Skip to main content

System Biology Approach: Gene Network Analysis for Muscular Dystrophy

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1687))

Abstract

Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.

In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.

This is a preview of subscription content, log in via an institution.

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Chen YW, Zhao P, Borup R, Hoffman EP (2000) Expression profiling in the muscular dystrophies: identification of novel aspects of molecular pathophysiology. J Cell Biol 151(6):1321–1336

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Chen YW, Nagaraju K, Bakay M, McIntyre O, Rawat R et al (2005) Early onset of inflammation and later involvement of TGFbeta in Duchenne muscular dystrophy. Neurology 65(6):826–834

    Article  CAS  PubMed  Google Scholar 

  3. Pescatori M, Broccolini A, Minetti C, Bertini E, Bruno C et al (2007) Gene expression profiling in the early phases of DMD: a constant molecular signature characterizes DMD muscle from early postnatal life throughout disease progression. FASEB J 21(4):1210–1226

    Article  CAS  PubMed  Google Scholar 

  4. Noguchi S, Tsukahara T, Fujita M, Kurokawa R, Tachikawa M et al (2003) cDNA microarray analysis of individual Duchenne muscular dystrophy patients. Hum Mol Genet 12(6):595–600

    Article  CAS  PubMed  Google Scholar 

  5. Haslett JN, Sanoudou D, Kho AT, Bennett RR, Greenberg SA et al (2002) Gene expression comparison of biopsies from Duchenne muscular dystrophy (DMD) and normal skeletal muscle. Proc Natl Acad Sci U S A 99(23):15000–15005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Allison DB, Cui X, Page GP, Sabripour M (2006) Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 7(1):55–70

    Article  CAS  PubMed  Google Scholar 

  7. Wilkins AS (2007) For the biotechnology industry the penny drops (at last): genes are not autonomous agents but function within networks. Bioessays 29:1179–1181

    Article  PubMed  Google Scholar 

  8. Van Regenmortel MHV (2007) The rational design of biological complexity: a deceptive metaphor. Proteomics 7:965–975

    Article  PubMed  Google Scholar 

  9. Giuliani A (2010) Collective motions and specific effectors: a statistical mechanics perspective on biological regulation. BMC Genomics 11(suppl. 1):S2

    Article  PubMed  PubMed Central  Google Scholar 

  10. Noble D (2008) Genes and causation. Philos Transact A Math Phys Eng Sci 366(1878):3001

    Article  CAS  Google Scholar 

  11. Romualdi C, Giuliani A, Millino C, Celegato B, Benigni R et al (2009) Correlation between gene expression and clinical data through linear and nonlinear principal components analyses: muscular dystrophies as case studies. Omics 13(3):173–184

    Article  CAS  PubMed  Google Scholar 

  12. Giuliani A (2017) The application of Principal Component Analysis to drug discovery and biomedical data. Drug Discov Today. http://dx.doi.org/10.1016/j.drudis.2017.01.005

  13. Roden JC, King BW, Trout D, Mortazavi A, Wold BJ et al (2006) Mining gene expression data by interpreting principal components. BMC Bioinformatics 7:194

    Article  PubMed  PubMed Central  Google Scholar 

  14. Tsuchiya M, Giuliani A, Hashimoto M, Erenpreisa K, Yoshikawa K (2016) Self-organizing global gene expression regulated through criticality: mechanism of the cell-fate change. PLoS One 11(12):e0167912. doi:10.1371/journal.pone.0167912

    Article  PubMed  PubMed Central  Google Scholar 

  15. Huang S (2009) Reprogramming cell fates: reconciling rarity with robustness. Bioessays 31(5):546–560

    Article  CAS  PubMed  Google Scholar 

  16. Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci U S A 96(12):6745–6750

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wei X, Ker-Chau L (2010) Exploring the within- and between-class correlation distributions for tumor classification. Proc Natl Acad Sci U S A 107(15):6737–6742

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gorban N, Smirnova EV, Tyukina TA (2010) Correlations, risk and crisis: from physiology to finance. Phys A 389(16):3193–3217

    Article  Google Scholar 

  19. Barabási AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5(2):101–113

    Article  PubMed  Google Scholar 

  20. Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138(3):963–971

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B et al (2003) Summaries of affymetrix GeneChip probe level data. Nucleic Acids Res 31:e15

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Federica Censi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Censi, F., Calcagnini, G., Mattei, E., Giuliani, A. (2018). System Biology Approach: Gene Network Analysis for Muscular Dystrophy. In: Bernardini, C. (eds) Duchenne Muscular Dystrophy. Methods in Molecular Biology, vol 1687. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7374-3_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7374-3_6

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7373-6

  • Online ISBN: 978-1-4939-7374-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics