Biclustering Analysis of Co-regulation Patterns in Nuclear-Encoded Mitochondrial Genes and Metabolic Pathways

  • Robert B. BenthamEmail author
  • Kevin Bryson
  • Gyorgy Szabadkai
Part of the Methods in Molecular Biology book series (MIMB, volume 1928)


Transcription of a large set of nuclear-encoded genes underlies biogenesis of mitochondria, regulated by a complex network of transcription factors and co-regulators. A remarkable heterogeneity can be detected in the expression of these genes in different cell types and tissues, and the recent availability of large gene expression compendiums allows the quantification of specific mitochondrial biogenesis patterns. We have developed a method to effectively perform this task. Massively correlated biclustering (MCbiclust) is a novel bioinformatics method that has been successfully applied to identify co-regulation patterns in large genesets, underlying essential cellular functions and determining cell types. The method has been recently evaluated and made available as a package in Bioconductor for R. One of the potential applications of the method is to compare expression of nuclear-encoded mitochondrial genes or larger sets of metabolism-related genes between different cell types or cellular metabolic states. Here we describe the essential steps to use MCbiclust as a tool to investigate co-regulation of mitochondrial genes and metabolic pathways.

Key words

Biclustering MCbiclust Mitochondria Metabolism Gene expression 



Funding was provided by the University College London COMPLeX/British Heart Foundation Fund (SP/08/004), the Biochemical and Biophysical Research Council (BB/L020874/1, BB/P018726/1), the Wellcome Trust (097815/Z/11/Z) in the UK, and the Association for Cancer Research (AIRC, IG13447) in Italy.


  1. 1.
    Lopez MF, Kristal BS, Chernokalskaya E, Lazarev A, Shestopalov AI, Bogdanova A, Robinson M (2000) High-throughput profiling of the mitochondrial proteome using affinity fractionation and automation. Electrophoresis 21:3427–3440CrossRefGoogle Scholar
  2. 2.
    Pagliarini DJ, Calvo SE, Chang B, Sheth SA, Vafai SB, Ong S-E, Walford GA, Sugiana C, Boneh A, Chen WK et al (2008) A mitochondrial protein compendium elucidates complex I disease biology. Cell 134:112–123CrossRefGoogle Scholar
  3. 3.
    Thor Johnson D, Harris RA, French S, Blair PV, You J, Bemis KG, Wang M, Balaban RS (2007) Tissue heterogeneity of the mammalian mitochondrial proteome. Am J Physiol Cell Physiol 292:689–697CrossRefGoogle Scholar
  4. 4.
    Kuznetsov AV, Hermann M, Saks V, Hengster P, Margreiter R (2009) The cell-type specificity of mitochondrial dynamics. Int J Biochem Cell Biol 41:1928–1939CrossRefGoogle Scholar
  5. 5.
    Scarpulla RC (2008) Transcriptional paradigms in mammalian mitochondrial biogenesis and function. Physiol Rev 88:611–638CrossRefGoogle Scholar
  6. 6.
    Hock MB, Kralli A (2009) Transcriptional control of mitochondrial biogenesis and function. Annu Rev Physiol 71:177–203CrossRefGoogle Scholar
  7. 7.
    Duchen MR, Szabadkai G (2010) Roles of mitochondria in human disease: figure 1. Essays Biochem 47:115–137CrossRefGoogle Scholar
  8. 8.
    Jones AWE, Yao Z, Vicencio JM, Karkucinska-Wieckowska A, Szabadkai G (2012) PGC-1 family coactivators and cell fate: roles in cancer, neurodegeneration, cardiovascular disease and retrograde mitochondria-nucleus signalling. Mitochondrion 12:86–99CrossRefGoogle Scholar
  9. 9.
    Bentham RB, Bryson K, Szabadkai G (2017) MCbiclust: a novel algorithm to discover large-scale functionally related genesets from massive transcriptomics data collections. Nucleic Acids Res 45:8712–8730CrossRefGoogle Scholar
  10. 10.
    Gentleman R, Carey V, Bates D, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80CrossRefGoogle Scholar
  11. 11.
    Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehár J, Kryukov GV, Sonkin D et al (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483:603–607CrossRefGoogle Scholar
  12. 12.
    Calvo SE, Clauser KR, Mootha VK (2015) MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins. Nucleic Acids Res 44(D1):D1251–D1257. Scholar
  13. 13.
    Smith AC, Blackshaw J a, Robinson AJ (2012) MitoMiner: a data warehouse for mitochondrial proteomics data. Nucleic Acids Res 40:D1160–D1167CrossRefGoogle Scholar
  14. 14.
    Consortium TGO (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29CrossRefGoogle Scholar
  15. 15.
    Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65CrossRefGoogle Scholar
  16. 16.
    Huang DW, Lempicki R a, Sherman BT (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57CrossRefGoogle Scholar
  17. 17.
    Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al (2005) Geneset enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550CrossRefGoogle Scholar
  18. 18.
    Reimand J, Arak T, Adler P, Kolberg L, Reisberg S, Peterson H, Vilo J (2016) g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res 44:W83–W89CrossRefGoogle Scholar
  19. 19.
    Luo W, Brouwer C (2013) Pathview: an R/bioconductor package for pathway-based data integration and visualization. Bioinformatics 29:1830–1831CrossRefGoogle Scholar
  20. 20.
    Barbie DA, Tamayo P, Boehm JS, Kim SY, Susan E, Dunn IF, Schinzel AC, Sandy P, Meylan E, Fröhling S et al (2010) Systematic RNA interference reveals that oncogenic KRAS- driven cancers require TBK1. Nature 462:108–112CrossRefGoogle Scholar
  21. 21.
    Hanzelmann S, Castelo R, Guinney J (2013) GSVA: geneset variation analysis for microarray and RNA-Seq data. BMC Bioinf 14:7CrossRefGoogle Scholar
  22. 22.
    Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–127CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Robert B. Bentham
    • 1
    Email author
  • Kevin Bryson
    • 2
  • Gyorgy Szabadkai
    • 1
    • 3
    • 4
  1. 1.Department of Cell and Developmental Biology, Consortium for Mitochondrial ResearchUniversity College LondonLondonUK
  2. 2.Department of Computer SciencesUniversity College LondonLondonUK
  3. 3.Department of Biomedical SciencesUniversity of PaduaPaduaItaly
  4. 4.The Francis Crick InstituteLondonUK

Personalised recommendations