The MITRE trial protocol: a study to evaluate the microbiome as a biomarker of efficacy and toxicity in cancer patients receiving immune checkpoint inhibitor therapy.

BACKGROUND: The gut microbiome is implicated as a marker of response to immune checkpoint inhibitors (ICI) based on preclinical mouse models and preliminary observations in limited patient series. Furthermore, early studies suggest faecal microbial transfer may have therapeutic potential, converting ICI non-responders into responders. So far, identification of specific responsible bacterial taxa has been inconsistent, which limits future application. The MITRE study will explore and validate a microbiome signature in a larger scale prospective study across several different cancer types. METHODS: Melanoma, renal cancer and non-small cell lung cancer patients who are planned to receive standard immune checkpoint inhibitors are being recruited to the MITRE study. Longitudinal stool samples are collected prior to treatment, then at 6 weeks, 3, 6 and 12 months during treatment, or at disease progression/recurrence (whichever is sooner), as well as after a severe (≥grade 3 CTCAE v5.0) immune-related adverse event. Additionally, whole blood, plasma, buffy coat, RNA and peripheral blood mononuclear cells (PBMCs) is collected at similar time points and will be used for exploratory analyses. Archival tumour tissue, tumour biopsies at progression/relapse, as well as any biopsies from body organs collected after a severe toxicity are collected. The primary outcome measure is the ability of the microbiome signature to predict 1 year progression-free survival (PFS) in patients with advanced disease. Secondary outcomes include microbiome correlations with toxicity and other efficacy end-points. Biosamples will be used to explore immunological and genomic correlates. A sub-study will evaluate both COVID-19 antigen and antibody associations with the microbiome. DISCUSSION: There is an urgent need to identify biomarkers that are predictive of treatment response, resistance and toxicity to immunotherapy. The data generated from this study will both help inform patient selection for these drugs and provide information that may allow therapeutic manipulation of the microbiome to improve future patient outcomes. TRIAL REGISTRATION: NCT04107168 , ClinicalTrials.gov, registered 09/27/2019. Protocol V3.2 (16/04/2021).

RCandy: an R package for visualizing homologous recombinations in bacterial genomes.

SUMMARY: Homologous recombination is an important evolutionary process in bacteria and other prokaryotes, which increases genomic sequence diversity and can facilitate adaptation. Several methods and tools have been developed to detect genomic regions recently affected by recombination. Exploration and visualization of such recombination events can reveal valuable biological insights, but it remains challenging. Here, we present RCandy, a platform-independent R package for rapid, simple and flexible visualization of recombination events in bacterial genomes. AVAILABILITY AND IMPLEMENTATION: RCandy is an R package freely available for use under the MIT license. It is platform-independent and has been tested on Windows, Linux and MacOSX. The source code comes together with a detailed vignette available on GitHub at https://github.com/ChrispinChaguza/RCandy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A community-driven resource for genomic epidemiology and antimicrobial resistance prediction of Neisseria gonorrhoeae at Pathogenwatch.

