Analysis of Pleiotropic Transcriptional Profiles: A Case Study of DNA Gyrase Inhibition

Open Access
Publisher Website
Google Scholar
Abstract
Genetic and environmental perturbations often result in complex transcriptional responses involving multiple genes and regulons. In order to understand the nature of a response, one has to account for the contribution of the downstream effects to the formation of a response. Such analysis can be carried out within a statistical framework in which the individual effects are independently collected and then combined within a linear model. Here, we modeled the contribution of DNA replication, supercoiling, and repair to the transcriptional response of inhibition of the Escherichia coli gyrase. By representing the gyrase inhibition as a true pleiotropic phenomenon, we were able to demonstrate that: (1) DNA replication is required for the formation of spatial transcriptional domains; (2) the transcriptional response to the gyrase inhibition is coordinated between at least two modules involved in DNA maintenance, relaxation and damage response; (3) the genes whose transcriptional response to the gyrase inhibition does not depend on the main relaxation activity of the cell can be classified on the basis of a GC excess in their upstream and coding sequences; and (4) relaxation by topoisomerase I dominates the transcriptional response, followed by the effects of replication and RecA. We functionally tested the effect of the interaction between relaxation and repair activities, and found support for the model derived from the microarray data. We conclude that modeling compound transcriptional profiles as a combination of downstream transcriptional effects allows for a more realistic, accurate, and meaningful representation of the transcriptional activity of a genome. Pleiotropism—a movement, or reaction, in multiple directions: although it was initially used specifically to describe the effect of a single genetic mutation on multiple characters in the offspring, the transcriptional responses of cells are often best described in terms of pleiotropy, when a single input affects multiple components inside the cell. This, in turn, presents a dilemma with the analysis and interpretation of the observed effects: which effects are directly due to the input itself and which are not? How are the effects related to each other and which are more important? And finally, can the overall transcriptional response be summarized as a combination of the effects? There is, however, a problem with recording the effects when they occur almost simultaneously in the same organism. The authors approached this by recording the effects independently, using mutants that could generate all of the effects of interest but one, and then estimating the effects and their interactions from a multivariate linear model. The authors applied this method to explain the transcriptional response of Escherichia coli to a quinolone antibacterial, a relative of Cipro (ciprofloxacin hydrochloride), and discovered unexpected interactions between DNA maintenance modules in the cell.