Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcriptome analysis of Escherichia coli during dGTP Starvation

ABSTRACT: Our laboratory has recently discovered that E. coli cells starved for the DNA precursor dGTP are killed efficiently (dGTP starvation) in a manner similar to that described for Thymineless Death (TLD). Conditions for specific dGTP starvation can be achieved by depriving an E. coli optA1 gpt strain of the purine nucleotide precursor hypoxanthine (Hx). To gain insight into the mechanisms underlying dGTP starvation, we conducted genome-wide gene expression analyses on actively growing optA1 gpt strains subjected to hypoxanthine deprivation for increasing periods of time. The data show that, upon Hx withdrawal, the optA1 gpt strain displays a diminished ability to de-repress the de novo purine biosynthesis genes, and this is likely due to internal guanine accumulation. The impairment to fully induce the purR regulon may be a contributing factor to the lethality of dGTP starvation. At later time points, and coinciding with cell lethality, strong induction of the SOS is observed, supporting the concept of replication stress as a final cause of death. No evidence was observed for the participation of other stress responses, including the rpoS-mediated global stress response in the starved cells, and reinforcing the lack of feedback of replication stress into the global metabolism of the cell. The genome-wide expression data also provide direct evidence for increased genome complexity during dGTP starvation, as a markedly increased gradient is observed for expression of genes located nearby the replication origin relative to those located towards the replication terminus. The present study was aimed at understanding the metabolic changes in dGTP-starved cells. To do so, we designed two experiments. The first experiment was aimed at investigating the transcriptional consequences of loss of the Hx purine source in each of the four strains (wt, optA1, gpt, and optA1 gpt). Here, hypoxanthine was withdrawn from actively growing cells starting at OD 0.1, and periodic 2-fold dilutions were applied to keep to OD of the growing cultures below or only slightly above 0.20 (low-dilution protocol). Samples were taken for microarray analysis at 15, 30, 45, 60, and 120 min. At each time point RNA samples were taken and analyzed for gene expression changes using the Affymetrix GeneChip E. coli Genome 2.0 Array. This experiment was conducted three times, independently. A second experiment was aimed at following the transcriptional changes in the optA1 gpt strain during the entire 6-hour time course including the filamentation and cell death stage. Here, we used a slightly different dilution protocol (high-dilution protocol), in which the strain was initially highly diluted (OD630 = 0.01) and further diluted at strategic points to prevent the OD to rise above 0.1. These conditions optimize the cell death phenomenon. This experiment was also conducted three times independently.

ORGANISM(S): Escherichia coli str. K-12 substr. MG1655

SUBMITTER: NIEHS Microarray Core

PROVIDER: E-GEOD-80002 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:The ability of Leishmania to survive in their insect or mammalian host is dependent upon an ability to sense and adapt to changes in the microenvironment. However, little is known about the molecular mechanisms underlying the parasite response to environmental changes, such as nutrient availability. To elucidate nutrient stress response pathways in Leishmania donovani, we have used purine starvation as the paradigm. The salvage of purines from the host milieu is obligatory for parasite replication; nevertheless, purine-starved parasites can persist in culture without supplementary purine for over 3 months, indicating that the response to purine starvation is robust and engenders parasite survival under conditions of extreme scarcity. To understand metabolic reprogramming during purine starvation we have employed global approaches. Whole proteome comparisons between purine-starved and purine-replete parasites over a 6-48 h span have revealed a temporal and coordinated response to purine starvation. Purine transporters and enzymes involved in acquisition at the cell surface are upregulated within a few hours of purine removal from the media, while other key purine salvage components are upregulated later in the time-course and more modestly.Â After 48 h, the proteome of purine-starved parasites is extensively remodeled and adaptations to purine stress appear tailored to deal with both purine deprivation and general stress. To probe the molecular mechanisms affecting proteome remodeling in response to purine starvation, comparative RNA-seq analyses, qRT-PCR, and luciferase reporter assays were performed on purine-starved versus purine-replete parasites.Â While the regulation of a minority of proteins tracked with changes at the mRNA level, for many regulated proteins it appears that proteome remodeling during purine stress occurs primarily via translational and post-translational mechanisms. One mRNA sample from each of Purine-Starved and Purine-Replete cells were analyzed using SL RNA-Seq methodology

Project description:Transcription profiling of wild type, relA-, and relA-spoT-, crp-, dksA-, rpoS-, lrp- mutant strains of E. coli starved for isoleucine Bacteria comprehensively reorganize their global gene expression when faced with nutrient exhaustion. In Escherichia coli and other free-living bacteria, the alarmone ppGpp facilitates this massive response by directly or indirectly coordinating the down-regulation of genes of the translation apparatus, and the induction of biosynthetic genes and the general stress response. Such a large reorientation likely requires the cooperative activities of many different genetic regulators, yet the structure of the transcription network below the level of ppGpp remains poorly defined. Using isoleucine starvation as an experimental model system for amino acid starvation, we identified genes that required ppGpp, Lrp, and RpoS for their induction. Surprisingly, despite the fact that the overwhelming majority of genes controlled by Lrp and RpoS required ppGpp for their activation, we found that these two regulons were not induced simultaneously. The data reported here suggest that metabolic genes, such as those of the Lrp regulon, require only a low basal level of ppGpp for their efficient induction. In contrast, the RpoS-dependent general stress response is not robustly induced until relatively high levels of ppGpp accumulate. Here we describe a data-driven conceptual model that explains how bacterial cells allocate transcriptional resources between metabolic and stress survival processes by discretely tuning regulatory activities to a central indicator of cellular physiology. The regulatory structure that emerges is consistent with a rheostatic model of the stringent response that allows cells to efficiently adapt to a wide range of nutritional environments. Keywords: genetic modification design; stress response; isoleucine starvation Two experiments were run. First experiment: WT and several mutant strains were starved for isoleucine (exhaustion of 60 uM ile). 40 minutes after starvation, RNA was extracted. All samples were compared to RNA from rapidly growing WT cells in identical medium replete with isoleucine (400 uM). 16 samples were hybridized: duplicates of 8 strains/conditions. Wildtype in log phase (OD = 0.4) with replete ile was control for ile starved samples. Second experiment: WT strain was starved for isoleucine (exhaustion of 60 uM ile). RNA was extracted at 12 timepoints as the cells entered stationary phase. All samples were compared to RNA from rapidly growing WT cells in identical medium replete with isoleucine (400 uM). 14 samples were hybridized: duplicates of the control condition and single timepoints during starvation. Wildtype in log phase (OD = 0.4) with replete ile was control for ile starved samples.

Dataset Information

Transcriptome analysis of Escherichia coli during dGTP Starvation

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets