Comparison of core-genome MLST, coreSNP and PFGE methods for Klebsiella pneumoniae cluster analysis

A rare waterborne outbreak of Bacillus paranthracis in Shandong province, China, 2020: epidemiologic survey, genomic insights, and virulence characteristics

Bacillus paranthracis, a Gram-positive conditional pathogen of Bacillus cereus group species, is capable of causing foodborne and waterborne illnesses, leading to intestinal diseases in humans characterized by diarrhoea and vomiting. However, documented cases of B. paranthracis infection outbreaks are rare in the world, and the genomic background of outbreak strains is seldom characterized. This study retrospectively analyzed strains obtained from an outbreak in schools, as well as from water systems in peri-urban areas, China, in 2020. In total, 28 B. cereus group isolates were retrieved, comprising 6 from stool samples and 22 from water samples. Epidemiological and phylogenetic investigations indicated that the B. paranthracis isolate from drinking water as the causative agent of the outbreak. The genomic comparison revealed a high degree of consistency among 8 outbreak-related strains in terms of antimicrobial resistance gene profiles, virulence gene profiles, genomic content, and multilocus sequence typing (MLST). The strains related to the outbreak show highly similar genomic ring diagrams and close phylogenetic relationships. Additionally, this study shed light on the pathogenic potential and complexity of B. cereus group through its diversity in virulence genes and mice infection model. The findings highlight the usefulness of B. paranthracis genomes in understanding genetic diversity within specific environments and in tracing the source of pathogens during outbreak situations, thereby enabling targeted infection control interventions.

First imported case of Candida auris infection in Milan, Italy: genomic characterisation

PURPOSE

Candida auris, an emerging multidrug-resistant yeast, has been reported worldwide. In Italy, the first case was reported in 2019. We describe the first case of C. auris, imported from Greece, in Milan, using whole genome sequencing to characterise mutations associated with antifungal resistance.

CASE PRESENTATION

On October 2022 an 80-year-old Italian man was hospitalised in Greece. In the absence of clinical improvement, the patient was transferred to our hospital, in Italy, where blood culture resulted positive for C. auris. Despite therapy, the patient died of septic shock. In a phylogenetic analysis the genome was assigned to Clade I with strains from Kenya, United Arab Emirates and India. D1/D2 region resulted identical to a Greek strain, as for many other strains from different World regions, highlighting the diffusion of this strain.

CONCLUSION

Importation of C. auris from abroad has been previously described. We report the first case of C. auris imported into Italy from Greece, according to phylogenetic analysis. This case reinforces the need for monitoring critically ill hospitalised patients also for fungi and addresses the need for the standardisation of susceptibility testing and strategies for diagnosis and therapy.

Core genome multilocus sequence typing (cgMLST) applicable to the monophyletic Klebsiella oxytoca species complex

The environmental bacterium Klebsiella oxytoca displays an alarming increase of antibiotic-resistant strains that frequently cause outbreaks in intensive care units. Due to its prevalence in the environment and opportunistic presence in humans, molecular surveillance (including resistance marker screening) and high-resolution cluster analysis are of high relevance. Furthermore, K. oxytoca previously described in studies is rather a species complex (KoSC) than a single species comprising at least six closely related species that are not easily differentiated by standard typing methods. To reach a discriminatory power high enough to identify and resolve clusters within these species, whole genome sequencing is necessary. The resolution is achievable with core genome multilocus sequence typing (cgMLST) extending typing of a few housekeeping genes to thousands of core genome genes. CgMLST is highly standardized and provides a nomenclature enabling cross laboratory reproducibility and data exchange for routine diagnostics. Here, we established a cgMLST scheme not only capable of resolving the KoSC species but also producing reliable and consistent results for published outbreaks. Our cgMLST scheme consists of 2,536 core genome and 2,693 accessory genome targets, with a percentage of good cgMLST targets of 98.31% in 880 KoSC genomes downloaded from the National Center for Biotechnology Information (NCBI). We also validated resistance markers against known resistance gene patterns and successfully linked genetic results to phenotypically confirmed toxic strains carrying the til gene cluster. In conclusion, our novel cgMLST enables highly reproducible typing of four different clinically relevant species of the KoSC and thus facilitates molecular surveillance and cluster investigations.

Cultivation and sequencing-free protocol for Serratia marcescens detection and typing

Serratia marcescens is an opportunistic pathogen that survives in inhospitable environments causing large outbreaks, particularly in neonatal intensive care units (NICUs). Genomic studies revealed that most S. marcescens nosocomial infections are caused by a specific clone (here "Infectious clone"). Whole genome sequencing (WGS) is the only portable method able to identify this clone, but it requires days to obtain results. We present a cultivation-free hypervariable-locus melting typing (HLMT) protocol for the fast detection and typing of S. marcescens, with 100% detection capability on mixed samples and a limit of detection that can reach the 10 genome copies. The protocol was able to identify the S. marcescens infectious clone with 97% specificity and 96% sensitivity when compared to WGS, yielding typing results portable among laboratories. The protocol is a cost and time saving method for S. marcescens detection and typing for large environmental/clinical surveillance screenings, also in low-middle income countries.

Genetic barriers more than environmental associations explain Serratia marcescens population structure

Bacterial species often comprise well-separated lineages, likely emerged and maintained by genetic isolation and/or ecological divergence. How these two evolutionary actors interact in the shaping of bacterial population structure is currently not fully understood. In this study, we investigate the genetic and ecological drivers underlying the evolution of Serratia marcescens, an opportunistic pathogen with high genomic flexibility and able to colonise diverse environments. Comparative genomic analyses reveal a population structure composed of five deeply-demarcated genetic clusters with open pan-genome but limited inter-cluster gene flow, partially explained by Restriction-Modification (R-M) systems incompatibility. Furthermore, a large-scale research on hundred-thousands metagenomic datasets reveals only a partial habitat separation of the clusters. Globally, two clusters only show a separate gene composition coherent with ecological adaptations. These results suggest that genetic isolation has preceded ecological adaptations in the shaping of the species diversity, an evolutionary scenario coherent with the Evolutionary Extended Synthesis.

Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae

Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics.

Nosocomial surveillance of multidrug-resistant Acinetobacter baumannii: a genomic epidemiological study

Acinetobacter baumannii is a major opportunistic pathogen causing hospital-acquired infections, and it is imperative to comprehend its evolutionary and epidemiological dynamics in hospitals to prevent and control nosocomial transmission. Here, we present a comprehensive genomic epidemiological study involving the genomic sequencing and antibiotic resistance profiling of 634 A. baumannii strains isolated from seven intensive care units (ICUs) of a Chinese general hospital over 2 consecutive years. Our study reveals that ST2 is highly dominant (90.54%) in the ICUs, with 98.90% of the ST2 exhibiting multidrug resistant or extensively drug resistant. Phylogenetic analyses of newly sequenced genomes and public data suggest that nosocomial isolates originated outside the hospital but evolved inside. The major lineages appear to be stable, with 9 of the 28 identified nosocomial epidemic clones infecting over 60% of the affected patients. However, outbreaks of two highly evolved clones have been observed in different hospitals, suggesting significant inter-hospital transmission chains. By coupling patient medical records and genomic divergence of the ST2, we found that cross-ward patient transfer played a crucial role in pathogen's nosocomial transmission. Additionally, we identified 831 potential adaptive evolutionary loci and 44 associated genes by grouping and comparing the genomes of clones with different prevalence. Overall, our study provides a comprehensive and contemporary survey on the epidemiology and genomic evolution of A. baumannii in a large Chinese general hospital. These findings shed light on the nosocomial evolution and transmission of A. baumannii and offers valuable information for transmission prevention and antibiotic therapy.IMPORTANCEThis study delved into the genomic evolution and transmission of nosocomial Acinetobacter baumannii on a large scale, spanning both an extended time period and the largest sample size to date. Through molecular epidemiological investigations based on genomics, we can directly trace the origin of the pathogen, detecting and monitoring outbreaks of infectious diseases in a timely manner, and ensuring public health safety. In addition, this study also collects a large amount of genomic and antibiotic resistance detection data, which is helpful for phenotype prediction based on genomic sequencing. It enables patients to receive personalized antibiotic treatment quickly, helps doctors select antibiotics more accurately, and contributes to reducing the use of antibiotics and lowering the risk of antibiotic resistance development.

Core-genome multilocus sequence typing and core-SNP analysis of Clostridium neonatale strains isolated in different spatio-temporal settings

IMPORTANCE

Clostridium neonatale has been isolated from the fecal samples of asymptomatic neonates and cases of necrotizing enterocolitis (NEC). Taking advantage of a large collection of independent strains isolated from different spatio-temporal settings, we developed and established a cgMLST scheme for the molecular typing of C. neonatale. Both the cgMLST and cgSNP methods demonstrate comparable discrimination power. Results indicate geographic- and temporal- independent clustering of C. neonatale NEC-associated strains. No specific cgMLST clade of C. neonatale was genetically associated with NEC.

Classification and prediction of Klebsiella pneumoniae strains with different MLST allelic profiles via SERS spectral analysis

The Gram-negative non-motile Klebsiella pneuomoniae is currently a major cause of hospital-acquired (HA) and community-acquired (CA) infections, leading to great public health concern globally, while rapid identification and accurate tracing of the pathogenic bacterium is essential in facilitating monitoring and controlling of K. pneumoniae outbreak and dissemination. Multi-locus sequence typing (MLST) is a commonly used typing approach with low cost that is able to distinguish bacterial isolates based on the allelic profiles of several housekeeping genes, despite low resolution and labor intensity of the method. Core-genome MLST scheme (cgMLST) is recently proposed to sub-type and monitor outbreaks of bacterial strains with high resolution and reliability, which uses hundreds or thousands of genes conserved in all or most members of the species. However, the method is complex and requires whole genome sequencing of bacterial strains with high costs. Therefore, it is urgently needed to develop novel methods with high resolution and low cost for bacterial typing. Surface enhanced Raman spectroscopy (SERS) is a rapid, sensitive and cheap method for bacterial identification. Previous studies confirmed that classification and prediction of bacterial strains via SERS spectral analysis correlated well with MLST typing results. However, there is currently no similar comparative analysis in K. pneumoniae strains. In this pilot study, 16 K. pneumoniae strains with different sequencing typings (STs) were selected and a phylogenetic tree was constructed based on core genome analysis. SERS spectra (N = 45/each strain) were generated for all the K. pneumoniae strains, which were then comparatively classified and predicted via six representative machine learning (ML) algorithms. According to the results, SERS technique coupled with the ML algorithm support vector machine (SVM) could achieve the highest accuracy (5-Fold Cross Validation = 100%) in terms of differentiating and predicting all the K. pneumoniae strains that were consistent to corresponding MLSTs. In sum, we show in this pilot study that the SERS-SVM based method is able to accurately predict K. pneumoniae MLST types, which has the application potential in clinical settings for tracing dissemination and controlling outbreak of K. pneumoniae in hospitals and communities with low costs and high rapidity.

Genomic Epidemiologic Investigation of a Multispecies Hospital Outbreak of NDM-5-Producing Enterobacterales Infections

Background

New Delhi metallo-β-lactamase (NDM) represents an emergent mechanism of carbapenem resistance associated with high mortality and limited antimicrobial treatment options. Because the blaNDM resistance gene is often carried on plasmids, traditional infection prevention and control (IP&C) surveillance methods like speciation, antimicrobial resistance testing, and reactive whole genome sequencing (WGS) may not detect plasmid transfer in multispecies outbreaks.

Methods

Initial outbreak detection of NDM-producing Enterobacterales identified at an acute care hospital occurred via traditional IP&C methods and was supplemented by real-time WGS surveillance, which was performed weekly using the Illumina platform. To resolve NDM-encoding plasmids, we performed long-read Oxford Nanopore sequencing and constructed hybrid assemblies using Illumina and Nanopore sequencing data. Reports of relatedness between NDM-producing organisms and reactive WGS for suspected outbreaks were shared with the IP&C team for assessment and intervention.

