Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1132744 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Surfing motility is a novel form of surface adaptation exhibited by the nosocomial pathogen, Pseudomonas aeruginosa, in the presence of the glycoprotein mucin that is found in high abundance at mucosal surfaces especially the lungs of cystic fibrosis and bronchiectasis patients. Here we investigated the adaptive antibiotic resistance of P. aeruginosa under conditions in which surfing occurs compared to cells undergoing swimming. P. aeruginosa surfing cells were significantly more resistant to several classes of antibiotics including aminoglycosides, carbapenems, polymyxins, and fluroquinolones. This was confirmed by incorporation of antibiotics into growth medium, which revealed a concentration-dependent inhibition of surfing motility that occurred at concentrations much higher than those needed to inhibit swimming. To investigate the basis of resistance, RNA-Seq was performed and revealed that surfing influenced the expression of numerous genes. Included amongst genes dysregulated under surfing conditions were multiple genes from the Pseudomonas resistome, which are known to affect antibiotic resistance when mutated. Screening transposon mutants in these surfing-dysregulated resistome genes revealed that several of these mutants exhibited changes in susceptibility to one or more antibiotics under surfing conditions, consistent with a contribution to the observed adaptive resistance. In particular, several mutants in resistome genes, including armR, recG, atpB, clpS, nuoB, and certain hypothetical genes such as PA5130, PA3576 and PA4292, showed contributions to broad-spectrum resistance under surfing conditions and could be complemented by their respective cloned genes. Therefore, we propose that surfing adaption led to extensive multidrug adaptive resistance as a result of the collective dysregulation of diverse genes.
2018-06-26 | GSE110044 | GEO
Project description:Improper use of antibiotics in swine could reduce commensal bacteria and possibly increase pathogen infections via the gut resistome. This study aimed to compare the metaproteomic profiles of gut resistome and related metabolism in the cecal microbiota of fattening pigs raised under antibiotic-free (ABF) conditions with those of ordinary industrial pigs (CTRL).
2023-10-09 | PXD037218 | JPOST Repository
Project description:First insight into environmental adaptability of antibiotic resistome from surface water to deep sediments in anthropogenic lakes by metagenomics
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Project description:Water column microbiome of two Canadian boreal shield lakes
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Project description:Today, many contaminants of emerging concern can be measured in waters across the United States, including the tributaries of the Great Lakes. However, just because the chemicals can be measured does not mean that they necessarily result in harm to fish and other aquatic species. Complicating risk assessment in these waters is the fact that aquatic species are encountering the chemicals as mixtures, which may have additive or synergistic risks that cannot be calculated using single chemical hazard and concentration-response information. We developed an in vitro effects-based screening approach to help us predict potential liver toxicity and cancer in aquatic organisms using water from specific Great Lakes tributaries: St. Louis River (MN), Bad River (WI), Fox River (WI), Manitowoc River (WI), Milwaukee River (WI), Indiana Harbor Canal (IN), St. Joseph River (MI), Grand River (MI), Clinton River (MI), River Rouge (MI), Maumee River (OH), Vermilion River (OH), Cuyahoga River (OH), Genesee River (NY), and Oswego River (NY). We exposed HepG2 cells for 48hrs to medium spiked with either field collected water (final concentration of environmental samples in the exposure medium were 75% of the field-collected water samples) or purified water. Using a deep neural network we clustered our collection sites from each tributary based on water chemistry. We also performed high throughput transcriptomics on the RNA obtained from the HepG2 cells. We used the transcriptomics data with our Bayesian Inferene for Sustance and Chemical Toxicity (BISCT) Bayesian Network for Steatosis to predict the probability of the field samples yielding a gene expression pattern consistent with predicting steatosis as an outcome. Surprisingly, we found that the probability of steatosis did not correspond to the surface water chemistry clustering. Our analysis suggests that chemical signatures are not informative in predicting biological effects. Furthermore, recent reports published after we obtained our samples, suggest that chemical levels in the sediment may be more relevant for predicting potential biological effects in the fish species developing tumors in the Great Lakes basin.
2022-12-01 | GSE146903 | GEO