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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., We used Neisseria gonorrhoeae genomes together with clinical and demographic data to study transmission in New York City. Our results indicate transmission across demographic groups and suggest that antibiotic resistance is not the main driver of gonorrhea spread.

Summary 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.

Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is a global concern. Phylogenetic analyses resolve uncertainties regarding genetic relatedness of isolates with identical phenotypes and inform whether AMR is due to new mutations and clonal expansion or separate introductions by importation. We sequenced 1,277 isolates with associated epidemiologic and antimicrobial susceptibility data collected during 2013–2016 to investigate N. gonorrhoeae genomic variability in England. Comparing genetic markers and phenotypes for AMR, we identified 2 N. gonorrhoeae lineages with different antimicrobial susceptibility profiles and 3 clusters with elevated MICs for ceftriaxone, varying mutations in the penA allele, and different epidemiologic characteristics. Our results indicate N. gonorrhoeae with reduced antimicrobial susceptibility emerged independently and multiple times in different sexual networks in England, through new mutation or recombination events and by importation. Monitoring and control for AMR in N. gonorrhoeae should cover the entire population affected, rather than focusing on specific risk groups or locations.

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.

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.

Pathogens of theMycobacterium tuberculosiscomplex (MTBC) are considered to be monomorphic, with little gene content variation between strains. Nevertheless, several genotypic and phenotypic factors separate strains of the different MTBC lineages (L), especially L5 and L6 (traditionally termedMycobacterium africanum) strains, from each other. However, this genome variability and gene content, especially of L5 strains, has not been fully explored and may be important for pathobiology and current approaches for genomic analysis of MTBC strains, including transmission studies. By comparing the genomes of 355 L5 clinical strains (including 3 complete genomes and 352 Illumina whole-genome sequenced isolates) to each other and to H37Rv, we identified multiple genes that were differentially present or absent between H37Rv and L5 strains. Additionally, considerable gene content variability was found across L5 strains, including a split in the L5.3 sub-lineage into L5.3.1 and L5.3.2. These gene content differences had a small knock-on effect on transmission cluster estimation, with clustering rates influenced by the selected reference genome, and with potential overestimation of recent transmission when using H37Rv as the reference genome. We conclude that full capture of the gene diversity, especially high-resolution outbreak analysis, requires a variation of the single H37Rv-centric reference genome mapping approach currently used in most whole-genome sequencing data analysis pipelines. Moreover, the high within-lineage gene content variability suggests that the pan-genome ofM. tuberculosisis at least several kilobases larger than previously thought, implying that a concatenated or reference-free genome assembly (de novo) approach may be needed for particular questions.

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.

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.

Purpose. Differentiation of the Mycobacterium tuberculosis complex (MTBc) from non-tuberculous mycobacteria (NTM) is important for tuberculosis diagnosis and is a prerequisite for reliable phenotypic drug-resistance testing. We evaluated the performance of the rapid MPT64 antigen identification test for the detection of Mycobacterium africanum lineage 5 (MAF L5). Methodology. Smear-positive tuberculosis patients' sputa were included prospectively. Culture was performed on Lowenstein-Jensen medium and, when positive, the MPT64 test and the classical para-nitro benzoic acid susceptibility and heat-labile catalase (PNB/catalase) identification tests were performed. The MPT64 test was repeated 14 days after an initially negative first testing. Direct spoligotyping was performed for MTBc lineage determination. Results. In total, 333 isolates were tested for all of the methods. Three hundred and twenty-two (96.7 %) were pure MTBc, by agreement between spoligotyping and PNB/catalase, and 11 were NTM or a mixture of MTBc/NTM. The MPT64 test conducted on day zero of culture-positivity correctly identified most of the pure MTBc isolates (93.2 %, 300/322), but it failed to detect 24% of the L5 isolates (18/75) versus 2% (4/202) of the L4 ones [OR=15.6 (5.3-45.8), P

