- Research article
- Open Access
Breed, sex and anatomical location-specific gene expression profiling of the porcine skeletal muscles
© Zhang et al.; licensee BioMed Central Ltd. 2013
- Received: 11 March 2013
- Accepted: 7 June 2013
- Published: 15 June 2013
Skeletal muscle is one of the most important economic traits in agricultural animals, especially in pigs. In the modern pig industry, lean type pigs have undergone strong artificial selection for muscle growth, which has led to remarkable phenotypic variations compared with fatty type pigs, making these different breeds an ideal model for comparative studies.
Here, we present comprehensive gene expression profiling for the white (longissimus dorsi muscle) and the red (psoas major muscle) skeletal muscles among male and female fatty Rongchang, feral Tibetan and lean Landrace pigs, using a microarray approach. We identified differentially expressed genes that may be associated the phenotypic differences of porcine muscles among the breeds, between the sexes and the anatomical locations. We also used a clustering method to identify sets of functionally coexpressed genes that are linked to different muscle phenotypes. We showed that, compared with the white muscles, which primarily modulate metabolic processes, the red muscles show a tendency to be a risk factor for inflammation and immune-related disorders.
This analysis presents breed-, sex- and anatomical location-specific gene expression profiles and further identified genes that may be associated with the phenotypic differences in porcine muscles among breeds, between the sexes and the anatomical locations.
- Gene expression
Skeletal muscle is the most abundant tissue, comprising approximately 50% of the total body mass in mammals . It is not only a motor organ, but also part of the endocrine system, participating in the regulation of whole body metabolism . Skeletal muscle, as a highly heterogeneous tissue, is composed of a variety of functionally diverse myofibre types ; mainly the red (type I and IIa) and the white (type IIb) fibers. Red skeletal muscles, such as the psoas major muscles (PMM), have a higher percentage of capillaries, myoglobin, lipids and mitochondria , making them a better aerobic machine than the paler-appearing white muscle . White skeletal muscles, such as the longissimus doris muscles (LDM) , are required for anaerobic glycolytic metabolism to support the high transient energy demand .
Deciphering the different gene expression patterns between the different tissues would aid in our understanding of their distinct metabolic features. Mo et al. identified various candidate genes involved in cell adhesion, energy balance, muscle atrophy and myogenesis by comparing patterns of gene expression in three independent mouse models of Kennedy disease/spinal bulbar muscular atrophy . Wolfs et al. reported that coexpressed immune and metabolic genes are associated with plasma high density lipoprotein and glucose levels by comparing genome-wide transcription profiling of subcutaneous and visceral adipose tissues obtained from obese patients . Previous reports also suggested that ethnic group and sex are also the important factors that affect physiological and biochemical features of skeletal muscles in mammals [9–12].
Pigs are important agricultural animals and ideal biomedical models . In the modern pig industry, pigs have undergone strong artificial selection for lean meat or adipose production, which has led to remarkable phenotypic variations, making these different breeds a perfect model for comparative studies [14, 15]. Using a microarray approach, Bai et al. noted that most differentially expressed genes between porcine PMM and LDM were of mitochondrial origin . Li et al. (2010) reported that the differentially expressed genes between the LDM and soleus muscle of Chinese Meishan pigs were mainly over-represented in various signaling pathways (particularly TGF-β, MAPK, Wnt, mTOR and insulin pathways) . Nonetheless, the different gene expression profiles associated with breed and sex in skeletal muscle tissues has been long overdue, and elucidation of this information will benefit the development of strategies for skeletal muscle manipulation.
Here, using a microarray technology, we present a comprehensive survey of gene expression profiles between two phenotypically distinct skeletal muscles and sexes of three well-defined pig breeds displaying distinct muscle phenotypes. This study will contribute to our understanding of the molecular process of muscle fiber type formulation and provide a theoretical basis for breed and meat quality improvement in pigs.
Our previous report, based on the same individuals, demonstrated that the myofibre cross-sectional area (CSA) and myofibre ratio were significant different between the two skeletal tissues, between the male and female and among the three breeds  (Additional file 1: Figure S1). In addition, 24 representative metabolism indicators in serum also revealed the same ranking from the leaner Landrace, the wild Tibetan and the fatty Rongchang pigs  (Additional file 2: Table S1).
