MALDI-TOF MS Using a Custom-Made Database, Biomarker Assignment, or Mathematical Classifiers Does Not Differentiate Shigella spp. and Escherichia coli

Shigella spp. and E. coli are closely related and cannot be distinguished using matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF MS) with commercially available databases. Here, three alternative approaches using MALDI-TOF MS to identify and distinguish Shigella spp., E. coli, and its pathotype EIEC were explored and evaluated using spectra of 456 Shigella spp., 42 E. coli, and 61 EIEC isolates. Identification with a custom-made database resulted in >94% Shigella identified at the genus level and >91% S. sonnei and S. flexneri at the species level, but the distinction of S. dysenteriae, S. boydii, and E. coli was poor. With biomarker assignment, 98% S. sonnei isolates were correctly identified, although specificity was low. Discriminating markers for S. dysenteriae, S. boydii, and E. coli were not assigned at all. Classification models using machine learning correctly identified Shigella in 96% of isolates, but most E. coli isolates were also assigned to Shigella. None of the proposed alternative approaches were suitable for clinical diagnostics for identifying Shigella spp., E. coli, and EIEC, reflecting their relatedness and taxonomical classification. We suggest the use of MALDI-TOF MS for the identification of the Shigella spp./E. coli complex, but other tests should be used for distinction

Genome-wide association studies of Shigella spp. and Enteroinvasive Escherichia coli isolates demonstrate an absence of genetic markers for prediction of disease severity

BACKGROUND: We investigated the association of symptoms and disease severity of shigellosis patients with genetic determinants of infecting Shigella and entero-invasive Escherichia coli (EIEC), because determinants that predict disease outcome per individual patient could be used to prioritize control measures. For this purpose, genome wide association studies (GWAS) were performed using presence or absence of single genes, combinations of genes, and k-mers. All genetic variants were derived from draft genome sequences of isolates from a multicenter cross-sectional study conducted in the Netherlands during 2016 and 2017. Clinical data of patients consisting of binary/dichotomous representation of symptoms and their calculated severity scores were also available from this study. To verify the suitability of the methods used, the genetic differences between the genera Shigella and Escherichia were used as control. RESULTS: The isolates obtained were representative of the population structure encountered in other Western European countries. No association was found between single genes or combinations of genes and separate symptoms or disease severity scores. Our benchmark characteristic, genus, resulted in eight associated genes and > 3,000,000 k-mers, indicating adequate performance of the algorithms used. CONCLUSIONS: To conclude, using several microbial GWAS methods, genetic variants in Shigella spp. and EIEC that can predict specific symptoms or a more severe course of disease were not identified, suggesting that disease severity of shigellosis is dependent on other factors than the genetic variation of the infecting bacteria. Specific genes or gene fragments of isolates from patients are unsuitable to predict outcomes and cannot be used for development, prioritization and optimization of guidelines for control measures of shigellosis or infections with EIEC

A Multifactorial Approach for Surveillance of Shigella spp. and Entero-Invasive Escherichia coli Is Important for Detecting (Inter)national Clusters

Shigella spp. and entero-invasive Escherichia coli (EIEC) can cause mild diarrhea to dysentery. In Netherlands, although shigellosis is a notifiable disease, there is no laboratory surveillance for Shigella spp. and EIEC in place. Consequently, the population structure for circulating Shigella spp. and EIEC isolates is not known. This study describes the phenotypic and serological characteristics, the phenotypic and genetic antimicrobial resistance (AMR) profiles, the virulence gene profiles, the classic multi-locus sequence types (MLST) and core genome (cg)MLST types, and the epidemiology of 414 Shigella spp. and EIEC isolates collected during a cross-sectional study in Netherlands in 2016 and 2017. S. sonnei (56%), S. flexneri (25%), and EIEC (15%) were detected predominantly in Netherlands, of which the EIEC isolates were most diverse according to their phenotypical profile, O-types, MLST types, and cgMLST clades. Virulence gene profiling showed that none of the isolates harbored Shiga toxin genes. Most S. flexneri and EIEC isolates possessed nearly all virulence genes examined, while these genes were only detected in approximately half of the S. sonnei isolates, probably due to loss of the large invasion plasmid upon subculturing. Phenotypical resistance correlated well with the resistant genotype, except for the genes involved in resistance to aminoglycosides. A substantial part of the characterized isolates was resistant to antimicrobials advised for treatment, i.e., 73% was phenotypically resistant to co-trimoxazole and 19% to ciprofloxacin. AMR was particularly observed in isolates from male patients who had sex with men (MSM) or from patients that had traveled to Asia. Furthermore, isolates related to international clusters were also circulating in Netherlands. Travel-related isolates formed clusters with isolates from patients without travel history, indicating their emergence into the Dutch population. In conclusion, laboratory surveillance using whole genome sequencing as high-resolution typing technique and for genetic characterization of isolates complements the current epidemiological surveillance, as the latter is not sufficient to detect all (inter)national clusters, emphasizing the importance of multifactorial public health approaches

