRSF11B
Superpathway of serine and glycine biosynthesis I
PSPH, PHGDH, SHMT2
Differentially modulated pathways and pig lipid metabolism
FFAs derived from adipose tissue and VLDL-associated triglycerides derived from the liver are important sources of the FA supply to muscle, playing an important role in determining the intramuscular FA composition [15, 23]. In fact, high, positive phenotypic correlations between adipose tissue and muscle FA composition were found for C14:0 (rC14:0 = 0.59, p-value = 1.14 × 10-14), C16:0 (rC16:0 = 0.72, p-value = 2.2 × 10-16) and C17:0 (rC17:0 = 0.65, p-value = 2.2 × 10-16), and moderate, positive phenotypic correlations were found for C16:1 n-7 (rC16:1 n-7 = 0.50, p-value = 3.3 × 10-10), C16:1 n-9 (rC16:1 n-9 = 0.47, p-value = 3.9 × 10-09), C18:0 (rC16:0 = 0.43, p-value = 9.8 × 10-08) and C18:1 n-9 (rC18:1 n-9 = 0.40, p-value = 9.2 × 10-07) in our animal material (Muñoz et al. (2013), submitted). In a previous study  that analyzed the liver transcriptome in the same groups of animals, it was suggested that a higher PUFA content observed in the H group induced a greater stimulation of both peroxisomal and mitochondrial β-oxidation and reduced triglyceride and cholesterol synthesis. This increase of FA oxidation observed in the liver of animals of the H group, jointly with ketone body production, is a “glucose sparing” mechanism of regulation in fasting conditions  which the animals were in at slaughter. In adipose tissue, the fasting condition induces the lipolysis of triglycerides storage and the blood transport of FFAs bound to albumin (ALB) (fold difference = 3.05, p-value = 1.23 × 10-07) to organs such as the heart and skeletal muscle to fulfill their energy requirements . Previous studies performed in 3T3-L1 pre-adipocytes demonstrated that over-expression of ALB stimulates long-chain FAs uptake by direct interaction with adipose cells and suggested that this stimulatory effect may be a general phenomenon in other types of cells . Hence, data obtained may explain the greater uptake of FAs into hepatocytes and their degradation in the β-oxidation pathway in the liver of the H-group animals. The negative effect that dietary PUFA causes on de novo FA synthesis [33, 49, 50] is well known, and this effect was also observed in our data. The down-regulation of this pathway in the group with the higher content of PUFA (that is, the H group) may be caused by the inhibitory effect of n-3 and n-6 PUFA on the expression of receptor subfamily 1, group H, member 3 (NR1H3) . The NR1H3 gene, also called liver X receptor α (LXRα), is a nuclear receptor which is highly expressed in adipose tissue. Studies performed in NR1H3(−/−) mice showed a decrease on de novo FA synthesis, due to the down-regulation of SREBF1 and its target genes  (Figure 3). Other studies confirmed that ChREBP is a key transcriptional regulator for the coordinated inhibition of glycolytic and lipogenic genes by PUFA . PUFA also suppresses the ChREBP gene function in a LXR-dependent manner, increasing its mRNA decay and altering ChREBP protein translocation from the cytosol to the nucleus . In addition, we cannot rule out a direct inhibition of SCD expression by PUFA  in animals of the H group. The repression of SCD increases the intracellular pool of saturated fatty acyl-CoAs inhibiting the ACACA enzyme and de novo lipogenesis and activating the carnitine palmitoyltransferase 1 (CPT1) gene, which is responsible for the rate-limiting step in the importing and oxidation of FAs into the mitochondria . Thus, altogether, our results are in agreement with a functional and anatomical separation of de novo lipid synthesis and β-oxidation in the porcine adipose tissue and liver, respectively. This suggests a tightly coordinated process among different hormones (peptides and/or lipids), transcription factors and nuclear receptors to avoid the simultaneous activation of antagonistic pathways. However, there is great controversy in explaining the relevance of β-oxidation in porcine adipose tissue. PPARA is considered to be the main transcription factor controlling FA oxidation. There are some studies that described a greater expression of pig PPARA in adipose tissue than in the liver, suggesting that adipose tissue could oxidize FAs to any extent . In contrast, other authors did not find PPARA expression in porcine adipose tissues . Consistent with this, we found higher levels of PPARA expression in the liver, as compared to adipose tissue, which suggests an important role of the liver in porcine β-oxidation.
