Role of<i>sul2</i>Gene Linked to Transposase in Resistance to Trimethoprim/Sulfamethoxazole Among<i>Stenotrophomonas maltophilia</i>Isolates

Hu, Li; Xu, Xi Hai; Yang, Hai Fei; Yan, Ying; Li, Jia Bin

doi:10.3343/alm.2016.36.1.73

Cited by 6 publications

(5 citation statements)

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“…These sul2 -associated GlmM sequences lack the entire GlmM C-terminal region, including three of its functional domains (Mehra-Chaudhary et al, 2011), and it can therefore be safely assumed that they are not functional as phosphoglucosamine mutases. This genetic arrangement has been reported previously as a feature of sul2 isolates (Kehrenberg and Schwarz, 2005; Hu et al, 2016), and it is strongly conserved in the genomic surroundings of chromosomal folP genes in the Gammaproteobacteria, the Betaproteobacteria and several Alphaproteobacteria lineages (Figure 1C). Analysis of the folP genetic surroundings in complete genomes of the Spirochaetes and the Alphaproteobacteria shows clear differences between the genes coding for the identified Rhodobiaceae and Leptospiraceae FolP ∗ proteins harboring the two-amino acid insertion pattern and those without it (Figure 1C).…”

Section: Resultssupporting

confidence: 79%

Origin of the Mobile Di-Hydro-Pteroate Synthase Gene Determining Sulfonamide Resistance in Clinical Isolates

et al. 2019

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Sulfonamides are synthetic chemotherapeutic agents that work as competitive inhibitors of the di-hydro-pteroate synthase (DHPS) enzyme, encoded by the folP gene. Resistance to sulfonamides is widespread in the clinical setting and predominantly mediated by plasmid- and integron-borne sul1-3 genes encoding mutant DHPS enzymes that do not bind sulfonamides. In spite of their clinical importance, the genetic origin of sul1-3 genes remains unknown. Here we analyze sul genes and their genetic neighborhoods to uncover sul signature elements that enable the elucidation of their genetic origin. We identify a protein sequence Sul motif associated with sul-encoded proteins, as well as consistent association of a phosphoglucosamine mutase gene (glmM) with the sul2 gene. We identify chromosomal folP genes bearing these genetic markers in two bacterial families: the Rhodobiaceae and the Leptospiraceae. Bayesian phylogenetic inference of FolP/Sul and GlmM protein sequences clearly establishes that sul1-2 and sul3 genes originated as a mobilization of folP genes present in, respectively, the Rhodobiaceae and the Leptospiraceae, and indicate that the Rhodobiaceae folP gene was transferred from the Leptospiraceae. Analysis of %GC content in folP/sul gene sequences supports the phylogenetic inference results and indicates that the emergence of the Sul motif in chromosomally encoded FolP proteins is ancient and considerably predates the clinical introduction of sulfonamides. In vitro assays reveal that both the Rhodobiaceae and the Leptospiraceae, but not other related chromosomally encoded FolP proteins confer resistance in a sulfonamide-sensitive Escherichia coli background, indicating that the Sul motif is associated with sulfonamide resistance. Given the absence of any known natural sulfonamides targeting DHPS, these results provide a novel perspective on the emergence of resistance to synthetic chemotherapeutic agents, whereby preexisting resistant variants in the vast bacterial pangenome may be rapidly selected for and disseminated upon the clinical introduction of novel chemotherapeuticals.

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Section: Resultssupporting

confidence: 79%

Origin of the Mobile Di-Hydro-Pteroate Synthase Gene Determining Sulfonamide Resistance in Clinical Isolates

et al. 2019

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“…In contrast, the sul2 was detected in sediment only at the discharge site, suggesting that its presence may have resulted from effluent discharge. However, both genes ( sul1 and sul2 ) have been previously reported in antibiotic polluted (Luo et al, 2010 ; Kristiansson et al, 2011 ; Bengtsson-Palme et al, 2014 ) and unpolluted sediments (Czekalski et al, 2015 ; Archundia et al, 2017 ), which is likely due to their genetic localization on mobile elements that could be easily transferred among bacteria (Heuer et al, 2011 ; Hu et al, 2016 ; Johnson et al, 2016 ; Koczura et al, 2016 ).…”

