Characterization of an outbreak caused by Elizabethkingia miricola using Fourier-transform infrared (FTIR) spectroscopy.

Fourier-transform infrared (FTIR) spectroscopy has the potential to be used for bacterial typing and outbreak characterization. We evaluated FTIR for the characterization of an outbreak caused by Elizabethkingia miricola. During the 2020-2021 period, 26 isolates (23 clinical and 3 environmental) were collected and analyzed by FTIR (IR Biotyper) and core-genome MLST (cgMLST), in addition to antimicrobial susceptibility testing. FTIR spectroscopy and cgMLST showed that 22 of the isolates were related to the outbreak, including the environmental samples, with only one discordance between both methods. Then, FTIR is useful for E. miricola typing and can be easily implemented in the laboratory.

CamPype: an open-source workflow for automated bacterial whole-genome sequencing analysis focused on Campylobacter.

BACKGROUND: The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the growth of data at a faster rate than can be effectively analyzed and critical gaps in bioinformatics knowledge. RESULTS: To address both issues, CamPype was developed as a new bioinformatics workflow for the genomics analysis of sequencing data of bacteria, especially Campylobacter, which is the main cause of gastroenteritis worldwide making a negative impact on the economy of the public health systems. CamPype allows fully customization of stages to run and tools to use, including read quality control filtering, read contamination, reads extension and assembly, bacterial typing, genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants. All results are processed and resumed in an interactive HTML report for best data visualization and interpretation. CONCLUSIONS: The minimal user intervention of CamPype makes of this workflow an attractive resource for microbiology laboratories with no expertise in bioinformatics as a first line method for bacterial typing and epidemiological analyses, that would help to reduce the costs of disease outbreaks, or for comparative genomic analyses. CamPype is publicly available at https://github.com/JoseBarbero/CamPype .

Evaluation of Fourier Transform Infrared spectroscopy (IR Biotyper) as a complement to Whole genome sequencing (WGS) to characterise Enterobacter cloacae, Citrobacter freundii and Klebsiella pneumoniae isolates recovered from hospital sinks.

Whole genome sequencing (WGS) of healthcare-associated pathogens is recognised as the gold standard for isolate typing and the recognition of transmission networks and outbreaks. However, it remains reasonably expensive to process small numbers of isolates in real-time, and frequently requires specific expertise to enable both sequencing and the analysis of sequencing outputs, limiting its generalisability and turnaround. Spectrometry has revolutionised species identification in clinical laboratory workflows, and has more recently been applied to strain-level identification to facilitate low-cost, routine strain typing in clinical laboratories. However, studies to date of its clinical performance for strain-level typing are conflicting, and limited evaluation has been undertaken on environmental healthcare-associated isolates. We therefore compared its performance with WGS for Enterobacter cloacae, Citrobacter freundii and Klebsiella pneumoniae isolated from sink drains across nine hospitals and investigated whether it could be used as a screening tool prior to WGS. We found its sensitivity and specificity to cluster isolates when compared with WGS were generally poor and highly variable dependent on species and the single nucleotide polymorphism (SNP) distance threshold used to cluster isolates.

Real-Time Nanopore Q20+ Sequencing Enables Extremely Fast and Accurate Core Genome MLST Typing and Democratizes Access to High-Resolution Bacterial Pathogen Surveillance.

Next-generation whole-genome sequencing is essential for high-resolution surveillance of bacterial pathogens, for example, during outbreak investigations or for source tracking and escape variant analysis. However, current global sequencing and bioinformatic bottlenecks and a long time to result with standard technologies demand new approaches. In this study, we investigated whether novel nanopore Q20+ long-read chemistry enables standardized and easily accessible high-resolution typing combined with core genome multilocus sequence typing (cgMLST). We set high requirements for discriminatory power by using the slowly evolving bacterium Bordetella pertussis as a model pathogen. Our results show that the increased raw read accuracy enables the description of epidemiological scenarios and phylogenetic linkages at the level of gold-standard short reads. The same was true for our variant analysis of vaccine antigens, resistance genes, and virulence factors, demonstrating that nanopore sequencing is a legitimate competitor in the area of next-generation sequencing (NGS)-based high-resolution bacterial typing. Furthermore, we evaluated the parameters for the fastest possible analysis of the data. By combining the optimized processing pipeline with real-time basecalling, we established a workflow that allows for highly accurate and extremely fast high-resolution typing of bacterial pathogens while sequencing is still in progress. Along with advantages such as low costs and portability, the approach suggested here might democratize modern bacterial typing, enabling more efficient infection control globally.

