Project description:RNA sequencing was performed on E. coli K12 MG1655 on three media (M9, CA-MHB, R10LB) treated with four antibiotics (Ciprofloxacin, Trimethoprim-sulfamethoxazole, Ceftriaxone, Meropenem) at their media-specific MIC90s
Project description:Microarrays allow us to monitor the change in transcription of every gene in the genome in response to a change in cellular state. We use cDNA microarrays to measure the response of E. coli to 13 different antibiotics and 3 synergistic combinations. Hierarchichal clustering reveals 4 distinct classes of antibiotics distinguished by their modes of action, and allows us to predict the mechanism for promethazine, a drug whose mode of action has not previously been established. The expression profiles of the synergistic combinations exhibit a complex relationship between the two component antibiotics, with similarity to one of the two drugs, as well as a surprising number of new gene responses exhibited by E. coli in response to neither drug alone. The subset of drugs which act in synergy with each other suggests that only very specific combination of mechanisms give rise to synergistic behavior. Keywords: stress response, antibiotic response, synergy
Project description:To assay every gene in the E. coli genome to identify those that contribute to increased or decreased susceptibility to the antibiotics trimethoprim and sulfamethoxazole. This will help to define more accurately those bacterial cell mechanisms that contribute to these phenomena and provide information that will contribute to the development of new antibiotics, or compounds or known antibiotics that synergise with those already in clinical use. Thus, this set of experiments confirmed that AZT, widely known for its antiviral activity, acts synergistically with trimehoprim.
Project description:To investigate and compare transcriptomic changes of Escherichia coli K-12 MG1655, the bacterium was exposed to nine antibiotics (tetracycline, mitomycin C ,imipenem, ceftazidime, kanamycin, ciprofloxacin, polymyxin E, erythromycin, and chloramphenicol) , and RNA-Seq was performed to determine comparative transcriptomic changes.
Project description:Identifying the mode of action (MOA) of antibacterial compounds is the fundamental basis for the development of new antibiotics, and the challenge increases with the emerging secondary and indirect effect from antibiotic stress. Although various omics-based system biology approaches of defining antibiotic MOA are currently available, they still need improved throughput, accuracy and comprehensiveness. Using high resolution accurate mass (HR/AM) based proteomics, we present here a comprehensive reference map of proteomic signatures of Escherichia coli under antibiotics challenge. With state-of-the-art label-free approach, we quantified > 1,500 protein groups in response to 19 FDA-approved antibiotics. Applying several machine learning techniques, we derived a panel of 14 proteins that can be used to classify antibiotics into different MOAs with nearly 100% accuracy. Interestingly, these proteins tend to mediate diverse bacterial cellular and metabolic processes. Transcriptomic level profiling correlates well with changes in protein expression in discriminating different antibiotics. Such expression signatures will aid future studies in identifying MOA of unknown compounds and facilitate the discovery of novel antibiotics. In summary, our study offers a practical approach for effective and rapid proteomic profiling, establishes a high quality reference compendium of microbial proteome in response to a wide range of antibiotics exposure, and provides a previously undescribed group of proteins and RNAs that allows rapid antibiotic classification and MOA determination.
Project description:Identifying the mode of action (MOA) of antibacterial compounds is the fundamental basis for the development of new antibiotics, and the challenge increases with the emerging secondary and indirect effect from antibiotic stress. Although various omics-based system biology approaches of defining antibiotic MOA are currently available, they still need improved throughput, accuracy and comprehensiveness. Using high resolution accurate mass (HR/AM) based proteomics, we present here a comprehensive reference map of proteomic signatures of Escherichia coli under antibiotics challenge. With state-of-the-art label-free approach, we quantified > 1,500 protein groups in response to 19 FDA-approved antibiotics. Applying several machine learning techniques, we derived a panel of 14 proteins that can be used to classify antibiotics into different MOAs with nearly 100% accuracy. Interestingly, these proteins tend to mediate diverse bacterial cellular and metabolic processes. Transcriptomic level profiling correlates well with changes in protein expression in discriminating different antibiotics. Such expression signatures will aid future studies in identifying MOA of unknown compounds and facilitate the discovery of novel antibiotics. In summary, our study offers a practical approach for effective and rapid proteomic profiling, establishes a high quality reference compendium of microbial proteome in response to a wide range of antibiotics exposure, and provides a previously undescribed group of proteins and RNAs that allows rapid antibiotic classification and MOA determination.
Project description:Microarrays allow us to monitor the change in transcription of every gene in the genome in response to a change in cellular state. We use cDNA microarrays to measure the response of E. coli to 13 different antibiotics and 3 synergistic combinations. Hierarchichal clustering reveals 4 distinct classes of antibiotics distinguished by their modes of action, and allows us to predict the mechanism for promethazine, a drug whose mode of action has not previously been established. The expression profiles of the synergistic combinations exhibit a complex relationship between the two component antibiotics, with similarity to one of the two drugs, as well as a surprising number of new gene responses exhibited by E. coli in response to neither drug alone. The subset of drugs which act in synergy with each other suggests that only very specific combination of mechanisms give rise to synergistic behavior. Keywords: stress response, antibiotic response, synergy Each array has two spots to monitor the response of E coli to one of the treatments, and two control spots (ie no treatment). These are background corrected, normalized for total intensity, and then the average volume difference is calculated. Each treatment has two replicates, and the result is average across these two replicates. Our final processed data is a relative volume difference, in which the aforementioned volume difference is divided by an estimate of the error in the data. This relative volume difference gives us greater confidence that the changes we see are real. Any relative volume difference >= 2 or <= -2 (ie where the absolute volume difference is twice as much as the estimated error or more) is considered to be significant. Total of 68 hybridizations: 17 samples X 2 replicates for each sample X (1 sample + 1 control for each replicate)
Project description:We have reported that bicarbonate (NaHCO3) potentiates the activity of aminoglycosides in Escherichia coli, but the action mechanism was not identified. To eventually understand how NaHCO3 can potentiate antibiotics, we thought that a rational first step was to examine the effect of NaHCO3 separately and to inspect initial gene expression changes triggered by it. In this work we started by confirming that NaHCO3 can reduce the number of viable E. coli bacteria. We then investigated, via RNAseq, gene expression changes induced by NaHCO3. There were upregulated and downregulated genes, among the top upregulated genes (~10-fold increase in expression) was tnaA, the gene encoding tryptophanase (TnaA), the enzyme that degrades tryptophan to indole. Considering that higher expression of tnaA likely led to increases in indole, we tested the effect of indole and found both growth inhibition and synergy with NaHCO3. We suggest that indole may participate in growth inhibition of E. coli. The RNAseq analysis also revealed upregulation (≥4-fold) of genes encoding proteins for the acquisition of iron and downregulation (≥16-fold) of genes encoding iron-sulfur-holding proteins, hence NaHCO3 apparently triggered also an iron deficit response. We suggest that iron deficiency may also be involved in growth inhibition by NaHCO3.