The Swiss Pathogen Surveillance Platform - towards a nation-wide One Health data exchange platform for bacterial, viral and fungal genomics and associated metadata.

The Swiss Pathogen Surveillance Platform (SPSP) is a shared secure surveillance platform between human and veterinary medicine, to also include environmental and foodborne isolates. It enables rapid and detailed transmission monitoring and outbreak surveillance of pathogens using whole genome sequencing data and associated metadata. It features controlled data access, complex dynamic queries, dedicated dashboards and automated data sharing with international repositories, providing actionable results for public health and the vision to improve societal well-being and health.

Text-Mining Services of the Swiss Variant Interpretation Platform for Oncology.

The Swiss Variant Interpretation Platform for Oncology is a centralized, joint and curated database for clinical somatic variants piloted by a board of Swiss healthcare institutions and operated by the SIB Swiss Institute of Bioinformatics. To support this effort, SIB Text Mining designed a set of text analytics services. This report focuses on three of those services. First, the automatic annotations of the literature with a set of terminologies have been performed, resulting in a large annotated version of MEDLINE and PMC. Second, a generator of variant synonyms for single nucleotide variants has been developed using publicly available data resources, as well as patterns of non-standard formats, often found in the literature. Third, a literature ranking service enables to retrieve a ranked set of MEDLINE abstracts given a variant and optionally a diagnosis. The annotation of MEDLINE and PMC resulted in a total of respectively 785,181,199 and 1,156,060,212 annotations, which means an average of 26 and 425 annotations per abstract and full-text article. The generator of variant synonyms enables to retrieve up to 42 synonyms for a variant. The literature ranking service reaches a precision (P10) of 63%, which means that almost two-thirds of the top-10 returned abstracts are judged relevant. Further services will be implemented to complete this set of services, such as a service to retrieve relevant clinical trials for a patient and a literature ranking service for full-text articles.

NGS-Based S. aureus Typing and Outbreak Analysis in Clinical Microbiology Laboratories: Lessons Learned From a Swiss-Wide Proficiency Test.

Whole genome sequencing (WGS) enables high resolution typing of bacteria up to the single nucleotide polymorphism (SNP) level. WGS is used in clinical microbiology laboratories for infection control, molecular surveillance and outbreak analyses. Given the large palette of WGS reagents and bioinformatics tools, the Swiss clinical bacteriology community decided to conduct a ring trial (RT) to foster harmonization of NGS-based bacterial typing. The RT aimed at assessing methicillin-susceptible Staphylococcus aureus strain relatedness from WGS and epidemiological data. The RT was designed to disentangle the variability arising from differences in sample preparation, SNP calling and phylogenetic methods. Nine laboratories participated. The resulting phylogenetic tree and cluster identification were highly reproducible across the laboratories. Cluster interpretation was, however, more laboratory dependent, suggesting that an increased sharing of expertise across laboratories would contribute to further harmonization of practices. More detailed bioinformatic analyses unveiled that while similar clusters were found across laboratories, these were actually based on different sets of SNPs, differentially retained after sample preparation and SNP calling procedures. Despite this, the observed number of SNP differences between pairs of strains, an important criterion to determine strain relatedness given epidemiological information, was similar across pipelines for closely related strains when restricting SNP calls to a common core genome defined by S. aureus cgMLST schema. The lessons learned from this pilot study will serve the implementation of larger-scale RT, as a mean to have regular external quality assessments for laboratories performing WGS analyses in a clinical setting.

Viral Metagenomics in the Clinical Realm: Lessons Learned from a Swiss-Wide Ring Trial.

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.

A comparative integrated transcript analysis and functional characterization of differential mechanisms for induction of liver hypertrophy in the rat.

The main goal of the present work was to better understand the molecular mechanisms underlying liver hypertrophy (LH), a recurrent finding observed following acute or repeated drug administration to animals, using transcriptomic technologies together with the results from conventional toxicology methods. Administration of 5 terminated proprietary drug candidates from participating companies involved in the EU Innomed PredTox Project or the reference hepatotoxicant troglitazone to rats for up to a 14-day duration induced LH as the main liver phenotypic toxicity outcome. The integrated analysis of transcriptomic liver expression data across studies turned out to be the most informative approach for the generation of mechanistic models of LH. In response to a xenobiotic stimulus, a marked increase in the expression of xenobiotic metabolizing enzymes (XME) was observed in a subset of 4 studies. Accumulation of these newly-synthesized proteins within the smooth endoplasmic reticulum (SER) would suggest proliferation of this organelle, which most likely is the main molecular process underlying the LH observed in XME studies. In another subset of 2 studies (including troglitazone), a marked up-regulation of genes involved in peroxisomal fatty acid ß-oxidation was noted, associated with induction of genes involved in peroxisome proliferation. Therefore, an increase in peroxisome abundance would be the main mechanism underlying LH noted in this second study subset. Together, the use of transcript profiling provides a means to generate putative mechanistic models underlying the pathogenesis of liver hypertrophy, to distinguish between subtle variations in subcellular organelle proliferation and creates opportunities for improved mechanism-based risk assessment.

Characterization of immune cell subsets during the active phase of multiple sclerosis reveals disease and c-Jun N-terminal kinase pathway biomarkers.

BACKGROUND: Autoimmune activation and deregulated apoptosis of T lymphocytes are involved in multiple sclerosis (MS). c-Jun N-terminal kinase (JNK) plays a role in T-cell survival and apoptosis. OBJECTIVES: The aim of this work was to investigate the role of the JNK-dependent apoptosis pathway in relapsing-remitting MS (RRMS). METHODS: The immunomodulatory effect of AS602801, a JNK inhibitor, was firstly evaluated on activated peripheral blood mononuclear cells (PBMCs) from healthy volunteers (HVs) and secondly in unstimulated purified CD4+, CD8+ and CD11b+ cells from RRMS patients and HVs. Moreover JNK/inflammation/apoptosis related genes were investigated in RRMS and HV samples. RESULTS: In activated PBMCs from HVs, we showed that AS602801 blocked T-lymphocyte proliferation and induced apoptosis. In RRMS CD4+ and CD8+ cells, AS602801 induced apoptosis genes and expression of surface markers, while in RRMS CD11b+ cells it induced expression of innate immunity receptors and co-stimulatory molecules. Untreated cells from RRMS active-phase patients significantly released interleukin-23 (IL-23) and interferon-gamma (IFN-γ) and expressed less apoptosis markers compared to the cells of HVs. Moreover, gene expression was significantly different in cells from RRMS active-phase patients vs. HVs. By comparing RRMS PBMCs in the active and stable phases, a specific genomic signature for RRMS was indentified. Additionally, CASP8AP2, CD36, ITGAL, NUMB, OLR1, PIAS-1, RNASEL, RTN4RL2 and THBS1 were identified for the first time as being associated to the active phase of RRMS. CONCLUSIONS: The analysis of the JNK-dependent apoptosis pathway can provide biomarkers for activated lymphocytes in the active phase of RRMS and a gene expression signature for disease status. The reported results might be useful to stratify patients, thereby supporting the development of novel therapies.