From SNP co-association to RNA co-expression: Novel insights into gene networks for intramuscular fatty acid composition in porcine
© Ramayo-Caldas et al.; licensee BioMed Central Ltd. 2014
Received: 19 July 2013
Accepted: 21 March 2014
Published: 26 March 2014
Fatty acids (FA) play a critical role in energy homeostasis and metabolic diseases; in the context of livestock species, their profile also impacts on meat quality for healthy human consumption. Molecular pathways controlling lipid metabolism are highly interconnected and are not fully understood. Elucidating these molecular processes will aid technological development towards improvement of pork meat quality and increased knowledge of FA metabolism, underpinning metabolic diseases in humans.
The results from genome-wide association studies (GWAS) across 15 phenotypes were subjected to an Association Weight Matrix (AWM) approach to predict a network of 1,096 genes related to intramuscular FA composition in pigs. To identify the key regulators of FA metabolism, we focused on the minimal set of transcription factors (TF) that the explored the majority of the network topology. Pathway and network analyses pointed towards a trio of TF as key regulators of FA metabolism: NCOA2, FHL2 and EP300. Promoter sequence analyses confirmed that these TF have binding sites for some well-know regulators of lipid and carbohydrate metabolism. For the first time in a non-model species, some of the co-associations observed at the genetic level were validated through co-expression at the transcriptomic level based on real-time PCR of 40 genes in adipose tissue, and a further 55 genes in liver. In particular, liver expression of NCOA2 and EP300 differed between pig breeds (Iberian and Landrace) extreme in terms of fat deposition. Highly clustered co-expression networks in both liver and adipose tissues were observed. EP300 and NCOA2 showed centrality parameters above average in the both networks. Over all genes, co-expression analyses confirmed 28.9% of the AWM predicted gene-gene interactions in liver and 33.0% in adipose tissue. The magnitude of this validation varied across genes, with up to 60.8% of the connections of NCOA2 in adipose tissue being validated via co-expression.
Our results recapitulate the known transcriptional regulation of FA metabolism, predict gene interactions that can be experimentally validated, and suggest that genetic variants mapped to EP300, FHL2, and NCOA2 modulate lipid metabolism and control energy homeostasis in pigs.
KeywordsPig Fatty acid Gene network Transcription factor Co-association Co-expression
Fatty acids (FA) are a major energy source and important constituents of cell membranes, playing a relevant role as cellular signaling molecules in various metabolic pathways, including metabolic diseases . Environmental and genetic effects determining FA composition in pigs have been the subject of many studies. Supporting a genetic influence on FA composition moderate to high heritability estimates have been reported [2, 3]. However, the molecular process controlling FA composition and metabolism is far from being fully understood. Technological, nutritional and organoleptic properties of pork meat quality are highly dependent on lipid content and FA composition [4–6]. Thus, elucidating this molecular process could aid improve meat quality for healthy human consumption and increase knowledge of FA metabolism, underpinning metabolic diseases. Pigs are important models for metabolic diseases such as obesity, type II diabetes (T2D) and atherosclerosis [7–10].
Molecular pathways controlling lipid metabolism are highly interconnected. Also, they interact with other related pathways, such as carbohydrate metabolism and energy homeostasis pathways. Together, these pathways and its interactions constitute an essential metabolic network for homeostatic control and normal organism development . In this context, a system biology approach focused on the connections and functional interactions between genes that underpin these metabolic pathways is an attractive alternative to the classical “single-gene-single-trait” approach found in most genome-wide association studies (GWAS) using single nucleotide polymorphisms (SNP).
The main goal of this study was to employ a previously described system biology approach termed Association Weight Matrix (AWM)  and, based on a SNP-to-SNP co-association evidence, infer a gene network for intramuscular (IMF) FA composition in pigs. This multi-trait approach was applied to data from 15 phenotypes related to FA composition and metabolism from an Iberian x Landrace intercross. Iberian pigs are a local Mediterranean breed extreme for obesity and appetite , whereas Landrace is a lean international breed. The analysis of the predicted gene network revealed key transcription factors that are network hubs and would be critical to determining meat quality, FA composition and controlling energy homeostasis. Finally, we experimentally validated some of the AWM network predictions using real-time PCR gene co-expression analyses in adipose and liver tissues.
Description of the ten most connected nodes in the co-association network
Illumina Chip SNP
Splice region variant
Gene ontology (GO) and pathway enrichment analyses were performed to gain insight into the predicted gene network. Overrepresented GO terms in the network included: “Cellular component organization” (P = 4.02 × 10-6, FDR = 3.95 × 10-2), “Cellular component organization or biogenesis” (P = 7.34 × 10-6, FDR = 3.6 × 10-2), “Cell projection morphogenesis” (P = 9.59 × 10-5, FDR = 9.42 × 10-2), “Fatty acid metabolic process” (P = 5.89 × 10-4, FDR = 1.03 × 10-2), “Glycerolipid metabolic process” (P = 1.2371 × 10-3, FDR = 1.66 × 10-2), “Sphingolipid metabolic process” (P = 7.45 × 10-4, FDR = 1.16 × 10-2) and “Unsaturated fatty acid biosynthetic process” (P = 2.13 × 10-3, FDR = 2.27 × 10-2). Additional file 2: Table S2 provides the full list of overrepresented GO terms. Pathway analyses revealed an enrichment for “Regulation of actin cytoskeleton (hsa04810)”, “Focal adhesion (hsa04510)”, “Pathways in cancer (hsa05200)”, “Chemokine signalling pathway (hsa04062)”, “Phosphatidylinositol signalling system (hsa04070)” and “Inositol phosphate metabolism (hsa00562)” (Additional file 3: Table S3).
To identify potential regulators of the above-mentioned pathways and GO categories, we focused on TF found in the gene network. We applied an information lossless approach that explored the 64,824 possible trios among the available 74 TF (see Methods and Additional file 4: Table S4 for complete list of TF) and identified the TF trio that spanned most of the network topology with minimum redundancy. These three TF were: Nuclear receptor coactivator 2 (NCOA2, alias TIF2), E1A binding protein p300 (EP300, alias p300) and four and a half LIM domains 2 (FHL2, alias SLIM-3). Interestingly, the promoter region of these TF contain binding sites for some well-known TF that are considered as important regulators of lipid and carbohydrate metabolism such as: SREBP-1, PPARG, PPAR-α, HNF1A, HNF4-α, ER-α and GR-α. In the predicted network, a total of 730 genes show co-association with the three key TF (Figure 2B). A detailed examination of the most representative pathways related to these 730 predicted target genes showed a significant overrepresentation for “HIF-1 signaling pathway (hsa04066)”, “Acute myeloid leukemia (hsa05221)”, “Colorectal cancer (hsa05210)”, “Renal cell carcinoma (hsa05211)” and “Type II diabetes mellitus (hsa04930)” (Additional file 5: Figure S1). Admittedly, some of the above-mentioned GO terms and pathways could have been expected from a network predicted from GWAS of FA-related phenotypes and this gives confidence in the reliability of the results. Others, however, were unexpected and might lead to new insights on FA physiology.