Molecular Characterization of Capnocytophaga canimorsus and Other Canine Capnocytophaga spp. and Assessment by PCR of Their Frequencies in Dogs ▿ †

Capnocytophaga canimorsus can be a virulent pathogen, whereas C. cynodegmi is of low virulence. Heterogeneity within these species, their frequency in dogs, and pathogenicity factors are largely unknown. Strains from blood cultures from patients presumptively identified as C. canimorsus (n = 25) and as C. cynodegmi by rrs analysis (n = 4), blood cultures from dogs (n = 8), blood cultures from cats (n = 2), and cultures from swabs from dog mouths (n = 53) were analyzed. PCR-restriction fragment length polymorphism (PCR-RFLP), a species-specific PCR on rpoB, and rrs sequencing were used. All 29 strains from human blood cultures could be grouped into three PCR-RFLP types. One included the C. canimorsus type strain, and the other types were closely related. Two canine strains were C. canimorsus and grouped into the least common RLFP pattern group. Five were C. cynodegmi and clustered with the reference strain. One canine and both feline strains were distinct. Four human strains that presumptively had been identified as C. cynodegmi by RNA gene sequence analysis clustered with the C. canimorsus strains by both PCR-RFLP and the sequence-specific PCR of the rpoB gene. C. canimorsus DNA was present in 73% (range, 61 to 85%) of dogs' mouths, and C. cynodegmi DNA was present in 96% (range, 94 to 100%) of dogs' mouths. As defined by rpoB PCR-RFLP and by PCRs using specific primers, all strains from human blood were C. canimorsus. The sequencing of rrs genes suggested the presence of different gene copies in a few strains, indicating that the method is less appropriate for species identification. Both species are present in the majority of dogs. Additional Capnocytophaga species occur in dogs' and cats' mouths

Tularemia Transmission to Humans, the Netherlands, 2011-2021

We used national registry data on human cases of Francisella tularensis subspecies holarctica infection to assess transmission modes among all 26 autochthonous cases in the Netherlands since 2011. The results indicate predominance of terrestrial over aquatic animal transmission sources. We recommend targeting disease-risk communication toward hunters, recreationists, and outdoor professionals

Multicenter evaluation of molecular and culture-dependent diagnostics for Shigella species and Entero-invasive Escherichia coli in the Netherlands

An inter-laboratory collaborative trial for the evaluation of diagnostics for detection and identification of Shigella species and Entero-invasive Escherichia coil (EIEC) was performed. Sixteen Medical Microbiological Laboratories (MMLs) participated. MMLs were interviewed about their diagnostic methods and a sample panel, consisting of DNA-extracts and spiked stool samples with different concentrations of Shigella flexneri, was provided to each MML The results of the trial showed an enormous variety in culture-dependent and molecular diagnostic techniques currently used among MMLs. Despite the various molecular procedures, 15 out of 16 MMLs were able to detect Shigella species or EIEC in all the samples provided, showing that the diversity of methods has no effect on the qualitative detection of Shigella flexneri. In contrast to semi quantitative analysis, the minimum and maximum values per sample differed by approximately five threshold cycles (Ct-value) between the MMLs included in the study. This indicates that defining a uniform Ct-value cut-off for notification to health authorities is not advisable. (C) 2016 Elsevier B.V. All rights reserved

Genome-wide association studies of Shigella spp. and Enteroinvasive Escherichia coli isolates demonstrate an absence of genetic markers for prediction of disease severity

