- 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.
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.
Results and discussion
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.
- Motta VF, de Lacerda CAM: Beneficial Effects of Exercise Training (Treadmill) on Body Mass and Skeletal Muscle Capillaries/Myocyte Ratio in C57BL/6 Mice Fed High-Fat Diet. Int J Morpho. 2012, 30 (1): 205-210. 10.4067/S0717-95022012000100037.View ArticleGoogle Scholar
- Matsakas A, Patel K: Skeletal muscle fibre plasticity in response to selected environmental and physiological stimuli. Histol Histopathol. 2009, 24 (5): 611-629.PubMedGoogle Scholar
- Choi Y, Kim B: Muscle fiber characteristics, myofibrillar protein isoforms, and meat quality. Livest Sci. 2009, 122 (2): 105-118.View ArticleGoogle Scholar
- Kim NK, Joh JH, Park HR, Kim OH, Park BY, Lee CS: Differential expression profiling of the proteomes and their mRNAs in porcine white and red skeletal muscles. Proteomics. 2004, 4 (11): 3422-3428. 10.1002/pmic.200400976.View ArticlePubMedGoogle Scholar
- Pette D, Staron R: Cellular and molecular diversities of mammalian skeletal muscle fibers. Rev Physiol Biochem Pharmacol. 1990, 116: 1-76.PubMedGoogle Scholar
- Campbell WG, Gordon SE, Carlson CJ, Pattison JS, Hamilton MT, Booth FW: Differential global gene expression in red and white skeletal muscle. Am J Physiol Cell Physiol. 2001, 280 (4): C763-C768.PubMedGoogle Scholar
- Mo K, Razak Z, Rao P, Yu Z, Adachi H, Katsuno M, Sobue G, Lieberman AP, Westwood JT, Monks DA: Microarray analysis of gene expression by skeletal muscle of three mouse models of Kennedy disease/spinal bulbar muscular atrophy. PLoS One. 2010, 5 (9): e12922-10.1371/journal.pone.0012922.PubMed CentralView ArticlePubMedGoogle Scholar
- Wolfs M, Rensen S, Bruin-Van Dijk E, Verdam F, Greve JW, Sanjabi B, Bruinenberg M, Wijmenga C, Van Haeften T, Buurman W: Co-expressed immune and metabolic genes in visceral and subcutaneous adipose tissue from severely obese individuals are associated with plasma HDL and glucose levels: a microarray study. BMC Med Genomics. 2010, 3 (1): 34-10.1186/1755-8794-3-34.PubMed CentralView ArticlePubMedGoogle Scholar
- Laughlin MH, Schrage WG, McAllister RM, Garverick H, Jones A: Interaction of gender and exercise training: vasomotor reactivity of porcine skeletal muscle arteries. J Appl Physiol. 2001, 90 (1): 216-227.PubMedGoogle Scholar
- Laughlin MH, Welshons WV, Sturek M, Rush JWE, Turk JR, Taylor JA, Judy BM, Henderson KK, Ganjam V: Gender, exercise training, and eNOS expression in porcine skeletal muscle arteries. J Appl Physiol. 2003, 95 (1): 250-264.View ArticlePubMedGoogle Scholar
- Glenmark B, Nilsson M, Gao H, Gustafsson JÅ, Dahlman-Wright K, Westerblad H: Difference in skeletal muscle function in males vs. females: role of estrogen receptor-β. Am J Physiol Endocrinol Metab. 2004, 287 (6): E1125-E1131. 10.1152/ajpendo.00098.2004.View ArticlePubMedGoogle Scholar
- Roth SM, Ferrell RE, Peters DG, Metter EJ, Hurley BF, Rogers MA: Influence of age, sex, and strength training on human muscle gene expression determined by microarray. Physiol Genomics. 2002, 10 (3): 181-190.PubMed CentralView ArticlePubMedGoogle Scholar
- Prather RS, Shen M, Dai Y: Genetically modified pigs for medicine and agriculture. Biotechnol Genet Eng Rev. 2008, 25 (1): 245-265.PubMedGoogle Scholar
- Rocha D, Plastow G: Commercial pigs: an untapped resource for human obesity research?. Drug Discov Today. 2006, 11 (11–12): 475-477.View ArticlePubMedGoogle Scholar
- Andersson L: How selective sweeps in domestic animals provide new insight into biological mechanisms. J Int Medicine. 2011, 271 (1): 1-14.View ArticleGoogle Scholar