Experimental validation: From co-association to co-expression analysis in liver and adipose tissues
The expression patterns of 43 genes in liver and 40 genes in adipose tissue were successfully measured across 55 backcross animals. In liver, the expression data of twelve additional genes were also included in the co-expression analysis (see Methods). Co-expression analysis revealed highly connected networks in both liver and adipose tissue, suggesting strong functional interconnections among the studied genes. Topology of liver co-expression network showed 55 nodes connected by 425 edges (Additional file 6: Figure S2A) and in adipose tissue 40 nodes and 261 edges were observed (Additional file 6: Figure S2B). Network parameters such as average degree (Deg) and average distance (AvDG) were slightly higher in liver co-expression network compared to adipose tissue network (DegLiver = 15.45 AvDG = 1.81 vs DegAdipose = 13.05 and AvDG = 1.75). Based on network centrality, the relevance of individual genes differs within each network. For example, topological properties of the liver co-expression network suggest an important role for ARNT in the regulation of hepatic lipogenic and glucoconeogenesis activity, and these findings agree with published results [16, 17]. It should be noted that BCL9 showed the highest centrality value in the liver co-expression network (Additional file 6: Figure S2A). In addition, degree analysis showed that BCL9, EP300, PBX1, SIRT1, PIP5K1A and ARNT were the most central genes in the liver co-expression network. However, in the adipose co-expression network, degree analysis suggested that ANK2, NCOA2, SIRT1, EIF4E, HMBOX1 are the most central genes (Additional file 6: Figure S2B). When analysing a sub-network of the liver co-expression network, formed only by the same 40 genes included in the adipose co-expression network, five genes (BCL9, EP300, PBX1, PIP5K1A, and SIRT1) were still the most central genes and this finding underscores their relevant role in the function and structure of the liver co-expression network.
Concordance validation between the co-association and the co-expression networks for the best TF trio and in adipose and liver tissues
Connections in the AWM co-association networkA
Connections in the qPCR co-expression networkA
Molecular processes controlling FA metabolism are highly interconnected and linked with related pathways, such as lipid, carbohydrate and energy metabolism. In fact, FA are a major energy source and together with several factors, such as total energy intake, dietary fat/carbohydrate ratio, or glucose and/or insulin concentration, regulate de novo lipogenesis [19, 20]. As a consequence, it is expected that at the selected threshold (P < 0.035) our best trio of TF (NCOA2, EP300, FHL2) show co-association with a large number of genes and other TF relevant for lipid, carbohydrate and energy metabolism. For instance, 39 of the predicted target genes via SNP co-association (Additional file 8: Table S6) have been recently reported in two large-scale meta-analysis studies for plasma lipids in humans [21, 22]. Interestingly, many of these genes, including our TF trio and other FA relevant genes, would have been missed by traditional single-trait GWAS due to the lack of an acceptably significant association level (i.e. P > 0.05 after correction for multiple testing). As noted before  and confirmed by this study, AWM points to new candidate genes, TF and gene interactions via exploring SNP co-associations across multiple traits beyond the one-dimensional approach for identifying genes affecting single traits. However, results should be interpreted with caution due to the limited sample size used in our study (144 pigs), which reduces the power to identify small effects and may introduce spurious results. Therefore, these TF might regulate other important genes for IMF FA composition not represented in this network and false positive results may be included in the network. However, only the SNPs associated with a large number of phenotypes were included in the AWM analysis and, due the multi-trait nature of the AWM methodology, the probability that the same SNP was associated with several phenotypes by chance is much lower than the probability of being associated with a single phenotype.
In the predicted network, NCOA2, a key TF regulating energy homeostasis [20, 23] and adipogenesis , showed co-association with a total of 326 genes, including relevant TF and genes associated with lipid and carbohydrate metabolisms, such as PROX1, PBX1, ARNT, MYB, MTF2, TCF7L1, SCD5, ABCC2, INSIG1, ACACB, FABP4, FABP3, ME1, AASDH, ABCC5 and SORT1. A role for PROX1 in the control of energy homeostasis has been proposed . Moreover, association of SNPs mapped to PROX1 and SLC30A8 with fasting glucose levels and increased risk for T2D has been reported in humans . Both PROX1 and SLC30A8, together with other T2D risk loci (IL6R, TCF7L2, HNF1A) and 21 genes reported as associated with plasma lipids in humans  were predicted as target genes of NCOA2 in our study. Co-expression analysis in adipose tissue validated 60.8% of the NCOA2 co-association target genes, including INSIG1 (rco-expression = 0.68), FDFT1 (rco-expression = 0.70), SETD2 (rco-expression = 0.59) and ABCC5 (rco-expression = 0.65). In liver, 34.6% of the predicted targets of NCOA2 were validated, including the above-mentioned PROX1 (rco-expression = 0.48), HNF1A (rco-expression = 0.56) and TCF7L2 (rco-expression = 0.50). It should be noted that previous studies in pigs show a correlation between NCOA2 expression (r = 0.605, P < 0.01) and IMF content of LD muscle . Also, NCOA2 was reported as modulating an AWM-network predicted for puberty in cattle , which included fat deposition measurements as traits related to puberty. Furthermore, knockout NCOA2-/- mice are protected against obesity, showing lean phenotype and decreased expression of genes involved in the uptake and storage of FA . A decreased expression of genes required for FA synthesis in liver tissue of NCOA2-/- mice was observed . In agreement with these previous results and the phenotypic difference in fat deposition between Iberian and Landrace breeds, a significant higher activity of NCOA2 in the liver of Iberian pigs was detected (FC = 1.56, P < 0.01) relative to Landrace pigs (Figure 3).
Another TF predicted as critical for FA regulation was EP300, which encodes the adenovirus E1A-associated cellular p300 transcriptional co-activator protein. It functions as histone acetyltransferase that regulates transcription by chromatin remodelling. Via histone acetyltransferase activity, EP300 regulates the transcription of liver X receptor (LXR) . EP300 is also required for adipocyte differentiation through the regulation of peroxisome proliferator-activated receptor gamma (PPARG) . Remarkably, EP300 has been reported as transcriptional co-activator of estrogen receptor (ER), hepatocyte nuclear factor 4 α (HNF4-α), aryl hydrocarbon receptor nuclear translocator (ARNT) and hepatocyte Nuclear Factor-1 α (HNF1A) [31–33]. All these above-mentioned TF co-regulated by EP300 (PPARG, LXR, HNF4, HNF1A, ER, ARNT) influence lipid and carbohydrate metabolisms and have been extensively studied in this context [17, 34–41]. Among the 180 AWM-predicted target genes for EP300, there are 30 genes known to be involved in lipid metabolism including ARNT a member of the HIF-1 pathway. ARNT is a relevant TF regulating hepatic gluconeogenesis and lipogenic gene expression . Interestingly, we observed a significant co-expression between ARNT and EP300 (r = 0.61) in the liver network. Additionally, other genes related to carbohydrate and lipid metabolism were predicted as EP300 AWM-target genes. These included: ADCY2, MMP9, ECHS1, ARRB1, EIF4E, ANK2, NR2E1, SLC2A6, SLC5A2, LEP, ELOVL6, MTTP, ACSM5, UCP2 and CYP2E1 (for a full list see Additional file 9: Table S7). Similarly to NCOA2, a significant higher expression of EP300 in the liver of Iberian pigs was detected (FC = 1.23, P < 0.05) in comparison with Landrace pigs (Figure 3). Our results, predicting targets for EP300 and studying their co-expression contributes to the knowledge on lipid and carbohydrate metabolism. It is well known that TF require co-regulators to modify and epigenetically remodel chromatin structure to facilitate the basal transcriptional machinery. EP300 is a chromatin remodeling gene opening new possibilities to study the roll of epigenetic modifications in the regulation of pork meat quality and the molecular control of energy homeostasis.
