c Escherichia coli O157:H7 is a notorious foodborne pathogen due to its low infectious dose and the disease symptoms it causes, which include bloody diarrhea and severe abdominal cramps. In some cases, the disease progresses to hemorrhagic colitis (HC) and hemolytic uremic syndrome (HUS), due to the expression of one or more Shiga toxins (Stx). Isoforms of Stx, including Stx2a, are encoded within temperate prophages. In the presence of certain antibiotics, phage induction occurs, which also increases the expression of toxin genes. Additionally, increased Stx2 accumulation has been reported when O157:H7 was cocultured with phage-susceptible nonpathogenic E. coli. This study characterized an E. coli O157:H7 strain, designated PA2, that belongs to the hypervirulent clade 8 cluster. Stx2a levels after ciprofloxacin induction were lower for PA2 than for the prototypical outbreak strains Sakai and EDL933. However, during coculture with the nonpathogenic strain E. coli C600, PA2 produced Stx2a levels that were 2-to 12-fold higher than those observed during coculture with EDL933 and Sakai, respectively. Germfree mice cocolonized by PA2 and C600 showed greater kidney damage, increased Stx2a accumulation in feces, and more visible signs of disease than mice given PA2 or C600 alone. These data suggest one mechanism by which microorganisms associated with the colonic microbiota could enhance the virulence of E. coli O157:H7, particularly a subset of clade 8 strains.E scherichia coli can be either nonpathogenic, as exemplified by the bacteria associated with the normal human gut microflora, or pathogenic, classified as intestinal or extraintestinal (1). The intestinal pathogenic E. coli group is further subdivided into six pathotypes, among which enterohemorrhagic E. coli (EHEC) is a causative agent of bloody diarrhea, hemorrhagic colitis (HC), and hemolytic uremic syndrome (HUS). Shiga toxins (Stx) are one of the defining virulence factors of E. coli O157:H7 (2, 3). The stx genes are carried within lambdoid prophages downstream of the promoter for late gene expression. The toxin is released after phage-induced lysis of the bacterial cell (4). Shiga toxins are approximately 70-kDa proteins, consisting of an enzymatically active A subunit and a pentameric B subunit. The A subunit is an N-glycosidase and inactivates the 60S ribosomal subunit. This leads to inhibition of protein synthesis in the eukaryotic cell (5). There are two antigenically distinct Stx proteins, designated Stx1 and Stx2 (6). Despite having an amino acid identity of 57%, mouse and primate studies have shown that Stx2 causes more severe disease outcomes than Stx1 (7, 8). Several allelic variants of Stx2 have been described, based on the amino acid differences in the A and B subunits. The most common forms of Stx2 expressed by E. coli O157:H7 human isolates are Stx2a and Stx2c (9, 10).Since its first report in 1983 (11), the serotype O157:H7 rapidly became a notorious foodborne pathogen due to its severe disease outcomes and an infectious dose of less than 100...

BackgroundShiga toxin-producing Escherichia coli O157:H7 is a foodborne pathogen that causes severe human diseases including hemolytic uremic syndrome (HUS). The virulence factor that mediates HUS, Shiga toxin (Stx), is encoded within the genome of a lambdoid prophage. Although draft sequences are publicly available for a large number of E. coli O157:H7 strains, the high sequence similarity of stx-converting bacteriophages with other lambdoid prophages poses challenges to accurately assess the organization and plasticity among stx-converting phages due to assembly difficulties.MethodsTo further explore genome plasticity of stx-converting prophages, we enriched phage DNA from 45 ciprofloxacin-induced cultures for subsequent 454 pyrosequencing to facilitate assembly of the complete phage genomes. In total, 22 stx2a-converting phage genomes were closed.ResultsComparison of the genomes distinguished nine distinct phage sequence types (PSTs) delineated by variation in obtained sequences, such as single nucleotide polymorphisms (SNPs) and insertion sequence element prevalence and location. These nine PSTs formed three distinct clusters, designated as PST1, PST2 and PST3. The PST2 cluster, identified in two clade 8 strains, was related to stx2a-converting phages previously identified in non-O157 Shiga-toxin producing E. coli (STEC) strains associated with a high incidence of HUS. The PST1 cluster contained phages related to those from E. coli O157:H7 strain Sakai (lineage I, clade 1), and PST3 contained a single phage that was distinct from the rest but most related to the phage from E. coli O157:H7 strain EC4115 (lineage I/II, clade 8). Five strains carried identical stx2a-converting phages (PST1-1) integrated at the same chromosomal locus, but these strains produced different levels of Stx2.ConclusionThe stx2a-converting phages of E. coli O157:H7 can be categorized into at least three phage types. Diversification within a phage type is mainly driven by IS629 and by a small number of SNPs. Polymorphisms between phage genomes may help explain differences in Stx2a production between strains, however our data indicates that genes encoded external to the phage affect toxin production as well.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1934-1) contains supplementary material, which is available to authorized users.