The ongoing Streptococcus pyogenes (Group A Streptococcus) outbreak in London, United Kingdom, in December 2022: a molecular epidemiology study.

OBJECTIVES: Epidemiological and whole-genome sequencing analysis of an ongoing outbreak of Streptococcus pyogenes (Group A Streptococcus) in London (United Kingdom). METHODS: Prospective identification of Group A Streptococcus cases from a diagnostic laboratory serving central and south London between 27 November and 10 December 2022. Case notes were reviewed and isolates were retrieved. Case numbers were compared with the previous 5 years. Whole-genome sequencing was performed with long-read, nanopore technology for emm typing and identification of superantigen genes. Associations of pathogen-related factors with an invasive disease were assessed by single-variable and multi-variable logistic regression. RESULTS: Case numbers began increasing in October 2022 from a baseline of 2.0 cases per day, and in December 2022, the average daily case numbers reached 10.8 cases per day, four-fold the number usually seen in winter. A total of 113 cases were identified during the prospective study period. Three quarters (86/113, 76%) were paediatric cases, including 2 deaths. Of 113 cases, 11 (10%) were invasive. In total, 56 isolates were successfully sequenced, including 10 of 11 (91%) invasive isolates. The emm12 (33/56, 59%) and emm1 (9/56, 16%) types were predominant, with 7 of 9 (78%) emm1 isolates being from the M1uk clone. The majority of invasive isolates had superantigen genes spea (7/10, 70%) and spej (8/10, 80%), whereas, in non-invasive isolates, these superantigen genes were found less frequently (spea: 5/46, 11% and spej: 7/46, 15%). By multivariable analysis of pathogen-related factors, spea (OR 8.9, CI 1.4-57, p 0.020) and spej (OR 12, CI 1.8-78, p 0.011) were associated with invasive disease. CONCLUSIONS: emm12 and emm1 types predominate in the ongoing outbreak, which mainly affects children. In this outbreak, the spea and spej superantigen genes are associated with the severity of presentation.

Analysis of the influencing factors of urinary tract infection in pregnant women with diabetes and the distribution characteristics of pathogens in the middle urinary tract / 中国医师杂志

Objective:To investigate the influencing factors of urinary tract infection in pregnant women with diabetes and the distribution characteristics of pathogens in the middle urinary tract.Methods:A total of 220 patients with gestational diabetes who visited the Second People′s Hospital of Lianyungang City from December 2018 to December 2021 were selected as the study subjects, and the incidence of urinary tract infection was counted. According to the diagnosis results of urinary tract infection, they were divided into infected group and uninfected group. The infected group took the middle urine for pathogen culture, and the resistance rate of main gram-negative bacteria to antibiotics was analyzed; Logistic regression model was used to analyze the influencing factors of urinary tract infection in pregnant women with diabetes.Results:There were 32 cases of urinary tract infection in 220 patients with gestational diabetes, and the infection rate was 14.55%(32/220). 43 strains of pathogenic bacteria were identified, mainly gram-negative bacilli [72.09%(31/43)], followed by gram-positive cocci [20.93%(9/43)] and fungi [6.98%(3/43)]. Amongthe main gram-negative bacteria, escherichia coli had a high resistance rate to ampicillin and levofloxacin, while Klebsiella pneumoniae had a high resistance rate to ampicillin and cefazolin; There were significant differences between the infected group and the non infected group in age, hospital stay, personal urinary tract infection history, pregnancy sexual life history, use of antibiotics, fasting blood sugar, serum albumin, and glycated hemoglobin (all P<0.05); Multivariate logistic regression results showed that personal history of urinary tract infection, sexual life during pregnancy, non-standard use of antibiotics, serum albumin<30 g/L, glycated hemoglobin ≥7%, and fasting blood sugar ≥8.5 mmol/L were independent risk factors for urinary tract infection in pregnant diabetes patients (all P<0.05). Conclusions:There is a high incidence of urinary tract infection in patients with gestational diabetes, and the risk factors are complex. Gram negative bacilli are the main pathogenic bacteria. Antibacterial drugs can be reasonably selected for intervention according to drug sensitivity test in clinical practice.

An efficient Pulsed-Field Gel Electrophoresis (PFGE) method for typing autolytic Lacticaseibacillus rhamnosus strains.