Functional enrichment analysis of differentially expressed genes
Tissue-specific DEGs were significantly enriched in energy metabolism related processes (i.e. generation of precursor metabolites and energy, respiratory electron transport chain, fatty acid metabolic process, oxidative phosphorylation, lipid metabolic process, tricarboxylic acid cycle and coenzyme metabolic process) (Figure 2B), which is consistent with the distinct features of energy expenditure regulation between the LDM and PMM . Energy availability is important in the formation of mature muscle fibers and is essential for muscle proliferation and differentiation. Louis et al. reported that the energy content of cultured satellite cells is related to the hypertrophy of myofibres in vitro, which indicated a direct connection between energy metabolism and myogenesis . Cagnazzo et al. also demonstrated that myogenic differentiation and energy metabolism were directly connected processes . Genes involved in energy metabolism were identified. For example, MDH1, PDK3 and GOT1 play important roles in sympathetic-induced metabolism, which is involved in modulating the activity of glyceroneogenesis . MDH1, PDK3 and GOT1 showed lower gene expression levels in the LDM than in PMM (Additional file 5: Figure S2), which agreed with previous reports [29–32]. We also found that tissue-specific DEGs were over-represented in the ubiquitin-proteasome pathway (Figure 2B), which plays a critical role in the adaptation of skeletal muscle to persistent decreases or increases in muscle activity. The ubiquitin-proteasome pathway is constitutively active in muscle and continually regulates protein turnover .
Validation of gene expression changes by Quantitative PCR (Q-PCR)
Six genes (ADIPOR1, ADIPOR2, CAV1, CAV2, INSIG1, and MDH1) were selected to confirm their expression patterns using Q-PCR. The results indicated that the expression patterns of these genes were consistent with the microarray (average Pearson’s r = 0.86; Additional file 6: Figure S3).
Analysis of coexpressed gene modules
To extract more biological information within the genome-wide expression data set that could not be provided by individual, we constructed coexpressed gene modules and performed association analysis with the phenotypic traits, as did previous reports .
Gene Ontology (GO) categories enriched for coexpressed gene modules that correlated with phenotypic traits
Tissues (gene module no.)
Involved gene no.
1.06 × 10-7
Primary metabolic process
4.55 × 10-8
Protein metabolic process
5.03 × 10-4
Carbohydrate metabolic process
Protein modification process
Protein amino acid phosphorylation
Cellular metabolic process
5.22 × 10-6
5.58 × 10-5
2.47 × 10-4
Cellular nitrogen compound metabolic process
Primary metabolic process
Immune system process
2.12 × 10-5
Response to external stimulus
Response to wounding
Regulation of immune system process
Regulation of response to stimulus
Positive regulation of immune system process
Regulation of immune response
Positive regulation of lymphocyte activation
2.19 × 10-5
Immune system process
1.79 × 10-6
Cellular defense response
Response to stimulus
4.87 × 10-4
2.24 × 10-3
We also found that two coexpressed gene modules in PMM were significantly negatively correlated with amount of orexin-B (OX-B) (Spearman’s r = −0.81, P =5.75 × 10-5) and the orexin receptor (OXR) (Spearman’s r = −0.68, P = 1.04 × 10-6, Figure 4B) in serum, which are representative indicators for the inflammatory process and the immune system in serum. The genes within these two gene modules were mainly enriched in the categories of the immune system process (29 genes, P = 2.12 × 10-5), inflammatory response (16 genes, P = 0.001), immune response (22 genes, P = 0.001), lymphocyte activation (11 genes, P = 0.02), leukocyte activation (11 genes, P = 0.03), and cellular defense response (6 genes, P = 0.02) (Table 1), which suggests that the PMM is a metabolic risk factor. This finding is consistent with evidence that shows that the PMM is supplied by venous blood from the lumbar spine and has lymphatics overlying the muscle from nearby intra-abdominal organs, making it highly susceptible to contiguous infection and inflammation from organs such as the colon, appendix, terminal ileum and several intra-abdominal structures [42–44].
The analysis presented the gene expression profiles and identified DEGs that may be related to the phenotypic differences in porcine muscles among breeds, between the sexes and the anatomical locations. The results provide a basis for further exploration of the molecular process of muscle fiber type formulation, and may also help the further development of biomarkers for important economic traits (i.e. pork quality and yield) in pigs.