How the gut microflora influences the progression of bacterial infections is only beginning to be understood. Antibiotics are counterindicated for E. coli O157:H7 infections, limiting treatment options. An increased understanding of how the gut microflora directs O157:H7 virulence gene expression may lead to additional treatment options. This work identified E. coli strains that enhance the production of Shiga toxin by O157:H7 through the secretion of a proposed microcin. Microcins are natural antimicrobial peptides that target specific species, can act as alternatives to antibiotics, and mediate microbial competition. This work demonstrates another mechanism by which non-O157 E. coli strains may increase Shiga toxin production and adds to our understanding of microcins, a group of antimicrobials less well understood than colicins.

Intestinal colonization by the foodborne pathogen Escherichia coli O157:H7 leads to serious disease symptoms, including hemolytic uremic syndrome (HUS) and hemorrhagic colitis (HC). Synthesis of one or more Shiga toxins (Stx) is essential for HUS and HC development. The genes encoding Stx, including Stx2a, are found within a lambdoid prophage integrated in the E. coli O157:H7 chromosome. Enhanced Stx2a expression was reported when specific non-pathogenic E. coli strains were co-cultured with E. coli O157:H7, and it was hypothesized that this phenotype required the non-pathogenic E. coli to be sensitive to stx-converting phage infection. We tested this hypothesis by generating phage resistant non-pathogenic E. coli strains where bamA (an essential gene and Stx phage receptor) was replaced with an ortholog from other species. Such heterologous gene replacement abolished the ability of the laboratory strain E. coli C600 to enhance toxin production when co-cultured with E. coli O157:H7 strain PA2, which belongs to the hypervirulent clade 8. The extracellular loops of BamA (loop 4, 6, 7) were further shown to be important for infection by stx2a-converting phages. However, similar gene replacement in another commensal E. coli, designated 1.1954, revealed a bamA-independent mechanism for toxin amplification. Toxin enhancement by 1.1954 was not the result of phage infection through an alternative receptor (LamB or FadL), lysogen formation by stx2a-converting phages, or the production of a secreted molecule. Collectively, these data suggest that non-pathogenic E. coli can enhance toxin production through at least two mechanisms.

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset. Methods We identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study. Results The benchmark datasets focus on the two most widely used sequencing platforms: long read sequencing data from the Oxford Nanopore Technologies platform and short read sequencing data from the Illumina platform. There are six datasets: three were derived from recent publications; two were derived from data mining public databases to answer common questions not covered by published datasets; one unique dataset representing common sequence failures was obtained by rigorously scrutinizing data that did not pass quality checks. The dataset summary table, data mining script and quality control (QC) values for all sequence data are publicly available on GitHub: https://github.com/CDCgov/datasets-sars-cov-2. Discussion The datasets presented here were generated to help public health laboratories build sequencing and bioinformatics capacity, benchmark different workflows and pipelines, and calibrate QC thresholds to ensure sequencing quality. Together, improvements in these areas support accurate and timely outbreak investigation and surveillance, providing actionable data for pandemic management. Furthermore, these publicly available and standardized benchmark data will facilitate the development and adjudication of new pipelines.