Species of lactic acid bacteria, due to their versatile metabolism, are commonly used in food and feed products, both as technological starters and as health- and welfare-promoting agents. Correct strain identification in microbe-containing products is vital, and the Pulsed-Field Gel Electrophoresis (PFGE) typing method is considered the 'gold standard' for this purpose. This typing technique is widely used in molecular epidemiology, especially for the early detection of emerging isolates with food-safety implications, for outbreak surveillance, and for infection control. The autolytic behavior that we encountered when typing Lacticaseibacillus rhamnosus strains using the PFGE technique led us to modify the current method used for typing lactic acid bacteria. This study describes a PFGE method for the molecular typing of autolytic members of the lactic acid bacteria.•An efficient method for overcoming DNA degradation during PFGE analysis for typing Lacticaseibacillus rhamnosus strains is described.•The method described herein could be considered for typing autolytic lactic acid bacteria.

Comparison of FT-IR with whole-genome sequencing for identification of maternal-to­neonate transmission of antibiotic-resistant bacteria.

Following a previous study in which we evaluated the carriage rates of extended spectrum ß-lactamase (ESBL) -producing Enterobacterales (ESBL+ E), carbapenem-resistant Enterobacterales (CRE), vancomycin-resistant Enterococci (VRE), and methicillin-resistant Staphylococcus aureus (MRSA) among pregnant women and their neonates, in the current study we used, for the first time, Fourier transform infrared spectrometry (FT-IR) in order to determine whether antibiotic-resistant bacteria colonization in neonates has resulted from a vertical transmission from the mothers. To this end, 28 pairs of maternal and neonatal isolates of antibiotic-resistant bacteria, including ESBL-producing E. coli (ESBL+E.coli), ESBL-producing K. pneumoniae (ESBL+K. pneumoniae) and MRSA isolates, were subjected to a FT-IR analysis to assess the similarity between maternal and new-born isolates. We compared the FT-IR analysis results with whole genome sequencing of the isolates, in order to define whether FT-IR spectroscopy can be applied for bio-typing of bacteria and for assessment of mother-to­neonate transmission. The FT-IR analysis showed that all neonatal isolates were similar to their corresponding maternal isolates and belonged to the same cluster. Alignments of the DNA sequences of the maternal and neonatal isolates pairs revealed above 99% identity, thus confirming the FT-IR results. In conclusion, FT-IR spectroscopy can be applied to monitor bacterial transmission and specifically maternal-to­neonate transmission.

Using deep learning for gene detection and classification in raw nanopore signals.

Recently, nanopore sequencing has come to the fore as library preparation is rapid and simple, sequencing can be done almost anywhere, and longer reads are obtained than with next-generation sequencing. The main bottleneck still lies in data postprocessing which consists of basecalling, genome assembly, and localizing significant sequences, which is time consuming and computationally demanding, thus prolonging delivery of crucial results for clinical practice. Here, we present a neural network-based method capable of detecting and classifying specific genomic regions already in raw nanopore signals-squiggles. Therefore, the basecalling process can be omitted entirely as the raw signals of significant genes, or intergenic regions can be directly analyzed, or if the nucleotide sequences are required, the identified squiggles can be basecalled, preferably to others. The proposed neural network could be included directly in the sequencing run, allowing real-time squiggle processing.

Comparison of fast Fourier transform infrared spectroscopy biotyping with whole genome sequencing-based genotyping in common nosocomial pathogens.

Early detection of bacterial transmission and outbreaks in hospitals is important because nosocomial infections can result in health complications and longer hospitalization. Current practice to detect outbreaks uses genotyping methods amplified fragment length polymorphism (AFLP) and whole genome sequencing (WGS), which are not suitable methods for real-time transmission screening of both susceptible and resistant bacteria. The aim was to assess the typing technique Fourier transform infrared (FTIR) spectroscopy as real-time screening method to discriminate large amounts of susceptible and resistant bacteria at strain level when there is no evident outbreak in comparison with the WGS reference. Isolates of past hospital outbreak strains of Acinetobacter baumannii/calcoaceticus complex (n = 25), Escherichia coli (n = 31), Enterococcus faecium (n = 22), Staphylococcus aureus (n = 37) and Pseudomonas aeruginosa (n = 30) were used for validation of FTIR. Subsequently, Enterococcus faecalis (n = 106) and Enterococcus faecium (n = 104) isolates from weekly routine screening samples when no potential outbreak was present were analysed. FTIR showed reproducibility and congruence of cluster composition with WGS for A. baumannii/calcoaceticus complex and E. faecium outbreak isolates. The FTIR results of E. faecalis and E. faecium isolates from routine samples showed reproducibility, but the congruence of cluster composition with WGS was low. For A. baumannii/calcoaceticus complex and E. faecium outbreak isolates, FTIR appears to be a discriminatory typing tool. However, our study shows the discriminatory power is too low to screen real-time for transmission of E. faecium and E. faecalis at patient wards based on isolates acquired in routine surveillance cultures when there is no clear suspicion of an ongoing outbreak.

