Ruud Jansen

High-Throughput Multilocus Sequence Typing: Bringing Molecular Typing to the Next Level

Multilocus sequence typing (MLST) is a widely used system for typing microorganisms by sequence analysis of housekeeping genes. The main advantage of MLST in comparison to other typing techniques is the unambiguity and transferability of sequence data. However, a main disadvantage is the high cost of DNA sequencing. Here we introduce a high-throughput MLST (HiMLST) method that employs next-generation sequencing (NGS) technology (Roche 454), to generate large quantities of high-quality MLST data at low costs. The HiMLST protocol consists of two steps. In the first step MLST target genes are amplified by PCR in multi-well plates. During this PCR the amplicons of each bacterial isolate are provided with a unique DNA barcode, the multiplex identifier (MID). In the second step all amplicons are pooled and sequenced in a single NGS-run. The MLST profile of each individual isolate can be retrieved easily using its unique MID. With HiMLST we have profiled 575 isolates of Legionella pneumophila, Staphylococcus aureus, Pseudomonas aeruginosa and Streptococcus pneumoniae in mixed species HiMLST experiments. In conclusion, the introduction of HiMLST paves the way for a broad employment of the MLST as a high-quality and cost-effective method for typing microbial species.

Prevalence, characterisation and clinical profiles of Shiga toxin-producing Escherichia coli in The Netherlands.

Detection of Shiga toxin-producing Escherichia coli (STEC) in The Netherlands is traditionally limited to serogroup O157. To assess the relative importance of STEC, including non-O157 serogroups, stool samples submitted nationwide for investigation of enteric pathogens or diarrhoea were screened with real-time PCR for the presence of the Shiga toxin genes. Patients were selected if their stool contained blood upon macroscopic examination, if they had a history of bloody diarrhoea, were diagnosed with haemolytic uraemic syndrome, or were aged <6 years (irrespective of the bloody aspect of the stool). PCR-positive stools were forwarded to a central laboratory for STEC isolation and typing. In total, 4069 stools were examined, with 68 (1.7%) positive PCR results. The highest prevalence was for stools containing macroscopic blood (3.5%), followed by stools from patients with a history of bloody diarrhoea (2.4%). Among young children, the prevalence (1.0%) was not significantly higher than among random, non-bloody, stool samples from diarrhoeal patients (1.4%). STEC strains were isolated from 25 (38%) PCR-positive stools. Eleven O-serogroups were detected, including five STEC O157 strains. As serogroup O157 represented only 20% of the STEC isolates, laboratories should be encouraged to use techniques enabling them to detect non-O157 serogroups, in parallel with culture, for isolation and subsequent characterisation of STEC strains for public health surveillance and detection of outbreaks.

Genome Analysis of Legionella pneumophila Strains Using a Mixed-Genome Microarray

Background Legionella, the causative agent for Legionnaires’ disease, is ubiquitous in both natural and man-made aquatic environments. The distribution of Legionella genotypes within clinical strains is significantly different from that found in environmental strains. Developing novel genotypic methods that offer the ability to distinguish clinical from environmental strains could help to focus on more relevant (virulent) Legionella species in control efforts. Mixed-genome microarray data can be used to perform a comparative-genome analysis of strain collections, and advanced statistical approaches, such as the Random Forest algorithm are available to process these data. Methods Microarray analysis was performed on a collection of 222 Legionella pneumophila strains, which included patient-derived strains from notified cases in the Netherlands in the period 2002–2006 and the environmental strains that were collected during the source investigation for those patients within the Dutch National Legionella Outbreak Detection Programme. The Random Forest algorithm combined with a logistic regression model was used to select predictive markers and to construct a predictive model that could discriminate between strains from different origin: clinical or environmental. Results Four genetic markers were selected that correctly predicted 96% of the clinical strains and 66% of the environmental strains collected within the Dutch National Legionella Outbreak Detection Programme. Conclusions The Random Forest algorithm is well suited for the development of prediction models that use mixed-genome microarray data to discriminate between Legionella strains from different origin. The identification of these predictive genetic markers could offer the possibility to identify virulence factors within the Legionella genome, which in the future may be implemented in the daily practice of controlling Legionella in the public health environment.