Findings

We observed a multispecies outbreak of NDM-5-producing Enterobacterales isolated from 15 patients between February 2021 and February 2023. The 19 clinical and surveillance isolates sequenced included seven bacterial species and each encoded the same NDM-5 plasmid, which showed high homology to NDM plasmids previously observed in Asia. WGS surveillance and epidemiologic investigation characterized ten horizontal plasmid transfer events and six bacterial transmission events between patients housed in varying hospital units. Transmission prevention focused on enhanced observation and adherence to basic infection prevention measures.

Interpretation

Our investigation revealed a complex, multispecies outbreak of NDM that involved multiple plasmid transfer and bacterial transmission events, increasing the complexity of outbreak identification and transmission prevention. Our investigation highlights the utility of combining traditional IP&C and prospective genomic methods in identifying and containing plasmid-associated outbreaks.

Funding

This work was funded in part by the National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH) (R01AI127472) (R21AI1783691).

The Swiss Pathogen Surveillance Platform - towards a nation-wide One Health data exchange platform for bacterial, viral and fungal genomics and associated metadata

The Swiss Pathogen Surveillance Platform (SPSP) is a shared secure surveillance platform between human and veterinary medicine, to also include environmental and foodborne isolates. It enables rapid and detailed transmission monitoring and outbreak surveillance of pathogens using whole genome sequencing data and associated metadata. It features controlled data access, complex dynamic queries, dedicated dashboards and automated data sharing with international repositories, providing actionable results for public health and the vision to improve societal well-being and health.

Hospital sanitary facilities on wards with high antibiotic exposure play an important role in maintaining a reservoir of resistant pathogens, even over many years

BACKGROUND

Hospitals with their high antimicrobial selection pressure represent the presumably most important reservoir of multidrug-resistant human pathogens. Antibiotics administered in the course of treatment are excreted and discharged into the wastewater system. Not only in patients, but also in the sewers, antimicrobial substances exert selection pressure on existing bacteria and promote the emergence and dissemination of multidrug-resistant clones. In previous studies, two main clusters were identified in all sections of the hospital wastewater network that was investigated, one K. pneumoniae ST147 cluster encoding NDM- and OXA-48 carbapenemases and one VIM-encoding P. aeruginosa ST823 cluster. In the current study, we investigated if NDM- and OXA-48-encoding K. pneumoniae and VIM-encoding P. aeruginosa isolates recovered between 2014 and 2021 from oncological patients belonged to those same clusters.

METHODS

The 32 isolates were re-cultured, whole-genome sequenced, phenotypically tested for their antimicrobial susceptibility, and analyzed for clonality and resistance genes in silico.

RESULTS

Among these strains, 25 belonged to the two clusters that had been predominant in the wastewater, while two others belonged to a sequence-type less prominently detected in the drains of the patient rooms.

CONCLUSION

Patients constantly exposed to antibiotics can, in interaction with their persistently antibiotic-exposed sanitary facilities, form a niche that might be supportive for the emergence, the development, the dissemination, and the maintenance of certain nosocomial pathogen populations in the hospital, due to antibiotic-induced selection pressure. Technical and infection control solutions might help preventing transmission of microorganisms from the wastewater system to the patient and vice versa, particularly concerning the shower and toilet drainage. However, a major driving force might also be antibiotic induced selection pressure and parallel antimicrobial stewardship efforts could be essential.

Split k-mer analysis compared to cgMLST and SNP-based core genome analysis for detecting transmission of vancomycin-resistant enterococci: results from routine outbreak analyses across different hospitals and hospitals networks in Berlin, Germany

The increase of Vancomycin-resistant Enterococcus faecium (VREfm) in recent years has been partially attributed to the rise of specific clonal lineages, which have been identified throughout Germany. To date, there is no gold standard for the interpretation of genomic data for outbreak analyses. New genomic approaches such as split k-mer analysis (SKA) could support cluster attribution for routine outbreak investigation. The aim of this project was to investigate frequent clonal lineages of VREfm identified during suspected outbreaks across different hospitals, and to compare genomic approaches including SKA in routine outbreak investigation. We used routine outbreak laboratory data from seven hospitals and three different hospital networks in Berlin, Germany. Short-read libraries were sequenced on the Illumina MiSeq system. We determined clusters using the published Enterococcus faecium-cgMLST scheme (threshold ≤20 alleles), and assigned sequence and complex types (ST, CT), using the Ridom SeqSphere+ software. For each cluster as determined by cgMLST, we used pairwise core-genome SNP-analysis and SKA at thresholds of ten and seven SNPs, respectively, to further distinguish cgMLST clusters. In order to investigate clinical relevance, we analysed to what extent epidemiological linkage backed the clusters determined with different genomic approaches. Between 2014 and 2021, we sequenced 693 VREfm strains, and 644 (93 %) were associated within cgMLST clusters. More than 74 % (n=475) of the strains belonged to the six largest cgMLST clusters, comprising ST117, ST78 and ST80. All six clusters were detected across several years and hospitals without apparent epidemiological links. Core SNP analysis identified 44 clusters with a median cluster size of three isolates (IQR 2-7, min-max 2-63), as well as 197 singletons (41.4 % of 475 isolates). SKA identified 67 clusters with a median cluster size of two isolates (IQR 2-4, min-max 2-19), and 261 singletons (54.9 % of 475 isolates). Of the isolate pairs attributed to clusters, 7 % (n=3064/45 596) of pairs in clusters determined by standard cgMLST, 15 % (n=1222/8500) of pairs in core SNP-clusters and 51 % (n=942/1880) of pairs in SKA-clusters showed epidemiological linkage. The proportion of epidemiological linkage differed between sequence types. For VREfm, the discriminative ability of the widely used cgMLST based approach at ≤20 alleles difference was insufficient to rule out hospital outbreaks without further analytical methods. Cluster assignment guided by core genome SNP analysis and the reference free SKA was more discriminative and correlated better with obvious epidemiological linkage, at least recently published thresholds (ten and seven SNPs, respectively) and for frequent STs. Besides higher overall discriminative power, the whole-genome approach implemented in SKA is also easier and faster to conduct and requires less computational resources.