Different studies have determined the importance of several adipose tissue-derived hormones in the regulation of systemic carbohydrate and lipid homeostasis [57, 58]. Communication between adipose tissue and distant organs has been previously described through the lipokine palmitoleate (C16:1 n-7), which strongly stimulates muscle insulin action while it suppresses hepatosteatosis . Studies performed in vivo in humans showed a clear increase of SREBF1c caused by insulin in muscle and, consequently, the induction of key lipogenic enzymes [59, 60]. The mean comparison of C16:1 n-7 FA composition between the L and H groups showed suggestive differences in both muscle (Additional file 1: Table S1) and adipose tissue (data not shown). Hence, different levels of C16:1 n-7 may determine a differential systemic regulation that may explain the phenotypic variations observed between groups. Lipogenesis in muscle is produced by the small proportion of adipocytes in this tissue. Therefore, their lipogenic activity is lower in comparison with other extra-muscular adipose tissues . Despite the lower rate of lipogenesis observed in muscle, de novo FA synthesis directly contributes to the in situ intramuscular FA composition . In this sense, the study of muscle transcriptome in pigs, together with liver and adipose tissue transcriptomes, would be important in order to obtain a complete view of FA metabolism.
Finally, other adipose tissue-derived hormones such as leptin or adiponectin cannot be discarded, as well as other interesting genes not annotated in the current pig genome assembly. The differentially expressed genes identified seem to be relevant in controlling the overall FA composition in adipose tissue and muscle, and they should be considered as candidate genes for meat quality traits in pigs. The knowledge of these genes and their regulatory networks may help in the design of new strategies for improving pork meat quality by increasing the MUFA/SFA and n-3/n-6 PUFA ratios . The maintenance of these ratios is essential in order to reduce the imbalanced FA intake of today’s consumers and to avoid several diseases, including cancers and coronary heart disease. The great similarities between pigs and humans in body size and other physiological/anatomical features convert the pig into an excellent biomedical model for human disease. Hence, analysis of the porcine backfat transcriptome showed the role of several genes in regulating the lipid metabolism not only in pigs but also in humans, due to the metabolic similarities between both species. Additionally, several lipid-related diseases affected both human and pig (obesity, including diabetes, metabolic syndrome or other lipid-related diseases), therefore, data generated in this study can be used to identify polymorphism with a major effect on these diseases.
In this study, we provide a global view of the adipose tissue (backfat) transcriptome of six pigs and extensive new knowledge about transposable elements, new putative protein-coding genes and the expression levels of known genes in adipose tissue. Animals were classified into two groups according to their intramuscular FA composition, and 396 genes were found to be differentially expressed between groups. These genes belong to molecular functions and gene networks related to lipid and FA metabolism. Pathway analysis showed a different modulation of lipogenesis between phenotypically extreme animals, probably caused by differences in PUFA levels (mainly linolenic and α-linolenic). Finally, the crucial role of IMF FA composition in the technological and the nutritional and organoleptic quality of pork meat is well-known. Hence, this study will allow for the identification of candidate genes and gene networks for FA composition traits which may help in the design of better selection strategies to improve porcine meat quality traits.
The IBMAP cross was originated by crossing three Iberian (Guadyerbas line) boars with 31 Landrace sows . Animals used in this study belong to a backcross (BC1_LD) generated by crossing five F1 (Iberian × Landrace) boars with 26 Landrace sows and producing 144 backcrossed animals. All pigs were raised in a normal intensive system and feeding was ad libitum with a cereal-based commercial diet. Pigs were slaughtered at an average age of 179.8 days ± 2.6 days following national and institutional guidelines for the ethical use and treatment of animals in experiments. Samples of adipose tissue (backfat) were collected at the slaughterhouse, snap-frozen in liquid nitrogen and stored at −80°C until analyzed. A total of 48 traits related to growth, carcass quality and intramuscular FA composition were measured. In Ramayo-Caldas et al. (2012), the phenotypic information from twenty-six of the total traits was used to classify BC1_LD animals into two groups (H and L) according to the first component of a PCA . A total of six animals was selected for the study, considering pedigree information representing the parental genetic diversity, and that only females were retained for RNA sequencing (three per group). Phenotypic mean comparison between groups was performed using a linear model implemented in R.
RNA isolation, library preparation and sequencing
Total RNA was isolated from backfat using the RiboPure™ Isolation of High Quality Total RNA (Ambion®; Austin, TX) following the manufacturer’s recommendations. RNA was quantified using the Nano-Drop ND-1000 spectrophotometer (NanoDrop products; Wilmington, USA) and checked for purity and integrity in a Bioanalyser-2100 (Agilent Technologies, Inc., Santa Clara CA, USA). For each sample, one paired-end library with an approximately 300-bp insert size was prepared using TruSeq RNA Sample Prep Kit v2 (Illumina, Inc.; San Diego CA, USA). To discriminate among samples, libraries were labeled by barcoding and pooled to be run in different lanes. Libraries were sequenced, in CNAG (Centro Nacional de Análisis Genómico), on an Illumina HiSeq2000 instrument (Illumina, Inc.; San Diego CA, USA) that generated paired-end reads of 75 nucleotides. More than 236 million reads were generated in this study.