Development and evaluation of a culture-free microbiota profiling platform (MYcrobiota) for clinical diagnostics

Microbiota profiling has the potential to greatly impact on routine clinical diagnostics by detecting DNA derived from live, fastidious, and dead bacterial cells present within clinical samples. Such results could potentially be used to benefit patients by influencing antibiotic prescribing practices or to generate new classical-based diagnostic methods, e.g., culture or PCR. However, technical flaws in 16S rRNA gene next-generation sequencing (NGS) protocols, together with the requirement for access to bioinformatics, currently hinder the introduction of microbiota analysis into clinical diagnostics. Here, we report on the development and evaluation of an “end-to-end” microbiota profiling platform (MYcrobiota), which combines our previously validated micelle PCR/NGS (micPCR/NGS) methodology with an easy-to-use, dedicated bioinformatics pipeline. The newly designed bioinformatics pipeline processes micPCR/NGS data automatically and summarizes the results in interactive, but simple web reports. In order to explore the utility of MYcrobiota in clinical diagnostics, 47 clinical samples (40 “damaged skin” samples and 7 synovial fluids) were investigated using routine bacterial culture as comparator. MYcrobiota confirmed the presence of bacterial DNA in 37/37 culture-positive samples and detected bacterial taxa in 2/10 culture-negative samples. Moreover, 36/38 potentially relevant aerobic bacterial taxa and 3/3 mixtures of anaerobic bacteria were identified using culture and MYcrobiota, with the sensitivity and specificity being 95%. Interestingly, the majority of the 448 bacterial taxa identified using MYcrobiota were not identified using culture, which could potentially have an impact on clinical decision-making. Taken together, the development of MYcrobiota is a promising step towards the introduction of microbiota analysis into clinical diagnostic laboratories.

Monitoring of microbial dynamics in a drinking water distribution system using the culture-free, user-friendly, MYcrobiota platform

Drinking water utilities currently rely on a range of microbiological detection techniques to evaluate the quality of their drinking water (DW). However, microbiota profiling using culture-free 16S rRNA gene next-generation sequencing (NGS) provides an opportunity for improved monitoring of the microbial ecology and quality of DW. Here, we evaluated the utility of a previously validated microbiota profiling platform (MYcrobiota) to investigate the microbial dynamics of a full-scale, non-chlorinated DW distribution system (DWDS). In contrast to conventional methods, we observed spatial and temporal bacterial genus changes (expressed as operational taxonomic units - OTUs) within the DWDS. Further, a small subset of bacterial OTUs dominated with abundances that shifted across the length of the DWDS, and were particularly affected by a post-disinfection step. We also found seasonal variation in OTUs within the DWDS and that many OTUs could not be identified, even though MYcrobiota is specifically designed to reduce potential PCR sequencing artefacts. This suggests that our current knowledge about the microbial ecology of DW communities is limited. Our findings demonstrate that the user-friendly MYcrobiota platform facilitates culture-free, standardized microbial dynamics monitoring and has the capacity to facilitate the introduction of microbiota profiling into the management of drinking water quality.

Characterization of the nasopharyngeal and middle ear microbiota in gastroesophageal reflux-prone versus gastroesophageal reflux non-prone children

Otitis media (OM) is one of the most common pediatric infections worldwide, but the complex microbiology associated with OM is poorly understood. Previous studies have shown an association between OM and gastroesophageal reflux (GER) in children. Therefore, in order to bridge the gap in our current understanding of the interaction between GER and OM, we investigated the nasopharyngeal and middle ear microbiota of children suffering from GER-associated OM and OM only, using culture-independent 16S rRNA gene sequencing. Middle ear fluid, nasopharyngeal swabs, and clinical data were collected as part of a prospective pilot study conducted at the Department of Otorhinolaryngology of the Erasmus MC-Sophia Children’s Hospital, Rotterdam, the Netherlands. A total of 30 children up to 12 years of age who suffered from recurrent acute otitis media (AOM) (5), chronic otitis media with effusion (OME) (23), or both (2), and who were listed for tympanostomy tube placement, were included in the study. Nine children were included in the GER-associated OM cohort and 21 in the OM-only cohort. We found no obvious effect of GER on the nasopharyngeal and middle ear microbiota between the two groups of children. However, our results highlight the need to assess the true role of Alloiococcus spp. and Turicella spp. in children presenting with a high prevalence of recurrent AOM and chronic OME.

Correction: Genome Analysis of Legionella pneumophila Strains Using a Mixed-Genome Microarray

In Table 3, the sequence locations of marker 7B8 and marker 15D6 in the Lorraine strain have been switched. The correct sequence location for marker 7B8 in the Lorraine strain is: 2631852–2632252. The correct sequence location for marker 15D6 in the Lorraine strain is: 713281–713837.