BACKGROUND: Antimicrobial-resistant (AMR) Neisseria gonorrhoeae is an urgent threat to public health, as strains resistant to at least one of the two last-line antibiotics used in empiric therapy of gonorrhoea, ceftriaxone and azithromycin, have spread internationally. Whole genome sequencing (WGS) data can be used to identify new AMR clones and transmission networks and inform the development of point-of-care tests for antimicrobial susceptibility, novel antimicrobials and vaccines. Community-driven tools that provide an easy access to and analysis of genomic and epidemiological data is the way forward for public health surveillance. METHODS: Here we present a public health-focussed scheme for genomic epidemiology of N. gonorrhoeae at Pathogenwatch ( https://pathogen.watch/ngonorrhoeae ). An international advisory group of experts in epidemiology, public health, genetics and genomics of N. gonorrhoeae was convened to inform on the utility of current and future analytics in the platform. We implement backwards compatibility with MLST, NG-MAST and NG-STAR typing schemes as well as an exhaustive library of genetic AMR determinants linked to a genotypic prediction of resistance to eight antibiotics. A collection of over 12,000 N. gonorrhoeae genome sequences from public archives has been quality-checked, assembled and made public together with available metadata for contextualization. RESULTS: AMR prediction from genome data revealed specificity values over 99% for azithromycin, ciprofloxacin and ceftriaxone and sensitivity values around 99% for benzylpenicillin and tetracycline. A case study using the Pathogenwatch collection of N. gonorrhoeae public genomes showed the global expansion of an azithromycin-resistant lineage carrying a mosaic mtr over at least the last 10 years, emphasising the power of Pathogenwatch to explore and evaluate genomic epidemiology questions of public health concern. CONCLUSIONS: The N. gonorrhoeae scheme in Pathogenwatch provides customised bioinformatic pipelines guided by expert opinion that can be adapted to public health agencies and departments with little expertise in bioinformatics and lower-resourced settings with internet connection but limited computational infrastructure. The advisory group will assess and identify ongoing public health needs in the field of gonorrhoea, particularly regarding gonococcal AMR, in order to further enhance utility with modified or new analytic methods.

Phylogenomics of Mycobacterium africanum reveals a new lineage and a complex evolutionary history.

Human tuberculosis (TB) is caused by members of the Mycobacterium tuberculosis complex (MTBC). The MTBC comprises several human-adapted lineages known as M. tuberculosis sensu stricto, as well as two lineages (L5 and L6) traditionally referred to as Mycobacterium africanum. Strains of L5 and L6 are largely limited to West Africa for reasons unknown, and little is known of their genomic diversity, phylogeography and evolution. Here, we analysed the genomes of 350 L5 and 320 L6 strains, isolated from patients from 21 African countries, plus 5 related genomes that had not been classified into any of the known MTBC lineages. Our population genomic and phylogeographical analyses showed that the unclassified genomes belonged to a new group that we propose to name MTBC lineage 9 (L9). While the most likely ancestral distribution of L9 was predicted to be East Africa, the most likely ancestral distribution for both L5 and L6 was the Eastern part of West Africa. Moreover, we found important differences between L5 and L6 strains with respect to their phylogeographical substructure and genetic diversity. Finally, we could not confirm the previous association of drug-resistance markers with lineage and sublineages. Instead, our results indicate that the association of drug resistance with lineage is most likely driven by sample bias or geography. In conclusion, our study sheds new light onto the genomic diversity and evolutionary history of M. africanum, and highlights the need to consider the particularities of each MTBC lineage for understanding the ecology and epidemiology of TB in Africa and globally.

The Distribution and Spread of Susceptible and Resistant Neisseria gonorrhoeae Across Demographic Groups in a Major Metropolitan Center.

BACKGROUND: Genomic epidemiology studies of gonorrhea in the United States have primarily focused on national surveillance for antibiotic resistance, and patterns of local transmission between demographic groups of resistant and susceptible strains are unknown. METHODS: We analyzed a convenience sample of genome sequences, antibiotic susceptibility, and patient data from 897 gonococcal isolates cultured at the New York City (NYC) Public Health Laboratory from NYC Department of Health and Mental Hygiene (DOHMH) Sexual Health Clinic (SHC) patients, primarily in 2012-2013. We reconstructed the gonococcal phylogeny, defined transmission clusters using a 10 nonrecombinant single nucleotide polymorphism threshold, tested for clustering of demographic groups, and placed NYC isolates in a global phylogenetic context. RESULTS: The NYC gonococcal phylogeny reflected global diversity with isolates from 22/23 of the prevalent global lineages (96%). Isolates clustered on the phylogeny by patient sexual behavior (P < .001) and race/ethnicity (P < .001). Minimum inhibitory concentrations were higher across antibiotics in isolates from men who have sex with men compared to heterosexuals (P < .001) and white heterosexuals compared to black heterosexuals (P < .01). In our dataset, all large transmission clusters (≥10 samples) of N. gonorrhoeae were susceptible to ciprofloxacin, ceftriaxone, and azithromycin, and comprised isolates from patients across demographic groups. CONCLUSIONS: All large transmission clusters were susceptible to gonorrhea therapies, suggesting that resistance to empiric therapy was not a main driver of spread, even as risk for resistance varied across demographic groups. Further study of local transmission networks is needed to identify drivers of transmission.