Three females and three males at 210-days-old for each of the leaner Landrace pigs, the wild Tibetan pigs and the fatty Rongchang pigs were used in this study as previously described . Animals were humanely sacrificed, according to the Regulations for the Administration of Affairs Concerning Experimental Animals (Ministry of Science and Technology, China, revised in June 2004) and approved by the Institutional Animal Care and Use Committee in the College of Animal Science and Technology, Sichuan Agricultural University, Sichuan, China. The longissimus dorsi muscle (LDM, typical white muscle) near the last 3rd or 4th rib and the intermediate section of psoas major muscle (PMM, typical red muscle) were rapidly separated from each carcass. Samples were frozen in liquid nitrogen, and stored at −80°C until RNA extraction. For more information, please refer to Li et al. .
Measurements of skeletal muscle-related phenotype
Measurements of concentrations of 24 serum-circulating indicators of metabolism, myofibre cross-sectional area and myofibre ratio (type I vs. II) are from our previous report based on same individuals. For more information, please refer to Li et al. .
Extraction of RNA
Total RNA was extracted from 36 samples using TRIzol (Invitrogen). RNA was purified and DNase treated using an RNeasy column (Qiagen) according to the manufacturer’s instructions. The quantity of each RNA sample was examined by the NanoDrop ND-1000 spectrophotometer (Nano Drop) at 260/280 nm (ratio > 2.0). The integrity of total RNA also passed analysis with the Bioanalyzer 2100 and RNA 6000 Nano LabChip Kit (Agilent Technologies) with RIN number > 6 (7.6 ± 0.3, n = 36).
Agilent Oligo microarrays were used to determine global gene expression of 36 samples. Individual microarrays were performed for each sample. Hybridization, washing, and scanning were done according to standard Agilent protocols. Generated array images were loaded into Feature Extraction Software (Agilent Technologies) for feature data extraction, and data analysis was performed with MultiExperiment Viewer (MeV) . Array data have been uploaded to NCBI’s Gene Expression Omnibus (GEO) [accession number GSE30343]. For more information, please refer to Li et al..
To obtain high-confidence gene expression data, we mapped 43,603 probes (60 mer in length) to the pig reference genome allowing up to one mismatch, and further filtered unannotated pig target sequences which resulting 4,309 genes were used in subsequent analysis. (Tables S2). To identify differentially expressed mRNAs (P < 0.05) for the clustering analysis, we used three-way ANOVA for comparisons. Resulting P-values of above tests were corrected with adjusted Bonferroni method (FDR < 0.01, 1,000 permutations).
Construct modules of coexpressed genes
For LDM and PMM separately, modules of highly coexpressed genes were constructed using pair wise average-linkage cluster analysis as previously described [8, 46]. We kept repeating this as an iterative process until the most significantly correlated pair was r < 0.8. To visualize the correlations between probes within the modules, we constructed colored heatmaps by plotting pair-wise correlation values of expression of all the probes within the modules. To calculate significance of overlap in gene content between modules and between different datasets, we performed Fisher’s exact tests.
Function enrichment analysis of genes
To elucidate the biological mechanisms associated with the genes that are correlated to the phenotypic traits, we performed functional enrichment analysis of Gene Ontology (GO) for genes using DAVID software .
Quantitative PCR (Q-PCR)
We selected six genes randomly to validation experiment using Q-PCR. Primer sequences used for the Q-PCR are shown in Additional file 9: Table S6. Porcine ACTB, TBP and TOP2B were simultaneously used as endogenous control genes . Relative expression levels of objective mRNAs were calculated using the ΔΔCt method.
This work was supported by grants from the Specialized Research Fund of Ministry of Agriculture of China (NYCYTX-009), the Project of Provincial Twelfth Five Years’ Animal Breeding of Sichuan Province (2011YZGG15) and the National Special Foundation for Transgenic Species of China (2011ZX08006-003) to X.L., the National High Technology Research and Development Program of China (863 Program) (2013AA102502) to M.L., the Chongqing Fund for Distinguished Young Scientists (CSTC2010BA1007) to J.W.
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