The third key TF was FHL2, an evolutionarily conserved gene that can interact with an important range of proteins from different functional classes, including receptors, signal transducers, TF and cofactors . FHL2 plays an important role as molecular transmitter linking various signalling pathways to transcriptional regulation. For instance, FHL2 is involved in the co-activation of human androgen receptor (AR), ER and peroxisome proliferator-activated receptor alpha (PPARα) [42–44]. In addition, FHL2 mediates interaction with β-catenin and promotes myoblast C2C12 differentiation in mice . The gene B-cell CLL/lymphoma 9 (BCL9), an activator of the Wnt/β-catenin  and Wingless-type MMTV integration site family, member 4 (WNT4) was among the 251 targets predicted for FHL2 in our network. The growth factor WNT4 is a member of the Wnt signaling pathway involved in developmental processes and relevant for gonad development and sex-determination . Liver expression analyses provided supporting evidence for the predicted interaction between FHL2 and WNT4, as a significant co-expression (r = 0.44, P < 0.001) was observed. Other genes and TF associated with development process, lipid and carbohydrate metabolism, such as FHL5, MYO1E, MYB, RORC, JARID2, ZFHX4, WNK1, LIPC, CREB5, CDC42, ACSL1, FABP5, ABCB11, FLT1 and HTR2A were also predicted as targets of FHL2 according to the co-association network. FHL2 was not differentially expressed in the comparison between Iberian and Landrace pigs. Also, FHL2 showed a proportion of validated interactions in the co-expression analysis (20% adipose tissues and 14.3% in liver) lower than for the other two TF, NCOA2 and EP300. These somewhat less promising results could be a consequence of the tissue-specific activity of FHL2, as it has been reported for the co-activation of AR.
Although, some gene to gene interactions predicted by the AWM approach were not corroborated by the co-expression analysis, the possibility of these interactions occurring in other spatial temporal and/or tissues cannot be ruled out, or indeed manifesting their joint effect through other means than co-expression. TF and their target genes interact in a temporal and tissue dependent manner, so the examination of networks spanning multiple tissues is critical to highlight interactions that could otherwise be unknown from individual tissue analysis . In spite of this tissue/time limitation, two of the three TF from the best trio (EP300 and NCOA2) showed higher than average centrality values in both liver and adipose tissue co-expression networks. Moreover, we observed a significant co-expression between NCOA2 and EP300 in the liver network with some other TF considered master regulators of the lipid metabolism. For instance, NCOA2 was significantly co-expressed with PPARα (r = 0.39, P < 0.01), HNF1A (r = 0.56, P < 0.001) and HNF4α (r = 0.36, P < 0.01), and EP300 was co-expressed with PPARD (r = 0.38, P < 0.01) and HNF1A (r = 0.64, P < 0.001) (Additional file 6: Figure S2 A, B). The liver plays a central role in maintaining overall energy balance by controlling lipid and carbohydrate metabolism. In pigs, the liver is the primary site of de novo cholesterol synthesis and fatty acid oxidation and, together with adipose tissue, has a crucial role in regulating lipid metabolism [49, 50]. All these observations, together with the higher expression of NCOA2 and EP300 observed in the liver of the Iberian pigs compared with Landrace pigs, suggest a relevant role of these genes in the hepatic transcriptional regulation of lipid metabolism in pigs.
Overall, our GWAS and network predictions, supported by literature and co-expression analysis in liver and adipose tissue, suggest a co-operative role for the three TF (NCOA2, EP300, FHL2) in the transcriptional regulation of IMF, FA composition and the control of energy homeostasis in pigs. We hypothesize that these TF mediate a highly inter-connected regulatory cascade including pathways such as HIF-1, AR, ER and Wnt/β-catenin that seem pivotal for lipid metabolism. The role of these pathways in the transcriptional regulation of lipid metabolism is a subject of intense studies [17, 38, 39, 51–54]. A functional cooperation between the three TF in the modulation of these pathways is evident from our results and supported by literature evidence. For example, according to String database [55, 56] (http://string-db.org/), experimental data confirmed that protein-protein interaction exists among, EP300, NCOA2, FHL2, AR and ESR1 (Additional file 10: Figure S3). In addition, EP300 and NCOA2 take part on the AR and ER pathways and both, NCOA2 and FHL2 are AR co-regulators [43, 57, 58]. Studying the combined effect of NCOA2, EP300, and FHL2 in the regulation of specific genes will lead to new knowledge related to FA pathways.
The most overrepresented pathway corresponding to the 730 AWM-predicted target genes of the three TF was HIF-1 (Additional file 5: Figure S1). The HIF-1 pathway is central to adaptive regulation of cellular energy metabolism; by regulating the expression of glycolytic enzymes and hepatic lipid metabolism [17, 54, 59, 60]. Our liver co-expression analysis supports previously reported evidence  for the relevance of ARNT gene (member of HIF pathway) in the hepatic lipogenic gene expression. Additionally, HIF-1α, which is another member of HIF pathway and β-catenin co-ordinately enhance AR transactivation. The interaction between β-catenin and both HIF-1 and AR pathways has been documented [61–63]. Moreover, β-catenin is a ligand-dependent co-activator of AR and a functional cooperation in the synergistic activation of AR-mediated transcription among EP300, FHL2 and β-catenin have been reported . Ours results showing the interactions between the three key TF, recapitulate these pathways interactions that are known mammalian biology, extending its significance to pigs.
In summary, our results suggest that common genetic variants mapped to (or in linkage disequilibrium with) EP300, FHL2 and NCOA2 together with other candidate genes including ARNT, BCL9, SIRT1, PBX1, PROX1, HNF1A, SLC30A8, TCF7L2 and ANK2 modulate lipid metabolism and control energy homeostasis in pigs. Furthermore, epistatic predicted interactions between TF and their target genes are likely to contribute to the complex inheritance of FA composition and related polygenic traits (lipid metabolism and energy homeostasis). It is generally accepted that metabolic diseases such as obesity and T2D are linked to disturbance of energy homeostasis or homeostatic imbalance. It should be noted that among the 730 predicted target genes, an overrepresentation of genes from the T2D pathway was observed (Additional file 5: Figure S1). Also, 39 of the 730 genes are known to control plasma lipid content in humans [21, 22].
Further studies will be required to elucidate the specific cellular and molecular processes of interaction among the three TF and its target genes that determine FA composition and control energy homeostasis in pigs. The implications of research in this area are broad, ranging from applications from pork meat quality to modeling mammal biology.
Phenotypic traits, animals and genotypes
Data from 144 pigs (25% Iberian × 75% Landrace), representing 26 full-sib families, from backcrossing five F1 males with 26 Landrace sows was utilized. Details about the management conditions and the phenotype information have been previously reported [65–67]. For this study and based on an previous principal components analysis  we selected 15 of the total 48 traits representing the most informative phenotypes within the dataset. Nine of the 15 traits were related to IMF fatty acid (FA) composition in LD muscle, seven correspond to indices of FA metabolism and the last one is the IMF percentage (Additional file 11: Table S8). The Porcine SNP60K BeadChip (Illumina)  was used to genotype a total 197 pigs, including the 144 phenotyped animals and the founder population. Quality control excluded SNPs with minor allele frequency < 5% and with call rate < 95%. A subset of 48,119 SNPs were retained for subsequent analysis, in addition, previously detected polymorphisms in the MTTP, FABP4, FABP5, and ELOVL6 genes were also tested [67, 69, 70]. The genomic coordinates of the SNP correspond to the Sus scrofa genome sequence assembly (Sscrofa10.2, August 2011)  and were annotated using as reference the pig assembly 10.2 [ftp://ftp.ncbi.nlm.nih.gov/genomes/Sus_scrofa/GFF/].
Animal care and procedures were performed following national and institutional guidelines for the Good Experimental Practices and approved by the Ethical Committee of the Institution (IRTA- Institut de Recerca i Tecnologia Agroalimentàries).
where: yij represents the vector of observations from the ith pig at the jth trait ; X is the incidence matrix relating fixed effects in ß with observation in yij; Z is the incidence matrix relating random additive polygenic effects in u with observation in yij; sj,k represents the additive association of the kth SNP on the jth trait and eij is the vector of random residual effects. Fixed effects included in ß were, sex (two levels), batch (five levels) and carcass weight as covariate. Polygenic effects were treated as random and distributed as N(0, Aσu) where A is a numerator of kinship matrix. Then, the allele substitution effect of the ith SNP on the jth trait was z-score standardized and employed to constructing the AWM .
