pyngoST: fast, simultaneous and accurate multiple sequence typing of Neisseria gonorrhoeae genome collections.

Extensive gonococcal surveillance has been performed using molecular typing at global, regional, national and local levels. The three main genotyping schemes for this pathogen, multi-locus sequence typing (MLST), Neisseria gonorrhoeae multi-antigen sequence typing (NG-MAST) and N. gonorrhoeae sequence typing for antimicrobial resistance (NG-STAR), allow inter-laboratory and inter-study comparability and reproducibility and provide an approximation to the gonococcal population structure. With whole-genome sequencing (WGS), we obtain a substantially higher and more accurate discrimination between strains compared to previous molecular typing schemes. However, WGS remains unavailable or not affordable in many laboratories, and thus bioinformatic tools that allow the integration of data among laboratories with and without access to WGS are imperative for a joint effort to increase our understanding of global pathogen threats. Here, we present pyngoST, a command-line Python tool for fast, simultaneous and accurate sequence typing of N. gonorrhoeae from WGS assemblies. pyngoST integrates MLST, NG-MAST and NG-STAR, and can also designate NG-STAR clonal complexes, NG-MAST genogroups and penA mosaicism, facilitating multiple sequence typing from large WGS assembly collections. Exact and closest matches for existing alleles and sequence types are reported. The implementation of a fast multi-pattern searching algorithm allows pyngoST to be rapid and report results on 500 WGS assemblies in under 1 min. The mapping of typing results on a core genome tree of 2375 gonococcal genomes revealed that NG-STAR is the scheme that best represents the population structure of this pathogen, emphasizing the role of antimicrobial use and antimicrobial resistance as a driver of gonococcal evolution. This article contains data hosted by Microreact.

Genomic Surveillance of Salmonella from the Comunitat Valenciana (Spain).

Salmonella enterica subspecies enterica is one of the most important foodborne pathogens and the causative agent of salmonellosis, which affects both humans and animals producing numerous infections every year. The study and understanding of its epidemiology are key to monitoring and controlling these bacteria. With the development of whole-genome sequencing (WGS) technologies, surveillance based on traditional serotyping and phenotypic tests of resistance is being replaced by genomic surveillance. To introduce WGS as a routine methodology for the surveillance of food-borne Salmonella in the region, we applied this technology to analyze a set of 141 S. enterica isolates obtained from various food sources between 2010 and 2017 in the Comunitat Valenciana (Spain). For this, we performed an evaluation of the most relevant Salmonella typing methods, serotyping and sequence typing, using both traditional and in silico approaches. We extended the use of WGS to detect antimicrobial resistance determinants and predicted minimum inhibitory concentrations (MICs). Finally, to understand possible contaminant sources in this region and their relationship to antimicrobial resistance (AMR), we performed cluster detection combining single-nucleotide polymorphism (SNP) pairwise distances and phylogenetic and epidemiological data. The results of in silico serotyping with WGS data were highly congruent with those of serological analyses (98.5% concordance). Multi-locus sequence typing (MLST) profiles obtained with WGS information were also highly congruent with the sequence type (ST) assignment based on Sanger sequencing (91.9% coincidence). In silico identification of antimicrobial resistance determinants and minimum inhibitory concentrations revealed a high number of resistance genes and possible resistant isolates. A combined phylogenetic and epidemiological analysis with complete genome sequences revealed relationships among isolates indicative of possible common sources for isolates with separate sampling in time and space that had not been detected from epidemiological information. As a result, we demonstrate the usefulness of WGS and in silico methods to obtain an improved characterization of S. enterica enterica isolates, allowing better surveillance of the pathogen in food products and in potential environmental and clinical samples of related interest.

Molecular Epidemiology of Methicillin-Resistant Staphylococcus aureus in a Tertiary Hospital from the Comunidad Valenciana (Spain).

To reduce the high rates of morbidity and mortality caused by methicillin-resistant Staphylococcus aureus (MRSA) strains, it is essential to prevent their transmission. This can be achieved through molecular surveillance of the infecting strains, for which the detection of the entry of new strains, the analysis of antimicrobial resistance, and their containment are essential. In this study, we have analyzed 190 MRSA isolates obtained at the Consorcio Hospital General Universitario de Valencia (Spain) from 2013 to 2018 with three approaches: Multilocus Sequence Typing, spa, and SCCmec typing. Although the incidence of S. aureus infections detected in the hospital increased in the study period, the frequency of MRSA isolates decreased from 33% to 18%. One hundred seventy-two MRSA isolates were resistant to three or more classes of antimicrobials, especially to fluoroquinolones. No relevant temporal trend in the distribution of antibiotic susceptibility was observed. The combination of the three typing schemes allowed the identification of 74 different clones, of which the combination ST125-t067-IV was the most abundant in the study (27 cases). Members of three clonal complexes, CC5, CC8, and CC22, comprised 91% of the isolates, and included 32 STs and 32 spa types. The emergence of low incidence strains throughout the study period and a large number of isolates resistant to different classes of antibiotics shows the need for epidemiological surveillance of this pathogen. Our study demonstrates that epidemiological and molecular surveillance is a powerful tool to detect the emergence of clinically important MRSA clones.