Mapping, assembling and annotation of reads
In order to map all reads generated, the software TopHat v2.0.1 [63, 64] was employed, using as reference version 10.2 of the pig genome (Sscrofa10.2) and the annotation database Ensembl Genes 67 [http://www.ensembl.org/info/data/ftp/index.html]. Tophat was used with an expected mean inner-distance of 160-bp between paired-end reads. Quality control and reads statistics were determined with FASTQC [http://www.bioinformatics.babraham.ac.uk/projects/fastqc/]. Transcripts were assembled and quantified by Cufflinks v2.0.2 [64, 65] with a minimum alignment count per locus of 20. Additionally, for counting the number of reads mapping to exons, introns and intergenic positions, the intersectBED tool from BEDtools was used .
Orthology detection and transposable element analysis
Intergenic-expressed regions, according to the current pig genome assembly (Sscrofa10.2), were extracted with Cuffcompare  and custom Python and R scripts. Putative coding transcripts were identified with Augustus , providing exon boundaries and allowing for complete protein translation. Functional annotation was performed by using BLASTP option from BLAST2GO, with the following parameters: E-value hit filter 1.00E-6, annotation cutoff 55, gene ontology (GO) weight 5 and HSP-hit coverage cutoff 0 . Additionally, an InterProScan tool implemented in the BLAST2GO software and the ANNEX data set was employed to refine the functional annotations. GO terms were summarized according to the three principal GO categories: cellular component, biological process and molecular functions. Enzyme mapping of annotated sequences was performed using direct GO to enzyme mapping and used to query the Kyoto Encyclopedia of Genes and Genomes (KEGG) to define the main metabolic pathways involved [68, 69].
Furthermore, the software RepeatMasker (http://www.repeatmasker.org/) version open-3.30 was employed with the ‘quick search’ option and ‘pig’ species, in order to indentify repetitive and transposable elements in the adipose tissue transcriptome. The Search Engine used was NCBI/RMBLAST with the complete rm-20120418 database.
Gene expression quantification and correlation analysis with expression microarrays
Qualimap v0.5 software was employed to count the number of reads mapped for each gene, and the total number of counts was considered as expression values . Pearson correlations between the mean expression values of each group were calculated using the cor.test function of R. Five of the animals sequenced were also assayed with high-density oligonucleotide microarray chips (GeneChip® Porcine) from Affymetrix (Santa Clara, CA), containing a total of 23,937 probe sets (23,256 transcripts), representing 20,201 Sus scrofa genes. Microarrays were hybridized and scanned at the Institut de Recerca Hospital Universitari Vall d’Hebron (Barcelona, Spain) following Affymetrix standard protocols. The Gene-Chip Operating Software (GCOS) was used to generate expression data, and probes were normalized and adjusted for background noises with the GCRMA R package . All probes correspond to a total of 7,885 Ensembl gene IDs expressed in backfat, and these genes were used to estimate the Spearman correlation between the log2 expression values of genes analyzed by RNA-Seq and microarrays.
Differential gene expression analysis
The DESeq R package was employed to detect genes differentially expressed between groups . DESeq mediates a negative binomial distribution by modeling the biological and technical variance for testing DE genes in two experimental conditions. DESeq uses the unambiguous table of counts per gene obtained from QualiMap software using the comp-counts option as the input file . Before the analysis, some exploratory tests were performed to validate both the good data quality and the variance estimation. Per-gene estimates of the base variance against the base levels showed that the fit (red line) followed the single-gene estimates well (Additional file 8: Figure S3). The residualEcdfPlot function used to check the uniformity of the cumulative probabilities revealed a similar curve pattern of the empirical cumulative density functions (ECDF) in both groups. Data were filtered by a minimum mean of 20 reads mapped per gene and only those genes with a fold difference between groups higher than 1.2-fold or lower than −1.2-fold were retained. Fold differences were calculated referring to the group of animals with the lower expression. Genes with a positive fold difference indicate that they are highly expressed in the H group, whereas genes with negative fold difference values represent that they are highly expressed in the L group. Then, the R package q-value  was employed to calculate the false-discovery rate, and genes with a p-value ≤ 0.01 (which is equivalent to a q-value ≤ 0.1) were retained in both classifications.