Molecular typing methods & resistance mechanisms of MDR Klebsiella pneumoniae

The emergence and transmission of carbapenem-resistant Klebsiella pneumoniae (CRKP) have been recognized as a major public health concern. Here, we investigated the molecular epidemiology and its correlation with the mechanisms of resistance in CRKP isolates by compiling studies on the molecular epidemiology of CRKP strains worldwide. CRKP is increasing worldwide, with poorly characterized epidemiology in many parts of the world. Biofilm formation, high efflux pump gene expression, elevated rates of resistance, and the presence of different virulence factors in various clones of K. pneumoniae strains are important health concerns in clinical settings. A wide range of techniques has been implemented to study the global epidemiology of CRKP, such as conjugation assays, 16S-23S rDNA, string tests, capsular genotyping, multilocus sequence typing, whole-genome sequencing-based surveys, sequence-based PCR, and pulsed-field gel electrophoresis. There is an urgent need to conduct global epidemiological studies on multidrug-resistant infections of K. pneumoniae across all healthcare institutions worldwide to develop infection prevention and control strategies. In this review, we discuss different typing methods and resistance mechanisms to explore the epidemiology of K. pneumoniae pertaining to human infections.

Sequence-based detection and typing procedures for Burkholderia mallei: Assessment and prospects

Although glanders has been eradicated in most of the developed world, the disease still persists in various countries such as Brazil, India, Pakistan, Bangladesh, Nepal, Iran, Bahrain, UAE and Turkey. It is one of the notifiable diseases listed by the World Organization for Animal Health. Occurrence of glanders imposes restriction on equestrian events and restricts equine movement, thus causing economic losses to equine industry. The genetic diversity and global distribution of the causing agent, Burkholderia (B.) mallei, have not been assessed in detail and are complicated by the high clonality of this organism. Among the identification and typing methods, PCR-based methods for distinguishing B. mallei from its close relative B. pseudomallei as well as genotyping using tandem repeat regions (MLVA) are established. The advent and continuous advancement of the sequencing techniques and the reconstruction of closed genomes enable the development of genome guided epidemiological tools. For achieving a higher genomic resolution, genotyping methods based on whole genome sequencing data can be employed, like genome-wide single nucleotide polymorphisms. One of the limitations in obtaining complete genomic sequences for further molecular characterization of B. mallei is its high GC content. In this review, we aim to provide an overview of the widely used detection and typing methods for B. mallei and illustrate gaps that still require development. The genomic features of Burkholderia, their high homology and clonality will be first described from a comparative genomics perspective. Then, the commonly used molecular detection (PCR systems) and typing systems (e.g., multilocus sequence typing, variable number of tandem repeat analysis) will be presented and put in perspective with recently developed genomic methods. Also, the increasing availability of B. mallei genomic sequences and evolution of the sequencing methods offers exciting prospects for further refinement of B. mallei typing, that could overcome the difficulties presently encountered with this particular bacterium.

Comparative analysis of two genomes of Chlamydia pecorum isolates from an Alpine chamois and a water buffalo

Background

To date, whole genome sequencing has been performed mainly for isolates of Chlamydia trachomatis, C. pneumoniae, C. psittaci and C. abortus, but only a few isolates of C. pecorum have been entirely sequenced and this makes it difficult to understand its diversity and population structure. In this study the genome of two C. pecorum strains isolated from the lung of an Alpine chamois affected with pneumonia (isolate PV7855) and the brain of a water buffalo affected with meningoencephalomyelitis (isolate PV6959), were completely sequenced with MiSeq system (Illumina) and analyzed in their most polymorphic regions.

Results

The genome length and GC content of the two isolates were found to be consistent with other C. pecorum isolates and the gene content of polymorphic membrane proteins and plasticity zone was found to be very similar. Some differences were observed in the phospholipase genes for both isolates and in the number of genes in the plasticity zone, such as the presence of some hypothetical proteins in PV6959, not present in any other genomes analyzed in this study. Interestingly, PV6959 possesses an extra pmp and has an incomplete tryptophan biosynthesis operon. Plasmids were detected in both isolates.

Conclusions

Genome sequencing of the two C. pecorum strains did not reveal differences in length and GC content despite the origin from different animal species with different clinical disease. In the plasticity zone, the differences in the genes pattern might be related to the onset of specific symptoms or infection of specific hosts. The absence of a tryptophan biosynthesis pathway in PV6959 may suggest a strict relationship between C. pecorum and its host.

Hypervariable-Locus Melting Typing: a Novel Approach for More Effective High-Resolution Melting-Based Typing, Suitable for Large Microbiological Surveillance Programs

Pathogen typing is pivotal to detecting the emergence of high-risk clones in hospital settings and to limit their spread. Unfortunately, the most commonly used typing methods (i.e., pulsed-field gel electrophoresis [PFGE], multilocus sequence typing [MLST], and whole-genome sequencing [WGS]) are expensive or time-consuming, limiting their application to real-time surveillance. High-resolution melting (HRM) can be applied to perform cost-effective and fast pathogen typing, but developing highly discriminatory protocols is challenging. Here, we present hypervariable-locus melting typing (HLMT), a novel approach to HRM-based typing that enables the development of more effective and portable typing protocols. HLMT types the strains by assigning them to melting types (MTs) on the basis of a reference data set (HLMT-assignment) and/or by clustering them using melting temperatures (HLMT-clustering). We applied the HLMT protocol developed on the capsular gene wzi for Klebsiella pneumoniae on 134 strains collected during surveillance programs in four hospitals. Then, we compared the HLMT results to those obtained using wzi, MLST, WGS, and PFGE typing. HLMT distinguished most of the K. pneumoniae high-risk clones with a sensitivity comparable to that of PFGE and MLST+wzi. It also drew surveillance epidemiological curves comparable to those obtained using MLST+wzi, PFGE, and WGS typing. Furthermore, the results obtained using HLMT-assignment were consistent with those of wzi typing for 95% of the typed strains, with a Jaccard index value of 0.9. HLMT is a fast and scalable approach for pathogen typing, suitable for real-time hospital microbiological surveillance. HLMT is also inexpensive, and thus, it is applicable for infection control programs in low- and middle-income countries. IMPORTANCE In this work, we describe hypervariable-locus melting typing (HLMT), a novel fast approach to pathogen typing using the high-resolution melting (HRM) assay. The method includes a novel approach for gene target selection, primer design, and HRM data analysis. We successfully applied this method to distinguish the high-risk clones of Klebsiella pneumoniae, one of the most important nosocomial pathogens worldwide. We also compared HLMT to typing using WGS, the capsular gene wzi, MLST, and PFGE. Our results show that HLMT is a typing method suitable for real-time epidemiological investigation. The application of HLMT to hospital microbiology surveillance can help to rapidly detect outbreak emergence, improving the effectiveness of infection control strategies.