An R script, available from the authors, was written to automate the process of building an AWM. Palmitoleic acid (C16:1 (n-7)) was used as the key phenotype and the procedure described by Fortes and colleagues  was followed, but we introduced a few modifications, specifically regarding the P-value threshold for selecting SNP from GWAS. The P-value threshold was chosen by exploring the sensitivity of the data instead of simply accepting the nominal P < 0.05. We took advantage of the biological knowledge concerning TF related to the analyzed traits and used it as a priori information.
In essence, instead of applying a hard-coded nominal P-value of, for instance, 0.05 or 0.01 or 0.001, we employed a knowledge-based approach to identify the P-value threshold at which the information content, in terms of fatty acid regulation, is maximized. To this effect, we mined the literature and relevant databases to compile a list of 340 TF of which 34 were known to be related to FA metabolism. The distribution of these 34 TF relative to the entire set of 340 was explored at various P-value thresholds. The P-value threshold that maximized the number of FA-related TF was used as the optimal P-value to apply when developing the Association Weight Matrix. Quite importantly, others in the past have employed a knowledge-based approach to identify the critical P-value threshold. More recently, and in the context of GWAS, Yang et al.  used an approach similar to ours to find the P-value that maximise the correlation between the proportion of significant SNPs and the heritability across 47 traits.
Step1: A total of 340 TF were located within 2.5 Kb of a SNP and therefore included in the initial dataset. For all these TF included in our dataset, those that are well known key regulators of the lipid metabolism were initially selected.
Step2: For each gene, those involved in the lipid metabolism and also reported in the census of human TF by Vaqueriza et al.  were included.
Step3: The Human Protein Reference Database (HPRD) and the Biomolecular Object Network Databank (BIND) were mined. Then other TF that have been reported to interact with some of the TF retained in the two previous steps were selected. After these first 3 steps, a total of 34 TF were retained (Additional file 12: Table S9).
Step4: Subsequently we compare the distribution of the 34 TF at different P-values from P = 1 to P = 10-4versus the distribution of the total number of TF included in the AWM (340). As a result, we have chosen P < 0.035 as the threshold. This specific P-value maximizes the difference between both groups of TF (Figure 5), imposing an informed bias towards lipid metabolism to the network.
After defining the threshold of P < 0.035, the selection of SNPs for building AWM continued. Those SNPs that were either associated (P < 0.035) with palmitoleic acid or with any ≥ 3 traits, and were located either ≤ 2,500 bp to or ≥ 850 kb from the nearest annotated gene (Sscrofa10.2 assembly), were selected to build the AWM matrix. PermutMatrix software  was employed to visualize hierarchical clustering of traits (AWM columns) and genes (AWM rows) using Euclidean distance and the Average linkage method. To identify and report gene-gene or gene-SNP interactions we used PCIT algorithm . Cytoscape software  was used to visualize the gene network and also to perform overrepresented GO terms analysis, using BiNGO plugin . Node centrality values and network topological parameters were calculated using CentiScaPe plugin . Pathway enrichment analysis were performed using FATIGO tool form BABELOMICS [81, 82]. Further, pathways analyses of the 730 predicted target genes (co-associated with the key TF) were performed using ClueGO, Cytoscape plugin . Pathway information was retrieved from the KEGG (http://www.genome.jp/kegg/) and BioCarta (http://www.biocarta.com/) databases. In all cases, the cut-off for considering a significance overrepresentation was established by Benjamini & Hochberg multiple testing correction of the P-value (FDR < 0.05) .
Expression and co-expression analysis
In order to provide supporting evidence for the in-silico AWM-network predictions we obtained and explored gene expression data by reverse transcription quantitative Real-Time PCR (RT-qPCR). The expression pattern of the 3 Key TF (NCOA2, EP300, FHL2) in LD muscle, liver and adipose tissues was tested in two phenotypically divergent breeds for fat deposition traits (Iberian and Landrace which are also the founders of our studied population, five animals per breed). Finally, liver and adipose co-expression analyses of 55 (43 from the present study, and twelve: ACSM5, APOA2, ARNT, CYP7A1, FABP5, FADS3, HNF4a, LIPC, MTTP, PPARA, PPARD and ELOVL6 genes from Ballester et al., 2013 submitted) and 40 genes, respectively, were performed using the PCIT algorithm  in 55 backcross animals. Since sex differences in liver transcriptome have been reported  only females were considered in the co-expression analyses of both tissues.
From the 55 genes explored in the liver co-expression analysis, 48 were present in the AWM network. The remaining seven were incorporated due to their biological relevance, including three well-know TF related to lipid metabolism (PPARα, PPARD, HNF4α) and four genes related to lipid metabolism (SIRT1, FADS3, APOA2, CYP7A1). Similarly, from the 40 genes employed in the adipose co-expression analysis, 39 were present in the AWM network. The one gene out, SIRT, was also included due to its relevant controlling lipolysis [86, 87] and promoting fat mobilization in white adipose tissue .
Total RNA was obtained from liver, muscle and adipose tissues using the RiboPure kit (Ambion), following the manufacturer’s recommendations. RNA was quantified using the NanoDrop ND-1000 spectrophotometer (NanoDrop products) and the RNA integrity was assessed by Agilent Bioanalyzer-2100 (Agilent Technologies). Approximately, one microgram of total RNA was reverse-transcribed into cDNA using the High-Capacity cDNA Reverse Transcription kit (Applied Biosystems) in 20 μl of reactions, following the manufacturer’s instructions.
To analyze the expression pattern of the 3 key transcription factors, an ABI PRISM 7900 Sequence Detection System (Applied Biosystems) in combination with FastStart Universal Sybr green master (Rox; Roche Applied Science) was used. PCR amplifications were performed in a total reaction volume of 20 μl containing 5 μl of cDNA diluted 1:25. All primers were used at 300 nM. The thermal cycle was 10 min at 95°C, 40 cycles of 15 s at 95°C and 1 min at 60°C. A dissociation curve was drawn for each primer pair to assess the specificity of the amplification. Three reference genes (ACTB, HPRT1, TBP) frequently used in RT-qPCR experiments were tested as endogenous controls. Using the GeNorm software , the ACTB and TBP genes were selected as the best endogenous controls for all tissues. After ensuring the possibility to use the 2-ΔΔCT method , data was analyzed using the RQ manager v1.2.1 and the DataAssist™v3.0 softwares (Applied Biosystems). The 2-ΔCT values were used to compare our data.
The 48.48 microfluidic dynamic array IFC chip (Fluidigm) was used to analyze the expression of 48 genes (44 target genes and 4 reference genes) in liver and adipose tissue of 55 backcross animals belonging to the same population in which the GWAS was performed. Two μl of 1:5 diluted cDNA was pre-amplified using 2X Taqman PreAmp Master Mix (Applied Biosystems) and 50 nM of each primer pair in 5 μl reaction volume, according to the manufacturer’s directions. The cycling program was 10 min at 95°C followed by 16 cycles of 15 s at 95°C and 4 min at 60°C. At the end of this pre-amplification step, the reactions were diluted 1:5 (diluted pre-amplification samples). RT-qPCR on the dynamic array chips was conducted on the BioMark™ system (Fluidigm). Five μl sample pre-mix containing 2.5 μl of SsoFast EvaGreen Supermix with Low ROX (Bio-Rad), 0.25 μl of DNA Binding Dye Sample Loading Reagent (Fluidigm) and 2.25 μl of diluted pre-amplification samples (1:16 or 1:64 from the diluted pre-amplification samples from liver and backfat, respectively), as well as 5 μl assay mix containing 2.5 μl of Assay Loading Reagent (Fluidigm), 2.25 μl of DNA Suspension Buffer (Teknova) and 0.25 μl of 100 μM primer pairs (500 nM in the final reaction) were mixed inside the chip using the IFC controller MX (Fluidigm). The thermal cycle was 60s at 95°C followed by 30 cycles of 5 s at 96°C and 20s at 60°C. A dissociation curve was also drawn for each primer pair.