The equine disease strangles, which is characterized by the formation of abscesses in the lymph nodes of the head and neck, is one of the most frequently diagnosed infectious diseases of horses around the world. The causal agent, Streptococcus equi subspecies equi , establishes a persistent infection in approximately 10 % of animals that recover from the acute disease. Such ‘carrier’ animals appear healthy and are rarely identified during routine veterinary examinations pre-purchase or transit, but can transmit S. equi to naïve animals initiating new episodes of disease. Here, we report the analysis and visualization of phylogenomic and epidemiological data for 670 isolates of S. equi recovered from 19 different countries using a new core-genome multilocus sequence typing (cgMLST) web bioresource. Genetic relationships among all 670 S. equi isolates were determined at high resolution, revealing national and international transmission events that drive this endemic disease in horse populations throughout the world. Our data argue for the recognition of the international importance of strangles by the Office International des Épizooties to highlight the health, welfare and economic cost of this disease. The Pathogenwatch cgMLST web bioresource described herein is available for tailored genomic analysis of populations of S. equi and its close relative S. equi subspecies zooepidemicus that are recovered from horses and other animals, including humans, throughout the world. This article contains data hosted by Microreact.

Coscolla et al., 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., M.C. is supported by the Ramón y Cajal programme from the Ministerio de Ciencia, Innovación y Universidades. This work was supported by the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) (research award to M.C.), Ministerio de Ciencia, Innovación y Universidades (grant number RTI2018-094399-A-I00 to M.C.) and Consellería de Educació de la Generalitat Valenciana (grant number SEJI/2019/011 to M.C.), the Swiss National Science Foundation (grants 310030_188888, IZRJZ3_164171, IZLSZ3_170834 and CRSII5_177163 to S. G.), the European Research Council (883582-ECOEVODRTB to S. G.) and Wellcome (grant number 097134/Z/11/Z to D. Y.-M).

Background The gut microbiome of cancer patients appears to be associated with response to Immune Checkpoint Inhibitor (ICIs) treatment.1–4 However, the bacteria linked to response differ between published studies. Methods Longitudinal stool samples were collected from 69 patients with advanced melanoma receiving approved ICIs in the Cambridge (UK) MELRESIST study. Pretreatment samples were analysed by Microbiotica, using shotgun metagenomic sequencing. Microbiotica’s sequencing platform comprises the world’s leading Reference Genome Database and advanced Microbiome Bioinformatics to give the most comprehensive and precise mapping of the gut microbiome. This has enabled us to identify gut bacteria associated with ICI response missed using public reference genomes. Published microbiome studies in advanced melanoma,1–3renal cell carcinoma (RCC) and non-small cell lung cancer (NSCLC)4 were reanalysed with the same platform. Results Analysis of the MELRESIST samples showed an overall change in the microbiome composition between advanced melanoma patients and a panel of healthy donor samples, but not between patients who subsequently responded or did not respond to ICIs. However, we did identify a discrete microbiome signature which correlated with response. This signature predicted response with an accuracy of 93% in the MELRESIST cohort, but was less predictive in the published melanoma cohorts.1–3 Therefore, we developed a bioinformatic analytical model, incorporating an interactive random forest model and the MELRESIST dataset, to identify a microbiome signature which was consistent across all published melanoma studies. This model was validated three times by accurately predicting the outcome of an independent cohort. A final microbiome signature was defined using the validated model on MELRESIST and the three published melanoma cohorts. This was very accurate at predicting response in all four studies combined (91%), or individually (82–100%). This signature was also predictive of response in a NSCLC study and to a lesser extent in RCC. The core of this signature is nine bacteria significantly increased in abundance in responders. Conclusions Analysis of the MELRESIST study samples, precision microbiome profiling by the Microbiotica Platform and a validated bioinformatic analysis, have enabled us to identify a unique microbiome signature predictive of response to ICI therapy in four independent melanoma studies. This removes the challenge to the field of different bacteria apparently being associated with response in different studies, and could represent a new microbiome biomarker with clinical application. Nine core bacteria may be driving response and hold potential for co-therapy with ICIs. Ethics Approval The study was approved by Newcastle & North Tyneside 2 Research Ethics Committee, approval number 11/NE/0312. References Matson V, Fessler J, Bao R, et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 2018;359(6371):104–108. Gopalakrishnan V, Spencer CN, Nezi L, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018;359(6371):97–103. Frankel AE, Coughlin LA, Kim J, et al. Metagenomic shotgun sequencing and unbiased metabolomic profiling identify specific human gut microbiota and metabolites associated with immune checkpoint therapy efficacy in melanoma patients. Neoplasia 2017;19(10):848–855. Routy B, Le Chatelier E, Derosa L, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 2018;359(6371):91–97.

