Genomics of rapid adaptation to antibiotics
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ABSTRACT:
REPOSITORIES: ENA
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Project description:Human activity is altering the environment at a rapid pace, challenging the adaptive capacities of genetic variation within animal populations. Animals also harbor extensive gut microbiomes, which play diverse roles in host health and fitness and may help expanding host capabilities. The unprecedented scale of human usage of xenobiotics and contamination with environmental toxins describes one challenge against which bacteria with their immense biochemical diversity are particularly suited to offer solutions. To explore the paths leading to bacteria-assisted rapid adaptation, we used Caenorhabditis elegans harboring a defined microbiome, and the antibiotic neomycin as a model toxin, harmful for the worm host and neutralized to different extents by microbiome members. Worms exposed to neomycin showed delayed development and decreased survival but were protected when colonized by neomycin-resistant members of the microbiome. Through a combination of 16S gene sequencing, counting of live bacteria and behavioral assays we identified two distinct mechanisms that facilitated adaptation: gut enrichment for a neomycin-modifying strain driven by altered bacterial competition; and host avoidance behavior, which depended on the stress-activated KGB-1/JNK and enabled preference of neomycin-protective bacteria. The straightforwardness of these mechanisms suggests that bacteria-assisted host adaptation may be more common than currently appreciated, protecting animals from novel stressors. However, gut remodeling may also cause dysbiosis, and additional experiments identified fitness trade-offs including increased susceptibility to infection as well as metabolic remodeling. Extending these results to other toxins suggests yet unaccounted-for microbiome-dependent long-term consequences of toxin exposure.
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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.
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