Gene functional classification, network and canonical pathways analyses
A bioinformatics approach was used to elucidate the biological importance of differentially expressed genes in the adipose tissue transcriptome. Ingenuity Pathway Analysis software (IPA; Ingenuity Systems, http://www.ingenuity.com) was applied to identify functions and pathways represented and for generating biological networks. The IPA program consists of the Ingenuity Pathway Knowledge Base (IPKB), which is derived from known functions and interactions of genes published in the literature. IPA presents the top canonical pathways associated with the uploaded data with a p-value calculated using the right-tailed Fisher’s exact test. Functional analysis was used to identify the biological functions that are differentially represented between both groups (H and L). Networks were algorithmically generated based on their connectivity, with a score representing the log probability of a particular network being found by random chance. Direct and indirect biological relationships between molecules (nodes) were represented as continuous and discontinuous lines, respectively. All lines are supported by at least one reference from the literature, from a textbook, or from canonical information stored in the Ingenuity Pathways Knowledge Base. The intensity of the node color indicates the degree of up-(red) or down-(green) regulation of the H versus L group.
The full data sets have been submitted to Gene Expression Omnibus (GEO) under accession GSE52012 and at NCBI Sequence Read Archive (SRA) under Accession SRP031783, Bioproject: PRJNA223356.
This work was funded by the Ministerio de Economía y Competitividad Project AGL2011-29821-C02, and by the Innovation Consolider-Ingenio 2010 Program (CSD2007-00036, Centre for Research in Agrigenomics). J. Corominas was funded by a FPI PhD grant from the Spanish Ministerio de Educación (BES-2009-081223), Y. Ramayo-Caldas was funded by a FPU PhD grant from the Spanish Ministerio de Educación (AP2008-01450) and A. Puig-Oliveras was funded by a PIF PhD grant from the Universitat Autónoma de Barcelona (458-01-1/2011).
- Dodson MVHG, Guan L, Du M, Rasmussen TP, Poulos SP, Mir P, Bergen WG, Fernyhough ME, McFarland DC, Rhoads RP, Soret B, Reecy JM, Velleman SG, Jiang Z: Lipid metabolism, adipocyte depot physiology and utilization of meat animals as experimental models for metabolic research. Int J Biol Sci. 2010, 6 (7): 691-699.PubMed CentralView ArticlePubMedGoogle Scholar
- Wood JD, Richardson RI, Nute GR, Fisher AV, Campo MM, Kasapidou E, Sheard PR, Enser M: Effects of fatty acids on meat quality: a review. Meat Sci. 2004, 66 (1): 21-32. 10.1016/S0309-1740(03)00022-6.View ArticlePubMedGoogle Scholar
- Chizzolini R, Zanardi E, Dorigoni V, Ghidini S: Calorific value and cholesterol content of normal and low-fat meat and meat products. Trends Food Sci Technol. 1999, 10 (4–5): 119-128.View ArticleGoogle Scholar
- Katan MB, Zock PL, Mensink RP: Effects of fats and fatty acids on blood lipids in humans: an overview. Am J Clin Nutr. 1994, 60 (6): 1017S-1022S.PubMedGoogle Scholar
- Food, Agriculture O, Agriculture Organization of the United N: Fats and Fatty Acids in Human Nutrition: Report of an Expert Consultation. 2008, Geneva: Food and Agriculture Organization of the United NationsGoogle Scholar
- de Lorgeril M, Salen P: New insights into the health effects of dietary saturated and omega-6 and omega-3 polyunsaturated fatty acids. BMC Med. 2012, 10 (1): 50-10.1186/1741-7015-10-50.PubMed CentralView ArticlePubMedGoogle Scholar
- Pérez-Enciso M, Clop A, Noguera JL, Ovilo C, Coll A, Folch JM, Babot D, Estany J, Oliver MA, Díaz I, et al: A QTL on pig chromosome 4 affects fatty acid metabolism: evidence from an Iberian by Landrace intercross. J Anim Sci. 2000, 78 (10): 2525-2531.PubMedGoogle Scholar
