Ontology highlight
ABSTRACT:
PROVIDER: PRJNA705736 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| SRR13834223_1.fastq.gz | Fastqsanger.gz | |||
| SRR13834223_2.fastq.gz | Fastqsanger.gz | |||
| SRR13834224_1.fastq.gz | Fastqsanger.gz | |||
| SRR13834224_2.fastq.gz | Fastqsanger.gz |
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Project description:<p>The gut microbiota is increasingly recognized for playing a critical role in human health and disease, especially in conferring resistance to both virulent pathogens such as Salmonella, which infects 1.2 million people in the United States every year [1], and opportunistic pathogens like Candida, which causes an estimated 46,000 cases of invasive candidiasis each year in the United States [2]. The dynamics of pathogen-microbiome interactions and the metabolites involved in this process remain largely unknown. </p><p>We use gnotobiotic mice infected with the virulent pathogen Salmonella enterica serovar Typhimurium or the opportunistic pathogen Candida albicans in combination with metagenomics and discovery metabolomics to identify changes in the community and metabolome during infection. To isolate the role of the microbiota in response to pathogens, we compared mice monocolonized with the pathogen, uninfected mice 'humanized' with a synthetic human microbiome, or infected humanized mice. We observe that changes in the community and in biosynthetic gene cluster potential occur within 3 days for the virulent Salmonella enterica serovar Typhimurium, but there are minimal changes with a poorly colonizing Candida albicans. In addition, the metabolome shifts depending on infection status, including changes in glutathione metabolites in response to Salmonella infection. The LC-MS metabolomic fingerprint of the cecum differed between mice monocolonized with either pathogen and humanized infected mice. Specifically, we identified an increase in glutathione disulfide, glutathione cysteine disulfide, inosine 5'-monophosphate, and hydroxybutyrylcarnitine in mice infected with Salmonella in contrast to uninfected mice and mice monocolonized with Salmonella. These metabolites potentially play a role in pathogen-induced oxidative stress. These results provide insight into how the microbiota community members interact with each other and with pathogens on a metabolic level.</p><p><br></p><p>Ref:</p><p>[1] Scallan E, Hoekstra RM, Angulo FJ, Tauxe RV, Widdowson MA, Roy SL, Jones JL, and Griffin PM. Foodborne Illness Acquired in the United States—Major Pathogens. Emerg Infect Dis 2011;17:7-15. doi.org/10.3201/eid1701.P11101</p><p>[2] Centers for Disease Control and Prevention, Antibiotic Resistance Threats in the United States, 2013</p>
2019-09-20 | MTBLS753 | MetaboLights
Project description:The pathogen Clostridioides difficile causes toxin-mediated diarrhea and is the leading cause of hospital-acquired infection in the United States. Due to growing antibiotic resistance and recurrent infection, targeting C. difficile metabolism presents a new approach to combat this infection. Genome-scale metabolic network reconstructions (GENREs) have been used to identify therapeutic targets and uncover properties that determine cellular behaviors. Thus, we constructed C. difficile GENREs for a hypervirulent isolate (strain [str.] R20291) and a historic strain (str. 630), validating both with in vitro and in vivo data sets. Growth simulations revealed significant correlations with measured carbon source usage (positive predictive value [PPV] ≥ 92.7%), and single-gene deletion analysis showed >89.0% accuracy. Next, we utilized each GENRE to identify metabolic drivers of both sporulation and biofilm formation. Through contextualization of each model using transcriptomes generated from in vitro and infection conditions, we discovered reliance on the pentose phosphate pathway as well as increased usage of cytidine and N-acetylneuraminate when virulence expression is reduced, which was subsequently supported experimentally. Our results highlight the ability of GENREs to identify novel metabolite signals in higher-order phenotypes like bacterial pathogenesis.