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., Antibiotic resistance in Neisseria gonorrhoeae is rising, yet sometimes strains emerge that have reverted to susceptibility. Here, the authors find that selective pressures from the host may influence susceptibility through loss-of-function mutations in genes that encode for efflux pumps.

Pathogens of theMycobacterium tuberculosiscomplex (MTBC) are considered monomorphic, with little gene content variation between strains. Nevertheless, several genotypic and phenotypic factors separate the different MTBC lineages (L), especially L5 and L6 (traditionally termedMycobacterium africanum), from each other. However, genome variability and gene content especially of L5 and L6 strains have not been fully explored and may be potentially important for pathobiology and current approaches for genomic analysis of MTBC isolates, including transmission studies.We compared the genomes of 358 L5 clinical isolates (including 3 completed genomes and 355 Illumina WGS (whole genome sequenced) isolates) to the L5 complete genomes and H37Rv, and identified multiple genes differentially present or absent between H37Rv and L5 strains. Additionally, considerable gene content variability was found across L5 strains, including a split in the L5.3 sublineage into L5.3.1 and L5.3.2. These gene content differences had a small knock on effect on transmission cluster estimation, with clustering rates influenced by the selection of reference genome, and with potential over-estimation of recent transmission when using H37Rv as the reference genome.Our data show that the use of H37Rv as reference genome results in missing SNPs in genes unique for L5 strains. This potentially leads to an underestimation of the diversity present in the genome of L5 strains and in turn affects the transmission clustering rates. As such, a full capture of the gene diversity, especially for high resolution outbreak analysis, requires a variation of the single H37Rv-centric reference genome mapping approach currently used in most WGS data analysis pipelines. Moreover, the high within-lineage gene content variability suggests that the pan-genome ofM. tuberculosisis at least several kilobases larger than previously thought, implying a concatenated or reference-free genome assembly (de novo) approach may be needed for particular questions.Data summarySequence data for the Illumina dataset are available at European Genome-phenome Archive (EGA;https://www.ebi.ac.uk/ega/) under the study accession numbers PRJEB38317 and PRJEB38656. Individual runs accession numbers are indicated in Table S8.PacBio raw reads for the L5 Benin genome are available on the ENA accession SAME3170744. The assembled L5 Benin genome is available on NCBI with accession PRJNA641267. To ensure naming conventions of the genes in the three L5 genomes can be followed, we have uploaded these annotated GFF files to figshare athttps://doi.org/10.6084/m9.figshare.12911849.v1.Custom python scripts used in this analysis can be found athttps://github.com/conmeehan/pathophy.

openaire: EC/H2020/742158/EU//SCARABEE 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.

Neisseria gonorrhoeaeis 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 on the largest collection ofN. gonorrhoeaeisolates to date (n=4852) to identify loss-of-function (LOF) mutations in the efflux pumpmtrCDEoperon as a mechanism of increased antibiotic susceptibility and demonstrate that these mutations are overrepresented in cervical isolates relative to urethral isolates (odds ratio (OR) = 3.74, 95% CI [1.98-6.70]). In support of a model in which pump expression incurs a fitness cost in this niche, cervical isolates were also enriched relative to urethral isolates in LOF mutations in themtrCDEactivatormtrA(OR = 8.60, 95% CI [4.96-14.57]) and infarA,a subunit of the FarAB efflux pump (OR = 6.25, 95% CI [3.90-9.83]). In total, approximately 2 in 5 cervical isolates (42.6%) contained a LOF mutation in either the efflux pump componentsmtrCorfarAor the activatormtrA.Our findings extend beyondN. gonorrhoeaeto otherNeisseria:mtrCLOF mutations are rare (N. meningitidisin a collection of 14,798 genomes but enriched in a heterosexual urethritis-associated lineage (8.6%, p = 9.90×10−5), indicating that efflux pump downregulation contributes broadly to the adaptation of pathogenicNeisseriato the female urogenital tract. 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.