Evaluation of Fourier Transform Infrared Spectroscopy as a First-Line Typing Tool for the Identification of Extended-Spectrum β-Lactamase-Producing Klebsiella pneumoniae Outbreaks in the Hospital Setting

Early detection of pathogen cross-transmission events and environmental reservoirs is needed to control derived nosocomial outbreaks. Whole-genome sequencing (WGS) is considered the gold standard for outbreak confirmation, but, in most cases, it is time-consuming and has elevated costs. Consequently, the timely incorporation of WGS results to conventional epidemiology (CE) investigations for rapid outbreak detection is scarce. Fourier transform infrared spectroscopy (FTIR) is a rapid technique that establishes similarity among bacteria based on the comparison of infrared light absorption patterns of bacterial polysaccharides and has been used as a typing tool in recent studies. The aim of the present study was to evaluate the performance of the FTIR as a first-line typing tool for the identification of extended-spectrum β-lactamase-producing Klebsiella pneumoniae (ESBL-Kp) outbreaks in the hospital setting in comparison with CE investigations using WGS as the gold standard method. Sixty-three isolates of ESBL-Kp collected from 2018 to 2021 and classified according to CE were typed by both FTIR and WGS. Concordance was measured using the Adjusted Rand index (AR) and the Adjusted Wallace coefficient (AW) for both CE and FTIR clustering considering WGS as the reference method. Both AR and AW were significantly higher for FTIR clustering than CE clustering (0.475 vs. 0.134, p = 0.01, and 0.521 vs. 0.134, p = 0.009, respectively). Accordingly, FTIR inferred more true clustering relationships than CE (38/42 vs. 24/42, p = 0.001). However, a similar proportion of genomic singletons was detected by both FTIR and CE (13/21 vs. 12/21, p = 1). This study demonstrates the utility of the FTIR method as a quick, low-cost, first-line tool for the detection of ESBL-Kp outbreaks, while WGS analyses are being performed for outbreak confirmation and isolate characterization. Thus, clinical microbiology laboratories would benefit from integrating the FTIR method into CE investigations for infection control measures in the hospital setting.

Whole-genome sequencing surveillance and machine learning for healthcare outbreak detection and investigation: A systematic review and summary

BACKGROUND

Whole-genome sequencing (WGS) has traditionally been used in infection prevention to confirm or refute the presence of an outbreak after it has occurred. Due to decreasing costs of WGS, an increasing number of institutions have been utilizing WGS-based surveillance. Additionally, machine learning or statistical modeling to supplement infection prevention practice have also been used. We systematically reviewed the use of WGS surveillance and machine learning to detect and investigate outbreaks in healthcare settings.

METHODS

We performed a PubMed search using separate terms for WGS surveillance and/or machine-learning technologies for infection prevention through March 15, 2021.

RESULTS

Of 767 studies returned using the WGS search terms, 42 articles were included for review. Only 2 studies (4.8%) were performed in real time, and 39 (92.9%) studied only 1 pathogen. Nearly all studies (n = 41, 97.6%) found genetic relatedness between some isolates collected. Across all studies, 525 outbreaks were detected among 2,837 related isolates (average, 5.4 isolates per outbreak). Also, 35 studies (83.3%) only utilized geotemporal clustering to identify outbreak transmission routes. Of 21 studies identified using the machine-learning search terms, 4 were included for review. In each study, machine learning aided outbreak investigations by complementing methods to gather epidemiologic data and automating identification of transmission pathways.

CONCLUSIONS

WGS surveillance is an emerging method that can enhance outbreak detection. Machine learning has the potential to identify novel routes of pathogen transmission. Broader incorporation of WGS surveillance into infection prevention practice has the potential to transform the detection and control of healthcare outbreaks.

A new and effective genes-based method for phylogenetic analysis of Klebsiella pneumoniae

The exponential increase in the number of genomes deposited in public databases can help us gain a more holistic understanding of the phylogeny and epidemiology of Klebsiella pneumoniae. However, inferring the evolutionary relationships of K. pneumoniae based on big genomic data is challenging for existing methods. In this study, core genes of K. pneumoniae were determined and analysed in terms of differences in GC content, mutation rate, size, and potential functions. We then developed a stable genes-based method for big data analysis and compared it with existing methods. Our new method achieved a higher resolution phylogenetic analysis of K. pneumoniae. Using this genes-based method, we explored global phylogenetic relationships based on a public database of nearly 953 genomes. The results provide useful information to facilitate the phylogenetic and epidemiological analysis of K. pneumoniae, and the findings are relevant for security applications.

