Browse AMR Genes
Explore antimicrobial resistance genes from the literature
Explore antimicrobial resistance genes from the literature
integrase
Overview
| Protein Change | Nucleotide Change | Mechanism | Organism | Resistance To | Database | Validation Status |
|---|---|---|---|---|---|---|
| - | - | - | tetracycline|sulfonamides|aminoglycosides|macrolides | Reslit | Candidate |
Genetic Environment of bla(TEM-1), bla(CTX-M-15), bla(CMY-42) and Characterization of Integrons of Escherichia coli Isolated From an Indian Urban Aquatic Environment.
The study characterizes the genetic environment of bla TEM-1, bla CTX-M-15, and bla CMY-42 in E. coli isolates from an Indian urban aquatic environment, identifying the presence of IS 26, IS Ecp1, and class 1 integrons.
Evolution of antibiotic resistance at low antibiotic concentrations including selection below the minimal selective concentration.
The study identifies ermF, intI1, and mphA as genes that show positive selection under specific antibiotic concentrations, highlighting their role in antibiotic resistance development at low concentrations.
Diversity of resistant determinants, virulence factors, and mobile genetic elements in Acinetobacter baumannii from India: A comprehensive in silico genome analysis.
The study identified multiple antibiotic resistance genes (ARGs) in 47 Acinetobacter baumannii isolates from India, including blaOXA-23, blaADC-73, aac(3)-I, aadA, aph(3')-Ib, sul1, sul2, and lpsB. These genes contribute to resistance against carbapenems, cephalosporins, aminoglycosides, sulfonamides, and polymyxins.
Impact of corrosion inhibitors on antibiotic resistance, metal resistance, and microbial communities in drinking water.
Zinc orthophosphate increased antibiotic resistant bacteria (ARB) and antibiotic-resistance genes (ARGs), while sodium silicate decreased ARB and ARGs. The study identified sul1, sul2, qacEΔ1, and intI1 as key ARGs influenced by corrosion inhibitors.
PanARGMiner (Pan-Genomic Antimicrobial Resistance Gene Miner): An advanced feature selection framework for extracting key resistance genes from pan-genomic datasets.
PanARGMiner effectively identifies key resistance genes from pan-genomic datasets, including both known and novel AMR genes, across multiple bacterial species.
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