The Bengal Bay clone (ST772) is a community-associated and multidrug-resistant Staphylococcus aureus lineage first isolated from Bangladesh and India in 2004. In this study, we showed that the Bengal Bay clone emerged from a virulent progenitor circulating on the Indian subcontinent. Its subsequent global transmission was associated with travel or family contact in the region. ST772 progressively acquired specific resistance elements at limited cost to its fitness and continues to be exported globally, resulting in small-scale community and health care outbreaks. The Bengal Bay clone therefore combines the virulence potential and epidemiology of community-associated clones with the multidrug resistance of health care-associated S. aureus lineages. This study demonstrates the importance of whole-genome sequencing for the surveillance of highly antibiotic-resistant pathogens, which may emerge in the community setting of regions with poor antibiotic stewardship and rapidly spread into hospitals and communities across the world., The evolution and global transmission of antimicrobial resistance have been well documented for Gram-negative bacteria and health care-associated epidemic pathogens, often emerging from regions with heavy antimicrobial use. However, the degree to which similar processes occur with Gram-positive bacteria in the community setting is less well understood. In this study, we traced the recent origins and global spread of a multidrug-resistant, community-associated Staphylococcus aureus lineage from the Indian subcontinent, the Bengal Bay clone (ST772). We generated whole-genome sequence data of 340 isolates from 14 countries, including the first isolates from Bangladesh and India, to reconstruct the evolutionary history and genomic epidemiology of the lineage. Our data show that the clone emerged on the Indian subcontinent in the early 1960s and disseminated rapidly in the 1990s. Short-term outbreaks in community and health care settings occurred following intercontinental transmission, typically associated with travel and family contacts on the subcontinent, but ongoing endemic transmission was uncommon. Acquisition of a multidrug resistance integrated plasmid was instrumental in the emergence of a single dominant and globally disseminated clade in the early 1990s. Phenotypic data on biofilm, growth, and toxicity point to antimicrobial resistance as the driving force in the evolution of ST772. The Bengal Bay clone therefore combines the multidrug resistance of traditional health care-associated clones with the epidemiological transmission of community-associated methicillin-resistant S. aureus (MRSA). Our study demonstrates the importance of whole-genome sequencing for tracking the evolution of emerging and resistant pathogens. It provides a critical framework for ongoing surveillance of the clone on the Indian subcontinent and elsewhere.

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 experimentally 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 NgoAX 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.

Highlights • Combined molecular and epidemiological data can describe the spread of gonorrhoea. • Sexual networks can be inferred from molecular clusters of infection. • Gender and sexual orientation are commonly used to characterise these networks. • Application of these data within gonorrhoea control interventions is limited. • Future studies should focus on evaluating molecular typing data in practice., Summary Objectives Neisseria gonorrhoeae (NG) is a significant global public health concern due to rising diagnoses rates and antimicrobial resistance. Molecular combined with epidemiological data have been used to understand the distribution and spread of NG, as well as relationships between cases in sexual networks, but the public health value gained from these studies is unclear. We conducted a systematic review to examine how molecular epidemiological studies have informed understanding of sexual networks and NG transmission, and subsequent public health interventions. Methods Five research databases were systematically searched up to 31st March 2017 for studies that used sequence-based DNA typing methods, including whole genome sequencing, and linked molecular data to patient-level epidemiological data. Data were extracted and summarised to identify common themes. Results Of the 49 studies included, 82% used NG Multi-antigen Sequence Typing. Gender and sexual orientation were commonly used to characterise sexual networks that were inferred using molecular clusters; clusters predominantly of one patient group often contained a small number of isolates from other patient groups. Suggested public health applications included using these data to target interventions at specific populations, confirm outbreaks, and inform partner management, but these were mainly untested. Conclusions Combining molecular and epidemiological data has provided insight into sexual mixing patterns, and dissemination of NG, but few studies have applied these findings to design or evaluate public health interventions. Future studies should focus on the application of molecular epidemiology in public health practice to provide evidence for how to prevent and control NG.