Data was collected using the Fluidigm Real-Time PCR analysis software 3.0.2 (Fluidigm) and analyzed using the DAG expression software 22.214.171.124  applying the relative standard curve method (see Applied Biosystems user bulletin #2). Standard curves with a four-fold dilutions series (1/4, 1/16, 1/64, 1/256, 1/1024) of a pool of 10 cDNA samples were constructed for each gene to extrapolate the quantity values of the studied samples. The PCR efficiencies were almost 100% in both tissues for all the assays (Additional file 13: Table S10) with low coefficients of inter-assay variation of threshold cycle (<2.4% in liver and <3.5% in adipose tissue). Of the four endogenous genes tested (ACTB, B2M, HPRT1, TBP), ACTB and TBP were the genes with the most stable expression  in both tissues. The normalized quantity values of each sample and assay were used to compare our data.
All the primers used in this study were designed using PrimerExpress 2.0 software (Applied Biosystems) and are shown in Additional file 13: Table S10. Prior to perform the Fluidigm Real-Time PCR, all the assays were tested for PCR specificity in an ABI PRISM 7900 Sequence Detection System (Applied Biosystems) using two-fold dilutions (1/20, 1/200) of a pool of ten cDNA samples and a minus RT control to check the presence of DNA contamination. Melting curve analysis was performed for all the assays.
The relevant information and full data sets are included as additional files.
This work was funded by MICINN project AGL2011-29821-C02 (Ministerio de Economía y Competitividad), and by the Innovation Consolider-Ingenio 2010 Program (CSD2007-00036, Centre for Research in Agrigenomics). We thank Dr. Nick Hudson for insightful suggestions and manuscript proofread. Y. Ramayo-Caldas was funded by a FPU PhD grant from the Spanish Ministerio de Educación (AP2008-01450).
- Wakil SJ, Abu-Elheiga LA: Fatty acid metabolism: target for metabolic syndrome. J Lipid Res. 2009, 50 (Supplement): S138-S143.PubMed CentralPubMedView ArticleGoogle Scholar
- Casellas J, Noguera JL, Reixach J, Diaz 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.PubMedView ArticleGoogle 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.PubMedView ArticleGoogle Scholar
- Wood JD, Enser M: Factors influencing fatty acids in meat and the role of antioxidants in improving meat quality. Br J Nutr. 1997, 78 (Suppl 1): S49-S60.PubMedView ArticleGoogle Scholar
- Wood JD, Enser M, Fisher AV, Nute GR, Richardson RI, Sheard PR: Manipulating meat quality and composition. Proc Nutr Soc. 1999, 58 (2): 363-370. 10.1017/S0029665199000488.PubMedView ArticleGoogle 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.PubMedView ArticleGoogle Scholar
- Ekser B, Ezzelarab M, Hara H, van der Windt DJ, Wijkstrom M, Bottino R, Trucco M, Cooper DK: Clinical xenotransplantation: the next medical revolution?. Lancet. 2012, 379 (9816): 672-683. 10.1016/S0140-6736(11)61091-X.PubMedView ArticleGoogle Scholar
- Seki Y, Williams L, Vuguin PM, Charron MJ: Minireview: Epigenetic programming of diabetes and obesity: animal models. Endocrinology. 2012, 153 (3): 1031-1038. 10.1210/en.2011-1805.PubMed CentralPubMedView ArticleGoogle Scholar
- Bendixen E, Danielsen M, Larsen K, Bendixen C: Advances in porcine genomics and proteomics–a toolbox for developing the pig as a model organism for molecular biomedical research. Brief Funct Genomics. 2010, 9 (3): 208-219. 10.1093/bfgp/elq004.PubMedView ArticleGoogle Scholar
- Houpt K, Houpt T, Pond W: The pig as a model for the study of obesity and of control of food intake: a review. Yale J Biol Med. 1979, 52 (3): 307-329.PubMed CentralPubMedGoogle Scholar
- Hardie DG: Organismal carbohydrate and lipid homeostasis. Cold Spring Harb Perspect Biol. 2012, 4 (5): doi:10.1101/cshperspect.a006031Google Scholar
- Fortes MR, Reverter A, Zhang Y, Collis E, Nagaraj SH, Jonsson NN, Prayaga KC, Barris W, Hawken RJ: Association weight matrix for the genetic dissection of puberty in beef cattle. Proc Natl Acad Sci U S A. 2010, 107 (31): 13642-13647. 10.1073/pnas.1002044107.PubMed CentralPubMedView ArticleGoogle Scholar
- Serra X, Gil F, Perez-Enciso M, Oliver MA, Vazquez JM, Gispert M, Diaz I, Moreno F, Latorre R, Noguera JL: A comparison of carcass, meat quality and histochemical characteristics of Iberian (Guadyerbas line) and Landrace pigs. Livestock Prod Sci. 1998, 56 (3): 215-223. 10.1016/S0301-6226(98)00151-1.View ArticleGoogle Scholar
- Reverter A, Chan EK: Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics. 2008, 24 (21): 2491-2497. 10.1093/bioinformatics/btn482.PubMedView ArticleGoogle Scholar
- Ravasi T, Suzuki H, Cannistraci CV, Katayama S, Bajic VB, Tan K, Akalin A, Schmeier S, Kanamori-Katayama M, Bertin N, Carninci P, Daub CO, Forrest AR, Gough J, Grimmond S, Han JH, Hashimoto T, Hide W, Hofmann O, Kamburov A, Kaur M, Kawaji H, Kubosaki A, Lassmann T, van Nimwegen E, MacPherson CR, Ogawa C, Radovanovic A, Schwartz A, Teasdale RD, et al: An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man. Cell. 2010, 140 (5): 744-752. 10.1016/j.cell.2010.01.044.PubMedView ArticleGoogle Scholar
- Wang XL, Suzuki R, Lee K, Tran T, Gunton JE, Saha AK, Patti M-E, Goldfine A, Ruderman NB, Gonzalez FJ, Kahn CR: Ablation of ARNT/HIF1β in liver alters gluconeogenesis, lipogenic gene expression, and serum ketones. Cell Metab. 2009, 9 (5): 428-439. 10.1016/j.cmet.2009.04.001.PubMed CentralPubMedView ArticleGoogle Scholar
- Rankin EB, Rha J, Selak MA, Unger TL, Keith B, Liu Q, Haase VH: Hypoxia-inducible factor 2 regulates hepatic lipid metabolism. Mol Cell Biol. 2009, 29 (16): 4527-4538. 10.1128/MCB.00200-09.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang L, Zheng W, Zhao H, Deng M: Statistical analysis reveals co-expression patterns of many pairs of genes in yeast are jointly regulated by interacting Loci. PLoS Genet. 2013, 9 (3): e1003414-10.1371/journal.pgen.1003414.PubMed CentralPubMedView ArticleGoogle Scholar
- Parks EJ, Krauss RM, Christiansen MP, Neese RA, Hellerstein MK: Effects of a low-fat, high-carbohydrate diet on VLDL-triglyceride assembly, production, and clearance. J Clin Invest. 1999, 104 (8): 1087-1096. 10.1172/JCI6572.PubMed CentralPubMedView ArticleGoogle Scholar