Adaptation to the cervical environment is associated with increased antibiotic susceptibility in Neisseria gonorrhoeae.

Neisseria gonorrhoeae is an urgent public health threat due to rapidly increasing incidence and antibiotic resistance. In contrast with the trend of increasing resistance, clinical isolates that have reverted to susceptibility regularly appear, prompting questions about which pressures compete with antibiotics to shape gonococcal evolution. Here, we used genome-wide association to identify loss-of-function (LOF) mutations in the efflux pump mtrCDE operon as a mechanism of increased antibiotic susceptibility and demonstrate that these mutations are overrepresented in cervical relative to urethral isolates. This enrichment holds true for LOF mutations in another efflux pump, farAB, and in urogenitally-adapted versus typical N. meningitidis, providing evidence for a model in which expression of these pumps in the female urogenital tract incurs a fitness cost for pathogenic Neisseria. Overall, our findings highlight the impact of integrating microbial population genomics with host metadata and demonstrate how host environmental pressures can lead to increased antibiotic susceptibility.

Genomic evolution of Neisseria gonorrhoeae since the preantibiotic era (1928-2013): antimicrobial use/misuse selects for resistance and drives evolution.

BACKGROUND: Multidrug-resistant Neisseria gonorrhoeae strains are prevalent, threatening gonorrhoea treatment globally, and understanding of emergence, evolution, and spread of antimicrobial resistance (AMR) in gonococci remains limited. We describe the genomic evolution of gonococci and their AMR, related to the introduction of antimicrobial therapies, examining isolates from 1928 (preantibiotic era) to 2013 in Denmark. This is, to our knowledge, the oldest gonococcal collection globally. METHODS: Lyophilised isolates were revived and examined using Etest (18 antimicrobials) and whole-genome sequencing (WGS). Quality-assured genome sequences were obtained for 191 viable and 40 non-viable isolates and analysed with multiple phylogenomic approaches. RESULTS: Gonococcal AMR, including an accumulation of multiple AMR determinants, started to emerge particularly in the 1950s-1970s. By the twenty-first century, resistance to most antimicrobials was common. Despite that some AMR determinants affect many physiological functions and fitness, AMR determinants were mainly selected by the use/misuse of gonorrhoea therapeutic antimicrobials. Most AMR developed in strains belonging to one multidrug-resistant (MDR) clade with close to three times higher genomic mutation rate. Modern N. gonorrhoeae was inferred to have emerged in the late-1500s and its genome became increasingly conserved over time. CONCLUSIONS: WGS of gonococci from 1928 to 2013 showed that no AMR determinants, except penB, were in detectable frequency before the introduction of gonorrhoea therapeutic antimicrobials. The modern gonococcus is substantially younger than previously hypothesized and has been evolving into a more clonal species, driven by the use/misuse of antimicrobials. The MDR gonococcal clade should be further investigated for early detection of strains with predispositions to develop and maintain MDR and for initiation of public health interventions.

Phylogenomic analysis of Neisseria gonorrhoeae transmission to assess sexual mixing and HIV transmission risk in England: a cross-sectional, observational, whole-genome sequencing study.

