Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.; Taylor, Ronald C.; Weisenhorn, Pamela; Olson, Robert D.; Stevens, Rick L.; Rocha, Miguel; Rocha, Isabel; Best, Aaron A.; DeJongh, Matthew; Tintle, Nathan L.; Parrello, Bruce; Overbeek, Ross; Henry, Christopher S.

doi:10.3389/fmicb.2016.01819

Title: Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

Journal Article · Thu Nov 24 00:00:00 UTC 2016 · Frontiers in Microbiology

DOI: https://doi.org/10.3389/fmicb.2016.01819 · OSTI ID:1339825

Faria, José P. ^[1]; Davis, James J. ^[2]; Edirisinghe, Janaka N. ^[2]; Taylor, Ronald C. ^[3]; Weisenhorn, Pamela ^[4]; Olson, Robert D. ^[2]; Stevens, Rick L. ^[5]; Rocha, Miguel ^[6]; Rocha, Isabel ^[6]; Best, Aaron A. ^[7]; DeJongh, Matthew ^[7]; Tintle, Nathan L. ^[8]; Parrello, Bruce ^[9]; Overbeek, Ross ^[10]; Henry, Christopher S. ^[2]

Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Minho, Braga (Portugal)
Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)
Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, Chicago, IL (United States)
Univ. of Minho, Braga (Portugal)
Hope College, Holand, MI (United States)
Dordt College, Sioux, IA (United States)
Argonne National Lab. (ANL), Argonne, IL (United States); Fellowship for Interpretation of Genomes, Burr Ridge, IL (United States)
Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States); Fellowship for Interpretation of Genomes, Burr Ridge, IL (United States)

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. A multitude of technologies, abstractions, and interpretive frameworks have emerged to answer the challenges presented by genome function and regulatory network inference. Here, we propose a new approach for producing biologically meaningful clusters of coexpressed genes, called Atomic Regulons (ARs), based on expression data, gene context, and functional relationships. We demonstrate this new approach by computing ARs for Escherichia coli, which we compare with the coexpressed gene clusters predicted by two prevalent existing methods: hierarchical clustering and k-means clustering. We test the consistency of ARs predicted by all methods against expected interactions predicted by the Context Likelihood of Relatedness (CLR) mutual information based method, finding that the ARs produced by our approach show better agreement with CLR interactions. We then apply our method to compute ARs for four other genomes: Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus. We compare the AR clusters from all genomes to study the similarity of coexpression among a phylogenetically diverse set of species, identifying subsystems that show remarkable similarity over wide phylogenetic distances. We also study the sensitivity of our method for computing ARs to the expression data used in the computation, showing that our new approach requires less data than competing approaches to converge to a near final configuration of ARs. We go on to use our sensitivity analysis to identify the specific experiments that lead most rapidly to the final set of ARs for E. coli. As a result, this analysis produces insights into improving the design of gene expression experiments.

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Research Organization:: Argonne National Laboratory (ANL); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: Fundacao para a Ciencia ea Tecnologia of Portugal; National Institutes of Health (NIH). National Institute of Allergy and Infectious Diseases (NIAID); National Science Foundation (NSF); U. S. Department of Health and Human Services; USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)

Grant/Contract Number:: AC02-06CH11357; AC05-76RL01830

OSTI ID:: 1339825

Report Number(s):: PNNL-SA--115054; KP1601010

Journal Information:: Frontiers in Microbiology, Journal Name: Frontiers in Microbiology Vol. 7; ISSN 1664-302X

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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Advantages and limitations of current network inference methods De Smet, Riet; Marchal, Kathleen Nature Reviews Microbiology, Vol. 8, Issue 10 https://doi.org/10.1038/nrmicro2419	journal	August 2010
Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis Chandrasekaran, Sriram; Price, Nathan D. Proceedings of the National Academy of Sciences, Vol. 107, Issue 41 https://doi.org/10.1073/pnas.1005139107	journal	September 2010
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RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more Salgado, Heladia; Peralta-Gil, Martin; Gama-Castro, Socorro Nucleic Acids Research, Vol. 41, Issue D1 https://doi.org/10.1093/nar/gks1201	journal	November 2012
PATRIC, the bacterial bioinformatics database and analysis resource Wattam, Alice R.; Abraham, David; Dalay, Oral Nucleic Acids Research, Vol. 42, Issue D1 https://doi.org/10.1093/nar/gkt1099	journal	November 2013
The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST) Overbeek, Ross; Olson, Robert; Pusch, Gordon D. Nucleic Acids Research, Vol. 42, Issue D1 https://doi.org/10.1093/nar/gkt1226	journal	November 2013
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DeGNServer: Deciphering Genome-Scale Gene Networks through High Performance Reverse Engineering Analysis Li, Jun; Wei, Hairong; Zhao, Patrick Xuechun BioMed Research International, Vol. 2013 https://doi.org/10.1155/2013/856325	journal	January 2013
Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data Tintle, Nathan L.; Sitarik, Alexandra; Boerema, Benjamin BMC Bioinformatics, Vol. 13, Issue 1 https://doi.org/10.1186/1471-2105-13-193	journal	August 2012
Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles Faith, Jeremiah J.; Hayete, Boris; Thaden, Joshua T. PLoS Biology, Vol. 5, Issue 1 https://doi.org/10.1371/journal.pbio.0050008	journal	January 2007
Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S. PLoS Computational Biology, Vol. 10, Issue 10 https://doi.org/10.1371/journal.pcbi.1003882	journal	October 2014
Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data Faria, José P.; Overbeek, Ross; Taylor, Ronald C. Frontiers in Microbiology, Vol. 7 https://doi.org/10.3389/fmicb.2016.00275	journal	March 2016

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59 BASIC BIOLOGICAL SCIENCES
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Atomic Regulon
CLR
Escherichia coli
atomic regulon
clustering
gene expression analysis
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hierarchical clustering
k-means clustering
transcriptomic data

Title: Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

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