- Picard F, Gehin M, Annicotte J-S, Rocchi S, Champy M-F, O'Malley BW, Chambon P, Auwerx J: SRC-1 and TIF2 control energy balance between white and brown adipose tissues. Cell. 2002, 111 (7): 931-941. 10.1016/S0092-8674(02)01169-8.PubMedView ArticleGoogle Scholar
- Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, Pirruccello JP, Ripatti S, Chasman DI, Willer CJ, Johansen CT, Fouchier SW, Isaacs A, Peloso GM, Barbalic M, Ricketts SL, Bis JC, Aulchenko YS, Thorleifsson G, Feitosa MF, Chambers J, Orho-Melander M, Melander O, Johnson T, Li X, Guo X, Li M, Shin Cho Y, Jin Go M, Jin Kim Y, et al: Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010, 466 (7307): 707-713. 10.1038/nature09270.PubMed CentralPubMedView ArticleGoogle Scholar
- Asselbergs FW, Guo Y, van Iperen EP, Sivapalaratnam S, Tragante V, Lanktree MB, Lange LA, Almoguera B, Appelman YE, Barnard J, Baumert J, Beitelshees AL, Bhangale TR, Chen YD, Gaunt TR, Gong Y, Hopewell JC, Johnson T, Kleber ME, Langaee TY, Li M, Li YR, Liu K, McDonough CW, Meijs MF, Middelberg RP, Musunuru K, Nelson CP, O'Connell JR, Padmanabhan S, et al: Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. Am J Hum Genet. 2012, 91 (5): 823-838. 10.1016/j.ajhg.2012.08.032.PubMed CentralPubMedView ArticleGoogle Scholar
- Duteil D, Chambon C, Ali F, Malivindi R, Zoll J, Kato S, Geny B, Chambon P, Metzger D: The transcriptional coregulators TIF2 and SRC-1 regulate energy homeostasis by modulating mitochondrial respiration in skeletal muscles. Cell Metab. 2010, 12 (5): 496-508. 10.1016/j.cmet.2010.09.016.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang X, Chen J, Liu H, Xu Y, Wang X, Xue C, Yu D, Jiang Z: The pig p160 co-activator family: Full length cDNA cloning, expression and effects on intramuscular fat content in Longissimus Dorsi muscle. Domest Anim Endocrinol. 2008, 35 (2): 208-216. 10.1016/j.domaniend.2008.05.006.PubMedView ArticleGoogle Scholar
- Dufour CR, Levasseur M-P, Pham NHH, Eichner LJ, Wilson BJ, Charest-Marcotte A, Duguay D, Poirier-Héon JF, Cermakian N, Giguère V: Genomic Convergence among ERRα, PROX1, and BMAL1 in the Control of Metabolic Clock Outputs. PLoS Genet. 2011, 7 (6): e1002143-10.1371/journal.pgen.1002143.PubMed CentralPubMedView ArticleGoogle Scholar
- Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, Wheeler E, Glazer NL, Bouatia-Naji N, Gloyn AL, Lindgren CM, Mägi R, Morris AP, Randall J, Johnson T, Elliott P, Rybin D, Thorleifsson G, Steinthorsdottir V, Henneman P, Grallert H, Dehghan A, Hottenga JJ, Franklin CS, Navarro P, Song K, Goel A, Perry JR, Egan JM, Lajunen T, et al: New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010, 42 (2): 105-116. 10.1038/ng.520.PubMed CentralPubMedView ArticleGoogle Scholar
- Fortes MRS, Reverter A, Nagaraj SH, Zhang Y, Jonsson NN, Barris W, Lehnert S, Boe-Hansen GB, Hawken RJ: A single nucleotide polymorphism-derived regulatory gene network underlying puberty in 2 tropical breeds of beef cattle. J Anim Sci. 2011, 89 (6): 1669-1683. 10.2527/jas.2010-3681.PubMedView ArticleGoogle Scholar
- Jeong J-W, Kwak I, Lee KY, White LD, Wang X-P, Brunicardi FC, O’ÄôMalley BW, DeMayo FJ: The genomic analysis of the impact of steroid receptor coactivators ablation on hepatic metabolism. Molecular Endocrinology. 2006, 20 (5): 1138-1152. 10.1210/me.2005-0407.PubMedView ArticleGoogle Scholar
- Huuskonen J, Fielding PE, Fielding CJ: Role of p160 coactivator complex in the activation of liver X receptor. Arterioscler Thromb Vasc Biol. 2004, 24 (4): 703-708. 10.1161/01.ATV.0000121202.72593.da.PubMedView ArticleGoogle Scholar
- Takahashi N, Kawada T, Yamamoto T, Goto T, Taimatsu A, Aoki N, Kawasaki H, Taira K, Yokoyama KK, Kamei Y, Fushiki T: Overexpression and ribozyme-mediated targeting of transcriptional coactivators CREB-binding protein and p300 revealed their indispensable roles in adipocyte differentiation through the regulation of peroxisome proliferator-activated receptor γ. J Biol Chem. 2002, 277 (19): 16906-16912. 10.1074/jbc.M200585200.PubMedView ArticleGoogle Scholar
- Torres-Padilla ME, Weiss MC: Effects of interactions of hepatocyte nuclear factor 4 α isoforms with coactivators and corepressors are promoter-specific. FEBS Lett. 2003, 539 (1-3): 19-23. 10.1016/S0014-5793(03)00174-1.PubMedView ArticleGoogle Scholar
- Chen D, Huang S-M, Stallcup MR: Synergistic, p160 coactivator-dependent enhancement of estrogen receptor function by CARM1 and p300. J Biol Chem. 2000, 275 (52): 40810-40816. 10.1074/jbc.M005459200.PubMedView ArticleGoogle Scholar
- Ban N, Yamada Y, Someya Y, Miyawaki K, Ihara Y, Hosokawa M, Toyokuni S, Tsuda K, Seino Y: Hepatocyte nuclear factor-1 α recruits the transcriptional Co-activator p300 on the GLUT2 gene promoter. Diabetes. 2002, 51 (5): 1409-1418. 10.2337/diabetes.51.5.1409.PubMedView ArticleGoogle Scholar
- Li AC, Glass CK: PPAR- and LXR-dependent pathways controlling lipid metabolism and the development of atherosclerosis. J Lipid Res. 2004, 45 (12): 2161-2173. 10.1194/jlr.R400010-JLR200.PubMedView ArticleGoogle Scholar
- Ulven SM, Dalen KT, Gustafsson J-Ö, Nebb HI: LXR is crucial in lipid metabolism. Prostaglandins Leukot Essent Fatty Acids. 2005, 73 (1): 59-63. 10.1016/j.plefa.2005.04.009.PubMedView ArticleGoogle Scholar
- Palanker L, Tennessen JM, Lam G, Thummel CS: Drosophila HNF4 regulates lipid mobilization and β-oxidation. Cell Metab. 2009, 9 (3): 228-239. 10.1016/j.cmet.2009.01.009.PubMed CentralPubMedView ArticleGoogle Scholar
- Marcil V, Seidman E, Sinnett D, Boudreau F, Gendron F-P, Beaulieu J-F, Menard D, Precourt L-P, Amre D, Levy E: Modification in oxidative stress, inflammation, and lipoprotein assembly in response to hepatocyte nuclear factor 4Œ ± knockdown in intestinal epithelial cells. J Biol Chem. 2010, 285 (52): 40448-40460. 10.1074/jbc.M110.155358.PubMed CentralPubMedView ArticleGoogle Scholar
- Alaynick WA: Nuclear receptors, mitochondria and lipid metabolism. Mitochondrion. 2008, 8 (4): 329-337. 10.1016/j.mito.2008.02.001.PubMed CentralPubMedView ArticleGoogle Scholar
