Tracking Down the Cause of Necrotizing Fasciitis in a Patient with Negative Cultures

In a patient suspected of lower abdominal necrotizing fasciitis cultures remained negative, possibly because broad spectrum antibiotics had already been given before tissue for culture was obtained. 16S-23S rDNANext Generation Sequencing showed that 99.7% of bacterial DNA was of Streptococcus pyogenes. 16S-23S rDNA can replace culture for identification of bacteria, also in polymicrobial infection

Development and validation of a reference data set for assigning Staphylococcus species based on next-generation sequencing of the 16S-23S rRNA region

Many members of the Staphylococcus genus are clinically relevant opportunistic pathogens that warrant accurate and rapid identification for targeted therapy. The aim of this study was to develop a careful assignment scheme for staphylococcal species based on next-generation sequencing (NGS) of the 16S-23S rRNA region. All reference staphylococcal strains were identified at the species level using Sanger sequencing of the 16S rRNA, sodA, tuf, and rpoB genes and NGS of the 16S-23S rRNA region. To broaden the database, an additional 100 staphylococcal strains, including 29 species, were identified by routine diagnostic methods, 16S rRNA Sanger sequencing and NGS of the 16S-23S rRNA region. The results enabled development of reference sequences encompassing the 16S-23S rRNA region for 50 species (including one newly proposed species) and 6 subspecies of the Staphylococcus genus. This study showed sodA and rpoB targets were the most discriminative but NGS of the 16S-23S rRNA region was more discriminative than tuf gene sequencing and much more discriminative than 16S rRNA gene sequencing. Almost all Staphylococcus species could be distinguished when the max score was 99.0% or higher and the sequence similarity between the best and second best species was equal to or >0.2% (min. 9 nucleotides). This study allowed development of reference sequences for 21 staphylococcal species and enrichment for 29 species for which sequences were publicly available. We confirmed the usefulness of NGS of the 16S-23S rRNA region by identifying the whole species content in 45 clinical samples and comparing the results to those obtained using routine diagnostic methods. Based on the developed reference database, all staphylococcal species can be reliably detected based on the 16S-23S rRNA sequences in samples composed of both single species and more complex polymicrobial communities. This study will be useful for introduction of a novel diagnostic tool, which undoubtedly is an improvement for reliable species identification in polymicrobial samples. The introduction of this new method is hindered by a lack of reference sequences for the 16S-23S rRNA region for many bacterial species. The results will allow identification of all Staphylococcus species, which are clinically relevant pathogens

A Comparison of Three Different Bioinformatics Analyses of the 16S–23S rRNA Encoding Region for Bacterial Identification

Rapid and reliable identification of bacterial pathogens directly from patient samples is required for optimizing antimicrobial therapy. Although Sanger sequencing of the 16S ribosomal RNA (rRNA) gene is used as a molecular method, species identification and discrimination is not always achievable for bacteria as their 16S rRNA genes have sometimes high sequence homology. Recently, next generation sequencing (NGS) of the 16S–23S rRNA encoding region has been proposed for reliable identification of pathogens directly from patient samples. However, data analysis is laborious and time-consuming and a database for the complete 16S–23S rRNA encoding region is not available. Therefore, a better, faster, and stronger approach is needed for NGS data analysis of the 16S–23S rRNA encoding region. We compared speed and diagnostic accuracy of different data analysis approaches: de novo assembly followed by Basic Local Alignment Search Tool (BLAST), operational taxonomic unit (OTU) clustering, or mapping using an in-house developed 16S–23S rRNA encoding region database for the identification of bacterial species. De novo assembly followed by BLAST using the in-house database was superior to the other methods, resulting in the shortest turnaround time (2 h and 5 min), approximately 2 h less than OTU clustering and 4.5 h less than mapping, and a sensitivity of 80%. Mapping was the slowest and most laborious data analysis approach with a sensitivity of 60%, whereas OTU clustering was the least laborious approach with 70% sensitivity. Although the in-house database requires more sequence entries to improve the sensitivity, the combination of de novo assembly and BLAST currently appears to be the optimal approach for data analysis

Assessing the Public Health Risk of Shiga-Toxin Producing Escherichia coli using a Rapid Diagnostic Screening Algorithm

Shiga toxin-producing Escherichia coli (STEC) is an enteropathogen of public health concern because of its ability to cause serious illness and outbreaks. In this prospective study a diagnostic screening algorithm to categorise STEC infections into risk groups was evaluated. The algorithm consists of pre-screening stool specimens with real-time PCR (qPCR) for the presence of stx genes. qPCR positive stool samples were cultured in enrichment broth and again screened for stx genes and additional virulence factors (escV, aggR, aat, bfpA) and O-serogroups (O26, O103, O104, O111, O121, O145, O157). Also, PCR guided culture was performed with SMAC and CHROMagar STEC medium. The presence of virulence factors and O-serogroups was used for presumptive pathotype (PT) categorisation in four PT groups. The potential risk for severe disease was categorised as high risk for PT group I to low risk for PT group III, whereas PT group IV consists of unconfirmed stx qPCR positive samples. In total, 5022 stool samples of patients with gastrointestinal symptoms were included. qPCR detected stx genes in 1.8% of samples. Extensive screening for virulence factors and O-serogroups was performed on 73 samples. After enrichment, the presence of stx genes was confirmed in 65 samples (89%). By culture on selective media STEC was isolated in 36% (26/73). Ct values for stx genes were significantly lower after enrichment compared to direct qPCR (p<0.001). In total, 11 (15%), 19 (26%), 35 (48%), and 8 samples (11%) were categorised in PT group I, group II, group III, and group IV, respectively. Several virulence factors were associated with PT group I and II (stx2, stx2a, stx2f, toxB, eae, efa1, cif, espA, tccP, espP, nleA/B, tir cluster), while others were associated with PT group III (stx1, eaaA, mch cluster, ireA). Furthermore, the number of virulence factors differed between PT groups (ANOVA, p<0.0001). In conclusion, a diagnostic algorithm enables fast discrimination of STEC infections associated with a high to moderate risk for severe disease (PT group I and II) from less virulent STEC (PT group III)