Antimicrobial Susceptibility, Virulence, and Genomic Features of a Hypervirulent Serotype K2, ST65 Klebsiella pneumoniae Causing Meningitis in Italy

The rise of a new hypervirulent variant of Klebsiella pneumoniae (hvKp) was recently reported, mainly linked to the ST23 lineage. The hvKp variants can cause severe infections, including hepatic abscesses, bacteremia, and meningitis, with a particularly disconcerting propensity to cause community-acquired, life-threatening infection among young and otherwise healthy individuals. The present study aimed to report the clinical characteristics of a hypermucoviscous K. pneumoniae strain isolated in Italy and sustaining recurrent meningitis in a patient of Peruvian origin. A further objective was to retrospectively investigate, by means of whole-genome sequencing (WGS) analysis, the genomic features of such an isolate. The hypermucoviscosity phenotype of the strain (sk205y205t) was determined using the string test. Genomic information was obtained by WGS (Illumina) and bioinformatic analysis. Strain sk205y205t was susceptible to most antibiotics, despite the presence of some resistance genes, including bla_SHV-11, bla_SHV-67, fosA, and acrR. The isolate belonged to ST65 and serotype K2, and exhibited several virulence factors related to the hvKp variant. Among these, were the siderophore genes entB, irp2, iroN, iroB, and iucA; the capsule-regulating genes rmpA and rmpA2; and the type 1 and 3 fimbriae fimH27 and mrkD, respectively. A further operon, encoding the genotoxin colibactin (clbA-Q), was also identified. The virulence plasmids pK2044, pRJA166b, and pNDM. MAR were also detected. Phylogenetic investigation showed that this Italian strain is highly similar to a Chinese isolate, suggesting a hidden circulation of this hvKp ST65 K2 lineage.

Whole Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection

BACKGROUND

Most hospitals use traditional infection prevention (IP) methods for outbreak detection. We developed the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines whole genome sequencing (WGS) surveillance and machine learning (ML) of the electronic health record (EHR) to identify undetected outbreaks and the responsible transmission routes, respectively.

METHODS

We performed WGS surveillance of healthcare-associated bacterial pathogens from November 2016 to November 2018. EHR ML was used to identify the transmission routes for WGS-detected outbreaks, which were investigated by an IP expert. Potential infections prevented were estimated and compared to traditional IP practice during the same period.

RESULTS

Of 3,165 isolates, there were 2,752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates were identified by WGS; clusters ranged from 2-14 patients. At least one transmission route was detected for 65.7% of clusters. During the same time, traditional IP investigation prompted WGS for 15 suspected outbreaks involving 133 patients, for which transmission events were identified for 5 (3.8%). If EDS-HAT had been running in real-time, 25-63 transmissions could have been prevented. EDS-HAT was found to be cost-saving and more effective than traditional IP practice, with overall savings of $192,408 - $692,532.

CONCLUSION

EDS-HAT detected multiple outbreaks not identified using traditional IP methods, correctly identified the transmission routes for most outbreaks, and would save the hospital substantial costs. Traditional IP practice misidentified outbreaks for which transmission did not occur. WGS surveillance combined with EHR ML has the potential to save costs and enhance patient safety.

Molecular Analysis and Antimicrobial Resistance Pattern of Tigecycline-Non-Susceptible K. pneumoniae Isolated from a Tertiary Care Hospital of East Asia

Introduction

Tigecycline is one of the last resorts for carbapenem-resistant K. pneumoniae (CRKP) infections. Indeed, tigecycline-non-susceptible K. pneumoniae (TNSKP) strains are increasingly treated with the use of tigecycline. In this study, we attempted to better understand their epidemiological trends and characteristics. K. pneumoniae were collected from 2017 to 2020 at the First Affiliated Hospital of Nanchang University.

Methods

Thirty-four TNSKP strains were selected during the study period, all of which were analyzed using antimicrobial susceptibility testing, multilocus sequence typing (MLST), and pulsed-field gel electrophoresis (PFGE). PCR and DNA sequencing were performed for the detection of β-lactamase genes and carbapenemase genes, and the mutation analysis of tet(A), tet(X), tet(L), tet(M), rpsJ, ramR, and oqxR, which are related to tigecycline resistance. Virulence gene and capsular genotype testing were conducted to identify whether the TNSKP strains were hypervirulent Klebsiella pneumoniae.

Results

An epidemiology analysis showed that Klebsiella pneumoniae carbapenemase-2 (KPC-2) was the predominant carbapenemase in tigecycline non-susceptible carbapenem-resistant K. pneumoniae (TNSCRKP) (96.7%), and the dominant clone type was ST11-K14K64 (82.4%). Among them, 55.9% (19/34) of strains were from each department of ICU, particularly EICU and neurosurgery ICU. In order to further understand the molecular mechanisms of the TNSKP, a polymerase chain reaction of the resistant determinants was carried out. The results detected many tigecycline-resistant genes, such as tet(A) (97.1%), tet(X) (17.6%), rpsJ (97.1%), and ramR (8.8%).

Conclusion

As the results of this study reveal, we should take effective measures to control the increase in TNSKP.

Investigation of MALDI-TOF Mass Spectrometry for Assessing the Molecular Diversity of Campylobacter jejuni and Comparison with MLST and cgMLST: A Luxembourg One-Health Study

There is a need for active molecular surveillance of human and veterinary Campylobacter infections. However, sequencing of all isolates is associated with high costs and a considerable workload. Thus, there is a need for a straightforward complementary tool to prioritize isolates to sequence. In this study, we proposed to investigate the ability of MALDI-TOF MS to pre-screen C. jejuni genetic diversity in comparison to MLST and cgMLST. A panel of 126 isolates, with 10 clonal complexes (CC), 21 sequence types (ST) and 42 different complex types (CT) determined by the SeqSphere+ cgMLST, were analysed by a MALDI Biotyper, resulting into one average spectra per isolate. Concordance and discriminating ability were evaluated based on protein profiles and different cut-offs. A random forest algorithm was trained to predict STs. With a 94% similarity cut-off, an AWC of 1.000, 0.933 and 0.851 was obtained for MLST_CC, MLST_ST and cgMLST profile, respectively. The random forest classifier showed a sensitivity and specificity up to 97.5% to predict four different STs. Protein profiles allowed to predict C. jejuni CCs, STs and CTs at 100%, 93% and 85%, respectively. Machine learning and MALDI-TOF MS could be a fast and inexpensive complementary tool to give an early signal of recurrent C. jejuni on a routine basis.