- Casellas J, Noguera JL, Reixach J, Díaz I, Amills M, Quintanilla R: Bayes factor analyses of heritability for serum and muscle lipid traits in Duroc pigs. J Anim Sci. 2010, 88 (7): 2246-2254. 10.2527/jas.2009-2205.View ArticlePubMedGoogle Scholar
- Ntawubizi M, Colman E, Janssens S, Raes K, Buys N, De Smet S: Genetic parameters for intramuscular fatty acid composition and metabolism in pigs. J Anim Sci. 2010, 88 (4): 1286-1294. 10.2527/jas.2009-2355.View ArticlePubMedGoogle Scholar
- Uemoto Y, Nakano H, Kikuchi T, Sato S, Ishida M, Shibata T, Kadowaki H, Kobayashi E, Suzuki K: 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): 225-228. 10.1111/j.1365-2052.2011.02236.x.View ArticlePubMedGoogle Scholar
- Estellé J, Fernández AI, Pérez-Enciso M, Fernández A, Rodríguez C, Sánchez A, Noguera JL, Folch JM: 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): 813-820. 10.1111/j.1365-2052.2009.01922.x.View ArticlePubMedGoogle Scholar
- Corominas J, Ramayo-Caldas Y, Castelló A, Muñoz M, Ibáñez-Escriche N, Folch JM, Ballester M: Evaluation of the porcine ACSL4 gene as a candidate gene for meat quality traits in pigs. Anim Genet. 2012, 43 (6): 714-720. 10.1111/j.1365-2052.2012.02335.x.View ArticlePubMedGoogle Scholar
- 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 (3): 338-344.PubMedGoogle Scholar
- Kershaw EE, Flier JS: Adipose tissue as an endocrine organ. J Clin Endocrinol Metabol. 2004, 89 (6): 2548-2556. 10.1210/jc.2004-0395.View ArticleGoogle Scholar
- Cao H, Gerhold K, Mayers JR, Wiest MM, Watkins SM, Hotamisligil GS: Identification of a lipokine, a lipid hormone linking adipose tissue to systemic metabolism. Cell. 2008, 134 (6): 933-944. 10.1016/j.cell.2008.07.048.PubMed CentralView ArticlePubMedGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Meth. 2008, 5 (7): 621-628. 10.1038/nmeth.1226.View ArticleGoogle Scholar
- Jager M, Ott C-E, Grunhagen J, Hecht J, Schell H, Mundlos S, Duda G, Robinson P, Lienau J: Composite transcriptome assembly of RNA-seq data in a sheep model for delayed bone healing. BMC Genomics. 2011, 12 (1): 158-10.1186/1471-2164-12-158.PubMed CentralView ArticlePubMedGoogle Scholar
- Li R, Rinaldi M, Capuco A: Characterization of the abomasal transcriptome for mechanisms of resistance to gastrointestinal nematodes in cattle. Vet Res. 2011, 42 (1): 114-10.1186/1297-9716-42-114.PubMed CentralView ArticlePubMedGoogle Scholar
- Cánovas A, Rincon G, Islas-Trejo A, Wickramasinghe S, Medrano J: SNP discovery in the bovine milk transcriptome using RNA-Seq technology. Mamm Genome. 2010, 21 (11–12): 592-598.PubMed CentralView ArticlePubMedGoogle Scholar
- Huang W, Khatib H: Comparison of transcriptomic landscapes of bovine embryos using RNA-Seq. BMC Genomics. 2010, 11 (1): 711-10.1186/1471-2164-11-711.PubMed CentralView ArticlePubMedGoogle Scholar
- Jung WY, Kwon SG, Son M, Cho ES, Lee Y, Kim JH, Kim B-W, Park DH, Hwang JH, Kim TW, et al: RNA-Seq approach for genetic improvement of meat quality in pig and evolutionary insight into the substrate specificity of animal carbonyl reductases. PLoS ONE. 2012, 7 (9): e42198-10.1371/journal.pone.0042198.PubMed CentralView ArticlePubMedGoogle Scholar
- Ramayo-Caldas Y, Mach N, Esteve-Codina A, Corominas J, Castello A, Ballester M, Estelle J, Ibanez-Escriche N, Fernandez A, Perez-Enciso M, et al: Liver transcriptome profile in pigs with extreme phenotypes of intramuscular fatty acid composition. BMC Genomics. 2012, 13 (1): 547-10.1186/1471-2164-13-547.PubMed CentralView ArticlePubMedGoogle Scholar
- Frayn K, Arner P, Yki-Järvinen H: Fatty acid metabolism in adipose tissue, muscle and liver in health and disease. Essays Biochem. 2006, 042: 89-103. 10.1042/bse0420089.View ArticleGoogle Scholar
- Webb EC, O’Neill HA: The animal fat paradox and meat quality. Meat Sci. 2008, 80 (1): 28-36. 10.1016/j.meatsci.2008.05.029.View ArticlePubMedGoogle Scholar
