Microreact

Antimicrobial resistance (AMR) mechanisms, especially those conferring resistance to critically important antibiotics, are a great concern for public health. 16S rRNA methyltransferases (16S-RMTases) abolish the effectiveness of most clinically used aminoglycosides, but some of them are considered sporadic, such as RmtE. The main goals of this work were the genomic analysis of bacteria producing 16S-RMTases from a ‘One Health’ perspective in Venezuela, and the study of the epidemiological and evolutionary scenario of RmtE variants and their related mobile genetic elements (MGEs) worldwide. A total of 21 samples were collected in 2014 from different animal and environmental sources in the Cumaná region (Venezuela). Highly aminoglycoside-resistant Enterobacteriaceae isolates were selected, identified and screened for 16S-RMTase genes. Illumina and Nanopore whole-genome sequencing data were combined to obtain hybrid assemblies and analyse their sequence type, resistome, plasmidome and pan-genome. Genomic collections of rmtE variants and their associated MGEs were generated to perform epidemiological and phylogenetic analyses. A single 16S-RMTase, the novel RmtE4, was identified in five Klebsiella isolates from wastewater samples of Cumaná. This variant possessed three amino acid modifications with respect to RmtE1–3 (Asn152Asp, Val216Ile and Lys267Ile), representing the most genetic distant among all known and novel variants described in this work, and the second most prevalent. rmtE variants were globally spread, and their geographical distribution was determined by the associated MGEs and the carrying bacterial species. Thus, rmtE4 was found to be confined to Klebsiella isolates from South America, where it was closely related to ISVsa3 and an uncommon IncL plasmid related with hospital environments. This work uncovered the global scenario of RmtE and the existence of RmtE4, which could potentially emerge from South America. Surveillance and control measures should be developed based on these findings in order to prevent the dissemination of this AMR mechanism and preserve public health worldwide.

Escherichia coli and Klebsiella spp. are important human pathogens that cause a wide spectrum of clinical disease. In healthcare settings, sinks and other wastewater sites have been shown to be reservoirs of antimicrobial-resistant E. coli and Klebsiella spp., particularly in the context of outbreaks of resistant strains amongst patients. Without focusing exclusively on resistance markers or a clinical outbreak, we demonstrate that many hospital sink drains are abundantly and persistently colonized with diverse populations of E. coli , Klebsiella pneumoniae and Klebsiella oxytoca , including both antimicrobial-resistant and susceptible strains. Using whole-genome sequencing of 439 isolates, we show that environmental bacterial populations are largely structured by ward and sink, with only a handful of lineages, such as E. coli ST635, being widely distributed, suggesting different prevailing ecologies, which may vary as a result of different inputs and selection pressures. Whole-genome sequencing of 46 contemporaneous patient isolates identified one (2 %; 95 % CI 0.05–11 %) E. coli urine infection-associated isolate with high similarity to a prior sink isolate, suggesting that sinks may contribute to up to 10 % of infections caused by these organisms in patients on the ward over the same timeframe. Using metagenomics from 20 sink-timepoints, we show that sinks also harbour many clinically relevant antimicrobial resistance genes including bla CTX-M, bla SHV and mcr, and may act as niches for the exchange and amplification of these genes. Our study reinforces the potential role of sinks in contributing to Enterobacterales infection and antimicrobial resistance in hospital patients, something that could be amenable to intervention. This article contains data hosted by Microreact.

Natural products (NPs), or specialized metabolites, are important for medicine and agriculture alike, and for the fitness of the organisms that produce them. NP genome-mining aims at extracting biosynthetic information from the genomes of microbes presumed to produce these compounds. Typically, canonical enzyme sequences from known biosynthetic systems are identified after sequence similarity searches. Despite this being an efficient process, the likelihood of identifying truly novel systems by this approach is low. To overcome this limitation, we previously introduced EvoMining, a genome-mining approach that incorporates evolutionary principles. Here, we release and use our latest EvoMining version, which includes novel visualization features and customizable databases, to analyse 42 central metabolic enzyme families (EFs) conserved throughout Actinobacteria , Cyanobacteria , Pseudomonas and Archaea. We found that expansion-and-recruitment profiles of these 42 families are lineage specific, opening the metabolic space related to ‘shell’ enzymes. These enzymes, which have been overlooked, are EFs with orthologues present in most of the genomes of a taxonomic group, but not in all. As a case study of canonical shell enzymes, we characterized the expansion and recruitment of glutamate dehydrogenase and acetolactate synthase into scytonemin biosynthesis, and into other central metabolic pathways driving Archaea and Bacteria adaptive evolution. By defining the origin and fate of enzymes, EvoMining complements traditional genome-mining approaches as an unbiased strategy and opens the door to gaining insights into the evolution of NP biosynthesis. We anticipate that EvoMining will be broadly used for evolutionary studies, and for generating predictions of unprecedented chemical scaffolds and new antibiotics. This article contains data hosted by Microreact.

The surveillance of antimicrobial-resistant isolates has proven to be one of the most valuable tools to understand the global rise of multidrug-resistant bacterial pathogens. We report the first insights into the current situation in the Caribbean, where a pilot project to monitor antimicrobial resistance (AMR) through phenotypic resistance measurements combined with whole-genome sequencing was set up in collaboration with the Caribbean Public Health Agency (CARPHA). Our first study focused on Klebsiella pneumoniae , a highly relevant organism amongst the Gram-negative opportunistic pathogens worldwide causing hospital- and community-acquired infections. Our results show that not only carbapenem resistance, but also hypervirulent strains, are circulating in patients in the Caribbean. Our current data does not allow us to infer their prevalence in the population. We argue for the urgent need to further support AMR surveillance and stewardship in this almost uncharted territory, which can make a significant impact on the reduction of antimicrobial usage. This article contains data hosted by Microreact (https://microreact.org).

