Genomic sequencing of Neisseria gonorrhoeae to respond to the urgent threat of antimicrobial-resistant gonorrhea

High performance Legionella pneumophila source attribution using genomics-based machine learning classification

Fundamental to effective Legionnaires' disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent, Legionella pneumophila. Genomics has revolutionized pathogen surveillance, but L. pneumophila has a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here, we present a powerful machine learning approach that assigns the geographical source of Legionnaires' disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534 L. pneumophila genome sequences, including 149 genomes linked to 20 previously reported Legionnaires' disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmental L. pneumophila genomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance-agreement with epidemiological information. Our multivariate statistical learning approach maximizes the use of genomic variation data and is thus well-suited for supporting Legionnaires' disease outbreak investigations.IMPORTANCEIdentifying the sources of Legionnaires' disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila, the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires' disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires' disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission.

Antimicrobial susceptibility profile of Neisseria gonorrhoeae from patients attending a medical laboratory, Institut Pasteur de Madagascar between 2014 and 2020: phenotypical and genomic characterisation in a subset of Neisseria gonorrhoeae isolates

OBJECTIVES

Antimicrobial-resistant Neisseria gonorrhoeae (NG) is a concern. Little is known about antimicrobial susceptibility profiles and associated genetic resistance mechanisms of NG in Madagascar. We report susceptibility data of NG isolates obtained by the medical laboratory (CBC) of the Institut Pasteur de Madagascar, Antananarivo, Madagascar, during 2014-2020. We present antimicrobial resistance mechanisms data and phenotype profiles of a subset of isolates.

METHODS

We retrieved retrospective data (N=395) from patients with NG isolated during 2014-2020 by the CBC. We retested 46 viable isolates including 6 found ceftriaxone and 2 azithromycin resistant, as well as 33 isolated from 2020. We determined minimal inhibitory concentrations for ceftriaxone, ciprofloxacin, azithromycin, penicillin, tetracycline and spectinomycin using Etest. We obtained whole-genome sequences and identified the gene determinants associated with antimicrobial resistance and the sequence types (STs).

RESULTS

Over the study period, ceftriaxone-resistant isolates exceeded the threshold of 5% in 2017 (7.4% (4 of 54)) and 2020 (7.1% (3 of 42)). All retested isolates were found susceptible to ceftriaxone, azithromycin and spectinomycin, and resistant to ciprofloxacin. The majority were resistant to penicillin (83% (38 of 46)) and tetracycline (87% (40 of 46)). We detected chromosomal mutations associated with antibiotic resistance in gyrA, parC, penA, ponA, porB and mtrR genes. None of the retested isolates carried the mosaic penA gene. The high rate of resistance to penicillin and tetracycline is explained by the presence of bla TEM (94.7% (36 of 38)) and tetM (97.5% (39 of 40)). We found a high number of circulating multilocus STs. Almost half of them were new types, and one new type was among the four most predominant.

CONCLUSIONS

Our report provides a detailed dataset obtained through phenotypical and genotypical methods which will serve as a baseline for future surveillance of NG. We could not confirm the occurrence of ceftriaxone-resistant isolates. Our results highlight the importance of implementing quality-assured gonococcal antimicrobial resistance surveillance in Madagascar.

Antimicrobial susceptibility testing and molecular characterization of Neisseria gonorrhoeae in Tehran, Iran

