Project description:Deeper understanding of antibiotic-induced physiological responses is critical to identifying means for enhancing our current antibiotic arsenal. Bactericidal antibiotics with diverse targets have been hypothesized to kill bacteria, in part, by inducing production of damaging reactive species. This notion has been supported by many groups, but recently challenged. Here we robustly test the hypothesis using biochemical, enzymatic and biophysical assays along with genetic and phenotypic experiments. We first used a novel intracellular hydrogen peroxide (H2O2) sensor, together with a chemically diverse panel of fluorescent dyes sensitive to an array of reactive species, to demonstrate that antibiotics broadly induce redox stress. Subsequent gene expression analyses reveal that complex antibiotic-induced oxidative stress responses are distinct from canonical responses generated by supra-physiological levels of H2O2. We next developed a method to dynamically quantify cellular respiration and found that bactericidal antibiotics elevate oxygen consumption, indicating significant alterations to bacterial redox physiology. We further show that catalase or DNA mismatch repair enzyme overexpression, as well as antioxidant pre-treatment limit antibiotic lethality, indicating that reactive oxygen species causatively contribute to antibiotic killing. Critically, the killing efficacy of antibiotics was diminished under strict anaerobic conditions, but could be enhanced by exposure to molecular oxygen or addition of alternative electron acceptors, suggesting that environmental factors play a role in killing cells physiologically primed for death. This work provides direct evidence that bactericidal antibiotics, downstream of their target-specific interactions, induce complex redox alterations that contribute to cellular damage and death, thus supporting an evolving, expanded model of antibiotic lethality. Here, we used microarrays to analyze oxidative stress responses to bactericidal antibiotic treatment in wildtype and mutant E coli

Project description:Our group used rectal biopsies from patients with active ulcerative colitis (UC) and differential response to glucocorticoid treatment, to generate a predictive computational strategy based on systems biology. Contrasting mRNA and miRNAs intestinal expression profiles with updated molecular information related to UC and glucocorticoids, we were able to identify a mechanism of action (MoA) associated with corticosteroid failure in UC. ith the aim of transferring these results to clinical practice, the MoA-models created previously were implemented with plasmatic miRNAs profiles, to describe in silico non-invasive potential biomarkers of glucocorticoid refractoriness. Small RNA-seq analysis was perfomed using Illumina next generation sequencing and standard statistical criteria were applied to compare the miRNA profiles, incluidng paired samples before clucocorticoid treatment to the third day of the steroidal treatment. The selection these miRNA with predictive capacity was done using two complementary strategies: the first one using all the obtained miRNAs, and the second taking into account those miRNAs that are directly related to MoA, through various statistical restrictions (RELIEF, ENTROPY + CORRELATION, WILCOXON, ROC, and simple REGRESSION). These computational approaches from all miRNAs identified hsa-miR218-5p as the best to classify patients according their respond at basal time (Accuracy = 84.21%, Generalization Capability = 84.21%, Sensitivity = 100% and Specificity = 66.67%). In addition, the combination of this miRNA with hsa-miR6754-5p and hsa-miR4767, increases the diagnosis accuracy of the disease (Accuracy = 94.74%, Generalization Capability = 75.44%, Sensitivity = 70% and specificity = 81.48%). On the other hand, the combination of hsa-miR218-5p and the plasma levels of the Protein C Reactive increases the ability to classify patients towards their response to glucocorticoids up to 89%. Taking in account all this data, we can conclude that by means of a computer strategy based on Biology of Systems we have identified the plasmatic miR-218-5p as a potential biomarker to glucocorticoid response in UC before start the treatment.

Project description:Deeper understanding of antibiotic-induced physiological responses is critical to identifying means for enhancing our current antibiotic arsenal. Bactericidal antibiotics with diverse targets have been hypothesized to kill bacteria, in part, by inducing production of damaging reactive species. This notion has been supported by many groups, but recently challenged. Here we robustly test the hypothesis using biochemical, enzymatic and biophysical assays along with genetic and phenotypic experiments. We first used a novel intracellular hydrogen peroxide (H2O2) sensor, together with a chemically diverse panel of fluorescent dyes sensitive to an array of reactive species, to demonstrate that antibiotics broadly induce redox stress. Subsequent gene expression analyses reveal that complex antibiotic-induced oxidative stress responses are distinct from canonical responses generated by supra-physiological levels of H2O2. We next developed a method to dynamically quantify cellular respiration and found that bactericidal antibiotics elevate oxygen consumption, indicating significant alterations to bacterial redox physiology. We further show that catalase or DNA mismatch repair enzyme overexpression, as well as antioxidant pre-treatment limit antibiotic lethality, indicating that reactive oxygen species causatively contribute to antibiotic killing. Critically, the killing efficacy of antibiotics was diminished under strict anaerobic conditions, but could be enhanced by exposure to molecular oxygen or addition of alternative electron acceptors, suggesting that environmental factors play a role in killing cells physiologically primed for death. This work provides direct evidence that bactericidal antibiotics, downstream of their target-specific interactions, induce complex redox alterations that contribute to cellular damage and death, thus supporting an evolving, expanded model of antibiotic lethality. Here, we used microarrays to analyze oxidative stress responses to bactericidal antibiotic treatment in wildtype and mutant E coli WT or mutant E coli cells were grown to OD ~0.3. Untreated cells were harvested at time 0 as controls. Treated cells given the appropriate chemical perturbation and harvested 1 hour post-treatment. All experiments were performed in technical triplicate.

Dataset Information

Predictive Signatures of 19 Antibiotic-Induced Escherichia coli Proteomes

Publications

Predictive Signatures of 19 Antibiotic-Induced <i>Escherichia coli</i> Proteomes.

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