Surveillance of Enterobacter cloacae complex colonization and comparative analysis of different typing methods on a neonatal intensive care unit in Germany.

BACKGROUND: Enterobacter cloacae complex is a group of common opportunistic pathogens on neonatal intensive care units. Active microbiological screening to guide empirical antimicrobial treatment or to detect transmission events is recommended in high-risk preterm neonates. A rise in colonization with E. cloacae complex was observed in a German perinatal centre. The aim of this study was to evaluate the performance of different typing techniques using whole genome sequencing (WGS) as a reference. METHODS: Enterobacter cloacae complex isolates from clinical and screening specimens with an epidemiological link to the neonatal intensive care units were further assessed. Identification and antibiotic susceptibility testing was performed by a combination of VITEK2 (bioMérieux) and MALDI-TOF (Bruker Daltonics), followed by RAPD/rep-PCR and PFGE (XbaI). Retrospectively, all isolates were analyzed by Fourier-transform infrared (FTIR) spectroscopy (IR Biotyper, Bruker Daltonics). Whole genome sequencing with SNP-based clustering was used as the reference method. Furthermore, resistome analysis, sequence type and species identification were derived from the WGS data. Transmission analysis was based on epidemiological and typing data. RESULTS: Between September 2017 and March 2018 32 mostly preterm neonates were found to be colonized with E. cloacae complex and 32 isolates from 24 patients were available for further typing. RAPD/rep-PCR and PFGE showed good concordance with WGS whereas FTIR displayed mediocre results [adjusted rand index (ARI) = 0.436]. A polyclonal increase and two dominant and overlapping clonal clusters of two different E. hormaechei subspecies were detected. Overall, four different species were identified. Genotyping confirmed third-generation cephalosporin resistance development in isolates of the same patient. During the six-month period several infection prevention interventions were performed and no E. cloacae complex isolates were observed during the following months. CONCLUSIONS: Interpretation of the microbiological results alone to detect transmission events is often challenging and bacterial typing is of utmost importance to implement targeted infection control measures in an epidemic occurrence of E. cloacae complex. WGS is the most discriminatory method. However, traditional methods such as PFGE or RAPD/rep-PCR can provide reliable and quicker results in many settings. Furthermore, research is needed to quickly identify E. cloacae complex to the species level in the microbiological laboratory.

Protein FT-IR amide bands are beneficial to bacterial typing.

Bacterial FT-IR signals are extremely specific and highly reproducible, making FT-IR an efficient tool for bacterial typing at the subspecies level. The polysaccharide and nucleic acid FT-IR regions (1200-900 cm-1) are recommended as a precise and reproducible pattern for bacterial typing. However, proteins are the major macromolecules present in bacteria, and the FT-IR spectral region of proteins (1800-1300 cm-1) is conceivably an important factor in bacterial typing. In this study, we investigated the influence of water on bacterial protein amide bands by comparing spectra obtained with and without FT-IR system dehydration. Eight Escherichia coli, ten Klebsiella pneumoniae, and eleven Staphylococcus aureus strains were typed by FT-IR under different conditions in a blinded experimental setup. Hierarchical clustering analysis (HCA) showed that, when protein signals were included (1800-900 cm-1), the typing accuracies for select E. coli, K. pn and S. aureus strains without system dehydration were 50%, 30% and 18.2%, respectively. However, the accuracies greatly improved to 100%, 90% and 90.9% when the FT-IR system was dehydrated. These results indicate that the FT-IR signals of protein amide bands are beneficial for bacterial typing.

Diversity identification of KPC- producing Klebsiella pneumoniae using multilocus variable number tandem repeat analysis.