- Huss JM, Torra IP, Staels B, Giguere V, Kelly DP: Estrogen-related receptor α directs peroxisome proliferator-activated receptor α signaling in the transcriptional control of energy metabolism in cardiac and skeletal muscle. Mol Cell Biol. 2004, 24 (20): 9079-9091. 10.1128/MCB.24.20.9079-9091.2004.PubMed CentralPubMedView ArticleGoogle Scholar
- Xie X, Liao H, Dang H, Pang W, Guan Y, Wang X, Shyy JY-J, Zhu Y, Sladek FM: Down-regulation of hepatic HNF4 α gene expression during hyperinsulinemia via SREBPs. Mol Endocrinol. 2009, 23 (4): 434-443. 10.1210/me.2007-0531.PubMed CentralPubMedView ArticleGoogle Scholar
- Liu Y, Qiu DK, Ma X: Liver X receptors bridge hepatic lipid metabolism and inflammation. J Dig Dis. 2012, 13 (2): 69-74. 10.1111/j.1751-2980.2011.00554.x.PubMedView ArticleGoogle Scholar
- Johannessen M, Moller S, Hansen T, Moens U, Ghelue MV: The multifunctional roles of the four-and-a-half-LIM only protein FHL2. Cell Mol Life Sci. 2006, 63 (3): 268-284. 10.1007/s00018-005-5438-z.PubMedView ArticleGoogle Scholar
- Muller JM, Isele U, Metzger E, Rempel A, Moser M, Pscherer A, Breyer T, Holubarsch C, Buettner R, Schule R: FHL2, a novel tissue-specific coactivator of the androgen receptor. EMBO J. 2000, 19 (3): 359-369. 10.1093/emboj/19.3.359.PubMed CentralPubMedView ArticleGoogle Scholar
- Ciarlo JD, Flores AM, McHugh NG, Aneskievich BJ: FHL2 expression in keratinocytes and transcriptional effect on PPARŒ±/RXRŒ±. J Dermatol Sci. 2004, 35 (1): 61-63. 10.1016/j.jdermsci.2004.02.012.PubMedView ArticleGoogle Scholar
- Martin B, Schneider R, Janetzky S, Waibler Z, Pandur P, Kuhl M, Behrens J, von der Mark K, Starzinski-Powitz A, Wixler V: The LIM-only protein FHL2 interacts with β-catenin and promotes differentiation of mouse myoblasts. J Cell Biol. 2002, 159 (1): 113-122. 10.1083/jcb.200202075.PubMed CentralPubMedView ArticleGoogle Scholar
- Brack AS, Murphy-Seiler F, Hanifi J, Deka J, Eyckerman S, Keller C, Aguet M, Rando TA: BCL9 is an essential component of canonical Wnt signaling that mediates the differentiation of myogenic progenitors during muscle regeneration. Dev Biol. 2009, 335 (1): 93-105. 10.1016/j.ydbio.2009.08.014.PubMed CentralPubMedView ArticleGoogle Scholar
- Bernard P, Harley VR: Wnt4 action in gonadal development and sex determination. Int J Biochem Cell Biol. 2007, 39 (1): 31-43. 10.1016/j.biocel.2006.06.007.PubMedView ArticleGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998, 95 (25): 14863-14868. 10.1073/pnas.95.25.14863.PubMed CentralPubMedView ArticleGoogle 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
- Nafikov RA, Beitz DC: Carbohydrate and lipid metabolism in farm animals. J Nutr. 2007, 137 (3): 702-705.PubMedGoogle Scholar
- Willert K, Brown JD, Danenberg E, Duncan AW, Weissman IL, Reya T, Yates JR, Nusse R: Wnt proteins are lipid-modified and can act as stem cell growth factors. Nature. 2003, 423 (6938): 448-452. 10.1038/nature01611.PubMedView ArticleGoogle Scholar
- Zhou D, Strakovsky RS, Zhang X, Pan YX: The skeletal muscle Wnt pathway may modulate insulin resistance and muscle development in a diet-induced obese rat model. Obesity (Silver Spring). 2012, 20 (8): 1577-1584. 10.1038/oby.2012.42.View ArticleGoogle Scholar
- Abiola M, Favier M, Christodoulou-Vafeiadou E, Pichard A-L, Martelly I, Guillet-Deniau I: Activation of Wnt/β-catenin signaling increases insulin sensitivity through a reciprocal regulation of Wnt10b and SREBP-1c in skeletal muscle cells. PLoS ONE. 2009, 4 (12): e8509-10.1371/journal.pone.0008509.PubMed CentralPubMedView ArticleGoogle Scholar
- Goda N, Kanai M: Hypoxia-inducible factors and their roles in energy metabolism. Int J Hematol. 2012, 95 (5): 457-463. 10.1007/s12185-012-1069-y.PubMedView ArticleGoogle Scholar
- Snel B, Lehmann G, Bork P, Huynen MA: STRING: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic Acids Res. 2000, 28 (18): 3442-3444. 10.1093/nar/28.18.3442.PubMed CentralPubMedView ArticleGoogle Scholar
- Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen LJ, von Mering C: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011, 39 (suppl 1): D561-D568.PubMed CentralPubMedView ArticleGoogle Scholar
- Heinlein C, Chang C: Androgen receptor (AR) coregulators: An overview. Endocr Rev. 2002, 23: 175-200. 10.1210/edrv.23.2.0460.PubMedView ArticleGoogle Scholar
- Urbanucci A, Waltering K, Suikki H, Helenius M, Visakorpi T: Androgen regulation of the androgen receptor coregulators. BMC Cancer. 2008, 8 (1): 219-10.1186/1471-2407-8-219.PubMed CentralPubMedView ArticleGoogle Scholar
- Hamaguchi T, Iizuka N, Tsunedomi R, Hamamoto Y, Miyamoto T, Iida M, Tokuhisa Y, Sakamoto K, Takashima M, Tamesa T, Oka M: Glycolysis module activated by hypoxia-inducible factor 1alpha is related to the aggressive phenotype of hepatocellular carcinoma. Int J Oncol. 2008, 33 (4): 725-731.PubMedGoogle Scholar
- Ke Q, Costa M: Hypoxia-inducible factor-1 (HIF-1). Mol Pharmacol. 2006, 70 (5): 1469-1480. 10.1124/mol.106.027029.PubMedView ArticleGoogle Scholar
- Mitani T, Harada N, Nakano Y, Inui H, Yamaji R: Coordinated action of hypoxia-inducible factor-1 α and β-catenin in androgen receptor signaling. J Biol Chem. 2012, 287 (40): 33594-33606. 10.1074/jbc.M112.388298.PubMed CentralPubMedView ArticleGoogle Scholar
- Kaidi A, Williams AC, Paraskeva C: Interaction between β-catenin and HIF-1 promotes cellular adaptation to hypoxia. Nat Cell Biol. 2007, 9 (2): 210-217. 10.1038/ncb1534.PubMedView ArticleGoogle Scholar
- Yang F, Li X, Sharma M, Sasaki CY, Longo DL, Lim B, Sun Z: Linking β-catenin to androgen-signaling pathway. J Biol Chem. 2002, 277 (13): 11336-11344. 10.1074/jbc.M111962200.PubMedView ArticleGoogle Scholar
- Labalette C, Renard C-A, Neuveut C, Buendia M-A, Wei Y: Interaction and functional cooperation between the LIM protein FHL2, CBP/p300, and β-catenin. Mol Cell Biol. 2004, 24 (24): 10689-10702. 10.1128/MCB.24.24.10689-10702.2004.PubMed CentralPubMedView ArticleGoogle Scholar
- Ramayo-Caldas Y, Mercade A, Castello A, Yang B, Rodriguez C, Alves E, Diaz I, Ibanez-Escriche N, Noguera JL, Perez-Enciso M, Fernández AI, Folch JM: Genome-wide association study for intramuscular fatty acid composition in an Iberian x Landrace cross. J Anim Sci. 2012, 90 (9): 2883-2893. 10.2527/jas.2011-4900.PubMedView ArticleGoogle Scholar
