Population-level comparisons of prokaryotic genomes must take into account the substantial differences in gene content resulting from horizontal gene transfer, gene duplication and gene loss. However, the automated annotation of prokaryotic genomes is imperfect, and errors due to fragmented assemblies, contamination, diverse gene families and mis-assemblies accumulate over the population, leading to profound consequences when analysing the set of all genes found in a species. Here, we introduce Panaroo, a graph-based pangenome clustering tool that is able to account for many of the sources of error introduced during the annotation of prokaryotic genome assemblies. Panaroo is available at 
https://github.com/gtonkinhill/panaroo

.

/pdf/producing-polished-prokaryotic-pangenomes-with-the-panaroo-hqn8oat36l.pdf

Producing polished prokaryotic pangenomes with the Panaroo pipeline

We describe a gonorrhoea case with combined high-level azithromycin resistance and ceftriaxone resistance. In February 2018, a heterosexual male was diagnosed with gonorrhoea in the United Kingdom following sexual intercourse with a locally resident female in Thailand and failed treatment with ceftriaxone plus doxycycline and subsequently spectinomycin. Resistance arose from two mechanisms combining for the first time in a genetic background similar to a commonly circulating strain. Urgent action is essential to prevent further spread.

Gonorrhoea treatment failure caused by a Neisseria gonorrhoeae strain with combined ceftriaxone and high-level azithromycin resistance, England, February 2018

Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.

Genome-Based Prediction of Bacterial Antibiotic Resistance.

Population-level comparisons of prokaryotic genomes must take into account the substantial differences in gene content, resulting from frequent horizontal gene transfer, gene duplication and gene loss. However, the automated annotation of prokaryotic genomes is imperfect, and errors due to fragmented assemblies, contamination, diverse gene families and mis-assemblies accumulate over the population, leading to profound consequences when analysing the set of all genes found in a species. Here we introduce Panaroo, a graph based pangenome clustering tool that is able to account for many of the sources of error introduced during the annotation of prokaryotic genome assemblies. We verified our approach through extensive simulations of de novo assemblies using the infinitely many genes model and by analysing a number of publicly available large bacterial genome datasets. Using a highly clonal Mycobacterium tuberculosis dataset as a negative control case, we show that failing to account for annotation errors can lead to pangenome estimates that are dominated by error. We additionally demonstrate the utility of the improved graphical output provided by Panaroo by performing a pan-genome wide association study in Neisseria gonorrhoeae and by analysing gene gain and loss rates across 51 of the major global pneumococcal sequence clusters. Panaroo is freely available under an open source MIT licence at https://github.com/gtonkinhill/panaroo.

https://www.biorxiv.org/content/biorxiv/early/2020/01/28/2020.01.28.922989.full.pdf

Producing Polished Prokaryotic Pangenomes with the Panaroo Pipeline

Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is a serious public health problem, compromising the management and control of gonorrhoea globally. Resistance in N. gonorrhoeae to ceftriaxone, the last option for first-line empirical monotherapy of gonorrhoea, has been reported from many countries globally, and sporadic failures to cure especially pharyngeal gonorrhoea with ceftriaxone monotherapy and dual antimicrobial therapies (ceftriaxone plus azithromycin or doxycycline) have been confirmed in several countries. In 2018, the first gonococcal isolates with ceftriaxone resistance plus high-level azithromycin resistance were identified in England and Australia. The World Health Organization (WHO) Global Gonococcal Antimicrobial Surveillance Program (GASP) is essential to monitor AMR trends, identify emerging AMR and provide evidence for refinements of treatment guidelines and public health policy globally. Herein we describe the WHO GASP data from 67 countries in 2015–16, confirmed gonorrhoea treatment failures with ceftriaxone with or without azithromycin or doxycycline, and international collaborative actions and research efforts essential for the effective management and control of gonorrhoea. In most countries, resistance to ciprofloxacin is exceedingly high, azithromycin resistance is present and decreased susceptibility or resistance to ceftriaxone has emerged. Enhanced global collaborative actions are crucial for the control of gonorrhoea, including improved prevention, early diagnosis, treatment of index patient and partner (including test-of-cure), improved and expanded AMR surveillance (including surveillance of antimicrobial use and treatment failures), increased knowledge of correct antimicrobial use and the pharmacokinetics and pharmacodynamics of antimicrobials and effective drug regulations and prescription policies (including antimicrobial stewardship). Ultimately, rapid, accurate and affordable point-of-care diagnostic tests (ideally also predicting AMR and/or susceptibility), new therapeutic antimicrobials and, the only sustainable solution, gonococcal vaccine(s) are imperative.

/pdf/world-health-organization-global-gonococcal-antimicrobial-4y06hkd0g9.pdf

World Health Organization Global Gonococcal Antimicrobial Surveillance Program (WHO GASP): review of new data and evidence to inform international collaborative actions and research efforts

Summary Background Traditional methods for molecular epidemiology of Neisseria gonorrhoeae are suboptimal. Whole-genome sequencing (WGS) offers ideal resolution to describe population dynamics and to predict and infer transmission of antimicrobial resistance, and can enhance infection control through linkage with epidemiological data. We used WGS, in conjunction with linked epidemiological and phenotypic data, to describe the gonococcal population in 20 European countries. We aimed to detail changes in phenotypic antimicrobial resistance levels (and the reasons for these changes) and strain distribution (with a focus on antimicrobial resistance strains in risk groups), and to predict antimicrobial resistance from WGS data. Methods We carried out an observational study, in which we sequenced isolates taken from patients with gonorrhoea from the European Gonococcal Antimicrobial Surveillance Programme in 20 countries from September to November, 2013. We also developed a web platform that we used for automated antimicrobial resistance prediction, molecular typing ( N gonorrhoeae multi-antigen sequence typing [NG-MAST] and multilocus sequence typing), and phylogenetic clustering in conjunction with epidemiological and phenotypic data. Findings The multidrug-resistant NG-MAST genogroup G1407 was predominant and accounted for the most cephalosporin resistance, but the prevalence of this genogroup decreased from 248 (23%) of 1066 isolates in a previous study from 2009–10 to 174 (17%) of 1054 isolates in this survey in 2013. This genogroup previously showed an association with men who have sex with men, but changed to an association with heterosexual people (odds ratio=4·29). WGS provided substantially improved resolution and accuracy over NG-MAST and multilocus sequence typing, predicted antimicrobial resistance relatively well, and identified discrepant isolates, mixed infections or contaminants, and multidrug-resistant clades linked to risk groups. Interpretation To our knowledge, we provide the first use of joint analysis of WGS and epidemiological data in an international programme for regional surveillance of sexually transmitted infections. WGS provided enhanced understanding of the distribution of antimicrobial resistance clones, including replacement with clones that were more susceptible to antimicrobials, in several risk groups nationally and regionally. We provide a framework for genomic surveillance of gonococci through standardised sampling, use of WGS, and a shared information architecture for interpretation and dissemination by use of open access software. Funding The European Centre for Disease Prevention and Control, The Centre for Genomic Pathogen Surveillance, Orebro University Hospital, and Wellcome.

Panayiota Maikanti-Charalambous

Papers

Public health surveillance of multidrug-resistant clones of Neisseria gonorrhoeae in Europe: a genomic survey.