%0 Journal Article %1 Imam2015Integrated %A Imam, Saheed %A Noguera, Daniel R. %A Donohue, Timothy J. %D 2015 %I Public Library of Science %J PLoS Comput Biol %K genome-scale reconstruction transcriptional-regulatory-network %N 2 %P e1004103+ %R 10.1371/journal.pcbi.1004103 %T An Integrated Approach to Reconstructing Genome-Scale Transcriptional Regulatory Networks %U http://dx.doi.org/10.1371/journal.pcbi.1004103 %V 11 %X 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. The ever growing amount of genomic data enables the assembly of large-scale network models that can provide important new insights into living systems. However, assembly and validation of such large-scale models can be challenging, since we often lack sufficient information to make accurate predictions. This work describes a new approach for constructing large-scale transcriptional regulatory networks of individual cells. We show that the reconstructed network captures a significantly larger fraction of cellular regulatory processes than networks generated by other existing approaches. We predict this approach, with appropriate refinements, will allow reconstruction of large-scale transcriptional network models for a variety of other organisms. As we work towards modeling the function of cells or complex ecosystems, individually reconstructed network models of signaling, information transfer and metabolism, can be integrated to provide high information predictions and insights not otherwise obtainable.

@article{Imam2015Integrated, abstract = {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. The ever growing amount of genomic data enables the assembly of large-scale network models that can provide important new insights into living systems. However, assembly and validation of such large-scale models can be challenging, since we often lack sufficient information to make accurate predictions. This work describes a new approach for constructing large-scale transcriptional regulatory networks of individual cells. We show that the reconstructed network captures a significantly larger fraction of cellular regulatory processes than networks generated by other existing approaches. We predict this approach, with appropriate refinements, will allow reconstruction of large-scale transcriptional network models for a variety of other organisms. As we work towards modeling the function of cells or complex ecosystems, individually reconstructed network models of signaling, information transfer and metabolism, can be integrated to provide high information predictions and insights not otherwise obtainable.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Imam, Saheed and Noguera, Daniel R. and Donohue, Timothy J.}, biburl = {https://www.bibsonomy.org/bibtex/2cfda1183335af48f4d7414bbf892c30a/karthikraman}, citeulike-article-id = {13562278}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1004103}, day = 27, doi = {10.1371/journal.pcbi.1004103}, interhash = {c7c51d410f7add548402ec2eb5bf0dfd}, intrahash = {cfda1183335af48f4d7414bbf892c30a}, journal = {PLoS Comput Biol}, keywords = {genome-scale reconstruction transcriptional-regulatory-network}, month = feb, number = 2, pages = {e1004103+}, posted-at = {2015-04-15 10:36:50}, priority = {2}, publisher = {Public Library of Science}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {An Integrated Approach to Reconstructing {Genome-Scale} Transcriptional Regulatory Networks}, url = {http://dx.doi.org/10.1371/journal.pcbi.1004103}, volume = 11, year = 2015 }