- Wood JD, Enser M, Fisher AV, Nute GR, Sheard PR, Richardson RI, Hughes SI, Whittington FM: Fat deposition, fatty acid composition and meat quality: a review. Meat Sci. 2008, 78 (4): 343-358. 10.1016/j.meatsci.2007.07.019.View ArticlePubMedGoogle Scholar
- Serra X, Gil F, Pérez-Enciso M, Oliver MA, Vázquez JM, Gispert M, Díaz I, Moreno F, Latorre R, Noguera JL: A comparison of carcass, meat quality and histochemical characteristics of Iberian (Guadyerbas line) and Landrace pigs. Livestock Production Science. 1998, 56 (3): 215-223. 10.1016/S0301-6226(98)00151-1.View ArticleGoogle Scholar
- Chen C, Ai H, Ren J, Li W, Li P, Qiao R, Ouyang J, Yang M, Ma J, Huang L: A global view of porcine transcriptome in three tissues from a full-sib pair with extreme phenotypes in growth and fat deposition by paired-end RNA sequencing. BMC Genomics. 2011, 12 (1): 448-10.1186/1471-2164-12-448.PubMed CentralView ArticlePubMedGoogle Scholar
- Esteve-Codina A, Kofler R, Palmieri N, Bussotti G, Notredame C, Perez-Enciso M: Exploring the gonad transcriptome of two extreme male pigs with RNA-seq. BMC Genomics. 2011, 12 (1): 552-10.1186/1471-2164-12-552.PubMed CentralView ArticlePubMedGoogle Scholar
- Stanke M, Diekhans M, Baertsch R, Haussler D: Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008, 24 (5): 637-644. 10.1093/bioinformatics/btn013.View ArticlePubMedGoogle Scholar
- Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y: RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008, 18 (9): 1509-1517. 10.1101/gr.079558.108.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10 (1): 57-63. 10.1038/nrg2484.PubMed CentralView ArticlePubMedGoogle Scholar
- Ferré P: The biology of peroxisome proliferator-activated receptors: relationship with lipid metabolism and insulin sensitivity. Diabetes. 2004, 53 (suppl 1): S43-S50.View ArticlePubMedGoogle Scholar
- Postic C, Dentin R, Denechaud P-D, Girard J: ChREBP, a transcriptional regulator of glucose and lipid metabolism. Annu Rev Nutr. 2007, 27 (1): 179-192. 10.1146/annurev.nutr.27.061406.093618.View ArticlePubMedGoogle Scholar
- Ramayo-Caldas Y, Mercadé A, Castelló A, Yang B, Rodríguez C, Alves E, Díaz I, Ibánez-Escriche N, Noguera JL, Pérez-Enciso M, et al: Genome-Wide Association Study for intramuscular fatty acid composition in an Iberian x Landrace Cross. J Anim Sci. 2012, 90: 1-11.View ArticleGoogle Scholar
- Ren Z-Q, Wang Y, Xu Y-J, Wang L-J, Lei M-G, Zuo B, Li F-E, Xu D-Q, Zheng R, Deng C-Y, et al: Identification of a differentially expressed gene, ACL, between Meishan x Large White and Large White x Meishan F1 hybrids and their parents. Genet Sel Evol. 2008, 40 (6): 625-637.PubMed CentralView ArticlePubMedGoogle Scholar
- Muñoz M, Alves E, Corominas J, Folch JM, Casellas J, Noguera JL, Silió L, Fernández AI: Survey of SSC12 regions affecting fatty acid composition of intramuscular fat using high density SNP data. Frontiers in Genetics. 2011, 2: 101-PubMed CentralPubMedGoogle Scholar
- Muñoz G, Alves E, Fernández A, Óvilo C, Barragán C, Estellé J, Quintanilla R, Folch JM, Silió L, Rodríguez MC, et al: QTL detection on porcine chromosome 12 for fatty-acid composition and association analyses of the fatty acid synthase, gastric inhibitory polypeptide and acetyl-coenzyme A carboxylase alpha genes. Anim Genet. 2007, 38 (6): 639-646. 10.1111/j.1365-2052.2007.01668.x.View ArticlePubMedGoogle Scholar
- Jayakumar A, Tai MH, Huang WY, al-Feel W, Hsu M, Abu-Elheiga L, Chirala SS, Wakil SJ: Human fatty acid synthase: properties and molecular cloning. Proc Natl Acad Sci. 1995, 92 (19): 8695-8699. 10.1073/pnas.92.19.8695.PubMed CentralView ArticlePubMedGoogle Scholar
- Wakil SJ, Stoops JK, Joshi VC: Fatty acid synthesis and its regulation. Annu Rev Biochem. 1983, 52 (1): 537-579. 10.1146/annurev.bi.52.070183.002541.View ArticlePubMedGoogle Scholar