- Bai Q, McGillivray C, Da Costa N, Dornan S, Evans G, Stear M, Chang KC: Development of a porcine skeletal muscle cDNA microarray: analysis of differential transcript expression in phenotypically distinct muscles. BMC Genomics. 2003, 4 (1): 8-15. 10.1186/1471-2164-4-8.PubMed CentralView ArticlePubMedGoogle Scholar
- Li Y, Xu Z, Li H, Xiong Y, Zuo B: Differential transcriptional analysis between red and white skeletal muscle of Chinese Meishan pigs. Int J Biol Sci. 2010, 6 (4): 350-360.PubMed CentralView ArticlePubMedGoogle Scholar
- Li M, Wu H, Luo Z, Xia Y, Guan J, Wang T, Gu Y, Chen L, Zhang K, Ma J, et al: An atlas of DNA methylomes in porcine adipose and muscle tissues. Nat Commun. 2012, 3: 850-PubMed CentralView ArticlePubMedGoogle Scholar
- Trendelenburg AU, Meyer A, Rohner D, Boyle J, Hatakeyama S, Glass DJ: Myostatin reduces Akt/TORC1/p70S6K signaling, inhibiting myoblast differentiation and myotube size. Am J Physiol Cell Physiol. 2009, 296 (6): C1258-C1270. 10.1152/ajpcell.00105.2009.View ArticlePubMedGoogle Scholar
- Hasty P, Bradley A, Morris JH, Edmondson DG, Venuti JM, Olson EN, Klein WH: Muscle deficiency and neonatal death in mice with a targeted mutation in the myogenin gene. Nature. 1993, 364 (6437): 501-506. 10.1038/364501a0.View ArticlePubMedGoogle Scholar
- Nabeshima Y, Hanaoka K, Hayasaka M, Esuml E, Li S, Nonaka I: Myogenin gene disruption results in perinatal lethality because of severe muscle defect. Nature. 1993, 364 (6437): 532-535. 10.1038/364532a0.View ArticlePubMedGoogle Scholar
- Kambadur R, Sharma M, Smith TP, Bass JJ: Mutations in myostatin (GDF8) in double-muscled Belgian Blue and Piedmontese cattle. Genome Res. 1997, 7 (9): 910-916.PubMedGoogle Scholar
- Rivera-Ferre MG, Aguilera JF, Nieto R: Muscle fractional protein synthesis is higher in Iberian than in Landrace growing pigs fed adequate or lysine-deficient diets. J Nutr. 2005, 135 (3): 469-478.PubMedGoogle Scholar
- Senaeme C, Istasse L, Baldwin P, Gabriel A, Haan V, Bienfait JM: Muscle protein turnover in young bulls in relation to breed and hormonal status. Asian-Austral J Animal Sci. 1989, 2 (3): 200-201.View ArticleGoogle Scholar
- Vidal-Puig A, Solanes G, Grujic D, Flier JS, Lowell BB: UCP3: an uncoupling protein homologue expressed preferentially and abundantly in skeletal muscle and brown adipose tissue. Biochem Biophys Res Commun. 1997, 235 (1): 79-82. 10.1006/bbrc.1997.6740.View ArticlePubMedGoogle Scholar
- Louis M, Van Beneden R, Dehoux M, Thissen JP, Francaux M: Creatine increases IGF-I and myogenic regulatory factor mRNA in C2C12 cells. FEBS Lett. 2004, 557 (1): 243-247.View ArticlePubMedGoogle Scholar
- Cagnazzo M, Te Pas MF, Priem J, De Wit AA, Pool MH, Davoli R, Russo V: Comparison of prenatal muscle tissue expression profiles of two pig breeds differing in muscle characteristics. J Anim Sci. 2006, 84 (1): 1-10.PubMedGoogle Scholar
- Cadoudal T, Distel E, Durant S, Fouque F, Blouin JM, Collinet M, Bortoli S, Forest C, Benelli C: Pyruvate dehydrogenase kinase 4: regulation by thiazolidinediones and implication in glyceroneogenesis in adipose tissue. Diabetes. 2008, 57 (9): 2272-2279. 10.2337/db08-0477.PubMed CentralView ArticlePubMedGoogle Scholar
- Muthny T, Kovarik M, Sispera L, Tilser I, Holecek M: Protein metabolism in slow- and fast-twitch skeletal muscle during turpentine-induced inflammation. Int J Exp Pathol. 2008, 89 (1): 64-71.PubMed CentralView ArticlePubMedGoogle Scholar
- Auclair D, Garrel DR, Chaouki Zerouala A, Ferland LH: Activation of the ubiquitin pathway in rat skeletal muscle by catabolic doses of glucocorticoids. Am J Physiol. 1997, 272 (3): C1007-1016.PubMedGoogle Scholar
- Cleveland BM, Evenhuis JP: Molecular characterization of atrogin-1/F-box protein-32 (FBXO32) and F-box protein-25 (FBXO25) in rainbow trout (Oncorhynchus mykiss): Expression across tissues in response to feed deprivation. Comp Biochem Physiol B Biochem Mol Biol. 2010, 157 (3): 248-257. 10.1016/j.cbpb.2010.06.010.View ArticlePubMedGoogle Scholar
