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.

Targeted next-generation sequencing of the 16S-23S rRNA region for culture-independent bacterial identification - increased discrimination of closely related species.

The aim of this study was to develop an easy-to-use culture-free diagnostic method based on next generation sequencing (NGS) of PCR amplification products encompassing whole 16S-23S rRNA region to improve the resolution of bacterial species identification. To determine the resolution of the new method 67 isolates were subjected to four identification methods: Sanger sequencing of the 16S rRNA gene; NGS of the 16S-23S rRNA region using MiSeq (Illumina) sequencer; Microflex MS (Bruker) and VITEK MS (bioMérieux). To evaluate the performance of this new method when applied directly on clinical samples, we conducted a proof of principle study with 60 urine samples from patients suspected of urinary tract infections (UTIs), 23 BacT/ALERT (bioMérieux) positive blood culture bottles and 21 clinical orthopedic samples. The resolution power of NGS of the 16S-23S rRNA region was superior to other tested identification methods. Furthermore, the new method correctly identified pathogens established as the cause of UTIs and blood stream infections with conventional culture. NGS of the 16S-23S rRNA region also showed increased detection of bacterial microorganisms in clinical samples from orthopedic patients. Therefore, we conclude that our method has the potential to increase diagnostic yield for detection of bacterial pathogenic species compared to current methods.

[Fatal encephalitis caused by a parasite]. / Fatale encefalitis door een parasiet.

BACKGROUND: Encephalitis caused by a free-living amoeba is relatively rare and usually fatal. This is because the diagnosis is often made late and treatment is difficult. CASE DESCRIPTION: A 41-year-old patient with a previous history including kidney transplant was admitted with clinical symptoms of encephalitis. Brain imaging showed a number of hypodense regions, which were possibly abscesses. Although an infectious cause seemed probable, even the most extensive antimicrobial treatment was ineffective. The cause was not found until 2 months after the patient's death: infection with Balamuthia mandrillaris. A PCR test was used to detect this amoeba. CONCLUSION: This case study describes the first patient in the Netherlands to be diagnosed with granulomatous amoebic encephalitis caused by B. mandrillaris. An amoeba may be the cause of encephalitis with either a fulminant course or with a gradual increase of symptoms, without conventional anti-infective therapy being effective.

Assessing the public health risk of Shiga toxin-producing Escherichia coli by use of 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 categorize STEC infections into risk groups was evaluated. The algorithm consists of prescreening stool specimens with real-time PCR (qPCR) for the presence of stx genes. The 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 sorbitol MacConkey agar (SMAC) and CHROMagar STEC medium. The presence of virulence factors and O serogroups was used for presumptive pathotype (PT) categorization in four PT groups. The potential risk for severe disease was categorized from 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, 5,022 stool samples of patients with gastrointestinal symptoms were included. The 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 samples). Threshold cycle (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 (11%) samples were categorized into PT groups I, II, III, and IV, respectively. Several virulence factors (stx2, stx2a, stx2f, toxB, eae, efa1, cif, espA, tccP, espP, nleA and/or nleB, tir cluster) were associated with PT groups I and II, while others (stx1, eaaA, mch cluster, ireA) were associated with PT group III. Furthermore, the number of virulence factors differed between PT groups (analysis of variance, 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 groups I and II) from less-virulent STEC (PT group III).