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Systems-level analyses identify extensive coupling among gene expression machines

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 prot...

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Detalles Bibliográficos
Autores principales: Maciag, Karolina, Altschuler, Steven J, Slack, Michael D, Krogan, Nevan J, Emili, Andrew, Greenblatt, Jack F, Maniatis, Tom, Wu, Lani F
Formato: Texto
Lenguaje:English
Publicado: 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1681477/
https://www.ncbi.nlm.nih.gov/pubmed/16738550
http://dx.doi.org/10.1038/msb4100045
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author Maciag, Karolina
Altschuler, Steven J
Slack, Michael D
Krogan, Nevan J
Emili, Andrew
Greenblatt, Jack F
Maniatis, Tom
Wu, Lani F
author_facet Maciag, Karolina
Altschuler, Steven J
Slack, Michael D
Krogan, Nevan J
Emili, Andrew
Greenblatt, Jack F
Maniatis, Tom
Wu, Lani F
author_sort Maciag, Karolina
collection PubMed
description 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.
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spelling pubmed-16814772007-01-25 Systems-level analyses identify extensive coupling among gene expression machines Maciag, Karolina Altschuler, Steven J Slack, Michael D Krogan, Nevan J Emili, Andrew Greenblatt, Jack F Maniatis, Tom Wu, Lani F Mol Syst Biol Article 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. 2006-01-17 /pmc/articles/PMC1681477/ /pubmed/16738550 http://dx.doi.org/10.1038/msb4100045 Text en Copyright © 2006, EMBO and Nature Publishing Group
spellingShingle Article
Maciag, Karolina
Altschuler, Steven J
Slack, Michael D
Krogan, Nevan J
Emili, Andrew
Greenblatt, Jack F
Maniatis, Tom
Wu, Lani F
Systems-level analyses identify extensive coupling among gene expression machines
title Systems-level analyses identify extensive coupling among gene expression machines
title_full Systems-level analyses identify extensive coupling among gene expression machines
title_fullStr Systems-level analyses identify extensive coupling among gene expression machines
title_full_unstemmed Systems-level analyses identify extensive coupling among gene expression machines
title_short Systems-level analyses identify extensive coupling among gene expression machines
title_sort systems-level analyses identify extensive coupling among gene expression machines
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1681477/
https://www.ncbi.nlm.nih.gov/pubmed/16738550
http://dx.doi.org/10.1038/msb4100045
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