Perturbation-Specific Transcriptional Mapping for unbiased target elucidation of antibiotics
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ABSTRACT:
ORGANISM(S): Pseudomonas aeruginosa UCBPP-PA14
PROVIDER: GSE251671 | GEO | 2024/10/11
REPOSITORIES: GEO
Similar Datasets
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.
2020-07-23 | PXD016001 | Pride
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.
2020-07-23 | PXD016074 | Pride
Project description:Treatment of bacteria with antibiotics at or close to the inhibitory concentration leads to specific transcriptional responses often affecting target genes and targets pathways. A dataset of transcriptional profiles (compendium) induced by antibiotics with known mode-of-action (MoA) can be used to gain information on the putative MoA of novel substances with unknown MoAs. We used a Pasteurella multocida microarray to generate a compendium of transcriptional profiles and to obtain information on the putative MoA of a novel antibiotic compound. We also show a strong impact of the bacteriostatic antibiotics on P. multocida virulence gene transcription. Keywords: antibiotica treatment, time course
2008-06-01 | GSE10051 | GEO
Project description:The comparative advantages of RNA-Seq and microarrays in transcriptome profiling were evaluated in the context of a comprehensive study design. Gene expression data from Illumina RNA-Seq and Affymetrix microarrays were obtained from livers of rats exposed to 27 agents that comprised of seven modes of action (MOAs); they were split into training and test sets and verified with real time PCR. contributor: DrugMatrix, National Toxicology program (NTP) 105 samples were selected from the DrugMatirx tissue/RNA bank that is now owned by the National Toxicology Program (NTP). The samples were split into 2 sets, training and test, to allow for the evaluation of classifiers derived from the data. There were 63 samples in the training set and 42 in the test set. Of the 63 samples in the training set 45 were derived from rats treated with test agent and 18 were control samples (3 sets of 6). 39 of the test set samples were derived from test agent treated animals and 6 were from vehicle and route matched controls. Five MOAs were represented in the training set and 4 MOAs were in the test set. Two of the MOAs were duplicated from the test set and two were without representation in the training set. For each test agent there were three rats treated, in accordance with the common practice in the field of toxicology. For each MOA there were three representative test agents to ensure adequate power for detecting the MOA signatures.
2014-08-08 | E-GEOD-47875 | biostudies-arrayexpress
Project description:Genome-wide transcriptomics were used to discover the MoA of the small-molecule TERT activator compound. We then performed gene expression profiling analysis using data obtained from RNA-seq.
2024-05-14 | GSE231454 | GEO
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants. The hybridization design was a universal reference design (a mixture of aliquots from control and exposed samples), which is recommended when class discovery is the main purpose of the experiment. One of the three biological replicates of each exposure condition was labeled with one dye, the remaining two samples were labeled with the second dye.
2012-02-01 | E-GEOD-29864 | biostudies-arrayexpress
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants. The hybridization design was a universal reference design (a mixture of aliquots from control and exposed samples), which is recommended when class discovery is the main purpose of the experiment. One of the three biological replicates of each exposure condition was labeled with one dye, the remaining two samples were labeled with the second dye.
2012-02-01 | E-GEOD-29867 | biostudies-arrayexpress
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants. The hybridization design was a universal reference design (a mixture of aliquots from control and exposed samples), which is recommended when class discovery is the main purpose of the experiment. One of the three biological replicates of each exposure condition was labeled with one dye, the remaining two samples were labeled with the second dye.
2012-02-01 | E-GEOD-29862 | biostudies-arrayexpress
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants. The hybridization design was a universal reference design (a mixture of aliquots from control and exposed samples), which is recommended when class discovery is the main purpose of the experiment. One of the three biological replicates of each exposure condition was labeled with one dye, the remaining two samples were labeled with the second dye.
2012-02-01 | E-GEOD-29856 | biostudies-arrayexpress
Project description:Structural analogues are assumed to elicit toxicity via similar predominant modes of action (MOAs). Currently, MOA categorization of chemicals in environmental risk assessment is mainly based on the physico-chemical properties of potential toxicants. It is often not known whether such classification schemes are also supported by mechanistic biological data. In this study, the toxic effects of two groups of structural analogues (alcohols and anilines) with predefined MOA (narcotics and polar narcotics) were investigated at different levels of biological organization (gene transcription, energy reserves and growth). Chemical similarity was not indicative of a comparable degree of toxicity and a similar biological response. Categorization of the test chemicals based on the different biological responses (growth, energy use and gene transcription) did not result in a classification of the predefined narcotics versus the predefined polar narcotics. Moreover, gene transcription based clustering profiles were indicative of the observed effects at higher level of biological organization. Furthermore, a small set of classifier genes could be identified that was discriminative for the clustering pattern. These classifier genes co-varied with the organismal and physiological responses. Compared to the physico-chemistry based MOA classification, integrated biological multi-level effect assessment can provide the necessary MOA information that is crucial in high-quality environmental risk assessment. Our findings support the view that transcriptomics tools hold considerable promise to be used in biological response based mechanistic profiling of potential (eco)toxicants. The hybridization design was a universal reference design (a mixture of aliquots from control and exposed samples), which is recommended when class discovery is the main purpose of the experiment. One of the three biological replicates of each exposure condition was labeled with one dye, the remaining two samples were labeled with the second dye.
2012-02-01 | E-GEOD-29854 | biostudies-arrayexpress