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Transcriptional profiling of Mycobacterium tuberculosis exposed to antibiotics and drug combinations in broth culture.

ABSTRACT: Transcriptional profiling of Mycobacterium tuberculosis exposed to antibiotics and drug combinations in broth culture.

PROVIDER: PRJNA1240346 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR32812558.fastq.gz	Fastqsanger.gz
	SRR32812559.fastq.gz	Fastqsanger.gz
	SRR32812560.fastq.gz	Fastqsanger.gz
	SRR32812561.fastq.gz	Fastqsanger.gz
	SRR32812562.fastq.gz	Fastqsanger.gz

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Transcriptional profiling of Mycobacterium tuberculosis exposed to antibiotics and drug combinations in broth culture.

Project description:We profiled Mtb transcriptional response to a range of different antibiotic drug exposures in 7H9 broth culture by RNA-seq. Compound abbreviations: (EMB = ethambutol, BTZ = BTZ043, TYL = tyloxapol). We also hypothesized that an improved Mtb TRN could be inferred using a large, diverse compendium of RNA-seq data and therefore sought to supplement the existing repository of Mtb expression data available on SRA with additional conditions that could inform on Mtb's underlying regulatory programs. We went on to infer Mtb's TRN using a collection of inference methods in combination with this compendium, and employed it to build an interpretable machine learning model of Mtb fitness under stress.

2025-03-21 | GSE292636 | GEO

Escherichia coli treated with 13 antibiotics, 3 synergistic combinations, and IPTG

2008-02-12 | GSE10440 | GEO

Escherichia coli treated with 13 antibiotics, 3 synergistic combinations, and IPTG

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)

2008-02-12 | E-GEOD-10440 | biostudies-arrayexpress

Transcriptional profiling of Mycobacterium tuberculosis exposed to antibiotic treatment in broth culture.

Project description:We profiled Mtb transcriptional response to a range of different antibiotic drug exposures in 7H9 broth culture by RNA-seq. Compound abbreviations: (AMK = amikacin, BDQ = bedaquiline, CPF = ciprofloxacin, LVF = levofloxacin, MXF = moxifloxacin, CFZ = clofazimine, PA824 = pretomanid, RPE = rifapentine, PZA = pyrazinamide). We also hypothesized that an improved Mycobacterium tuberculosis (Mtb) transcriptional regulatory network (TRN) could be inferred using a large, diverse compendium of RNA-seq data and therefore sought to supplement the existing repository of Mtb expression data available on SRA with additional conditions that could inform on Mtb's underlying regulatory programs. We went on to infer Mtb's TRN using a collection of inference methods in combination with this compendium, and employed it to build an interpretable machine learning model of Mtb fitness under stress.

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Emergent gene expression responses to drug combinations predict higher-order drug interactions

Project description:Effective design of combination therapies requires understanding the changes in cell physiology resulting from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations.

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Emergent gene expression responses to drug combinations predict higher-order drug interactions

Project description:Emergent gene expression responses to drug combinations predict higher-order drug interactions

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A network-based trans-omics approach for predicting synergistic drug combinations

Project description:We examined the mode-of-action of synergistic drug combinations by microarray analysis. We focused on the drug combination of capsaicin and mitoxantrone, which were the top predicted drug pair identified by SyndrumNET. Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses. The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML), and it outperformed the previous methods in terms of high accuracy. We performed in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibited synergistic anticancer effects. Our mode-of-action analysis also revealed that the drug synergy of the top predicted combination of capsaicin and mitoxantrone was due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway. The proposed method is expected to be useful for various complex diseases.

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Transcriptional profiling of Mycobacterium tuberculosis exposed to proton pump inhibitor treatment in broth culture.

Project description:We hypothesized that an improved Mtb TRN could be inferred using a large, diverse compendium of RNA-seq data and therefore sought to supplement the existing repository of Mtb expression data available on SRA with additional conditions that could inform on Mtb's underlying regulatory programs. We profiled Mtb transcriptional response to a range of different proton bump inhibitor exposures in 7H9 broth culture by RNA-seq. Compound abbreviations: (PAN = pantoprazole, RAB = rabeprazole, VER = verapamil, LAN = lansoprazole, OMP = omeprazole). We went on to infer Mtb's TRN using a collection of inference methods in combination with this compendium, and employed it to build an interpretable machine learning model of Mtb fitness under stress.

2025-03-19 | GSE292408 | GEO

fermentation broth

Project description:fermentation broth Raw sequence reads

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Fermentation broth

Project description:Fermentation broth Raw sequence reads

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