Gonorrhea is a sexually transmitted infection occurring worldwide. Antimicrobial resistance (AMR) surveillance in Neisseria gonorrhoeae and associated molecular epidemiological studies are crucial to ascertain the spread of antibiotic-resistant and developing the local treatment guidelines. This study was performed to determine the antimicrobial susceptibility testing (AST) and molecular epidemiology of N. gonorrhoeae isolates in Tehran, Iran. During 1 July 2018-30 July 2020, a total of 500 urogenital (468 endocervical, 32 urethral) swabs were collected from patients with signs and symptoms of genitourinary infections presenting to two women's hospitals and one health center located center and south of Tehran. Specimens were cultured and examined for the presence of N. gonorrhoeae isolates by biochemical tests. MIC Test Strip determined the MICs of ceftriaxone, azithromycin, and ciprofloxacin. Neisseria gonorrhoeae multiantigen sequence typing (NG-MAST) was also performed. A total of 38 N. gonorrhoeae isolates were identified. The proportions of resistant N. gonorrhoeae isolates were as follows: ceftriaxone (MIC ≥0.125 μg/mL) 10.5% (4/38), azithromycin (MIC >1 μg/mL) 34% (13/38), and ciprofloxacin (MIC ≥1 μg/mL) 31.5% (12/38). In total, 25 different NG-MAST STs were identified. The STs comprised 1-4 isolates each, and the predominant ST was ST266 (n = 4). Our study demonstrates a diverse gonococcal population with high rates of resistance to azithromycin and evidence of resistance to ceftriaxone. The results have potential implications for antibiotic choice for the gonococcal treatment and highlight the need to broaden gonococcal AMR monitoring in Iran.

Comparative Whole-Genome Analysis of Neisseria gonorrhoeae Isolates Revealed Changes in the Gonococcal Genetic Island and Specific Genes as a Link to Antimicrobial Resistance

Comparative whole-genome analysis was performed for Neisseria gonorrhoeae isolates belonging to the Neisseria gonorrhoeae multiantigen sequence typing (NG-MAST) types predominant worldwide — 225, 1407, 2400, 2992, and 4186 — and to genogroup 807, the most common genogroup in the Russian Federation. Here, for the first time, the complete genomes of 25 N. gonorrhoeae isolates from genogroup 807 were obtained. For NG-MAST types 225, 1407, 2400, 2992, and 4186, genomes from the Pathogenwatch database were used. The phylogenetic network constructed for 150 genomes showed that the clustering according to NG-MAST type corresponded to the clustering according to genome. Comparisons of genomes of the six sequence types revealed 8-20 genes specific to each sequence type, including the loci for phase variations and genetic components of the gonococcal genetic island (GGI). NG-MAST type 2992 and 4186 isolates either lacked the GGI or carried critical mutations in genes essential for DNA secretion. In all analyzed genogroup 807 isolates, substitution of the essential atlA gene with the eppA gene was found, accompanied by a change in the traG allele, replacement of the ych gene with ych1, and the absence of the exp1 gene, which is likely to result in loss of GGI functionality. For the NG-MAST type 225, 1407 and 2400 isolates, no premature stop codons or reading frameshifts were found in the genes essential for GGI function. A relationship between isolate susceptibility to ciprofloxacin, penicillin, tetracycline and the presence of lesions in GGI genes necessary for DNA secretion was established. The N. gonorrhoeae evolutionary pathways, which allow a particular sequence type to maintain long-term predominance in the population, may include changes in genes responsible for adhesion and virulence, changes in the GGI structure, preservation of genes carrying drug resistance determinants, and changes in genes associated with host adaptation or encoding enzymes of biochemical pathways.

Neisseria gonorrhoeae Antimicrobial Resistance: Past to Present to Future

Neisseria gonorrhoeae (gonococcus) is a Gram-negative bacterium that causes gonorrhoea-a sexually transmitted disease. This gonococcus has progressively developed resistance to most of the available antimicrobials. Only a few countries around the world have been able to run extensive surveillance programmes on gonococcal infection and antimicrobial resistance, raising a global concern. Thus, this review focuses on the mechanisms of resistance to recommended antimicrobials in the past and present time. The approaches by the scientific community in the development of novel technologies such as whole-genome sequencing to predict antimicrobial resistance, track gonococcal transmission, as well as, introduce new therapeutics like Solithromycin, Zoliflodacin, and Gepotidacin were also discussed.

Genomic Analysis Reveals Antibiotic-Susceptible Clones and Emerging Resistance in Neisseria gonorrhoeae in Saskatchewan, Canada

Whole-genome sequencing was used to identify mutations in antibiotic resistance-conferring genes to compare susceptibility predictions with MICs and to ascertain strain types in 99 isolates of Neisseria gonorrhoeae Genotypes associated with susceptibility, as well as MIC creep or emerging resistance, were noted. Phylogenomic analysis revealed three distinctive clades and putative gonococcal transmission linkages involving a tetracycline-resistant N. gonorrhoeae outbreak and the clonal spread of susceptible isolates in men.

