Functional & Integrative Genomics

, Volume 12, Issue 1, pp 93–103 | Cite as

Muscle-specific gene expression is underscored by differential stressor responses and coexpression changes

  • Natalia Moreno-SánchezEmail author
  • Julia Rueda
  • Antonio Reverter
  • María Jesús Carabaño
  • Clara Díaz
Original Paper


Variations on the transcriptome from one skeletal muscle type to another still remain unknown. The reliable identification of stable gene coexpression networks is essential to unravel gene functions and define biological processes. The differential expression of two distinct muscles, M. flexor digitorum (FD) and M. psoas major (PM), was studied using microarrays in cattle to illustrate muscle-specific transcription patterns and to quantify changes in connectivity regarding the expected gene coexpression pattern. A total of 206 genes were differentially expressed (DE), 94 upregulated in PM and 112 in FD. The distribution of DE genes in pathways and biological functions was explored in the context of system biology. Global interactomes for genes of interest were predicted. Fast/slow twitch genes, genes coding for extracellular matrix, ribosomal and heat shock proteins, and fatty acid uptake centred the specific gene expression patterns per muscle. Genes involved in repairing mechanisms, such as ribosomal and heat shock proteins, suggested a differential ability of muscles to react to similar stressing factors, acting preferentially in slow twitch muscles. Muscle attributes do not seem to be completely explained by the muscle fibre composition. Changes in connectivity accounted for 24% of significant correlations between DE genes. Genes changing their connectivity mostly seem to contribute to the main differential attributes that characterize each specific muscle type. These results underscore the unique flexibility of skeletal muscle where a substantial set of genes are able to change their behavior depending on the circumstances.


Differential expression Skeletal muscle Gene coexpression System biology Microarrays 



The authors thank the Asociación de Avileña-Negra Ibérica and the Consejo Regulador de Carne de Ávila for their contribution in the sample collection. This work was partially funded by RTA2007-00081-00-00 (Ministerio de Ciencia e Innovación of Spain) and S2009AGR-1704-GENR (Consejería de Educación, Comunidad de Madrid) projects.

Supplementary material

10142_2011_249_MOESM1_ESM.pdf (317 kb)
Fig. S1 IPA-associated network functions for the genes upregulated in FD (PDF 316 kb)
10142_2011_249_MOESM2_ESM.pdf (314 kb)
Fig. S2 IPA-associated network functions for the genes upregulated in PM (PDF 314 kb)
10142_2011_249_MOESM3_ESM.doc (142 kb)
Table S1A Genes upregulated in Psoas major (PM) (DOC 141 kb)
10142_2011_249_MOESM4_ESM.doc (154 kb)
Table S1B Genes overexpressed in Flexor digitorum (FD) (DOC 152 kb)