- Matsuzaka T, Shimano H, Yahagi N, Kato T, Atsumi A, Yamamoto T, Inoue N, Ishikawa M, Okada S, Ishigaki N, et al: Crucial role of a long-chain fatty acid elongase, Elovl6, in obesity-induced insulin resistance. Nat Med. 2007, 13 (10): 1193-1202. 10.1038/nm1662.View ArticlePubMedGoogle Scholar
- Corominas J, Ramayo-Caldas Y, Puig-Oliveras A, Pérez-Montarelo D, Noguera JL, Folch JM, Ballester M: Polymorphism in the ELOVL6 Gene Is Associated with a Major QTL Effect on Fatty Acid Composition in Pigs. PLoS ONE. 2013, 8 (1): e53687-10.1371/journal.pone.0053687.PubMed CentralView ArticlePubMedGoogle Scholar
- Vincent A, Louveau I, Gondret F, Lebret B, Damon M: Mitochondrial function, fatty acid metabolism, and immune system are relevant features of pig adipose tissue development. Physiol Genomics. 2012, 44 (22): 1116-1124. 10.1152/physiolgenomics.00098.2012.View ArticlePubMedGoogle Scholar
- Vidal O, Varona L, Oliver MA, Noguera JL, Sànchez A, Amills M: Malic enzyme 1 genotype is associated with backfat thickness and meat quality traits in pigs. Anim Genet. 2006, 37 (1): 28-32. 10.1111/j.1365-2052.2005.01366.x.View ArticlePubMedGoogle Scholar
- Colbert CL, Kim C-W, Moon Y-A, Henry L, Palnitkar M, McKean WB, Fitzgerald K, Deisenhofer J, Horton JD, Kwon HJ: Crystal structure of Spot 14, a modulator of fatty acid synthesis. Proc Natl Acad Sci. 2010, 107 (44): 18820-18825. 10.1073/pnas.1012736107.PubMed CentralView ArticlePubMedGoogle Scholar
- Li XJ, Yang H, Li GX, Zhang GH, Cheng J, Guan H, Yang GS: Transcriptome profile analysis of porcine adipose tissue by high-throughput sequencing. Anim Genet. 2012, 43 (2): 144-152. 10.1111/j.1365-2052.2011.02240.x.View ArticlePubMedGoogle Scholar
- Barea R, Isabel B, Nieto R, López-Bote C, Aguilera JF: Evolution of the fatty acid profile of subcutaneous back-fat adipose tissue in growing Iberian and Landrace x Large White pigs. Animal. 2012, 7 (04): 688-698.View ArticlePubMedGoogle Scholar
- van der Vusse GJ: Albumin as fatty acid transporter. Drug Metab Pharmacokinet. 2009, 24 (4): 300-307. 10.2133/dmpk.24.300.View ArticlePubMedGoogle Scholar
- Trigatti BL, Gerber GE: A direct role for serum albumin in the cellular uptake of long-chain fatty acids. Biochem J. 1995, 308 (1): 155-159.PubMed CentralView ArticlePubMedGoogle Scholar
- Bergen WG, Mersmann HJ: Comparative aspects of lipid metabolism: impact on contemporary research and use of animal models. J Nutr. 2005, 135 (11): 2499-2502.PubMedGoogle Scholar
- Schmitz G, Ecker J: The opposing effects of n-3 and n-6 fatty acids. Prog Lipid Res. 2008, 47 (2): 147-155. 10.1016/j.plipres.2007.12.004.View ArticlePubMedGoogle Scholar
- Peet DJ, Turley SD, Ma W, Janowski BA, Lobaccaro J-MA, Hammer RE, Mangelsdorf DJ: Cholesterol and bile acid metabolism are impaired in mice lacking the nuclear oxysterol receptor LXR alpha. Cell. 1998, 93 (5): 693-704. 10.1016/S0092-8674(00)81432-4.View ArticlePubMedGoogle Scholar
- Dentin R, Benhamed F, Pegorier J-P, Foufelle F, Viollet B, Vaulont S, Girard J, Postic C: Polyunsaturated fatty acids suppress glycolytic and lipogenic genes through the inhibition of ChREBP nuclear protein translocation. J Clin Invest. 2005, 115 (10): 2843-2854. 10.1172/JCI25256.PubMed CentralView ArticlePubMedGoogle Scholar
- Ntambi JM: Regulation of stearoyl-CoA desaturase by polyunsaturated fatty acids and cholesterol. J Lipid Res. 1999, 40 (9): 1549-1558.PubMedGoogle Scholar
- Cohen P, Miyazaki M, Socci ND, Hagge-Greenberg A, Liedtke W, Soukas AA, Sharma R, Hudgins LC, Ntambi JM, Friedman JM: Role for Stearoyl-CoA Desaturase-1 in leptin-mediated weight loss. Science. 2002, 297 (5579): 240-243. 10.1126/science.1071527.View ArticlePubMedGoogle Scholar
- Ding ST, Schinckel AP, Weber TE, Mersmann HJ: Expression of porcine transcription factors and genes related to fatty acid metabolism in different tissues and genetic populations. J Anim Sci. 2000, 78 (8): 2127-2134.PubMedGoogle Scholar
