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

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

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

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

Sporadic Occurrence of Enteroaggregative Shiga Toxin-Producing Escherichia coli O104:H4 Similar to 2011 Outbreak Strain.

We describe the recent detection of 3 Shiga toxin–producing enteroaggregative Escherichia coli O104:H4 isolates from patients and 1 from pork in the Netherlands that were genetically highly similar to isolates from the 2011 large-scale outbreak in Europe. Our findings stress the importance of safeguarding food supply production chains to prevent future outbreaks

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

Increasing the efficiency of a national laboratory response to COVID-19; a nation-wide multicenter evaluation of 47 commercial SARS-CoV-2 immunoassays by 41 laboratories.

In response to the worldwide pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the subsequent antibody tests that flooded the market, a nationwide collaborative approach in the Netherlands was employed. Forty-one Dutch laboratories joined forces and shared their evaluation data to allow for the evaluation of a quantity of serological assays for SARS-CoV-2 that exceeds the capacity of each individual laboratory. As of April 2020, these performance data had been aggregated and shared in regularly updated reports with other laboratories, Dutch government, public health organizations, and the public. This frequently updated overview of assay performance increased the efficiency of our national laboratory response, supporting laboratories in their choice and implementation of assays. Aggregated performance data for 47 immunoassays for SARS-CoV-2 showed that none of the evaluated immunoassays that detect only IgM or IgA met the diagnostic criteria, indicating that they are not suitable for diagnosing acute infections. For the detection of IgG, only the Biozek Corona virus COVID rapid test, Euroimmun SARS-CoV-2 IgG, and Wantai SARS-CoV-2 antibody (Ab) ELISA met predefined performance criteria in hospitalized patients where samples were collected 14 days post-onset of symptoms (DPO), while for patients with mild or asymptomatic infections, only the Wantai SARS-CoV-2 Ab ELISA met the predefined performance criteria if samples were collected 14 days postonset. Here, we describe this unique nationwide collaboration during the onset of the COVID-19 pandemic; the collected data and their results are an example of what can be accomplished when forces are joined during a public health crisis