PURPOSE: The current study aimed to determine blaKPC, blaGES, blaVIM, blaNDM, blaOXA-23, and blaOXA-48 genes in clinical strains of Klebsiella pneumoniae isolated in Tehran, Iran to assess genetic diversity using MLVA as a typing method. METHODS: A total of 181 K. pneumoniae isolates were obtained from various clinical samples. CLSI 2018 (clinical and laboratory standards institute) guidelines were used to determine antibiotic susceptibility and the Modified Hodge Test (MHT). To detect blaKPC, blaGES, blaVIM, blaNDM, blaOXA-23, and blaOXA-48, the polymerase chain reaction (PCR) method was used. The MLVA method was used to type K. pneumoniae isolates by using PCR for 8 Variable Number Tandem Repeats (VNTRs). RESULTS: Imipenem was the most effective antibiotic against K. pneumoniae, with 36.5% susceptibility. 100 (55.24%) of the isolates tested positive for KPC, and 30 (30%) tested positive for six genes. Thirty MLVA genotypes were distinguished, and an examination of diversity indexes (DIs) for eight loci revealed that seven different alleles were the most polymorphic, with the highest DI of 0.780. CONCLUSIONS: The present study showed that MLVA could be helpful for typing clinical strains of K. pneumoniae. Our K. pneumoniae isolates are thought to be derived from a small number of clones that have undergone minor genetic changes over time. The results also showed that this method had great potential to differentiate those strains with high phenotypic similarity. The current study has revealed some intriguing facts about K. pneumoniae genetic relatedness in Tehran, Iran.

Large-scale comparative genomics to refine the organization of the global Salmonella enterica population structure.

The White-Kauffmann-Le Minor (WKL) scheme is the most widely used Salmonella typing scheme for reporting the disease prevalence of the enteric pathogen. With the advent of whole-genome sequencing (WGS), in silico methods have increasingly replaced traditional serotyping due to reproducibility, speed and coverage. However, despite integrating genomic-based typing by in silico serotyping tools such as SISTR, in silico serotyping in certain contexts remains ambiguous and insufficiently informative. Specifically, in silico serotyping does not attempt to resolve polyphyly. Furthermore, in spite of the widespread acknowledgement of polyphyly from genomic studies, the prevalence of polyphyletic serovars is not well characterized. Here, we applied a genomics approach to acquire the necessary resolution to classify genetically discordant serovars and propose an alternative typing scheme that consistently reflect natural Salmonella populations. By accessing the unprecedented volume of bacterial genomic data publicly available in GenomeTrakr and PubMLST databases (>180 000 genomes representing 723 serovars), we characterized the global Salmonella population structure and systematically identified putative non-monophyletic serovars. The proportion of putative non-monophyletic serovars was estimated higher than previous reports, reinforcing the inability of antigenic determinants to depict the complexity of Salmonella evolutionary history. We explored the extent of genetic diversity masked by serotyping labels and found significant intra-serovar molecular differences across many clinically important serovars. To avoid false discovery due to incorrect in silico serotyping calls, we cross-referenced reported serovar labels and concluded a low error rate in in silico serotyping. The combined application of clustering statistics and genome-wide association methods demonstrated effective characterization of stable bacterial populations and explained functional differences. The collective methods adopted in our study have practical values in establishing genomic-based typing nomenclatures for an entire microbial species or closely related subpopulations. Ultimately, we foresee an improved typing scheme to be a hybrid that integrates both genomic and antigenic information such that the resolution from WGS is leveraged to improve the precision of subpopulation classification while preserving the common names defined by the WKL scheme.

Large Increase in Azithromycin-Resistant Neisseria gonorrhoeae in Northern Spain.

The aim of this study was to characterize the evolution of gonorrhea in the general population by correlating epidemiological, genotypic, and antimicrobial resistance data of Neisseria gonorrhoeae isolates collected in northern Spain from 2014 to 2018. One hundred ninety-four strains underwent antimicrobial susceptibility testing and were genetically analyzed by N. gonorrhoeae multiantigen sequence typing. Increasing cases of gonococcal infections have been observed after 2015. Most occurred in male with urethritis. Sequence type (ST)-9972 and ST-1576, the predominant genotypes identified, have not been previously described as epidemic clones. Of great concern was the significant increase in azithromycin-resistant N. gonorrhoeae. More than 30% of these isolates were obtained from men who have sex with men (MSM). ST-12302 was the most prevalent clone among the azithromycin-resistant strains, and was also resistant to penicillin, ciprofloxacin, and tetracycline. This multidrug-resistant clone was exclusively isolated from MSM during 2018. The incidence rates of gonorrhea and azithromycin-resistant N. gonorrhoeae have significantly increased due to the emergence of new clones. ST-12302 has recently been recognized as an epidemic clone; therefore, its surveillance could be the key in controlling further dissemination of azithromycin resistance. These data highlight the need to perform local studies to update treatment guidelines and reinforce preventive measures against gonorrhea.

Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel.

Hierarchical genotyping approaches can provide insights into the source, geography and temporal distribution of bacterial pathogens. Multiple hierarchical SNP genotyping schemes have previously been developed so that new isolates can rapidly be placed within pre-computed population structures, without the need to rebuild phylogenetic trees for the entire dataset. This classification approach has, however, seen limited uptake in routine public health settings due to analytical complexity and the lack of standardized tools that provide clear and easy ways to interpret results. The BioHansel tool was developed to provide an organism-agnostic tool for hierarchical SNP-based genotyping. The tool identifies split k-mers that distinguish predefined lineages in whole genome sequencing (WGS) data using SNP-based genotyping schemes. BioHansel uses the Aho-Corasick algorithm to type isolates from assembled genomes or raw read sequence data in a matter of seconds, with limited computational resources. This makes BioHansel ideal for use by public health agencies that rely on WGS methods for surveillance of bacterial pathogens. Genotyping results are evaluated using a quality assurance module which identifies problematic samples, such as low-quality or contaminated datasets. Using existing hierarchical SNP schemes for Mycobacterium tuberculosis and Salmonella Typhi, we compare the genotyping results obtained with the k-mer-based tools BioHansel and SKA, with those of the organism-specific tools TBProfiler and genotyphi, which use gold-standard reference-mapping approaches. We show that the genotyping results are fully concordant across these different methods, and that the k-mer-based tools are significantly faster. We also test the ability of the BioHansel quality assurance module to detect intra-lineage contamination and demonstrate that it is effective, even in populations with low genetic diversity. We demonstrate the scalability of the tool using a dataset of ~8100 S. Typhi public genomes and provide the aggregated results of geographical distributions as part of the tool's output. BioHansel is an open source Python 3 application available on PyPI and Conda repositories and as a Galaxy tool from the public Galaxy Toolshed. In a public health context, BioHansel enables rapid and high-resolution classification of bacterial pathogens with low genetic diversity.

Centre-specific bacterial pathogen typing affects infection-control decision making.

Whole-genome sequencing is becoming the de facto standard for bacterial outbreak surveillance and infection prevention. This is accompanied by a variety of bioinformatic tools and needs bioinformatics expertise for implementation. However, little is known about the concordance of reported outbreaks when using different bioinformatic workflows. In this multi-centre proficiency testing among 13 major Dutch healthcare-affiliated centres, bacterial whole-genome outbreak analysis was assessed. Centres who participated obtained two randomized bacterial datasets of Illumina sequences, a Klebsiella pneumoniae and a Vancomycin-resistant Enterococcus faecium, and were asked to apply their bioinformatic workflows. Centres reported back on antimicrobial resistance, multi-locus sequence typing (MLST), and outbreak clusters. The reported clusters were analysed using a method to compare landscapes of phylogenetic trees and calculating Kendall-Colijn distances. Furthermore, fasta files were analysed by state-of-the-art single nucleotide polymorphism (SNP) analysis to mitigate the differences introduced by each centre and determine standardized SNP cut-offs. Thirteen centres participated in this study. The reported outbreak clusters revealed discrepancies between centres, even when almost identical bioinformatic workflows were used. Due to stringent filtering, some centres failed to detect extended-spectrum beta-lactamase genes and MLST loci. Applying a standardized method to determine outbreak clusters on the reported de novo assemblies, did not result in uniformity of outbreak-cluster composition among centres.

Harmonization of whole-genome sequencing for outbreak surveillance of Enterobacteriaceae and Enterococci.

