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SPINE: SParse eIgengene NEtwork Linking Gene Expression Clusters in Dehalococcoides mccartyi to Perturbations in Experimental Conditions

We present a statistical model designed to identify the effect of experimental perturbations on the aggregate behavior of the transcriptome expressed by the bacterium Dehalococcoides mccartyi strain 195. Strains of Dehalococcoides are used in sub-surface bioremediation applications because they orga...

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Autores principales: Mansfeldt, Cresten B., Logsdon, Benjamin A., Debs, Garrett E., Richardson, Ruth E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4340931/
https://www.ncbi.nlm.nih.gov/pubmed/25714365
http://dx.doi.org/10.1371/journal.pone.0118404
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author Mansfeldt, Cresten B.
Logsdon, Benjamin A.
Debs, Garrett E.
Richardson, Ruth E.
author_facet Mansfeldt, Cresten B.
Logsdon, Benjamin A.
Debs, Garrett E.
Richardson, Ruth E.
author_sort Mansfeldt, Cresten B.
collection PubMed
description We present a statistical model designed to identify the effect of experimental perturbations on the aggregate behavior of the transcriptome expressed by the bacterium Dehalococcoides mccartyi strain 195. Strains of Dehalococcoides are used in sub-surface bioremediation applications because they organohalorespire tetrachloroethene and trichloroethene (common chlorinated solvents that contaminate the environment) to non-toxic ethene. However, the biochemical mechanism of this process remains incompletely described. Additionally, the response of Dehalococcoides to stress-inducing conditions that may be encountered at field-sites is not well understood. The constructed statistical model captured the aggregate behavior of gene expression phenotypes by modeling the distinct eigengenes of 100 transcript clusters, determining stable relationships among these clusters of gene transcripts with a sparse network-inference algorithm, and directly modeling the effect of changes in experimental conditions by constructing networks conditioned on the experimental state. Based on the model predictions, we discovered new response mechanisms for DMC, notably when the bacterium is exposed to solvent toxicity. The network identified a cluster containing thirteen gene transcripts directly connected to the solvent toxicity condition. Transcripts in this cluster include an iron-dependent regulator (DET0096-97) and a methylglyoxal synthase (DET0137). To validate these predictions, additional experiments were performed. Continuously fed cultures were exposed to saturating levels of tetrachloethene, thereby causing solvent toxicity, and transcripts that were predicted to be linked to solvent toxicity were monitored by quantitative reverse-transcription polymerase chain reaction. Twelve hours after being shocked with saturating levels of tetrachloroethene, the control transcripts (encoding for a key hydrogenase and the 16S rRNA) did not significantly change. By contrast, transcripts for DET0137 and DET0097 displayed a 46.8±11.5 and 14.6±9.3 fold up-regulation, respectively, supporting the model. This is the first study to identify transcripts in Dehalococcoides that potentially respond to tetrachloroethene solvent-toxicity conditions that may be encountered near contamination source zones in sub-surface environments.
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spelling pubmed-43409312015-03-04 SPINE: SParse eIgengene NEtwork Linking Gene Expression Clusters in Dehalococcoides mccartyi to Perturbations in Experimental Conditions Mansfeldt, Cresten B. Logsdon, Benjamin A. Debs, Garrett E. Richardson, Ruth E. PLoS One Research Article We present a statistical model designed to identify the effect of experimental perturbations on the aggregate behavior of the transcriptome expressed by the bacterium Dehalococcoides mccartyi strain 195. Strains of Dehalococcoides are used in sub-surface bioremediation applications because they organohalorespire tetrachloroethene and trichloroethene (common chlorinated solvents that contaminate the environment) to non-toxic ethene. However, the biochemical mechanism of this process remains incompletely described. Additionally, the response of Dehalococcoides to stress-inducing conditions that may be encountered at field-sites is not well understood. The constructed statistical model captured the aggregate behavior of gene expression phenotypes by modeling the distinct eigengenes of 100 transcript clusters, determining stable relationships among these clusters of gene transcripts with a sparse network-inference algorithm, and directly modeling the effect of changes in experimental conditions by constructing networks conditioned on the experimental state. Based on the model predictions, we discovered new response mechanisms for DMC, notably when the bacterium is exposed to solvent toxicity. The network identified a cluster containing thirteen gene transcripts directly connected to the solvent toxicity condition. Transcripts in this cluster include an iron-dependent regulator (DET0096-97) and a methylglyoxal synthase (DET0137). To validate these predictions, additional experiments were performed. Continuously fed cultures were exposed to saturating levels of tetrachloethene, thereby causing solvent toxicity, and transcripts that were predicted to be linked to solvent toxicity were monitored by quantitative reverse-transcription polymerase chain reaction. Twelve hours after being shocked with saturating levels of tetrachloroethene, the control transcripts (encoding for a key hydrogenase and the 16S rRNA) did not significantly change. By contrast, transcripts for DET0137 and DET0097 displayed a 46.8±11.5 and 14.6±9.3 fold up-regulation, respectively, supporting the model. This is the first study to identify transcripts in Dehalococcoides that potentially respond to tetrachloroethene solvent-toxicity conditions that may be encountered near contamination source zones in sub-surface environments. Public Library of Science 2015-02-25 /pmc/articles/PMC4340931/ /pubmed/25714365 http://dx.doi.org/10.1371/journal.pone.0118404 Text en © 2015 Mansfeldt 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
Mansfeldt, Cresten B.
Logsdon, Benjamin A.
Debs, Garrett E.
Richardson, Ruth E.
SPINE: SParse eIgengene NEtwork Linking Gene Expression Clusters in Dehalococcoides mccartyi to Perturbations in Experimental Conditions
title SPINE: SParse eIgengene NEtwork Linking Gene Expression Clusters in Dehalococcoides mccartyi to Perturbations in Experimental Conditions
title_full SPINE: SParse eIgengene NEtwork Linking Gene Expression Clusters in Dehalococcoides mccartyi to Perturbations in Experimental Conditions
title_fullStr SPINE: SParse eIgengene NEtwork Linking Gene Expression Clusters in Dehalococcoides mccartyi to Perturbations in Experimental Conditions
title_full_unstemmed SPINE: SParse eIgengene NEtwork Linking Gene Expression Clusters in Dehalococcoides mccartyi to Perturbations in Experimental Conditions
title_short SPINE: SParse eIgengene NEtwork Linking Gene Expression Clusters in Dehalococcoides mccartyi to Perturbations in Experimental Conditions
title_sort spine: sparse eigengene network linking gene expression clusters in dehalococcoides mccartyi to perturbations in experimental conditions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4340931/
https://www.ncbi.nlm.nih.gov/pubmed/25714365
http://dx.doi.org/10.1371/journal.pone.0118404
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