Landscape of Epstein-Barr virus gene expression and perturbations in cancer.

Epstein-Barr virus (EBV) is the causative agent for multiple neoplastic diseases of epithelial and lymphocytic origin1-3. The heterogeneity of the viral elements expressed and the mechanisms by which these coding and non-coding genes maintain cancer cell properties in vivo remain elusive4,5. Here we conducted a multi-modal transcriptomic analysis of EBV-associated neoplasms and identified that the ubiquitously expressed RPMS1 non-coding RNAs support cancer cell properties by disruption of the interferon response. Our map of EBV expression shows a variable, but pervasive expression of BNLF2 discerned from the overlapping LMP1 RNA in bulk sequencing data. Using long-read single-molecule sequencing, we identified three new viral elements within the RPMS1 gene. Furthermore, single-cell sequencing datasets allowed for the separation of cancer cells and healthy cells from the same tissue biopsy and the characterization of a microenvironment containing interferon gamma excreted by EBV-stimulated T-lymphocytes. In comparison with healthy epithelium, EBV-transformed cancer cells exhibited increased proliferation and inhibited immune response induced by the RPMS1-encoded microRNAs. Our atlas of EBV expression shows that the EBV-transformed cancer cells express high levels of non-coding RNAs originating from RPMS1 and that the oncogenic properties are maintained by RPMS1 microRNAs. Through bioinformatic disentanglement of single cells from cancer tissues we identified a positive feedback loop where EBV-activated immune cells stimulate cancer cells to proliferate, which in turn undergo viral reactivation and trigger an immune response.

Epidemiology and clinical manifestations of different enterovirus and rhinovirus types show that EV-D68 may still have an impact on severity of respiratory infections.

Respiratory infections are often caused by enteroviruses (EVs). The aim of this study was to identify whether certain types of EV were more likely to cause severe illness in 2016, when an increasing spread of upper respiratory infections was observed in Gothenburg, Sweden. The EV strain in 137 of 1341 nasopharyngeal samples reactive for EV by polymerase chain reaction could be typed by sequencing the viral 5'-untranslated region and VP1 regions. Phylogenetic trees were constructed. Patient records were reviewed. Hospital care was needed for 46 of 74 patients with available medical records. The majority of the patients (83) were infected with the rhinovirus (RV). The remaining 54 were infected with EV A, B, C, and D strains of 13 different types, with EV-D68 and CV-A10 being the most common (17 vs. 14). Significantly more patients with EV-D68 presented with dyspnea, both when compared with other EV types (p = 0.003) and compared to all other EV and RV infections (p = 0.04). Phylogenetic analysis of the sequences revealed the spread of both Asian and European CV-A10 strains and 12 different RV C types. This study showed an abundance of different EV types spreading during a year with increased upper respiratory increased infections. EV-D68 infections were associated with more severe disease manifestation. Other EV and RV types were more evenly distributed between hospitalized and nonhospitalized patients. The EV type CV-A10 was also found in infected patients, which warrants further studies and surveillance, as this pathogen could cause more severe disease and outbreaks of hand, foot, and mouth disease.

Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics.

Infection in the central nervous system is a severe condition associated with high morbidity and mortality. Despite ample testing, the majority of encephalitis and meningitis cases remain undiagnosed. Metagenomic sequencing of cerebrospinal fluid has emerged as an unbiased approach to identify rare microbes and novel pathogens. However, several major hurdles remain, including establishment of individual limits of detection, removal of false positives and implementation of universal controls. Twenty-one cerebrospinal fluid samples, in which a known pathogen had been positively identified by available clinical techniques, were subjected to metagenomic DNA sequencing. Fourteen samples contained minute levels of Epstein-Barr virus. The detection threshold for each sample was calculated by using the total leukocyte content in the sample and environmental contaminants found in the bioinformatic classifiers. Virus sequences were detected in all ten samples, in which more than one read was expected according to the calculations. Conversely, no viral reads were detected in seven out of eight samples, in which less than one read was expected according to the calculations. False positive pathogens of computational or environmental origin were readily identified, by using a commonly available cell control. For bacteria, additional filters including a comparison between classifiers removed the remaining false positives and alleviated pathogen identification. Here we show a generalizable method for identification of pathogen species using DNA metagenomic sequencing. The choice of bioinformatic method mainly affected the efficiency of pathogen identification, but not the sensitivity of detection. Identification of pathogens requires multiple filtering steps including read distribution, sequence diversity and complementary verification of pathogen reads.