Whole-Genome Sequencing Investigation of a Large Nosocomial Outbreak Caused by ST131 H30Rx KPC-Producing Escherichia coli in Italy

KPC-producing Escherichia coli (KPC-Ec) remains uncommon, being mainly reported as the cause of sporadic episodes of infection rather than outbreak events. Here we retrospectively describe the dynamics of a large hospital outbreak sustained by KPC-Ec, involving 106 patients and 25 hospital wards, during a six-month period. Twenty-nine representative KPC-Ec isolates (8/29 from rectal swabs; 21/29 from other clinical specimens) have been investigated by Whole-Genome Sequencing (WGS). Outbreak isolates showed a multidrug-resistant profile and harbored several resistance determinants, including blaCTX-M-27, aadA5, dfrA17, sulI, gyrA1AB and parC1aAB. Phylogenomic analysis identified the ST131 cluster 1 (23/29 isolates), H30Rx clade C, as responsible for the epidemic event. A further two KPC-Ec ST131 clusters were identified: cluster 2 (n = 2/29) and cluster 3 (n = 1/29). The remaining KPC-Ec resulted in ST978 (n = 2/29) and ST1193 (n = 1/29), and were blaKPC-3 associated. The KPC-Ec ST131 cluster 1, originated in a previous KPC-Kp endemic context probably by plasmid transfer, and showed a clonal dissemination strategy. Transmission of the blaKPC gene to the globally disseminated high-risk ST131 clone represents a serious cause of concern. Application of WGS in outbreak investigations could be useful to better understand the evolution of epidemic events in order to address infection control and contrast interventions, especially when high-risk epidemic clones are involved.

Perspective on Clinically-Relevant Antimicrobial Resistant Enterobacterales in Food: Closing the Gaps Using Genomics

Antimicrobial resistance is one of the most important public health concerns—it causes 700,000 deaths annually according to the World Health Organization (WHO). Enterobacterales such as E. coli and Klebsiella pneumoniae, have become resistant to many relevant antimicrobials including carbapenems and extended spectrum cephalosporins. These clinically relevant resistant Enterobacterales (CRRE) members are now globally distributed in the environment including different food types (meats, produce, dairy). Unlike known foodborne pathogens, CRRE are not usually part of most food surveillance systems. However, numerous reports of CRRE highlight the importance of these bacteria in food and have been shown to contribute to the overall crisis of antimicrobial resistance. This is especially important in the context of carriage of these pathogens by immuno-compromised individuals. CRRE infections upon consumption of contaminated food could colonize the human gastrointestinal tract and eventually be a source of systemic infections such as urinary tract infections or septicemia. While different aspects need to be considered to elucidate this, whole genome sequencing along with metadata could be used to understand genomic relationships of CRRE obtained from foods and humans, including isolates from clinical infections. Once robust scientific data is available on the role of CRRE in food, countries could move forward to better survey and control CRRE in food.

Co-occurrence of Carbapenemase-encoding Genes Among Klebsiella pneumoniae Clinical Isolates: Positive Relationship of bla NDM and bla SIM with Imipenem Resistance

Background: Carbapenem-resistant Klebsiella pneumoniae (CR-KP), known as a significant public health threat, is the most common causative agent of nosocomial and community-acquired infections. Objectives: This study aimed to evaluate resistance to carbapenems and determine the prevalence of carbapenemase genes and multilocus sequence typing (MLST) of K. pneumoniae clinical isolates. Methods: One-hundred K. pneumoniae isolates were evaluated. The minimum inhibitory concentrations (MIC) of imipenem and meropenem were assessed by the broth microdilution method. Multiplex-polymerase chain reaction (PCR) was applied to detect 11 carbapenemase-encoding genes belonging to different classes. The alleles and sequence types (ST) of three isolates were identified by MLST. Results: The MIC of carbapenems for the isolates ranged from 0.062 to 32 µg/mL. Overall, resistance rates to imipenem and meropenem were reported 11% and 34%, respectively. The bla IMP gene was the most abundant (78.4%), followed by bla OXA-48 (48.6%), bla GIM (27%), bla KPC (27%), bla SIM (21.6%), bla BIC (21.6%), bla NDM (16.2%), bla AIM (16.2%), bla VIM (16.2%), bla DIM (8.1%), and bla SPM (8.1%). The co-existence of carbapenemase genes was observed in 81.8% of the isolates. A positive relationship was found between the presence of bla NDM and bla SIM and resistance to imipenem. Multilocus sequence typing results showed three different sequence types, including ST14, ST5188, and ST1861. Conclusions: This study revealed a high prevalence of CR-KP isolates that suggests a high risk of horizontal gene transfer and potential to spread resistance among other strains. Since STs are reported for the first time in Iran, they can be considered as emerging strains.

Rapid Open-Source SNP-Based Clustering Offers an Alternative to Core Genome MLST for Outbreak Tracing in a Hospital Setting

Traditional genotyping methods for infection control of antimicrobial-resistant bacteria in healthcare settings have been supplemented by whole-genome sequencing (WGS), often relying on a gene-based approach, e.g., core genome multilocus sequence typing (cgMLST), to cluster-related samples. In this study, we compared clusters of methicillin-resistant Staphylococcus aureus (MRSA) and Enterococcus faecium analyzed with the commercial cgMLST software Ridom SeqSphere+ and with an open-source single-nucleotide polymorphism (SNP)-based phylogenetic analysis pipeline (PAPABAC). A total of 5,655 MRSA and 2,572 E. faecium patient isolates, collected between 2013 and 2018, were processed. Clusters of 1,844 MRSA and 1,355 E. faecium isolates were compared to cgMLST results, and epidemiological data were included when available. The phylogenies inferred by the two different technologies were highly concordant, and the MRSA SNP tree re-captured known hospital-related outbreaks and epidemiologically linked samples. PAPABAC has the advantage over Ridom SeqSphere+ to generate stable, referable clusters without the need for sequence assembly, and it is a free-of-charge, open-source alternative to the commercial software.

