Unknown

Dataset Information

0

Systems-level analyses identify extensive coupling among gene expression machines.


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 approximately 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.

SUBMITTER: Maciag K 

PROVIDER: S-EPMC1681477 | BioStudies | 2006-01-01T00:00:00Z

REPOSITORIES: biostudies

Similar Datasets

1000-01-01 | S-EPMC3159981 | BioStudies
2017-01-01 | S-EPMC5599802 | BioStudies
2001-01-01 | S-EPMC99856 | BioStudies
2003-01-01 | S-EPMC187350 | BioStudies
2009-01-01 | S-EPMC2629578 | BioStudies
1000-01-01 | S-EPMC5388395 | BioStudies
2019-01-01 | S-EPMC6391453 | BioStudies
2012-01-01 | S-EPMC3273795 | BioStudies
1000-01-01 | S-EPMC1140607 | BioStudies
2001-01-01 | S-EPMC31267 | BioStudies