The standard workhorse for genomic analysis of the evolution of bacterial populations is phylogenetic modelling of mutations in the core genome. However, a notable amount of information about evolutionary and transmission processes in diverse populations can be lost unless the accessory genome is also taken into consideration. Here, we introduce panini (Pangenome Neighbour Identification for Bacterial Populations), a computationally scalable method for identifying the neighbours for each isolate in a data set using unsupervised machine learning with stochastic neighbour embedding based on the t-SNE (t-distributed stochastic neighbour embedding) algorithm. panini is browser-based and integrates with the Microreact platform for rapid online visualization and exploration of both core and accessory genome evolutionary signals, together with relevant epidemiological, geographical, temporal and other metadata. Several case studies with single- and multi-clone pneumococcal populations are presented to demonstrate the ability to identify biologically important signals from gene content data. panini is available at http://panini.pathogen.watch and code at http://gitlab.com/cgps/panini.

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.

Renibacterium salmoninarum is the causative agent of bacterial kidney disease (BKD), which is a commercially important disease of farmed salmonids. Typing by conventional methods provides limited information on the evolution and spread of this pathogen, as there is a low level of standing variation within the R. salmoninarum population. Here, we apply whole-genome sequencing to 42 R. salmoninarum isolates from Chile, primarily from salmon farms, in order to understand the epidemiology of BKD in this country. The patterns of genomic variation are consistent with multiple introductions to Chile, followed by rapid dissemination over a 30 year period. The estimated dates of introduction broadly coincide with major events in the development of the Chilean aquaculture industry. We find evidence for significant barriers to transmission of BKD in the Chilean salmon production chain that may also be explained by previously undescribed signals of host tropism in R. salmoninarum. Understanding the genomic epidemiology of BKD can inform disease intervention and improve sustainability of the economically important salmon industry. This article contains data hosted by Microreact.

There is growing evidence that patients with Clostridiumdifficile-associated diarrhoea often acquire their infecting strain before hospital admission. Wastewater is known to be a potential source of surface water that is contaminated with C. difficile spores. Here, we describe a study that used genome sequencing to compare C. difficile isolated from multiple wastewater treatment plants across the East of England and from patients with clinical disease at a major hospital in the same region. We confirmed that C. difficile from 65 patients were highly diverse and that most cases were not linked to other active cases in the hospital. In total, 186 C. difficile isolates were isolated from effluent water obtained from 18 municipal treatment plants at the point of release into the environment. Whole genome comparisons of clinical and environmental isolates demonstrated highly related populations, and confirmed extensive release of toxigenic C. difficile into surface waters. An analysis based on multilocus sequence types (STs) identified 19 distinct STs in the clinical collection and 38 STs in the wastewater collection, with 13 of 44 STs common to both clinical and wastewater collections. Furthermore, we identified five pairs of highly similar isolates (≤2 SNPs different in the core genome) in clinical and wastewater collections. Strategies to control community acquisition should consider the need for bacterial control of treated wastewater.

Serotype replacement has been reported in carriage and disease after pneumococcal conjugate vaccine (PCV) introductions in the UK and globally. We previously described concurrent expansion and decline of sequence types associated with serotype replacement over 5 years following PCV introductions in the UK. Here we use whole-genome sequencing to fully characterise the population structure of pneumococcal isolates collected over seven winters encompassing PCV7 and PCV13 introductions in the UK, investigating the importance of lineages in serotype replacement. We analysed 672 pneumococcal genomes from colonised children of 4 years old or less. The temporal prevalence of 20 lineages, defined by hierarchical Bayesian analysis of population structure (BAPS), was assessed in the context of serotype replacement. Multiple serotypes were detected in the primary winter of sampling within three vaccine-type (VT) lineages BAPS4, BAPS10 and BAPS11, in which serotype replacement were observed. In contrast, serotype replacement was not seen in the remaining three VT lineages (BAPS1, BAPS13 and BAPS14), that expressed a single serotype (6B, 6A and 3, respectively) in the primary winter. One lineage, BAPS1 serotype 6B was undetectable in the population towards the end of the study period. The dynamics of serotype replacement, in this UK population, was preceded by the presence or absence of multiple serotypes within VT lineages, in the pre-PCV population. This observation could help predict which non-vaccine types (NVTs) may be involved in replacement in future PCV introductions here and elsewhere. It could further indicate whether any antibiotic resistance associated with the lineages is likely to be affected by replacement.

Visualization is frequently used to aid our interpretation of complex datasets. Within microbial genomics, visualizing the relationships between multiple genomes as a tree provides a framework onto which associated data (geographical, temporal, phenotypic and epidemiological) are added to generate hypotheses and to explore the dynamics of the system under investigation. Selected static images are then used within publications to highlight the key findings to a wider audience. However, these images are a very inadequate way of exploring and interpreting the richness of the data. There is, therefore, a need for flexible, interactive software that presents the population genomic outputs and associated data in a user-friendly manner for a wide range of end users, from trained bioinformaticians to front-line epidemiologists and health workers. Here, we present Microreact, a web application for the easy visualization of datasets consisting of any combination of trees, geographical, temporal and associated metadata. Data files can be uploaded to Microreact directly via the web browser or by linking to their location (e.g. from Google Drive/Dropbox or via API), and an integrated visualization via trees, maps, timelines and tables provides interactive querying of the data. The visualization can be shared as a permanent web link among collaborators, or embedded within publications to enable readers to explore and download the data. Microreact can act as an end point for any tool or bioinformatic pipeline that ultimately generates a tree, and provides a simple, yet powerful, visualization method that will aid research and discovery and the open sharing of datasets.