Analysis of the Global Population Structure of Paenibacillus larvae and Outbreak Investigation of American Foulbrood Using a Stable wgMLST Scheme

Paenibacillus larvae causes the American foulbrood (AFB), a highly contagious and devastating disease of honeybees. Whole-genome sequencing (WGS) has been increasingly used in bacterial pathogen typing, but rarely applied to study the epidemiology of P. larvae. To this end, we used 125 P. larvae genomes representative of a species-wide diversity to construct a stable whole-genome multilocus sequence typing (wgMLST) scheme consisting of 5745 loci. A total of 51 P. larvae isolates originating from AFB outbreaks in Slovenia were used to assess the epidemiological applicability of the developed wgMLST scheme. In addition, wgMLST was compared with the core-genome MLST (cgMLST) and whole-genome single nucleotide polymorphism (wgSNP) analyses. All three approaches successfully identified clusters of outbreak-associated strains, which were clearly separated from the epidemiologically unlinked isolates. High levels of backward comparability of WGS-based analyses with conventional typing methods (ERIC-PCR and MLST) were revealed; however, both conventional methods lacked sufficient discriminatory power to separate the outbreak clusters. The developed wgMLST scheme provides an improved understanding of the intra- and inter-outbreak genetic diversity of P. larvae and represents an important progress in unraveling the genomic epidemiology of this important honeybee pathogen.

Whole Genome Sequencing Applied to Pathogen Source Tracking in Food Industry: Key Considerations for Robust Bioinformatics Data Analysis and Reliable Results Interpretation

Whole genome sequencing (WGS) has arisen as a powerful tool to perform pathogen source tracking in the food industry thanks to several developments in recent years. However, the cost associated to this technology and the degree of expertise required to accurately process and understand the data has limited its adoption at a wider scale. Additionally, the time needed to obtain actionable information is often seen as an impairment for the application and use of the information generated via WGS. Ongoing work towards standardization of wet lab including sequencing protocols, following guidelines from the regulatory authorities and international standardization efforts make the technology more and more accessible. However, data analysis and results interpretation guidelines are still subject to initiatives coming from distinct groups and institutions. There are multiple bioinformatics software and pipelines developed to handle such information. Nevertheless, little consensus exists on a standard way to process the data and interpret the results. Here, we want to present the constraints we face in an industrial setting and the steps we consider necessary to obtain high quality data, reproducible results and a robust interpretation of the obtained information. All of this, in a time frame allowing for data-driven actions supporting factories and their needs.

Genome-Based Analysis of a Sequence Type 1049 Hypervirulent Klebsiella pneumoniae Causing Bacteremic Neck Abscess

Hypervirulent Klebsiella pneumoniae (hvKP) has raised grave concerns in recent years and can cause severe infections with diverse anatomic locations including liver abscess, meningitis, and endophthalmitis. However, there is limited data about neck abscess caused by hvKP. A K. pneumoniae strain Kp_whw was isolated from neck abscess. We characterized the genetic background, virulence determinates of the strain by genomic analysis and dertermined the virulence level by serum resistance assay. Kp_whw belonged to sequence type (ST) 1049 K locus (KL) 5. Kp_whw showed hypermucoviscosity phenotype and was resistant to ampicillin but susceptible to the majority of the other antimicrobial agents. A pLVPK-like virulence plasmid and a chromosomal ICEKp5-like mobile genetic element were carried by Kp_whw, resulting in the risk of dissemination of hypervirulence. The strain exhibited relative higher level of core genome allelic diversity than accessory genome profile, in comparison to hvKP of K1/K2 serotype. Kp_whw was finally demonstrated as virulent as the ST23 K1 serotype hvKP strain NTUH-K2044 in vitro. In conclusion, this work elaborates the genetic background of a clinical hvKP strain with an uncommon ST, reinforcing our understanding of virulence mechanisms of hvKP.

Presence and infectivity of SARS-CoV-2 virus in wastewaters and rivers

The presence of SARS-CoV-2 in raw wastewaters has been demonstrated in many countries affected by this pandemic. Nevertheless, virus presence and infectivity in treated wastewaters, but also in the receiving water bodies are still poorly investigated. In this study, raw and treated samples from three wastewater treatment plants, and three river samples within the Milano Metropolitan Area, Italy, were surveyed for SARS-CoV-2 RNA detection by means of real time RT-PCR and infectivity test on culture cells. SARS-CoV-2 RNA was detected in raw, but not in treated wastewaters (four and two samples, respectively, sampled in two dates). The isolated virus genome was sequenced, and belonged to the strain most spread in Europe and similar to another found in the same region. RNA presence in raw wastewater samples decreased after eight days, probably following the epidemiological trend estimated for the area. Virus infectivity was always null, indicating the natural decay of viral pathogenicity in time from emission. Samples from receiving rivers (three sites, sampled in the same dates as wastewaters) showed in some cases a positivity to real time RT-PCR, probably due to non-treated, or inefficiently treated discharges, or to the combined sewage overflows. Nevertheless, also for rivers infectivity was null. Risks for public health should be limited, although a precautionary approach to risk assessment is here advocated, giving the preliminary nature of the presented data.

Presence and infectivity of SARS-CoV-2 virus in wastewaters and rivers

The presence of SARS-CoV-2 in raw wastewaters has been demonstrated in many countries affected by this pandemic. Nevertheless, virus presence and infectivity in treated wastewaters, but also in the receiving water bodies are still poorly investigated. In this study, raw and treated samples from three wastewater treatment plants, and three river samples within the Milano Metropolitan Area, Italy, were surveyed for SARS-CoV-2 RNA detection by means of real time RT-PCR and infectivity test on culture cells. SARS-CoV-2 RNA was detected in raw, but not in treated wastewaters (four and two samples, respectively, sampled in two dates). The isolated virus genome was sequenced, and belonged to the strain most spread in Europe and similar to another found in the same region. RNA presence in raw wastewater samples decreased after eight days, probably following the epidemiological trend estimated for the area. Virus infectivity was always null, indicating the natural decay of viral pathogenicity in time from emission. Samples from receiving rivers (three sites, sampled in the same dates as wastewaters) showed in some cases a positivity to real time RT-PCR, probably due to non-treated, or inefficiently treated discharges, or to the combined sewage overflows. Nevertheless, also for rivers infectivity was null. Risks for public health should be limited, although a precautionary approach to risk assessment is here advocated, giving the preliminary nature of the presented data.