Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using TranscriptomicsReport as inadecuate




Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics - Download this document for free, or read online. Document in PDF available to download.

The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions.



Author: Becky M. Hess, Junfeng Xue, Lye Meng Markillie, Ronald C. Taylor, H. Steven Wiley, Birgitte K. Ahring, Bryan Linggi

Source: http://plos.srce.hr/



DOWNLOAD PDF




Related documents