Dataset Information

Microarray genomotyping of key experimental strains of Neisseria gonorrhoeae reveals gene complement diversity and five new neisserial genes associated with Minimal Mobile Elements.

ABSTRACT: There are four widely used experimental strains of N. gonorrhoeae, one of which has been sequenced and used as the basis for the construction of a multi-strain, mutli-species pan-neisserial microarray. Although the N. gonorrhoeae population structure is thought to be less diverse than N. meningitidis, there are some recognized gene-complement differences between strains, including the 59 genes of the Gonococcal Genetic Island. In this study we have investigated the three experimental strains that have not been sequenced to determine the extent and nature of their similarities and differences.Using the Pan-Neisseria microarray, three commonly used gonococcal laboratory experimental strains were investigated (F62, MS11, & FA19). Genes absent from these strains, but present in strain FA1090, were assessed as is possible with typical microarrays. Due to the design of this microarray, additional genes were also identified. Differences were associated with Minimal Mobile Elements (MMEs) or known divergences. Genomotyping indicates the presence of genes previously only described in meningococci and shows the presence of the complete Gonococcal Genetic Island in N. gonorrhoeae strain FA19. Five new neisserial genes were identified through microarray genomotyping and subsequent sequencing of two divergent MMEs in N. gonorrhoeae strain MS11 and four MMEs in N. gonorrhoeae strain FA19. No differences were identified between N. gonorrhoeae strains FA1090 and F62, indicating that these strains are very similar.This study shows extensive similarity between the experimental strains, associated with a varying number of strain-specific genes. This provides a framework for those working with these strains to refer to the available gonococcal genome sequence, and is the first detailed comparison of gene complements between gonococcal strains.

SUBMITTER: Snyder LA

PROVIDER: S-EPMC406496 | biostudies-literature | 2004 Apr

REPOSITORIES: biostudies-literature

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Microarray genomotyping of key experimental strains of Neisseria gonorrhoeae reveals gene complement diversity and five new neisserial genes associated with Minimal Mobile Elements.

Snyder Lori A S LA Davies John K JK Saunders Nigel J NJ

BMC genomics 20040413 1

<h4>Background</h4>There are four widely used experimental strains of N. gonorrhoeae, one of which has been sequenced and used as the basis for the construction of a multi-strain, mutli-species pan-neisserial microarray. Although the N. gonorrhoeae population structure is thought to be less diverse than N. meningitidis, there are some recognized gene-complement differences between strains, including the 59 genes of the Gonococcal Genetic Island. In this study we have investigated the three exper ...[more]

PMID: 15084227

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Project description:OBJECTIVE:To investigate the in vitro antimicrobial susceptibility and resistant trends of Neisseria gonorrhoeae strains isolated in Guangzhou, from 1996 to 2001. METHODS:The agar dilution method was used to determine the minimum inhibitory concentrations (MICs) to four antimicrobials, penicillin G, ciprofloxacin, ceftriaxone, and spectinomycin. The resistance of all strains to four antibiotics was interpreted according to criteria used in the project of surveillance of gonococcal antibiotic susceptibility in the WHO Western Pacific Region. Penicillinase producing N gonorrhoeae (PPNG) was analysed by the paper acidometric method. RESULTS:793 consecutive N gonorrhoeae isolates collected in Guangzhou were studied from 1996 to 2001. A total of 55 strains of PPNG were identified and the prevalence rapidly spread from 2% to 21.8%. Of the four antibiotics examined, ceftriaxone and spectinomycin appeared to be the most effective agents although two spectinomycin resistant strains were isolated in 1996. Their MIC(50), MIC(90), and geometric mean MIC (MICmean) were all between the sensitive ranges of the interpretative criteria and remained stable over the years. However, resistance increased continuously to penicillin G and dramatically to ciprofloxacin. In 1996-2001, MIC(50), MIC(90), and MICmean of penicillin G increased from 1 micro g/ml to 2 micro g/ml, 4 micro g/ml to 32 micro g/ml, and 0.68 micro g/ml to 2.35 micro g/ml, respectively; those of ciprofloxacin steeply increased from 0.12 micro g/ml to 4 micro g/ml, 2 micro g/ml to 32 micro g/ml, and 0.14 micro g/ml to 2.62 micro g/ml in 1996-9, respectively, and then declined slightly in 2000-1. The prevalence of resistant isolates spread from 57.2% to 81.8% for penicillin G and from 17.6% to 72.7% for ciprofloxacin over the 6 years. CONCLUSION:Resistance to penicillin and ciprofloxacin increased greatly during 1996-2001. Ceftriaxone and spectinomycin should be used as the first line agents in treating gonorrhoea. It is of great importance to continuously survey the susceptibilities of N gonorrhoeae to antibiotics in controlling the spread of gonococcal infections.

Project description:The lowest concentration of an antimicrobial agent that can inhibit the visible growth of a microorganism after overnight incubation is called as minimum inhibitory concentration (MIC) and the drug prescriptions are made on the basis of MIC data to ensure successful treatment outcomes. Therefore, reliable antimicrobial susceptibility data is crucial, and it will help clinicians about which drug to prescribe. Although few prediction studies based on strategies have been conducted, however, no single machine learning (ML) modelling has been carried out to predict MICs in N. gonorrhoeae. In this study, we propose a ML based approach that can predict MICs of a specific antibiotic using unitigs sequences data. We retrieved N. gonorrhoeae genomes from European Nucleotide Archive and NCBI and analysed them combined with their respective MIC data for cefixime, ciprofloxacin, and azithromycin and then we constructed unitigs by using de Brujin graphs. We built and compared 35 different ML regression models to predict MICs. Our results demonstrate that RandomForest and CATBoost models showed best performance in predicting MICs of the three antibiotics. The coefficient of determination, R2, (a statistical measure of how well the regression predictions approximate the real data points) for cefixime, ciprofloxacin, and azithromycin was 0.75787, 0.77241, and 0.79009 respectively using RandomForest. For CATBoost model, the R2 value was 0.74570, 0.77393, and 0.79317 for cefixime, ciprofloxacin, and azithromycin respectively. Lastly, using feature importance, we explore the important genomic regions identified by the models for predicting MICs. The major mutations which are responsible for resistance against these three antibiotics were chosen by ML models as a top feature in case of each antibiotics. CATBoost, DecisionTree, GradientBoosting, and RandomForest regression models chose the same unitigs which are responsible for resistance. This unitigs-based strategy for developing models for MIC prediction, clinical diagnostics, and surveillance can be applicable for other critical bacterial pathogens.

Dataset Information

Microarray genomotyping of key experimental strains of Neisseria gonorrhoeae reveals gene complement diversity and five new neisserial genes associated with Minimal Mobile Elements.

Publications

Microarray genomotyping of key experimental strains of Neisseria gonorrhoeae reveals gene complement diversity and five new neisserial genes associated with Minimal Mobile Elements.

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