Four independent international groups have demonstrated that the pre-treatment gut microbiome of cancer patients is associated with the subsequent response to treatment with Immune Checkpoint Inhibitors (ICI) [1-4]. However, each study identified different bacteria as being linked to outcome, which has limited the development of drug response biomarkers and novel microbiome-based therapeutics. Here we describe the identification of a microbial signature predictive of response to ICI across multiple melanoma studies, and a derived Live Bacterial Therapeutic with potent anti-tumour activity. MELRESIST is a single centre, prospective melanoma patient data and biosample collection research study. We collected longitudinal stool samples from 69 patients with advanced melanoma who received standard anti-PD-1+/- anti-CTLA-4 antibodies. Shotgun metagenomic sequencing analysis of the baseline stool microbiome was done using Microbiotica's platform, which comprises the world's leading Reference Genome Database to give the most comprehensive and precise mapping of gut microbiomes. Using 6 months progression-free survival as our cut-off for response, the analysis revealed a small but discrete microbiome signature that differentiated responders and non-responders with an accuracy of 93%. We extended this signature by reanalysing another 3 melanoma patient stool sample sequence datasets [1-3] using the Microbiotica platform, and a machine learning-based bioinformatic model. The resultant bacterial signature accurately predicted response when all 4 studies when combined (91%), as well as when the cohorts were analysed individually (82-100%). We validated the model using independent cohorts and the signature using NSCLC and Renal Cell Carcinoma (RCC) datasets [4]. The latter indicated the bacteria associated with response may differ slightly between indications. At the core of the signature was 9 bacteria that were all overrepresented in patients that responded to ICI treatment. Notably as a consortium, these 9 bacteria demonstrated tumor growth inhibition when dosed in a syngeneic mouse model. These strains also stimulate primary immune cells in vitro leading to tumor cell killing. In summary, we have identified a microbiome biomarker that is predictive of response to ICI treatment in multiple clinical studies from different countries. In addition, a unique set of bacteria derived from the signature has great therapeutic potential in combination with ICIs. References 1 Matson V et al Science (2018) 359:104 2 Gopalakrishnan V Science (2018) 359:97 3 Frankel AE et al Neoplasia (2017) 19:848 4 Routy B et al Science (2018) 359:91 Citation Format: Matthew J. Robinson, Kevin Vervier, Simon Harris, Roy Rabbie, Doreen Milne, Catherine Booth, Christine Parkinson, Sarah J. Welsh, David Bruce, Emily Barker, David Adams, Pippa Corrie, Trevor D. Lawley. Precision microbiome profiling identifies a novel biomarker predictive of Immune Checkpoint Inhibitor response in multiple cohorts and a potent therapeutic consortium of bacteria [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1783.

e21546 Background: Four independent groups have demonstrated that the pre-treatment gut microbiome of cancer patients impacts the subsequent response to Immune Checkpoint Inhibitor (ICIs) therapy [1-4]. However, the patient’s outcome was linked to different bacteria in each study, which has limited the development of drug response biomarkers and clinic-first design of novel microbiome-based therapeutics. Methods: The Cambridge (UK) MELRESIST study includes a cohort of advanced melanoma patients receiving approved ICIs. Pretreatment stool samples from MELRESIST were analysed by Microbiotica using shotgun metagenomic sequencing. Microbiotica’s platform comprises the world’s leading Reference Genome Database to give the most comprehensive and precise mapping of the gut microbiome. Results: MELRESIST samples showed an overall difference in the microbiome composition between advanced melanoma patients and healthy donors, but not between patients who did or did not respond to ICIs. However, we did identify a discrete microbiome signature that differentiated responders and non-responders with an accuracy of 93%. We extended this signature by reanalysing three published melanoma cohorts [1-3] using the Microbiotica platform, and a propriety bioinformatic model. The resultant bacterial signature was very accurate at predicting response in all 4 published studies combined (91%), and each cohort individually (82-100%). We validated the model using independent validation cohorts and the signature using lung and renal cancer studies [4]. At the core of our microbiome signature was 9 bacteria most significantly associated with ICI efficacy. All 9 were overrepresented in patients who responded to immunotherapy suggesting high abundance of these bacteria is a central driver of ICI response. A consortium comprised of all 9 strains had very potent anti-tumor efficacy in a cancer syngeneic mouse model. The bacteria also demonstrate multiple interactions with primary human immune cells in vitro leading to dendritic cells activation, Cytotoxic T lymphocyte activation and tumor cell killing. These validate the potential of this consortium as a novel therapy for use in combination with ICIs. Conclusions: We have identified a unique microbiome signature predictive of ICI response in 4 independent melanoma cancer cohorts. This removes a major challenge to the field, and could represent a new highly accurate biomarker with clinical application. Nine core bacteria appear to be driving response, and demonstrate anti-tumor activity in vivo and in vitro. This consortium holds great potential as a co-therapy with ICIs. References:1 Matson V et al, Science (2018) 359:104; 2 Gopalakrishnan V et al, Science (2018) 359:97; 3 Frankel AE et al, Neoplasia (2017) 19:848; 4 Routy B et al, Science (2018) 359:91.