- Sundvold H, Grindflek E, Lien S: Tissue distribution of porcine peroxisome proliferator-activated receptor α: detection of an alternatively spliced mRNA. Gene. 2001, 273 (1): 105-113. 10.1016/S0378-1119(01)00562-5.View ArticlePubMedGoogle Scholar
- Yamauchi T, Kamon J, Minokoshi Y, Ito Y, Waki H, Uchida S, Yamashita S, Noda M, Kita S, Ueki K, et al: Adiponectin stimulates glucose utilization and fatty-acid oxidation by activating AMP-activated protein kinase. Nat Med. 2002, 8 (11): 1288-1295. 10.1038/nm788.View ArticlePubMedGoogle Scholar
- Baile CA, Della-Fera MA, Martin RJ: Regulation of metabolism and body fat mass by leptin. Annu Rev Nutr. 2000, 20 (1): 105-127. 10.1146/annurev.nutr.20.1.105.View ArticlePubMedGoogle Scholar
- Ducluzeau P-H, Perretti N, Laville M, Andreelli F, Vega N, Riou J-P, Vidal H: Regulation by insulin of gene expression in human skeletal muscle and adipose tissue: evidence for specific defects in type 2 diabetes. Diabetes. 2001, 50 (5): 1134-1142. 10.2337/diabetes.50.5.1134.View ArticlePubMedGoogle Scholar
- Guillet-Deniau I, Mieulet V, Le Lay S, Achouri Y, Carré D, Girard J, Foufelle F, Ferré P: Sterol regulatory element binding protein-1c expression and action in rat muscles: insulin-like effects on the control of glycolytic and lipogenic enzymes and UCP3 gene expression. Diabetes. 2002, 51 (6): 1722-1728. 10.2337/diabetes.51.6.1722.View ArticlePubMedGoogle Scholar
- Mourot J, Kouba M: Lipogenic enzyme activities in muscles of growing Large White and Meishan pigs. Livest Pro Sci. 1998, 55 (2): 127-133. 10.1016/S0301-6226(98)00129-8.View ArticleGoogle Scholar
- Perez-Enciso M, Clop A, Noguera JL, Ovilo C, Coll A, Folch JM, Babot D, Estany J, Oliver MA, Díaz I, et al: A QTL on pig chromosome 4 affects fatty acid metabolism: evidence from an Iberian by Landrace intercross. J Anim Sci. 2000, 78 (10): 2525-2531.PubMedGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009, 25 (9): 1105-1111. 10.1093/bioinformatics/btp120.PubMed CentralView ArticlePubMedGoogle Scholar
- Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protocols. 2012, 7 (3): 562-578. 10.1038/nprot.2012.016.View ArticlePubMedGoogle Scholar
- Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L: Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotech. 2010, 28 (5): 511-515. 10.1038/nbt.1621.View ArticleGoogle Scholar
- Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010, 26 (6): 841-842. 10.1093/bioinformatics/btq033.PubMed CentralView ArticlePubMedGoogle Scholar
- Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005, 21 (18): 3674-3676. 10.1093/bioinformatics/bti610.View ArticlePubMedGoogle Scholar
- Kanehisa M, Goto S: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000, 28 (1): 27-30. 10.1093/nar/28.1.27.PubMed CentralView ArticlePubMedGoogle Scholar
- Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, et al: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008, 36 (suppl 1): D480-D484.PubMed CentralPubMedGoogle Scholar
- García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, Dopazo J, Meyer TF, Conesa A: Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics. 2012, 28 (20): 2678-2679. 10.1093/bioinformatics/bts503.View ArticlePubMedGoogle Scholar
- Wu Z, Irizarry RA, Gentleman R, Martinez-Murillo F, Spencer F: A model-based background adjustment for oligonucleotide expression arrays. J Am Stat Assoc. 2004, 99 (468): 909-917. 10.1198/016214504000000683.View ArticleGoogle Scholar
- Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biol. 2010, 11 (10): R106-10.1186/gb-2010-11-10-r106.PubMed CentralView ArticlePubMedGoogle Scholar
- Storey JD, Tibshirani R: Statistical significance for genomewide studies. Proc Natl Acad Sci. 2003, 100 (16): 9440-9445. 10.1073/pnas.1530509100.PubMed CentralView ArticlePubMedGoogle Scholar
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