Association of Neisseria gonorrhoeae genogroups and specific PBP2/MtrR/PorB mutation patterns with susceptibility to penicillin in a susceptible gonococcal population

Objectives

To ascertain whether the antimicrobial susceptibility of Neisseria gonorrhoeae isolates with differing susceptibilities to penicillin is associated with genogroups (GGs) and combined mutation patterns in PBP2 (penA), the multiple transfer resistance repressor (MtrR; mtrR) and porin B (PorB; porB).

Methods

The susceptibility of 146 clinical N. gonorrhoeae isolates to penicillin was determined using the agar dilution method and the interpretation criteria of CLSI. The DNA sequences of penA, mtrR and porB in isolates were compared with WT sequences and mutation patterns were determined. Isolates were typed by N. gonorrhoeae multi-antigen sequence typing (NG-MAST) and STs were grouped into specific GGs.

Results

The isolates tested carried 9 mutation patterns in PBP2 and 12 mutation patterns in each of MtrR and PorB. Of the 146 isolates, 121 (82.9%) were grouped into 13 different GGs. Isolates with penicillin MICs of 0.03-0.06 mg/L were significantly associated with GG25 (P < 0.05) and PBP2/MtrR/PorB mutation pattern I/WT/WT (P < 0.01). Isolates with a penicillin MIC of 1.0 mg/L were associated (P < 0.05) with: (i) GG3655 and mutation pattern XXII/A-;G45D/G120K;A121N; (ii) GG921 and mutation pattern IX/G45D/G120D;A121N; and (iii) GG1109 and mutation pattern IX/G45D/WT. Sixty percent (9/15) of penicillin-resistant isolates (MIC ≥2 mg/L) were GG3654 (P < 0.0001) and carried mutation pattern IX/G45D/G120K;A121D or IX/G45D/G120D;A121D (P < 0.05).

Conclusions

Specific mutation patterns in PBP2/MtrR/PorB were associated with specific GGs and penicillin susceptibility. This approach of typing strains and resistance patterns is ideal for predicting antimicrobial resistance and should be used in instances in which gonococcal culture is not available but DNA can be obtained from clinical specimens.

Gen2Epi: an automated whole-genome sequencing pipeline for linking full genomes to antimicrobial susceptibility and molecular epidemiological data in Neisseria gonorrhoeae

BACKGROUND

Recent adva1nces in whole genome sequencing (WGS) based technologies have facilitated multi-step applications for predicting antimicrobial resistance (AMR) and investigating the molecular epidemiology of Neisseria gonorrhoeae. However, generating full scaffolds of N. gonorrhoeae genomes from short reads, and the assignment of molecular epidemiological information (NG-MLST, NG-MAST, and NG-STAR) to multiple assembled samples, is challenging due to required manual tasks such as annotating antimicrobial resistance determinants with standard nomenclature for a large number of genomes.

RESULTS

We present Gen2Epi, a pipeline that assembles short reads into full scaffolds and automatically assigns molecular epidemiological and AMR information to the assembled genomes. Gen2Epi is a command-line tool integrating third-party software and tailored specifically for N. gonorrhoeae. For its evaluation, the Gen2Epi pipeline successfully assembled the WGS short reads from 1484 N. gonorrhoeae samples into full-length genomes for both chromosomes and plasmids and was able to assign in silico molecular determinant information to each dataset automatically. The assemblies were generated using raw as well as trimmed short reads. The median genome coverage of full-length scaffolds and "N" statistics (N50, NG50, and NGA50) were higher than, or comparable to, previously published results and the scaffolding process improved the quality of the draft genome assemblies. Molecular antimicrobial resistant (AMR) determinants identified by Gen2Epi reproduced information for the 1484 samples as previously reported, including NG-MLST, NG-MAST, and NG-STAR molecular sequence types.