BACKGROUND: Characterising sexual networks with transmission of sexually transmitted infections might allow identification of individuals at increased risk of infection. We aimed to investigate sexual mixing in Neisseria gonorrhoeae transmission networks between women, heterosexual men, and men who report sex with men (MSM), and between people with and without HIV. METHODS: In this cross-sectional observational study, we whole-genome sequenced N gonorrhoeae isolates from the archive of the Gonococcal Resistance to Antimicrobials Surveillance Programme (GRASP).w Isolates that varied by five single nucleotide polymorphisms or fewer were grouped into clusters that represented sexual networks with N gonorrhoeae transmission. Clusters were described by gender, sexual risk group, and HIV status. FINDINGS: We sequenced 1277 N gonorrhoeae isolates with linked clinical and sociodemographic data that were collected in five clinics in England during 2013-16 (July 1 to Sept 30 in 2013-15; July 1 to Sept 9 in 2016). The isolates grouped into 213 clusters. 30 (14%) clusters contained isolates from heterosexual men and MSM but no women and three (1%) clusters contained isolates from only women and MSM. 146 (69%) clusters comprised solely people with negative or unknown HIV status and seven (3%) comprised only HIV-positive people. 60 (28%) clusters comprised MSM with positive and negative or unknown HIV status. INTERPRETATION: N gonorrhoeae molecular data can provide information indicating risk of HIV or other sexually transmitted infections for some individuals for whom such risk might not be known from clinical history. These findings have implications for sexual health care, including offering testing, prevention advice, and preventive treatment, such as HIV pre-exposure prophylaxis. FUNDING: National Institute for Health Research Health Protection Research Unit; Wellcome; Public Health England.

Genetic variation associated with infection and the environment in the accidental pathogen Burkholderia pseudomallei.

The environmental bacterium Burkholderia pseudomallei causes melioidosis, an important endemic human disease in tropical and sub-tropical countries. This bacterium occupies broad ecological niches including soil, contaminated water, single-cell microbes, plants and infection in a range of animal species. Here, we performed genome-wide association studies for genetic determinants of environmental and human adaptation using a combined dataset of 1,010 whole genome sequences of B. pseudomallei from Northeast Thailand and Australia, representing two major disease hotspots. With these data, we identified 47 genes from 26 distinct loci associated with clinical or environmental isolates from Thailand and replicated 12 genes in an independent Australian cohort. We next outlined the selective pressures on the genetic loci (dN/dS) and the frequency at which they had been gained or lost throughout their evolutionary history, reflecting the bacterial adaptability to a wide range of ecological niches. Finally, we highlighted loci likely implicated in human disease.

Genetic variation regulates the activation and specificity of Restriction-Modification systems in Neisseria gonorrhoeae.

Restriction-Modification systems (RMS) are one of the main mechanisms of defence against foreign DNA invasion and can have an important role in the regulation of gene expression. The obligate human pathogen Neisseria gonorrhoeae carries one of the highest loads of RMS in its genome; between 13 to 15 of the three main types. Previous work has described their organization in the reference genome FA1090 and has inferred the associated methylated motifs. Here, we studied the structure of RMS and target methylated motifs in 25 gonococcal strains sequenced with Single Molecule Real-Time (SMRT) technology, which provides data on DNA modification. The results showed a variable picture of active RMS in different strains, with phase variation switching the activity of Type III RMS, and both the activity and specificity of a Type I RMS. Interestingly, the Dam methylase was found in place of the NgoAXI endonuclease in two of the strains, despite being previously thought to be absent in the gonococcus. We also identified the real methylation target of NgoAXII as 5'-GCAGA-3', different from that previously described. Results from this work give further insights into the diversity and dynamics of RMS and methylation patterns in N. gonorrhoeae.

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.

Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation of factors that may influence performance of such models, how they might apply to and vary across clinical populations, and what the implications might be in the clinical setting. Here, we performed a meta-analysis of seven large Neisseria gonorrhoeae datasets, as well as Klebsiella pneumoniae and Acinetobacter baumannii datasets, with whole genome sequence data and antibiotic susceptibility phenotypes using set covering machine classification, random forest classification, and random forest regression models to predict resistance phenotypes from genotype. We demonstrate how model performance varies by drug, dataset, resistance metric, and species, reflecting the complexities of generating clinically relevant conclusions from machine learning-derived models. Our findings underscore the importance of incorporating relevant biological and epidemiological knowledge into model design and assessment and suggest that doing so can inform tailored modeling for individual drugs, pathogens, and clinical populations. We further suggest that continued comprehensive sampling and incorporation of up-to-date whole genome sequence data, resistance phenotypes, and treatment outcome data into model training will be crucial to the clinical utility and sustainability of machine learning-based molecular diagnostics.