Whole-genome sequencing (WGS) is becoming the de facto standard for bacterial typing and outbreak surveillance of resistant bacterial pathogens. However, interoperability for WGS of bacterial outbreaks is poorly understood. We hypothesized that harmonization of WGS for outbreak surveillance is achievable through the use of identical protocols for both data generation and data analysis. A set of 30 bacterial isolates, comprising of various species belonging to the Enterobacteriaceae family and Enterococcus genera, were selected and sequenced using the same protocol on the Illumina MiSeq platform in each individual centre. All generated sequencing data were analysed by one centre using BioNumerics (6.7.3) for (i) genotyping origin of replications and antimicrobial resistance genes, (ii) core-genome multi-locus sequence typing (cgMLST) for Escherichia coli and Klebsiella pneumoniae and whole-genome multi-locus sequencing typing (wgMLST) for all species. Additionally, a split k-mer analysis was performed to determine the number of SNPs between samples. A precision of 99.0% and an accuracy of 99.2% was achieved for genotyping. Based on cgMLST, a discrepant allele was called only in 2/27 and 3/15 comparisons between two genomes, for E. coli and K. pneumoniae, respectively. Based on wgMLST, the number of discrepant alleles ranged from 0 to 7 (average 1.6). For SNPs, this ranged from 0 to 11 SNPs (average 3.4). Furthermore, we demonstrate that using different de novo assemblers to analyse the same dataset introduces up to 150 SNPs, which surpasses most thresholds for bacterial outbreaks. This shows the importance of harmonization of data-processing surveillance of bacterial outbreaks. In summary, multi-centre WGS for bacterial surveillance is achievable, but only if protocols are harmonized.

Multilocus Sequence Analysis and Copper Ion Resistance Detection of 60 Xanthomonas arboricola pv. juglandis Isolates from China.

Walnut bacterial blight caused by Xanthomonas arboricola pv. juglandis has serious repercussions for walnut production around the world. Between 2015 and 2017, disease samples were collected from six counties (Danjiangkou, Baokang, Suizhou, Shennongjia, Zigui, and Xingshan) in Hubei Province, China. Fifty-nine X. arboricola pv. juglandis isolates were identified by morphology and specific PCR primers from 206 isolates. The genetic diversity of 60 X. arboricola pv. juglandis isolates (59 from Hubei plus 1 from Beijing) was evaluated by multilocus sequence analysis, and their resistance to copper ion (Cu2+) treatment was determined. A neighbor-joining phylogenetic dendrogram was constructed based on four sequences of housekeeping genes (atpD-dnaK-glnA-gyrB). Two groups of isolates whose clustering was consistent with that of glnA were identified. The minimal inhibitory concentration of Cu2+ on representative X. arboricola pv. juglandis strain DW3F3 (the first genome sequenced X. arboricola pv. juglandis from China) was 115 µg/ml. Setting the copper-resistant threshold value to 125 µg/ml, 47 and 13 isolates were considered sensitive and resistant to Cu2+, respectively. Furthermore, five isolates showed Cu2+ resistance at 270 µg/ml. Compared with the copper resistance gene B (copB) from sensitive isolates, the copB gene in resistant isolates had a 15-bp insertion and eight scattered single-nucleotide polymorphisms. Interestingly, the clustering based on multilocus sequence analysis was distinct between X. arboricola pv. juglandis Cu2+-resistant and -sensitive isolates.

MeltingPlot, a user-friendly online tool for epidemiological investigation using High Resolution Melting data.

BACKGROUND: The rapid identification of pathogen clones is pivotal for effective epidemiological control strategies in hospital settings. High Resolution Melting (HRM) is a molecular biology technique suitable for fast and inexpensive pathogen typing protocols. Unfortunately, the mathematical/informatics skills required to analyse HRM data for pathogen typing likely limit the application of this promising technique in hospital settings. RESULTS: MeltingPlot is the first tool specifically designed for epidemiological investigations using HRM data, easing the application of HRM typing to large real-time surveillance and rapid outbreak reconstructions. MeltingPlot implements a graph-based algorithm designed to discriminate pathogen clones on the basis of HRM data, producing portable typing results. The tool also merges typing information with isolates and patients metadata to create graphical and tabular outputs useful in epidemiological investigations and it runs in a few seconds even with hundreds of isolates. AVAILABILITY: https://skynet.unimi.it/index.php/tools/meltingplot/ . CONCLUSIONS: The analysis and result interpretation of HRM typing protocols can be not trivial and this likely limited its application in hospital settings. MeltingPlot is a web tool designed to help the user to reconstruct epidemiological events by combining HRM-based clustering methods and the isolate/patient metadata. The tool can be used for the implementation of HRM based real time large scale surveillance programs in hospital settings.