- Ramayo-Caldas Y, Mach N, Esteve-Codina A, Corominas J, Castello A, Ballester M, Estelle J, Ibanez-Escriche N, Fernandez AI, Perez-Enciso M, Folch JM: Liver transcriptome profile in pigs with extreme phenotypes of intramuscular fatty acid composition. BMC Genomics. 2012, 13: 547-10.1186/1471-2164-13-547.PubMed CentralPubMedView ArticleGoogle Scholar
- Corominas J, Ramayo-Caldas Y, Puig-Oliveras A, Perez-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 CentralPubMedView ArticleGoogle Scholar
- Ramos AM, Crooijmans RP, Affara NA, Amaral AJ, Archibald AL, Beever JE, Bendixen C, Churcher C, Clark R, Dehais P, Hansen MS, Hedegaard J, Hu ZL, Kerstens HH, Law AS, Megens HJ, Milan D, Nonneman DJ, Rohrer GA, Rothschild MF, Smith TP, Schnabel RD, Van Tassell CP, Taylor JF, Wiedmann RT, Schook LB, Groenen MA: Design of a high density SNP genotyping assay in the pig using SNPs identified and characterized by next generation sequencing technology. PLoS ONE. 2009, 4 (8): e6524-10.1371/journal.pone.0006524.PubMed CentralPubMedView ArticleGoogle Scholar
- Estelle J, Fernandez AI, Perez-Enciso M, Fernandez A, Rodriguez C, Sanchez 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.PubMedView ArticleGoogle Scholar
- Mercade A, Perez-Enciso M, Varona L, Alves E, Noguera JL, Sanchez A, Folch JM: Adipocyte fatty-acid binding protein is closely associated to the porcine FAT1 locus on chromosome 4. J Anim Sci. 2006, 84 (11): 2907-2913. 10.2527/jas.2005-663.PubMedView ArticleGoogle Scholar
- Groenen MAM, Archibald AL, Uenishi H, Tuggle CK, Takeuchi Y, Rothschild MF, Rogel-Gaillard C, Park C, Milan D, Megens H-J, Li S, Larkin DM, Kim H, Frantz LAF, Caccamo M, Ahn H, Aken BL, Anselmo A, Anthon C, Auvil L, Badaoui B, Beattie CW, Bendixen C, Berman D, Blecha F, Blomberg J, Bolund L, Bosse M, Botti S, Bujie Z, et al: Analyses of pig genomes provide insight into porcine demography and evolution. Nature. 2012, 491 (7424): 393-398. 10.1038/nature11622.PubMed CentralPubMedView ArticleGoogle Scholar
- Perez-Enciso M, Misztal I: Qxpak.5: old mixed model solutions for new genomics problems. BMC Bioinformatics. 2011, 12: 202-10.1186/1471-2105-12-202.PubMed CentralPubMedView ArticleGoogle Scholar
- Henderson CR: Best linear unbiased estimation and prediction under a selection model. Biometrics. 1975, 31 (2): 423-447. 10.2307/2529430.PubMedView ArticleGoogle Scholar
- Henderson CR: Applications of Linear Models in Animal Breeding. 1984, Guelph, Ontario, Canada.: University of GuelphGoogle Scholar
- Yang J, Lee T, Kim J, Cho M-C, Han B-G, Lee J-Y, Lee H-J, Cho S, Kim H: Ubiquitous polygenicity of human complex traits: genome-wide analysis of 49 traits in Koreans. PLoS Genet. 2013, 9 (3): e1003355-10.1371/journal.pgen.1003355.PubMed CentralPubMedView ArticleGoogle Scholar
- Vaquerizas JM, Kummerfeld SK, Teichmann SA, Luscombe NM: A census of human transcription factors: function, expression and evolution. Nat Rev Genet. 2009, 10 (4): 252-263. 10.1038/nrg2538.PubMedView ArticleGoogle Scholar
- Caraux G, Pinloche S: PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics. 2005, 21 (7): 1280-1281. 10.1093/bioinformatics/bti141.PubMedView ArticleGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13 (11): 2498-2504. 10.1101/gr.1239303.PubMed CentralPubMedView ArticleGoogle Scholar
- Maere S, Heymans K, Kuiper M: BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005, 21 (16): 3448-3449. 10.1093/bioinformatics/bti551.PubMedView ArticleGoogle Scholar
- Scardoni G, Petterlini M, Laudanna C: Analyzing biological network parameters with CentiScaPe. Bioinformatics. 2009, 25 (21): 2857-2859. 10.1093/bioinformatics/btp517.PubMed CentralPubMedView ArticleGoogle Scholar
- Medina I, Carbonell J, Pulido L, Madeira SC, Goetz S, Conesa A, Tárraga J, Pascual-Montano A, Nogales-Cadenas R, Santoyo J, García F, Marbà M, Montaner D, Dopazo J: Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Res. 2010, 38 (suppl 2): W210-W213.PubMed CentralPubMedView ArticleGoogle Scholar
- Al-Shahrour F, Minguez P, Tarraga J, Medina I, Alloza E, Montaner D, Dopazo J: FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. Nucleic Acids Res. 2007, 35 (suppl 2): W91-W96.PubMed CentralPubMedView ArticleGoogle Scholar
- Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman W-H, Pagés F, Trajanoski Z, Galon JRM: ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009, 25 (8): 1091-1093. 10.1093/bioinformatics/btp101.PubMed CentralPubMedView ArticleGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc Series B (Methodological). 1995, 57 (1): 289-300.Google Scholar
- Zhang Y, Klein K, Sugathan A, Nassery N, Dombkowski A, Zanger UM, Waxman DJ: Transcriptional profiling of human liver identifies Sex-biased genes associated with polygenic dyslipidemia and coronary artery disease. PLoS ONE. 2011, 6 (8): e23506-10.1371/journal.pone.0023506.PubMed CentralPubMedView ArticleGoogle Scholar
- Chakrabarti P, English T, Karki S, Qiang L, Tao R, Kim J, Luo Z, Farmer SR, Kandror KV: SIRT1 controls lipolysis in adipocytes via FOXO1-mediated expression of ATGL. J Lipid Res. 2011, 52 (9): 1693-1701. 10.1194/jlr.M014647.PubMed CentralPubMedView ArticleGoogle Scholar
- Schug TT, Li X: Sirtuin 1 in lipid metabolism and obesity. Ann Med. 2011, 43 (3): 198-211. 10.3109/07853890.2010.547211.PubMed CentralPubMedView ArticleGoogle Scholar
- Picard F, Kurtev M, Chung N, Topark-Ngarm A, Senawong T, Machado De Oliveira R, Leid M, McBurney MW, Guarente L: Sirt1 promotes fat mobilization in white adipocytes by repressing PPAR-gamma. Nature. 2004, 429 (6993): 771-776. 10.1038/nature02583.PubMed CentralPubMedView ArticleGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3 (7): RESEARCH0034-PubMed CentralPubMedView ArticleGoogle Scholar
- Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2- ΔΔCT method. Methods. 2001, 25 (4): 402-408. 10.1006/meth.2001.1262.PubMedView ArticleGoogle Scholar
- Ballester M, Cordón R, Folch JM: DAG Expression: high-throughput gene expression analysis of Real-Time PCR data using standard curves for relative quantification. Plos One. 2013, 8 (11): e80385-10.1371/journal.pone.0080385.PubMed CentralPubMedView ArticleGoogle Scholar
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