Author Correction: Atlas of group A streptococcal vaccine candidates compiled using large-scale comparative genomics.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The impact of antimicrobials on gonococcal evolution.

The sexually transmitted pathogen Neisseria gonorrhoeae is regarded as being on the way to becoming an untreatable superbug. Despite its clinical importance, little is known about its emergence and evolution, and how this corresponds with the introduction of antimicrobials. We present a genome-based phylogeographical analysis of 419 gonococcal isolates from across the globe. Results indicate that modern gonococci originated in Europe or Africa, possibly as late as the sixteenth century and subsequently disseminated globally. We provide evidence that the modern gonococcal population has been shaped by antimicrobial treatment of sexually transmitted infections as well as other infections, leading to the emergence of two major lineages with different evolutionary strategies. The well-described multidrug-resistant lineage is associated with high rates of homologous recombination and infection in high-risk sexual networks. A second, multisusceptible lineage is more associated with heterosexual networks, with potential implications for infection control.

Epidemic of carbapenem-resistant Klebsiella pneumoniae in Europe is driven by nosocomial spread.

Public health interventions to control the current epidemic of carbapenem-resistant Klebsiella pneumoniae rely on a comprehensive understanding of its emergence and spread over a wide range of geographical scales. We analysed the genome sequences and epidemiological data of >1,700 K. pneumoniae samples isolated from patients in 244 hospitals in 32 countries during the European Survey of Carbapenemase-Producing Enterobacteriaceae. We demonstrate that carbapenemase acquisition is the main cause of carbapenem resistance and that it occurred across diverse phylogenetic backgrounds. However, 477 of 682 (69.9%) carbapenemase-positive isolates are concentrated in four clonal lineages, sequence types 11, 15, 101, 258/512 and their derivatives. Combined analysis of the genetic and geographic distances between isolates with different ß-lactam resistance determinants suggests that the propensity of K. pneumoniae to spread in hospital environments correlates with the degree of resistance and that carbapenemase-positive isolates have the highest transmissibility. Indeed, we found that over half of the hospitals that contributed carbapenemase-positive isolates probably experienced within-hospital transmission, and interhospital spread is far more frequent within, rather than between, countries. Finally, we propose a value of 21 for the number of single nucleotide polymorphisms that optimizes the discrimination of hospital clusters and detail the international spread of the successful epidemic lineage, ST258/512.

Genome-wide epistasis and co-selection study using mutual information.

Covariance-based discovery of polymorphisms under co-selective pressure or epistasis has received considerable recent attention in population genomics. Both statistical modeling of the population level covariation of alleles across the chromosome and model-free testing of dependencies between pairs of polymorphisms have been shown to successfully uncover patterns of selection in bacterial populations. Here we introduce a model-free method, SpydrPick, whose computational efficiency enables analysis at the scale of pan-genomes of many bacteria. SpydrPick incorporates an efficient correction for population structure, which adjusts for the phylogenetic signal in the data without requiring an explicit phylogenetic tree. We also introduce a new type of visualization of the results similar to the Manhattan plots used in genome-wide association studies, which enables rapid exploration of the identified signals of co-evolution. Simulations demonstrate the usefulness of our method and give some insight to when this type of analysis is most likely to be successful. Application of the method to large population genomic datasets of two major human pathogens, Streptococcus pneumoniae and Neisseria meningitidis, revealed both previously identified and novel putative targets of co-selection related to virulence and antibiotic resistance, highlighting the potential of this approach to drive molecular discoveries, even in the absence of phenotypic data.

