Whole-genome sequencing (WGS) of microbial pathogens recovered from patients with infectious disease facilitates high-resolution strain characterization and molecular epidemiology. However, increasing reliance on culture-independent methods to diagnose infectious diseases has resulted in few isolates available for WGS. Here, we report a novel culture-independent approach to genome characterization of Bordetella pertussis, the causative agent of pertussis and a paradigm for insufficient genomic surveillance due to limited culture of clinical isolates. Sequencing libraries constructed directly from residual pertussis-positive diagnostic nasopharyngeal specimens were hybridized with biotinylated RNA "baits" targeting B. pertussis fragments within complex mixtures that contained high concentrations of host and microbial background DNA. Recovery of B. pertussis genome sequence data was evaluated with mock and pooled negative clinical specimens spiked with reducing concentrations of either purified DNA or inactivated cells. Targeted enrichment increased the yield of B. pertussis sequencing reads up to 90% while simultaneously decreasing host reads to less than 10%. Filtered sequencing reads provided sufficient genome coverage to perform characterization via whole-genome single nucleotide polymorphisms and whole-genome multilocus sequencing typing. Moreover, these data were concordant with sequenced isolates recovered from the same specimens such that phylogenetic reconstructions from either consistently clustered the same putatively linked cases. The optimized protocol is suitable for nasopharyngeal specimens with diagnostic IS481 Ct < 35 and >10 ng DNA. Routine implementation of these methods could strengthen surveillance and study of pertussis resurgence by capturing additional cases with genomic characterization.

Respiratory diphtheria is a serious infection caused by toxigenic Corynebacterium diphtheriae, and disease transmission mainly occurs through respiratory droplets. Between 2017 and 2019, a large diphtheria outbreak among forcibly displaced Myanmar nationals densely settled in Bangladesh was investigated. Here we utilized whole-genome sequencing (WGS) to characterize recovered isolates of C. diphtheriae and two co-circulating non-diphtheritic Corynebacterium (NDC) species - C. pseudodiphtheriticum and C. propinquum. C. diphtheriae isolates recovered from all 53 positive cases in this study were identified as toxigenic biovar mitis, exhibiting intermediate resistance to penicillin, and formed four phylogenetic clusters circulating among multiple refugee camps. Additional sequenced isolates collected from two patients showed co-colonization with non-toxigenic C. diphtheriae biovar gravis, one of which exhibited decreased susceptibility to the first-line antibiotics and harboured a novel 23-kb multidrug resistance plasmid. Results of phylogenetic reconstruction and virulence-related gene contents of the recovered NDC isolates indicated they were likely commensal organisms, though 80.4 %(45/56) were not susceptible to erythromycin, and most showed high minimum inhibition concentrations against azithromycin. These results demonstrate the high resolution with which WGS can aid molecular investigation of diphtheria outbreaks, through the quantification of bacterial genetic relatedness, as well as the detection of virulence factors and antibiotic resistance markers among case isolates.

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.

This report describes the complete genome sequences of four isolates of the nondiphtheritic Corynebacterium (NDC) species Corynebacterium pseudodiphtheriticum and Corynebacterium propinquum, recovered during investigation of a large diphtheria outbreak in Bangladesh. These data will assist in better delineating the boundary between these related species and understanding their virulence potential.

We report a toxigenic strain of Corynebacterium diphtheriae isolated from an oozing dermal wound in a pet cat in Texas, USA. We also describe the epidemiologic public health efforts conducted to identify potential sources of infection and mitigate its spread and the molecular and genetic studies performed to identify the bacterium.

Between July 2018 and May 2019, Corynebacterium diphtheriae was isolated from eight patients with non-respiratory infections, seven of whom experienced homelessness and had stayed at shelters in King County, WA, USA. All isolates were microbiologically identified as nontoxigenic C. diphtheriae biovar mitis. Whole-genome sequencing confirmed that all case isolates were genetically related, associated with sequence type 445 and differing by fewer than 24 single-nucleotide polymorphisms (SNPs). Compared to publicly available C. diphtheriae genomic data, these WA isolates formed a discrete cluster with SNP variation consistent with previously reported outbreaks. Virulence-related gene content variation within the highly related WA cluster isolates was also observed. These results indicated that genome characterization can readily support epidemiology of nontoxigenic C. diphtheriae.

Respiratory diphtheria, characterized by a firmly adherent pseudomembrane, is caused by toxin-producing strains of Corynebacterium diphtheriae, with similar illness produced occasionally by toxigenic Corynebacterium ulcerans or, rarely, Corynebacterium pseudotuberculosis While diphtheria laboratory confirmation requires culture methods to determine toxigenicity, real-time PCR (RT-PCR) provides a faster method to detect the toxin gene (tox). Nontoxigenic tox-bearing (NTTB) Corynebacterium isolates have been described, but impact of these isolates on the accuracy of molecular diagnostics is not well characterized. Here, we describe a new triplex RT-PCR assay to detect tox and distinguish C. diphtheriae from the closely related species C. ulcerans and C. pseudotuberculosis Analytical sensitivity and specificity of the assay were assessed in comparison to culture using 690 previously characterized microbial isolates. The new triplex assay characterized Corynebacterium isolates accurately, with 100% analytical sensitivity for all targets. Analytical specificity with isolates was 94.1%, 100%, and 99.5% for tox, Diph_rpoB, and CUP_rpoB targets, respectively. Twenty-nine NTTB Corynebacterium isolates, representing 5.9% of 494 nontoxigenic isolates tested, were detected by RT-PCR. Whole-genome sequencing of NTTB isolates revealed varied mutations putatively underlying their lack of toxin production, as well as eight isolates with no mutation in tox or the promoter region. This new Corynebacterium RT-PCR method provides a rapid tool to screen isolates and identify probable diphtheria cases directly from specimens. However, the sporadic occurrence of NTTB isolates reinforces the viewpoint that diphtheria culture diagnostics continue to provide the most accurate case confirmation.