  1. Alexeyenko A, Sonnhammer EL (2009) Global networks of functional coupling in eukaryotes from comprehensive data integration. Genome Res 19:1107–1116PubMedCrossRefGoogle Scholar
  2. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410PubMedGoogle Scholar
  3. Bai Q, McGillivray C, da Costa N, Dornan S, Evans G, Stear MJ, Chang K-C (2003) Development of a porcine skeletal muscle cDNA microarray: analysis of differential transcript expression in phenotypically distinct muscles. BMC Genomics 4:8PubMedCrossRefGoogle Scholar
  4. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc A 57:289–300Google Scholar
  5. Blais A, Tsikitis M, Acosta-Alvear D, Sharan R, Kluger Y, Dynlacht BD (2005) An initial blueprint for myogenic differentiation. Genes Dev 19:553–569PubMedCrossRefGoogle Scholar
  6. Byrne KA, Wang YH, Lehnert SA, Harper GS, McWilliam SM, Bruce HL, Reverter A (2005) Gene expression profiling of muscle tissue in Brahman steers during nutritional restriction. J Anim Sci 83:1–12PubMedGoogle Scholar
  7. Campbell WG, Gordon SE, Carlson CJ, Pattison JS, Hamilton MT, Booth FW (2001) Differential global gene expression in red and white skeletal muscle. Am J Physiol Cell Ph 280:C763–C768Google Scholar
  8. Cánovas A, Quintanilla R, Amills M, Pena RN (2010) Muscle transcriptomic profiles in pigs with divergent phenotypes for fatness traits. BMC Genomics 11:372PubMedGoogle Scholar
  9. Cha B, Lim JW, Kim KH, Kim H (2010) HSP90beta interacts with Rac1 to activate NADPH oxidase in Helicobacter pylori-infected gastric epithelial cells. Int J Biochem Cell Biol 42:1455–1461PubMedCrossRefGoogle Scholar
  10. Chang KC (2007) Key signalling factors and pathways in the molecular determination of skeletal muscle phenotype. Animal 1:681–698. doi: 10.1017/S1751731107702070 CrossRefGoogle Scholar
  11. Chanseaume E, Giraudet C, Gryson C, Walrand S, Rousset P, Boirie Y, Morio B (2007) Enhanced muscle mixed and mitochondrial protein synthesis rates after a high-fat or high-sucrose diet[ast]. Obesity 15:853–859PubMedCrossRefGoogle Scholar
  12. Díaz C, Moreno-Sánchez N, Rueda J, Reverter A, Wang YH, Carabaño MJ (2009) Model selection in a global analysis of a microarray experiment. J Anim Sci 87:88–98PubMedCrossRefGoogle Scholar
  13. Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z (2009) GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinforma 10:48CrossRefGoogle Scholar
  14. Frier BC, Locke M (2007) Heat stress inhibits skeletal muscle hypertrophy. Cell Stress Chaperones 12:132–141PubMedCrossRefGoogle Scholar
  15. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57CrossRefGoogle Scholar
  16. Hudson NJ, Reverter A, Wang Y, Greenwood PL, Dalrymple BP (2009) Inferring the transcriptional landscape of bovine skeletal muscle by integrating co-expression networks. PLoS One 4:e7249PubMedCrossRefGoogle Scholar
  17. Kim N-K, Joh J-H, Park H-R, Kim O-H, Park B-Y, Lee C-S (2004) Differential expression profiling of the proteomes and their mRNAs in porcine white and red skeletal muscles. Proteomics 4:3422–3428. doi: 10.1002/pmic.200400976 PubMedCrossRefGoogle Scholar
  18. Kovacs D, Rakacs M, Agoston B, Lenkey K, Semrad K, Schroeder R, Tompa P (2009) Janus chaperones: assistance of both RNA- and protein-folding by ribosomal proteins. FEBS Lett 583(1):88–92Google Scholar
  19. Lehnert SA, Wang YH, Byrne KA (2004) Development and application of a bovine cDNA microarray for expression profiling of muscle and adipose tissue. Aust J Exp Agric 44:1127–1133CrossRefGoogle Scholar
  20. Lehnert SA, Wang YH, Tan SH, Reverter A (2006) Gene expression-based approaches to beef quality research. Aust J Exp Agric 46:165–172CrossRefGoogle Scholar
  21. Lehnert SA, Reverter A, Byrne KA, Wang YH, Nattrass GS, Hudson NJ, Greenwood PL (2007) Gene expression studies of developing bovine longissimus muscle from two different beef cattle breeds. BMC Dev Biol 7:95PubMedCrossRefGoogle Scholar
  22. Lin J, Wu H, Tarr PT, Zhang C-Y, Wu Z, Boss O, Michael LF, Puigserver P, Isotani E, Olson EN, Lowell BB, Bassel-Duby R, Spiegelman BM (2002) Transcriptional co-activator PGC-1[alpha] drives the formation of slow-twitch muscle fibres. Nature 418:797–801PubMedCrossRefGoogle Scholar