Section: Resultsmentioning

confidence: 98%

Functional Repertoire of Antibiotic Resistance Genes in Antibiotic Manufacturing Effluents and Receiving Freshwater Sediments

et al. 2018

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Environments polluted by direct discharges of effluents from antibiotic manufacturing are important reservoirs for antibiotic resistance genes (ARGs), which could potentially be transferred to human pathogens. However, our knowledge about the identity and diversity of ARGs in such polluted environments remains limited. We applied functional metagenomics to explore the resistome of two Croatian antibiotic manufacturing effluents and sediments collected upstream of and at the effluent discharge sites. Metagenomic libraries built from an azithromycin-production site were screened for resistance to macrolide antibiotics, whereas the libraries from a site producing veterinary antibiotics were screened for resistance to sulfonamides, tetracyclines, trimethoprim, and beta-lactams. Functional analysis of eight libraries identified a total of 82 unique, often clinically relevant ARGs, which were frequently found in clusters and flanked by mobile genetic elements. The majority of macrolide resistance genes identified from matrices exposed to high levels of macrolides were similar to known genes encoding ribosomal protection proteins, macrolide phosphotransferases, and transporters. Potentially novel macrolide resistance genes included one most similar to a 23S rRNA methyltransferase from Clostridium and another, derived from upstream unpolluted sediment, to a GTPase HflX from Emergencia. In libraries deriving from sediments exposed to lower levels of veterinary antibiotics, we found 8 potentially novel ARGs, including dihydrofolate reductases and beta-lactamases from classes A, B, and D. In addition, we detected 7 potentially novel ARGs in upstream sediment, including thymidylate synthases, dihydrofolate reductases, and class D beta-lactamase. Taken together, in addition to finding known gene types, we report the discovery of novel and diverse ARGs in antibiotic-polluted industrial effluents and sediments, providing a qualitative basis for monitoring the dispersal of ARGs from environmental hotspots such as discharge sites of pharmaceutical effluents.

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“…Mutations in both dihydrofolate reductase and dihydropteroate synthetase have been demonstrated to elevate Plasmodium falciparum resistance to sulfadoxine-pyrimethamine, a known sulfonamide antibiotic [61]. Furthermore, we observed that tetracycline resistance genes, and transposase, linked to antibiotic resistance [63,64] appear to play a significant secondary role in Salmonella resistance to sulfisoxazole and trimethoprim-sulfamethoxazole in our dataset. Dihydrofolate reductase (four out of ten important genes) and dihydropteroate synthase type-2 (seven out of ten of the important genes) (Figure 7) are the principal contributors to high MIC in trimethoprim-sulfamethoxazole and sulfisoxazole, respectively [61].…”

Section: Quinolones (Ciprofloxacin Nalixidic Acid)mentioning

confidence: 49%

Section: Resultsmentioning

confidence: 87%

Predicting Salmonella MIC and Deciphering Genomic Determinants of Antibiotic Resistance and Susceptibility

Ayoola,

Das,

Krishnan

et al. 2024

Microorganisms

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Salmonella spp., a leading cause of foodborne illness, is a formidable global menace due to escalating antimicrobial resistance (AMR). The evaluation of minimum inhibitory concentration (MIC) for antimicrobials is critical for characterizing AMR. The current whole genome sequencing (WGS)-based approaches for predicting MIC are hindered by both computational and feature identification constraints. We propose an innovative methodology called the “Genome Feature Extractor Pipeline” that integrates traditional machine learning (random forest, RF) with deep learning models (multilayer perceptron (MLP) and DeepLift) for WGS-based MIC prediction. We used a dataset from the National Antimicrobial Resistance Monitoring System (NARMS), comprising 4500 assembled genomes of nontyphoidal Salmonella, each annotated with MIC metadata for 15 antibiotics. Our pipeline involves the batch downloading of annotated genomes, the determination of feature importance using RF, Gini-index-based selection of crucial 10-mers, and their expansion to 20-mers. This is followed by an MLP network, with four hidden layers of 1024 neurons each, to predict MIC values. Using DeepLift, key 20-mers and associated genes influencing MIC are identified. The 10 most significant 20-mers for each antibiotic are listed, showcasing our ability to discern genomic features affecting Salmonella MIC prediction with enhanced precision. The methodology replaces binary indicators with k-mer counts, offering a more nuanced analysis. The combination of RF and MLP addresses the limitations of the existing WGS approach, providing a robust and efficient method for predicting MIC values in Salmonella that could potentially be applied to other pathogens.

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Role ofsul2Gene Linked to Transposase in Resistance to Trimethoprim/Sulfamethoxazole AmongStenotrophomonas maltophiliaIsolates

Cited by 6 publications

References 6 publications

Origin of the Mobile Di-Hydro-Pteroate Synthase Gene Determining Sulfonamide Resistance in Clinical Isolates

Origin of the Mobile Di-Hydro-Pteroate Synthase Gene Determining Sulfonamide Resistance in Clinical Isolates

Functional Repertoire of Antibiotic Resistance Genes in Antibiotic Manufacturing Effluents and Receiving Freshwater Sediments

Predicting Salmonella MIC and Deciphering Genomic Determinants of Antibiotic Resistance and Susceptibility

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