Atlas of group A streptococcal vaccine candidates compiled using large-scale comparative genomics.

Group A Streptococcus (GAS; Streptococcus pyogenes) is a bacterial pathogen for which a commercial vaccine for humans is not available. Employing the advantages of high-throughput DNA sequencing technology to vaccine design, we have analyzed 2,083 globally sampled GAS genomes. The global GAS population structure reveals extensive genomic heterogeneity driven by homologous recombination and overlaid with high levels of accessory gene plasticity. We identified the existence of more than 290 clinically associated genomic phylogroups across 22 countries, highlighting challenges in designing vaccines of global utility. To determine vaccine candidate coverage, we investigated all of the previously described GAS candidate antigens for gene carriage and gene sequence heterogeneity. Only 15 of 28 vaccine antigen candidates were found to have both low naturally occurring sequence variation and high (>99%) coverage across this diverse GAS population. This technological platform for vaccine coverage determination is equally applicable to prospective GAS vaccine antigens identified in future studies.

Molecular epidemiology and whole genome sequencing analysis of clinical Mycobacterium bovis from Ghana.

BACKGROUND: Bovine tuberculosis (bTB) caused by Mycobacterium bovis is a re-emerging problem in both livestock and humans. The association of some M. bovis strains with hyper-virulence, MDR-TB and disseminated disease makes it imperative to understand the biology of the pathogen. METHODS: Mycobacterium bovis (15) among 1755 M. tuberculosis complex (MTBC) isolated between 2012 and 2014 were characterized and analyzed for associated patient demography and other risk factors. Five of the M. bovis isolates were whole-genome sequenced and comparatively analyzed against a global collection of published M. bovis genomes. RESULTS: Mycobacterium bovis was isolated from 3/560(0.5%) females and 12/1195(1.0%) males with pulmonary TB. The average age of M. bovis infected cases was 46.8 years (7-72years). TB patients from the Northern region of Ghana (1.9%;4/212) had a higher rate of infection with M. bovis (OR = 2.7,p = 0.0968) compared to those from the Greater Accra region (0.7%;11/1543). Among TB patients with available HIV status, the odds of isolating M. bovis from HIV patients (2/119) was 3.3 higher relative to non-HIV patients (4/774). Direct contact with livestock or their unpasteurized products was significantly associated with bTB (p<0.0001, OR = 124.4,95% CI = 30.1-508.3). Two (13.3%) of the M. bovis isolates were INH resistant due to the S315T mutation in katG whereas one (6.7%) was RIF resistant with Q432P and I1491S mutations in rpoB. M. bovis from Ghana resolved as mono-phyletic branch among mostly M. bovis from Africa irrespective of the host and were closest to the root of the global M. bovis phylogeny. M. bovis-specific amino acid mutations were detected among MTBC core genes such as mce1A, mmpL1, pks6, phoT, pstB, glgP and Rv2955c. Additional mutations P6T in chaA, G187E in mgtC, T35A in Rv1979c, S387A in narK1, L400F in fas and A563T in eccA1 were restricted to the 5 clinical M. bovis from Ghana. CONCLUSION: Our data indicate potential zoonotic transmission of bTB in Ghana and hence calls for intensified public education on bTB, especially among risk groups.

Mycobacterium ulcerans Population Genomics To Inform on the Spread of Buruli Ulcer across Central Africa.

