SPINE: SParse eIgengene NEtwork linking gene expression clusters in Dehalococcoides mccartyi to perturbations in experimental conditions

PLoS One. 2015 Feb 25;10(2):e0118404. doi: 10.1371/journal.pone.0118404. eCollection 2015.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Chloroflexi / genetics*
  • Cluster Analysis
  • Computational Biology / methods
  • Gene Expression Profiling
  • Gene Expression Regulation, Bacterial*
  • Gene Regulatory Networks*
  • Models, Biological*
  • Models, Statistical*
  • Reproducibility of Results
  • Stress, Physiological / genetics
  • Transcriptome*

Grants and funding

The authors would like to thank our funding sources, the Department of Defense Army Research Office (W911NF- 07-1-0249) and the National Science Foundation CBET Program (CBET-0731169), for supporting this work. Additionally, the authors would like to thank the NSF Graduate Research Fellowship Program for funding Cresten Mansfeldt and Cornell University’s Engineering Learning Initiative for funding Garrett Debs in this research. A portion of this research was performed using EMSL, a national scientific user facility sponsored by the Department of Energy’s Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.