Shigella flexneri serotype 1c derived from serotype 1a by acquisition of gtrIC gene cluster via a bacteriophage

Newly Emerged Serotype 1c of Shigella flexneri: Multiple Origins and Changing Drug Resistance Landscape

Bacillary dysentery caused by Shigella flexneri is a major cause of under-five mortality in developing countries, where a novel S. flexneri serotype 1c has become very common since the 1980s. However, the origin and diversification of serotype 1c remain poorly understood. To understand the evolution of serotype 1c and their antimicrobial resistance, we sequenced and analyzed the whole-genome of 85 clinical isolates from the United Kingdom, Egypt, Bangladesh, Vietnam, and Japan belonging to serotype 1c and related serotypes of 1a, 1b and Y/Yv. We identified up to three distinct O-antigen modifying genes in S. flexneri 1c strains, which were acquired from three different bacteriophages. Our analysis shows that S. flexneri 1c strains have originated from serotype 1a and serotype 1b strains after the acquisition of bacteriophage-encoding gtrIc operon. The maximum-likelihood phylogenetic analysis using core genes suggests two distinct S. flexneri 1c lineages, one specific to Bangladesh, which originated from ancestral serotype 1a strains and the other from the United Kingdom, Egypt, and Vietnam originated from ancestral serotype 1b strains. We also identified 63 isolates containing multiple drug-resistant genes in them conferring resistance against streptomycin, sulfonamide, quinolone, trimethoprim, tetracycline, chloramphenicol, and beta-lactamase. Furthermore, antibiotic susceptibility assays showed 83 (97.6%) isolates as either complete or intermediate resistance to the WHO-recommended first- and second-line drugs. This changing drug resistance pattern demonstrates the urgent need for drug resistance surveillance and renewed treatment guidelines.

Extensive inter-strain diversity among clinical isolates of Shigella flexneri with reference to its serotype, virulence traits and plasmid incompatibility types, a study from south India over a 6-year period

Background

Shigella has evolved as a result of acquiring extragenetic material through horizontal gene exchange. These aid in the rapid emergence of bacterial inter-strain diversity in virulence factors and serotype variants through O-antigenic switching. Plasmid incompatibility typing of isolates is insightful in understanding local expansion of virulence plasmids, as whether virulence dissemination involves diverse plasmids or one dominant ‘epidemic’ type. The broad question underlying this study was that of how inter-strain genetic, serotype and plasmid incompatibility type variations can help understand the emergence of Shigella as a highly virulent pathogen.

Results

A total of 101 confirmed isolates of S. flexneri were included in this study. The distribution of the subtypes were variable, type 2a (48/101, 47.5%), type 6 (15/101, 14.9%), type 1b (8/101, 7.9%), type 1 variant (7/101, 6.9%), type 3b (12/101, 11.9% 0, type 4 (6/101, 6.0%), variant Y (2/101, 1.9%) and variant X (1/101, 1%). All had the ipaH gene (101/101, 100%) followed by ompA (92/101, 91.1%), ial (84/101, 83.4%), sen (82/101, 81.2%), virF (84/101, 83.2%), set1A and set1B (59/101, 58.4%). Out of the total of 49 isolates that showed all the virulence related genes studied here the IncIγ plasmid was detected in all isolates studied followed by FII (33/49, 67.3%), FIIS (20/49, 40.8%). Inc K was positive in two isolates (2/49, 4%) studied. The inc groups IncI1-α, Inc T were detected in 1 isolate each and Inc L and Inc P formed part of the multireplicon in the same isolate.

Conclusions

In order to estimate the burden of the disease caused by the new serotypes, it is important to have knowledge of the locally prevalent serotype. This will prove helpful in developing strategies for prevention of same especially since, the immunity in such diseases is serotype specific. Thus, the emergence of non-typable atypical serotypes of S. flexneri from natural infections needs to be investigated further. This study highlights the emergence of genetic variants exhibiting resistance to many antibiotics which needs to be studied for understanding the ever-changing landscape of this pathogen.