- Wang X, Hu Z, Hu J, Du J, Mitch WE: Insulin resistance accelerates muscle protein degradation: Activation of the ubiquitin-proteasome pathway by defects in muscle cell signaling. Endocrinology. 2006, 147 (9): 4160-4168. 10.1210/en.2006-0251.View ArticlePubMedGoogle Scholar
- Reid MB: Response of the ubiquitin-proteasome pathway to changes in muscle activity. Am J Physiol Regul Integr Comp Physiol. 2005, 288 (6): R1423-R1431. 10.1152/ajpregu.00545.2004.View ArticlePubMedGoogle Scholar
- Agrawal P, Chen Y-T, Schilling B, Gibson BW, Hughes RE: Ubiquitin-specific Peptidase 9, X-linked (USP9X) Modulates Activity of Mammalian Target of Rapamycin (mTOR). J Biol Chem. 2012, 287 (25): 21164-21175. 10.1074/jbc.M111.328021.PubMed CentralView ArticlePubMedGoogle Scholar
- Chang Y, Yu Y, Wang N, Xu Y: Cloning and characterization of syap1, a down regulated gene in human hepatocellular carcinoma. Shi Yan Sheng Wu Xue Bao. 2001, 34 (4): 319-322.PubMedGoogle Scholar
- Lahn BT, Page DC: Functional coherence of the human Y chromosome. Science. 1997, 278 (5338): 675-680. 10.1126/science.278.5338.675.View ArticlePubMedGoogle Scholar
- Brocker C, Carpenter C, Nebert DW, Vasiliou V: Evolutionary divergence and functions of the human acyl-CoA thioesterase gene ( ACOT ) family. Hum Genomics. 2010, 4 (6): 411-420. 10.1186/1479-7364-4-6-411.PubMed CentralView ArticlePubMedGoogle Scholar
- Hakme A, Huber A, Dolle P, Schreiber V: The macroPARP genes Parp-9 and Parp-14 are developmentally and differentially regulated in mouse tissues. Dev Dyn. 2008, 237 (1): 209-215. 10.1002/dvdy.21399.View ArticlePubMedGoogle Scholar
- Lourim D, Lin JJ-C: Apolipoprotein A-1 expression is resistant to dimethyl sulfoxide inhibition of myogenic differentiation. Exp Cell Res. 1991, 197 (1): 57-65. 10.1016/0014-4827(91)90479-E.View ArticlePubMedGoogle Scholar
- Bass A, Brdiczka D, Eyer P, Hofer S, Pette D: Metabolic differentiation of distinct muscle types at the level of enzymatic organization. Euro J Biochem. 1969, 10 (2): 198-206. 10.1111/j.1432-1033.1969.tb00674.x.View ArticleGoogle Scholar
- Ryu Y, Kim B: The relationship between muscle fiber characteristics, postmortem metabolic rate, and meat quality of pig longissimus dorsi muscle. Meat Sci. 2005, 71 (2): 351-357. 10.1016/j.meatsci.2005.04.015.View ArticlePubMedGoogle Scholar
- Crum-Cianflone NF: Bacterial, fungal, parasitic, and viral myositis. Clin Microbiol Rev. 2008, 21 (3): 473-494. 10.1128/CMR.00001-08.PubMed CentralView ArticlePubMedGoogle Scholar
- Itskowitz MS, Jones SM: GI Consult: Appendicitis. Emerg Med. 2004, 36 (10): 10-15.Google Scholar
- Miller T, Al-Lozi M, Lopate G, Pestronk A: Myopathy with antibodies to the signal recognition particle: clinical and pathological features. J Neurol Neurosurg Psychiatry. 2002, 73 (4): 420-428. 10.1136/jnnp.73.4.420.PubMed CentralView ArticlePubMedGoogle Scholar
- Saeed A, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M: TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003, 34 (2): 374-378.PubMedGoogle Scholar
- Li M, Wu H, Wang T, Xia Y, Jin L, Jiang A, Zhu L, Chen L, Li R, Li X: Co-methylated Genes in Different Adipose Depots of Pig are Associated with Metabolic, Inflammatory and Immune Processes. Int J Biol Sci. 2012, 8 (6): 831-837.PubMed CentralView ArticlePubMedGoogle Scholar
- Da Huang W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4 (1): 44-57.View ArticlePubMedGoogle Scholar
- Erkens T, Van Poucke M, Vandesompele J, Goossens K, Van Zeveren A, Peelman L: Development of a new set of reference genes for normalization of real-time RT-PCR data of porcine backfat and longissimus dorsi muscle, and evaluation with PPARGC1A. BMC Biotechnol. 2006, 6 (1): 41-48. 10.1186/1472-6750-6-41.PubMed CentralView ArticlePubMedGoogle Scholar
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