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Meningitis, encephalitis, and myelitis are various forms of acute central nervous system (CNS) inflammation, which can coexist and lead to serious sequelae. Known aetiologies include infections and immune-mediated processes. Despite advances in clinical microbiology over the past decades, the cause of acute CNS inflammation remains unknown in approximately 50% of cases. High-throughput sequencing was performed to search for viral sequences in cerebrospinal fluid (CSF) samples collected from 26 patients considered to have acute CNS inflammation of unknown origin, and 10 patients with defined causes of CNS diseases. In order to better grasp the clinical significance of viral sequence data obtained in CSF, 30 patients without CNS disease who had a lumbar puncture performed during elective spinal anaesthesia were also analysed. One case of human astrovirus (HAstV)-MLB2-related meningitis and disseminated infection was identified. No other viral sequences that can easily be linked to CNS inflammation were detected. Viral sequences obtained in all patient groups are discussed. While some of them reflect harmless viral infections, others result from reagent or sample contamination, as well as index hopping. Altogether, this study highlights the potential of high-throughput sequencing in identifying previously unknown viral neuropathogens, as well as the interpretation issues related to its application in clinical microbiology.

Shotgun metagenomics using next generation sequencing (NGS) is a promising technique to analyze both DNA and RNA microbial material from patient samples. Mostly used in a research setting, it is now increasingly being used in the clinical realm as well, notably to support diagnosis of viral infections, thereby calling for quality control and the implementation of ring trials (RT) to benchmark pipelines and ensure comparable results. The Swiss NGS clinical virology community therefore decided to conduct a RT in 2018, in order to benchmark current metagenomic workflows used at Swiss clinical virology laboratories, and thereby contribute to the definition of common best practices. The RT consisted of two parts (increments), in order to disentangle the variability arising from the experimental compared to the bioinformatics parts of the laboratory pipeline. In addition, the RT was also designed to assess the impact of databases compared to bioinformatics algorithms on the final results, by asking participants to perform the bioinformatics analysis with a common database, in addition to using their own in-house database. Five laboratories participated in the RT (seven pipelines were tested). We observed that the algorithms had a stronger impact on the overall performance than the choice of the reference database. Our results also suggest that differences in sample preparation can lead to significant differences in the performance, and that laboratories should aim for at least 5−10 Mio reads per sample and use depth of coverage in addition to other interpretation metrics such as the percent of coverage. Performance was generally lower when increasing the number of viruses per sample. The lessons learned from this pilot study will be useful for the development of larger-scale RTs to serve as regular quality control tests for laboratories performing NGS analyses of viruses in a clinical setting.