Electronic supplementary material

The online version of this article (10.1186/s13099-019-0314-9) contains supplementary material, which is available to authorized users.

In Silico Serotyping Based on Whole-Genome Sequencing Improves the Accuracy of Shigella Identification

Bacteria of the genus Shigella, consisting of 4 species and >50 serotypes, cause shigellosis, a foodborne disease of significant morbidity, mortality, and economic loss worldwide. Classical Shigella identification based on selective media and serology is tedious, time-consuming, expensive, and not always accurate. A molecular diagnostic assay does not distinguish Shigella at the species level or from enteroinvasive Escherichia coli (EIEC). We inspected genomic sequences from 221 Shigella isolates and observed low concordance rates between conventional designation and molecular serotyping: 86.4% and 80.5% at the species and serotype levels, respectively. Serotype determinants for 6 additional serotypes were identified. Examination of differentiation gene markers commonly perceived as characteristic hallmarks in Shigella showed high variability among different serotypes. Using this information, we developed ShigaTyper, an automated workflow that utilizes limited computational resources to accurately and rapidly determine 59 Shigella serotypes using Illumina paired-end whole-genome sequencing (WGS) reads. Shigella serotype determinants and species-specific diagnostic markers were first identified through read alignment to an in-house curated reference sequence database. Relying on sequence hits that passed a threshold level of coverage and accuracy, serotype could be unambiguously predicted within 1 min for an average-size WGS sample of ∼500 MB. Validation with WGS data from 380 isolates showed an accuracy rate of 98.2%. This pipeline is the first step toward building a comprehensive WGS-based analysis pipeline of Shigella spp. in a field laboratory setting, where speed is essential and resources need to be more cost-effectively dedicated.IMPORTANCE Shigella causes diarrheal disease with serious public health implications. However, conventional Shigella identification methods are laborious and time-consuming and can be erroneous due to the high similarity between Shigella and enteroinvasive Escherichia coli (EIEC) and cross-reactivity between serotyping antisera. Further, serotype interpretation is complicated for inexperienced users. To develop an easier method with higher accuracy based on whole-genome sequencing (WGS) for Shigella serotyping, we systematically examined genomic information of Shigella isolates from 53 serotypes to define rules for differentiation and serotyping. We created ShigaTyper, an automated pipeline that accurately and rapidly excludes non-Shigella isolates and identifies 59 Shigella serotypes using Illumina paired-end WGS reads. A serotype can be unambiguously predicted at a data processing speed of 538 MB/min with 98.2% accuracy from a regular laptop. Once it is installed, training in bioinformatics analysis and Shigella genetics is not required. This pipeline is particularly useful to general microbiologists in field laboratories.

Diversity-Generating Machines: Genetics of Bacterial Sugar-Coating

Bacterial pathogens and commensals are surrounded by diverse surface polysaccharides which include capsules and lipopolysaccharides. These carbohydrates play a vital role in bacterial ecology and interactions with the environment. Here, we review recent rapid advancements in this field, which have improved our understanding of the roles, structures, and genetics of bacterial polysaccharide antigens. Genetic loci encoding the biosynthesis of these antigens may have evolved as bacterial diversity-generating machines, driven by selection from a variety of forces, including host immunity, bacteriophages, and cell-cell interactions. We argue that the high adaptive potential of polysaccharide antigens should be taken into account in the design of polysaccharide-targeting medical interventions like conjugate vaccines and phage-based therapies.

Evaluation of a Culture-Dependent Algorithm and a Molecular Algorithm for Identification of Shigella spp., Escherichia coli, and Enteroinvasive E. coli

Identification of Shigella spp., Escherichia coli, and enteroinvasive E. coli (EIEC) 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 the original identification. However, the resolution for certain serotypes was lower than that of previously described methods and lower than that of the culture-dependent algorithm. Although the resolution of the culture-dependent algorithm is high, 100% of noninvasive E. coli, Shigella sonnei, and Shigella dysenteriae, 93% of Shigella boydii and EIEC, and 85% of Shigella flexneri isolates 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 isolates and are applicable in low-resource settings, in contrast to previously 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 the serotype level.