  23. McLachlan GJ, Bean RW, Ben-Tovim J, Zhu JX (2005) Using mixture models to detect differentially expressed genes. Aust J Exp Agric 45:859–866CrossRefGoogle Scholar
  24. Mittendorfer B, Andersen JL, Plomgaard P, Saltin B, Babraj JA, Smith K, Rennie MJ (2005) Protein synthesis rates in human muscles: neither anatomical location nor fibre-type composition are major determinants. J Physiol 563:203–211PubMedCrossRefGoogle Scholar
  25. Moreno-Sánchez N, Díaz C, Carabaño MJ, Rueda J, Rivero JL (2008) A comprehensive characterisation of the fibre composition and properties of a limb (Flexor digitorum superficialis, membri thoraci) and a trunk (Psoas major) muscle in cattle. BMC Cell Biol 9:67PubMedCrossRefGoogle Scholar
  26. Moreno-Sánchez N, Rueda J, Carabaño MJ, Reverter A, McWilliam SM, González C, Díaz C (2010) Skeletal muscle specific genes networks in cattle. Funct Integr Genomics 10:609–618PubMedCrossRefGoogle Scholar
  27. Morton JP, MacLaren DPM, Cable NT, Bongers T, Griffiths RD, Campbell IT, Evans L, Kayani A, McArdle A, Drust B (2006) Time course and differential responses of the major heat shock protein families in human skeletal muscle following acute nondamaging treadmill exercise. J Appl Physiol 101:176–182. doi: 10.1152/japplphysiol.00046.2006 PubMedCrossRefGoogle Scholar
  28. Prieto C, Risueño A, Fontanillo C, De Las RJ (2008) Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles. PLoS One 3:e3911PubMedCrossRefGoogle Scholar
  29. Reverter A, Barris W, Moreno-Sánchez N, McWilliam S, Wang YH, Harper G, Lehnert SA, Dalrymple BP (2005) Construction of gene interaction and regulatory networks in bovine skeletal muscle from expression data. Aust J Exp Agric 45:821–829CrossRefGoogle Scholar
  30. Reverter A, Hudson NJ, Wang Y, Tan S, Barris W, Byrne KA, McWilliam SM, Bottema CDK, Kister A, Greenwood PL, Harper GS, Lehnert SA, Dalrymple BP (2006) A gene co-expression network for bovine skeletal muscle inferred from microarray data. Physiol Genomics 28:76–83PubMedCrossRefGoogle Scholar
  31. Salo DC, Donovan CM, Davies KJA (1991) HSP70 and other possible heat shock or oxidative stress proteins are induced in skeletal muscle, heart, and liver during exercise. Free Radic Biol Med 11:239–246PubMedCrossRefGoogle Scholar
  32. Sudre K, Leroux C, Pietu G, Cassar-Malek I, Petit E, Listrat A, Auffray C, Picard B, Martin P, Hocquette J-F (2003) Transcriptome analysis of two bovine muscles during ontogenesis. J Biochem (Tokyo) 133:745–756CrossRefGoogle Scholar
  33. Sudre K, Cassar-Malek I, Listrat A, Ueda Y, Leroux C, Jurie C, Auffray C, Renand G, Martin P, Hocquette J-F (2005) Biochemical and transcriptomic analyses of two bovine skeletal muscles in Charolais bulls divergently selected for muscle growth. Meat Sci 70:267–277PubMedCrossRefGoogle Scholar
  34. Talmant A, Monin G (1986) Activities of metabolic and contractile enzymes in 18 bovine muscles. Meat Sci 18:23–40PubMedCrossRefGoogle Scholar
  35. Wang YH, Byrne KA, Reverter A, Harper GS, Taniguchi M, McWilliam SM, Mannen H, Oyama K, Lehnert SA (2005) Transcriptional profiling of skeletal muscle tissue from two breeds of cattle. Mamm Genome 16:201–210PubMedCrossRefGoogle Scholar
  36. Warner JR, McIntosh KB (2009) How common are extraribosomal functions of ribosomal proteins? Mol Cell 34:3–11PubMedCrossRefGoogle Scholar
  37. Wu H, Gallardo T, Olson EN, Williams RS, Shohet RV (2003) Transcriptional analysis of mouse skeletal myofiber diversity and adaptation to endurance exercise. J Muscle Res Cell Motil 24:587–592PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Natalia Moreno-Sánchez
    • 1
    Email author
  • Julia Rueda
    • 2
  • Antonio Reverter
    • 3
  • María Jesús Carabaño
    • 1
  • Clara Díaz
    • 1
  1. 1.Departamento de Mejora Genética Animal, INIA (Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria)MadridSpain
  2. 2.Departamento de Genética, Facultad de BiologíaUniversidad Complutense de MadridMadridSpain
  3. 3.CSIRO Livestock Industries and Cooperative Research Centre for Beef Genetic Technologies, Queensland Bioscience PrecinctBrisbaneAustralia

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