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An Integrated Approach to Reconstructing Genome-Scale Transcriptional Regulatory Networks

Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene ex...

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Detalles Bibliográficos
Autores principales: Imam, Saheed, Noguera, Daniel R., Donohue, Timothy J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4344238/
https://www.ncbi.nlm.nih.gov/pubmed/25723545
http://dx.doi.org/10.1371/journal.pcbi.1004103
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author Imam, Saheed
Noguera, Daniel R.
Donohue, Timothy J.
author_facet Imam, Saheed
Noguera, Daniel R.
Donohue, Timothy J.
author_sort Imam, Saheed
collection PubMed
description Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied γ-proteobacterium Escherichia coli showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the E. coli TRN than expression-based approaches, potentially making them highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the α-Proteobacterium Rhodobacter sphaeroides that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in R. sphaeroides and/or other bacteria. Experimental validation of predictions from this R. sphaeroides TRN model showed that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to R. sphaeroides TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of integrating comparative genomics of closely related organisms with gene expression data to assemble large-scale TRN models with high-quality predictions.
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spelling pubmed-43442382015-03-04 An Integrated Approach to Reconstructing Genome-Scale Transcriptional Regulatory Networks Imam, Saheed Noguera, Daniel R. Donohue, Timothy J. PLoS Comput Biol Research Article Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied γ-proteobacterium Escherichia coli showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the E. coli TRN than expression-based approaches, potentially making them highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the α-Proteobacterium Rhodobacter sphaeroides that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in R. sphaeroides and/or other bacteria. Experimental validation of predictions from this R. sphaeroides TRN model showed that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to R. sphaeroides TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of integrating comparative genomics of closely related organisms with gene expression data to assemble large-scale TRN models with high-quality predictions. Public Library of Science 2015-02-27 /pmc/articles/PMC4344238/ /pubmed/25723545 http://dx.doi.org/10.1371/journal.pcbi.1004103 Text en © 2015 Imam et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Imam, Saheed
Noguera, Daniel R.
Donohue, Timothy J.
An Integrated Approach to Reconstructing Genome-Scale Transcriptional Regulatory Networks
title An Integrated Approach to Reconstructing Genome-Scale Transcriptional Regulatory Networks
title_full An Integrated Approach to Reconstructing Genome-Scale Transcriptional Regulatory Networks
title_fullStr An Integrated Approach to Reconstructing Genome-Scale Transcriptional Regulatory Networks
title_full_unstemmed An Integrated Approach to Reconstructing Genome-Scale Transcriptional Regulatory Networks
title_short An Integrated Approach to Reconstructing Genome-Scale Transcriptional Regulatory Networks
title_sort integrated approach to reconstructing genome-scale transcriptional regulatory networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4344238/
https://www.ncbi.nlm.nih.gov/pubmed/25723545
http://dx.doi.org/10.1371/journal.pcbi.1004103
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