2023-11-27 | MODEL2205060002 | BioModels
Project description:Understanding constraints which shape antibiotic resistance is key for predicting and controlling drug resistance. Here, we performed high-throughput laboratory evolution of Escherichia coli. The transcriptome, resistance, and genomic profiles for the evolved strains in 48 environments were quantitatively analyzed. By analyzing the quantitative datasets through interpretable machine learning techniques, the emergence of low dimensional phenotypic states within the 192 strains was observed. Further analysis revealed the underlying biological processes responsible for the distinct states. We also report a novel constraint which leads to decelerated evolution. These findings bridge the genotypic, gene expression, and drug resistance space, and lead to a comprehensive understanding of constraints for antibiotic resistance.
2020-10-12 | GSE137348 | GEO
Project description:Chronic Pseudomonas aeruginosa infections evades antibiotic therapy and are associated with mortality in cystic fibrosis (CF) patients. We find that in vitro resistance evolution of P.aeruginosa towards clinically relevant antibiotics leads to phenotypic convergence towards distinct states. These states are associated with collateral sensitivity towards several antibiotic classes and encoded by mutations in antibiotic resistance genes, including transcriptional regulator nfxB. Longitudinal analysis of isolates from CF patients reveals similar and defined phenotypic states, which are associated with extinction of specific sub-lineages in patients. In depth investigation of chronic P.aeruginosa populations in a CF patient during antibiotic therapy revealed dramatic genotypic and phenotypic convergence. Notably, fluoroquinolone-resistant subpopulations harboring nfxB mutations were eradicated by antibiotic therapy as predicted by our in vitro data. This study supports the hypothesis that antibiotic treatment of chronic infections can be optimized by targeting phenotypic states associated with specific mutations to improve treatment success in chronic infections.
2018-10-23 | PXD007972 | Pride
Project description:Resveratrol against Aeromonas hydrophila through proteomics analysis on an Q Exactive HF mass spectrometer (Thermo Scientific, United States) .
2024-01-29 | PXD048957 |
Project description:Bacterial evolution of antibiotic resistance frequently has deleterious side effects on microbial growth, virulence, and susceptibility to other antimicrobial agents. However, it is unclear how these trade-offs could be utilized for manipulating antibiotic resistance in the clinic, not least because the underlying molecular mechanisms are poorly understood. Using laboratory evolution, we demonstrate that clinically relevant resistance mutations in Escherichia coli constitutively rewire a large fraction of the transcriptome in a repeatable and stereotypic manner. Strikingly, lineages adapted to functionally distinct antibiotics and having no resistance mutations in common show a wide range of parallel gene expression changes that alter oxidative stress response, iron homeostasis, and the composition of the bacterial outer membrane and cell surface. These common physiological alterations are associated with changes in cell morphology and enhanced sensitivity to antimicrobial peptides. Finally, the constitutive transcriptomic changes induced by resistance mutations are largely distinct from those induced by antibiotic stresses in the wild-type. This indicates a limited role for genetic assimilation of the induced antibiotic stress response during resistance evolution. Our work suggests that diverse resistance mutations converge on similar global transcriptomic states that shape genetic susceptibility to antimicrobial compounds.
2023-01-26 | GSE223493 | GEO
Project description:Sex specificity of the C. elegans metabolome
Russell N. Burkhardt1, Alexander B. Artyukhin1,3, Erin Z. Aprison2, Brian J. Curtis1, Bennett W. Fox1, Andreas H. Ludewig1, Amaresh Chaturbedi4, Oishika Panda1, Chester J. J. Wrobel1, Siu S. Lee4, Ilya Ruvinsky2, and Frank C. Schroeder1,
1Boyce Thompson Institute and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States
2Department of Molecular Biosciences, Northwestern University, Evanston, IL 60208, United States
3Current address: Chemistry Department, College of Environmental Science and Forestry, State University of New York, Syracuse, New York 13210, United States
4Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853, United States
Correspondence to fs31@cornell.edu
2022-07-25 | MSV000089965 | MassIVE