Buruli ulcer is a neglected tropical disease of skin and subcutaneous tissue caused by infection with the pathogen Mycobacterium ulcerans Many critical issues for disease control, such as understanding the mode of transmission and identifying source reservoirs of M. ulcerans, are still largely unknown. Here, we used genomics to reconstruct in detail the evolutionary trajectory and dynamics of M. ulcerans populations at a central African scale and at smaller geographical village scales. Whole-genome sequencing (WGS) data were analyzed from 179 M. ulcerans strains isolated from all Buruli ulcer foci in the Democratic Republic of the Congo, The Republic of Congo, and Angola that have ever yielded positive M. ulcerans cultures. We used both temporal associations and the study of the mycobacterial demographic history to estimate the contribution of humans as a reservoir in Buruli ulcer transmission. Our phylogeographic analysis revealed one almost exclusively predominant sublineage of M. ulcerans that arose in Central Africa and proliferated in its different regions of endemicity during the Age of Discovery. We observed how the best sampled endemic hot spot, the Songololo territory, became an area of endemicity while the region was being colonized by Belgium (1880s). We furthermore identified temporal parallels between the observed past population fluxes of M. ulcerans from the Songololo territory and the timing of health policy changes toward control of the Buruli ulcer epidemic in that region. These findings suggest that an intervention based on detecting and treating human cases in an area of endemicity might be sufficient to break disease transmission chains, irrespective of other reservoirs of the bacterium.IMPORTANCE Buruli ulcer is a destructive skin and soft tissue infection caused by Mycobacterium ulcerans The disease is characterized by progressive skin ulceration, which can lead to permanent disfigurement and long-term disability. Currently, the major hurdles facing disease control are incomplete understandings of both the mode of transmission and environmental reservoirs of M. ulcerans As decades of spasmodic environmental sampling surveys have not brought us much closer to overcoming these hurdles, the Buruli ulcer research community has recently switched to using comparative genomics. The significance of our research is in how we used both temporal associations and the study of the mycobacterial demographic history to estimate the contribution of humans as a reservoir in Buruli ulcer transmission. Our approach shows that it might be possible to use bacterial population genomics to assess the impact of health interventions, providing valuable feedback for managers of disease control programs in areas where health surveillance infrastructure is poor.

Fast and flexible bacterial genomic epidemiology with PopPUNK.

The routine use of genomics for disease surveillance provides the opportunity for high-resolution bacterial epidemiology. Current whole-genome clustering and multilocus typing approaches do not fully exploit core and accessory genomic variation, and they cannot both automatically identify, and subsequently expand, clusters of significantly similar isolates in large data sets spanning entire species. Here, we describe PopPUNK (Population Partitioning Using Nucleotide K -mers), a software implementing scalable and expandable annotation- and alignment-free methods for population analysis and clustering. Variable-length k-mer comparisons are used to distinguish isolates' divergence in shared sequence and gene content, which we demonstrate to be accurate over multiple orders of magnitude using data from both simulations and genomic collections representing 10 taxonomically widespread species. Connections between closely related isolates of the same strain are robustly identified, despite interspecies variation in the pairwise distance distributions that reflects species' diverse evolutionary patterns. PopPUNK can process 103-104 genomes in a single batch, with minimal memory use and runtimes up to 200-fold faster than existing model-based methods. Clusters of strains remain consistent as new batches of genomes are added, which is achieved without needing to reanalyze all genomes de novo. This facilitates real-time surveillance with consistent cluster naming between studies and allows for outbreak detection using hundreds of genomes in minutes. Interactive visualization and online publication is streamlined through the automatic output of results to multiple platforms. PopPUNK has been designed as a flexible platform that addresses important issues with currently used whole-genome clustering and typing methods, and has potential uses across bacterial genetics and public health research.

Using genomics to understand antimicrobial resistance and transmission in Neisseria gonorrhoeae.

Gonorrhoea infections are on the increase and strains that are resistant to all antimicrobials used to treat the disease have been found worldwide. These observations encouraged the World Health Organization to include Neisseria gonorrhoeae on their list of high-priority organisms in need of new treatments. Fortunately, concurrent resistance to both antimicrobials used in dual therapy is still rare. The fight against antimicrobial resistance (AMR) must begin from an understanding of how it evolves and spreads in sexual networks. Genome-based analyses have allowed the study of the gonococcal population dynamics and transmission, giving a novel perspective on AMR gonorrhoea. Here, we will review past, present and future treatment options for gonorrhoea and explain how genomics is helping to increase our understanding of the changing AMR and transmission landscape. This article contains data hosted by Microreact.