CONCLUSIONS

Gen2Epi can be used to assemble short reads into full-length genomes and assign accurate molecular marker and AMR information automatically from NG-STAR, NG-MAST, and NG-MLST. Gen2Epi is publicly available under "CC BY-NC 2.0 CA" Creative Commons licensing as a VirtualBox image containing the constituent software components running on the LINUX operating system (CentOS 7). The image and associated documentation are available via anonymous FTP at ftp://www.cs.usask.ca/pub/combi or ftp://ftp.cs.usask.ca/pub/combi.

Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal Salmonella

Nontyphoidal Salmonella species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susceptibility data are available for Salmonella strains because of surveillance efforts from public health agencies. In this study, a collection of 5,278 nontyphoidal Salmonella genomes, collected over 15 years in the United States, was used to generate extreme gradient boosting (XGBoost)-based machine learning models for predicting MICs for 15 antibiotics. The MIC prediction models had an overall average accuracy of 95% within ±1 2-fold dilution step (confidence interval, 95% to 95%), an average very major error rate of 2.7% (confidence interval, 2.4% to 3.0%), and an average major error rate of 0.1% (confidence interval, 0.1% to 0.2%). The model predicted MICs with no a priori information about the underlying gene content or resistance phenotypes of the strains. By selecting diverse genomes for the training sets, we show that highly accurate MIC prediction models can be generated with less than 500 genomes. We also show that our approach for predicting MICs is stable over time, despite annual fluctuations in antimicrobial resistance gene content in the sampled genomes. Finally, using feature selection, we explore the important genomic regions identified by the models for predicting MICs. To date, this is one of the largest MIC modeling studies to be published. Our strategy for developing whole-genome sequence-based models for surveillance and clinical diagnostics can be readily applied to other important human pathogens.

The role of whole genome sequencing in monitoring antimicrobial resistance: A biosafety and public health priority in the Arabian Peninsula

The recent declaration by the United Nations to establish an interagency coordination group (IACG) on antimicrobial resistance (AMR) emphasises the global nature of the AMR threat. Rapid dissemination and spread of AMR is exacerbated by the movements of humans, animals, foods and materials. International monitoring and surveillance of AMR indicates to policy makers, regulators and auditors the magnitude of the problem and also informs appropriate and mindful interventions that will impact public health policy and mitigate AMR. Identifying the drivers of AMR requires a 'one-health' approach to capture cross-sectoral utilization, phenotypic and genetic data. Capacity building in diagnostic and reference laboratories is required for traditional phenotypic testing as well as newer technologies (e.g. whole genome sequencing, WGS), in order to enhance the detection, characterisation, tracking and surveillance of AMR. The Gulf Health Council (GHC) for the cooperation council states have developed national AMR plans and will standardise pathogen identification and susceptibility testing to gain useful, reliable and comparable data. Additional plans are to establish, for the region, a state-of-the-art 'one-health' WGS service to identify and examine emerging AMR issues as well as the associated healthcare and financial burden(s). Currently, there is a paucity of WGS based research for tackling AMR challenges in the GHC countries. In this article, we have considered the current surveillance landscape and the potential role of whole genome sequencing (WGS) for monitoring antimicrobial resistance in the Arabian Peninsula. We highlighted the importance of using WGS for monitoring AMR in these countries as there remains a dearth of microbial genomic data and studies from the GHC countries. Development of WGS-based AMR surveillance is required to identify the burden and prevalence of AMR in the GHC countries.

Neisseria genomics: current status and future perspectives

High-throughput whole genome sequencing has unlocked a multitude of possibilities enabling members of the Neisseria genus to be examined with unprecedented detail, including the human pathogens Neisseria meningitidis and Neisseria gonorrhoeae. To maximise the potential benefit of this for public health, it is becoming increasingly important to ensure that this plethora of data are adequately stored, disseminated and made readily accessible. Investigations facilitating cross-species comparisons as well as the analysis of global datasets will allow differences among and within species and across geographic locations and different times to be identified, improving our understanding of the distinct phenotypes observed. Recent advances in high-throughput platforms that measure the transcriptome, proteome and/or epigenome are also becoming increasingly employed to explore the complexities of Neisseria biology. An integrated approach to the analysis of these is essential to fully understand the impact these may have in the Neisseria genus. This article reviews the current status of some of the tools available for next generation sequence analysis at the dawn of the ‘post-genomic’ era.