Systems-level analyses identify extensive coupling among gene expression machines

Karolina Maciag, Steven J. Altschuler, Michael D. Slack, Nevan J. Krogan, Andrew Emili, Jack F. Greenblatt, Tom Maniatis, Lani F. Wu

Research output: Contribution to journalArticlepeer-review

Abstract

Here, we develop computational methods to assess and consolidate large, diverse protein interaction data sets, with the objective of identifying proteins involved in the coupling of multicomponent complexes within the yeast gene expression pathway. From among ∼43 000 total interactions and 2100 proteins, our methods identify known structural complexes, such as the spliceosome and SAGA, and functional modules, such as the DEAD-box helicases, within the interaction network of proteins involved in gene expression. Our process identifies and ranks instances of three distinct, biologically motivated motifs, or patterns of coupling among distinct machineries involved in different subprocesses of gene expression. Our results confirm known coupling among transcription, RNA processing, and export, and predict further coupling with translation and nonsense-mediated decay. We systematically corroborate our analysis with two independent, comprehensive experimental data sets. The methods presented here may be generalized to other biological processes and organisms to generate principled, systems-level network models that provide experimentally testable hypotheses for coupling among biological machines.

Original languageEnglish (US)
Article numbermsb4100045
Pages (from-to)2006.0003
JournalMolecular systems biology
Volume2
DOIs
StatePublished - May 16 2006
Externally publishedYes

Keywords

  • Gene expression
  • Network analysis
  • Protein interactions
  • RNA processing
  • Transcription

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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