BACKGROUND: We investigated the association of symptoms and disease severity of shigellosis patients with genetic determinants of infecting Shigella and entero-invasive Escherichia coli (EIEC), because determinants that predict disease outcome per individual patient could be used to prioritize control measures. For this purpose, genome wide association studies (GWAS) were performed using presence or absence of single genes, combinations of genes, and k-mers. All genetic variants were derived from draft genome sequences of isolates from a multicenter cross-sectional study conducted in the Netherlands during 2016 and 2017. Clinical data of patients consisting of binary/dichotomous representation of symptoms and their calculated severity scores were also available from this study. To verify the suitability of the methods used, the genetic differences between the genera Shigella and Escherichia were used as control. RESULTS: The isolates obtained were representative of the population structure encountered in other Western European countries. No association was found between single genes or combinations of genes and separate symptoms or disease severity scores. Our benchmark characteristic, genus, resulted in eight associated genes and > 3,000,000 k-mers, indicating adequate performance of the algorithms used. CONCLUSIONS: To conclude, using several microbial GWAS methods, genetic variants in Shigella spp. and EIEC that can predict specific symptoms or a more severe course of disease were not identified, suggesting that disease severity of shigellosis is dependent on other factors than the genetic variation of the infecting bacteria. Specific genes or gene fragments of isolates from patients are unsuitable to predict outcomes and cannot be used for development, prioritization and optimization of guidelines for control measures of shigellosis or infections with EIEC

Identification of Francisella tularensis Subspecies in a Clinical Setting Using MALDI-TOF MS: An In-House Francisella Library and Biomarkers.

Francisella tularensis is a zoonotic bacterium that is endemic in large parts of the world. It is absent in the standard library of the most applied matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) systems: the Vitek MS and the Bruker Biotyper system. The additional Bruker MALDI Biotyper Security library contains F. tularensis without subspecies differentiation. The virulence of F. tularensis differs between the subspecies. The F. tularensis subspecies (ssp.) tularensis is highly pathogenic, whereas the subspecies holarctica displays lower virulence and subspecies novicida and F. tularensis ssp. mediasiatica are hardly virulent. To differentiate the Francisellaceae and the F. tularensis-subspecies, an in-house Francisella library was built with the Bruker Biotyper system and validated together with the existing Bruker databases. In addition, specific biomarkers were defined based on the main spectra of the Francisella strains supplemented with in silico genome data. Our in-house Francisella library accurately differentiates the F. tularensis subspecies and the other Francisellaceae. The biomarkers correctly differentiate the various species within the genus Francisella and the F. tularensis subspecies. These MALDI-TOF MS strategies can successfully be applied in a clinical laboratory setting as a fast and specific method to identify F. tularensis to subspecies level

Evaluation of a culture dependent algorithm and a molecular algorithm for identification of Shigella spp., Escherichia coli, and enteroinvasive E. coli (EIEC).

Identification of Shigella spp., Escherichia coli and enteroinvasive E. coli is challenging, because of their close relatedness. Distinction is vital, as infections with Shigella spp. are under surveillance of health authorities, in contrast to EIEC infections. In this study, a culture dependent identification algorithm and a molecular identification algorithm were evaluated. Discrepancies between the two algorithms and original identification were assessed using Whole Genome Sequencing (WGS). After discrepancy analysis, with the molecular algorithm, 100% of the evaluated isolates were identified in concordance with original identification. However, the resolution for certain serotypes was lower than previously described methods and lower than the culture dependent algorithm. Although, the resolution of the culture dependent algorithm is high, 100% of non-invasive E. coli, S. sonnei, S. dysenteriae, 93% of S. boydii and EIEC and 85% of S. flexneri were identified in concordance with the original identification. Discrepancy analysis using WGS was able to confirm one of the used algorithms in four discrepant results. However, it failed to clarify three other discrepant results as it added yet another identification. Both proposed algorithms performed well for the identification of Shigella spp and EIEC, and are applicable in low-resource settings in contrast to earlier described methods that require WGS for daily diagnostics. Evaluation of the algorithms showed that both algorithms are capable of identifying Shigella species and EIEC isolates. The molecular algorithm is more applicable in clinical diagnostics for fast and accurate screening, while the culture dependent algorithm is more suitable for reference laboratories to identify Shigella spp. and EIEC up to serotype level