Rapid O serogroup identification of the six clinically relevant Shiga toxin-producing Escherichia coli by antibody microarray

Microarray Strategies for Exploring Bacterial Surface Glycans and Their Interactions With Glycan-Binding Proteins

Bacterial surfaces are decorated with distinct carbohydrate structures that may substantially differ among species and strains. These structures can be recognized by a variety of glycan-binding proteins, playing an important role in the bacteria cross-talk with the host and invading bacteriophages, and also in the formation of bacterial microcolonies and biofilms. In recent years, different microarray approaches for exploring bacterial surface glycans and their recognition by proteins have been developed. A main advantage of the microarray format is the inherent miniaturization of the method, which allows sensitive and high-throughput analyses with very small amounts of sample. Antibody and lectin microarrays have been used for examining bacterial glycosignatures, enabling bacteria identification and differentiation among strains. In addition, microarrays incorporating bacterial carbohydrate structures have served to evaluate their recognition by diverse host/phage/bacterial glycan-binding proteins, such as lectins, effectors of the immune system, or bacterial and phagic cell wall lysins, and to identify antigenic determinants for vaccine development. The list of samples printed in the arrays includes polysaccharides, lipopoly/lipooligosaccharides, (lipo)teichoic acids, and peptidoglycans, as well as sequence-defined oligosaccharide fragments. Moreover, microarrays of cell wall fragments and entire bacterial cells have been developed, which also allow to study bacterial glycosylation patterns. In this review, examples of the different microarray platforms and applications are presented with a view to give the current state-of-the-art and future prospects in this field.

Molecular serogrouping ofEscherichia coli

O-antigens present on the surface ofEscherichia coliprovide antigenic specificity for the strain and are the main components for O-serogroup designation. Serotyping using O-group-specific antisera for the identification ofE. coliO-serogroups has been traditionally the gold-standard for distinguishingE. colistrains. Knowledge of the O-group is important for determining pathogenic lineage, classifyingE. colifor epidemiological studies, for determining virulence, and for tracing outbreaks of diseases and sources of infection. However, serotyping has limitations, as the antisera generated against each specific O-group may cross-react, many strains are non-typeable, and others can autoagglutinate or be rough (lacking an O-antigen). Currently, the nucleotide sequences are available for most of the 187 designatedE. coliO-groups. Public health and other laboratories are considering whole genome sequencing to develop genotypic methods to determine O-groups. These procedures require instrumentation and analysis that may not be accessible and may be cost-prohibitive at this time. In this review, we have identified unique gene sequences within the O-antigen gene clusters and have targeted these genes for identification of O-groups using the polymerase chain reaction. This information can be used to distinguish O-groups by developing other platforms forE. colidiagnostics in the future.

Advances in Molecular Serotyping and Subtyping of Escherichia coli

Escherichia coli plays an important role as a member of the gut microbiota; however, pathogenic strains also exist, including various diarrheagenic E. coli pathotypes and extraintestinal pathogenic E. coli that cause illness outside of the GI-tract. E. coli have traditionally been serotyped using antisera against the ca. 186 O-antigens and 53 H-flagellar antigens. Phenotypic methods, including bacteriophage typing and O- and H- serotyping for differentiating and characterizing E. coli have been used for many years; however, these methods are generally time consuming and not always accurate. Advances in next generation sequencing technologies have made it possible to develop genetic-based subtyping and molecular serotyping methods for E. coli, which are more discriminatory compared to phenotypic typing methods. Furthermore, whole genome sequencing (WGS) of E. coli is replacing established subtyping methods such as pulsed-field gel electrophoresis, providing a major advancement in the ability to investigate food-borne disease outbreaks and for trace-back to sources. A variety of sequence analysis tools and bioinformatic pipelines are being developed to analyze the vast amount of data generated by WGS and to obtain specific information such as O- and H-group determination and the presence of virulence genes and other genetic markers.

Comparison of O-Antigen Gene Clusters of All O-Serogroups of Escherichia coli and Proposal for Adopting a New Nomenclature for O-Typing

Escherichia coli strains are classified based on O-antigens that are components of the lipopolysaccharide (LPS) in the cell envelope. O-antigens are important virulence factors, targets of both the innate and adaptive immune system, and play a role in host-pathogen interactions. Because they are highly immunogenic and display antigenic specificity unique for each strain, O-antigens are the biomarkers for designating O-types. Immunologically, 185 O-serogroups and 11 OX-groups exist for classification. Conventional serotyping for O-typing entails agglutination reactions between the O-antigen and antisera generated against each O-group. The procedure is labor intensive, not always accurate, and exhibits equivocal results. In this report, we present the sequences of 71 O-antigen gene clusters (O-AGC) and a comparison of all 196 O- and OX-groups. Many of the designated O-types, applied for classification over several decades, exhibited similar nucleotide sequences of the O-AGCs and cross-reacted serologically. Some O-AGCs carried insertion sequences and others had only a few nucleotide differences between them. Thus, based on these findings, it is proposed that several of the E. coli O-groups may be merged. Knowledge of the O-AGC sequences facilitates the development of molecular diagnostic platforms that are rapid, accurate, and reliable that can replace conventional serotyping. Additionally, with the scientific knowledge presented, new frontiers in the discovery of biomarkers, understanding the roles of O-antigens in the innate and adaptive immune system and pathogenesis, the development of glycoconjugate vaccines, and other investigations, can be explored.

Light scattering sensor for direct identification of colonies of Escherichia coli serogroups O26, O45, O103, O111, O121, O145 and O157

Background

Shiga-toxin producing Escherichia coli (STEC) have emerged as important foodborne pathogens, among which seven serogroups (O26, O45, O103, O111, O121, O145, O157) are most frequently implicated in human infection. The aim was to determine if a light scattering sensor can be used to rapidly identify the colonies of STEC serogroups on selective agar plates.

Methodology/Principal Findings

Initially, a total of 37 STEC strains representing seven serovars were grown on four different selective agar media, including sorbitol MacConkey (SMAC), Rainbow Agar O157, BBL CHROMagarO157, and R&F E. coli O157:H7, as well as nonselective Brain Heart Infusion agar. The colonies were scanned by an automated light scattering sensor, known as BARDOT (BActerial Rapid Detection using Optical scattering Technology), to acquire scatter patterns of STEC serogroups, and the scatter patterns were analyzed using an image classifier. Among all of the selective media tested, both SMAC and Rainbow provided the best differentiation results allowing multi-class classification of all serovars with an average accuracy of more than 90% after 10–12 h of growth, even though the colony appearance was indistinguishable at that early stage of growth. SMAC was chosen for exhaustive scatter image library development, and 36 additional strains of O157:H7 and 11 non-O157 serovars were examined, with each serogroup producing unique differential scatter patterns. Colony scatter images were also tested with samples derived from pure and mixed cultures, as well as experimentally inoculated food samples. BARDOT accurately detected O157 and O26 serovars from a mixed culture and also from inoculated lettuce and ground beef (10-h broth enrichment +12-h on-plate incubation) in the presence of natural background microbiota in less than 24 h.

Conclusions

BARDOT could potentially be used as a screening tool during isolation of the most important STEC serovars on selective agar plates from food samples in less than 24 h.