Biology subjects

Vracar, D.

Publications and source records attributed to Vracar, D..

2 recordsLinked to original sources

bioRxiv2022

Detection of latent Epstein-Barr virus gene expression in single-cell sequencing of peripheral blood mononuclear cells

Epstein-Barr virus (EBV) DNA is regularly found in the blood of patients with EBV associated diseases and occasionally in healthy individuals. However, EBV infected primary B-lymphocytes have not yet been detected using scRNA seq. Here, we screened the viral transcriptome in single cell RNA sequencing datasets from peripheral blood to identify virus infected cells. Whereas EBV RNA was detected in an immunocompromised patient, EBV associated nasopharyngeal carcinoma and multiple sclerosis samples did not display any levels of circulating EBV RNA. We further screened whole-blood samples from a cohort of immunosuppressed patients for viral transcripts using a custom enhanced RT-qPCR panel and detected latency programs dominated by noncoding RNAs (EBERs and RPMS1). To explore the interplay between the EBV and the host-cell transcriptome profile, we used enriched B-lymphocytes from a splenectomy patient with 30% EBER positivity estimated by in situ hybridization and performed 5 single-cell RNA sequencing with paired VDJ profiling. The EBV expression pattern of the patients B-lymphocytes confirmed the RT-qPCR assay with RPMS1 and LMP-1/BNLF2a/b significantly dominating the sequenced EBV polyadenylated RNA. A comparison between the expression profile of EBV positive B-lymphocytes and healthy controls B-lymphocytes revealed the upregulation in genes involved in cell population proliferation when infected with EBV. This is further supported by a measurable polyclonal expansion in the patient, as compared to a control, emphasizing EBVs role in a host-cells tendency for cellular expansion. However, when contrasting to cells that have undergone malignant transformation, the primary EBV infected cells display a rather dissimilar expression profile, even to cells that are supposed to simulate primary EBV infection (I.e. Lymphoblastoid Cell Lines). This implies that during primary infection of EBV, the host-cell enters a state of premalignancy rather than a complete oncogenic transformation at the initial time of infection

microbiology↗

bioRxiv2020

Optimization of cerebrospinal fluid microbial metagenomic sequencing diagnostics

BackgroundInfection 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 remains, including establishment of individual limits of detection, removal of false positives and implementation of universal controls. ResultsTwenty-one cerebrospinal fluid samples, in which a known pathogen had been positively identified by available clinical techniques, were subjected to metagenomic DNA sequencing using massive parallel sequencing. Fourteen samples contained minute levels of Epstein-Barr virus. Calculation of the detection threshold for each sample was made using total leukocyte content in the sample and environmental contaminants found in bioinformatic classifiers. Virus sequences were detected in all ten samples, in which more than one read was expected according to calculations. Conversely, no viral reads were detected in seven out of eight samples, in which less than one read was expected according to 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. ConclusionsHere we show a generalizable method for detection and identification of pathogen species using metagenomic sequencing. The sensitivity for each sample can be calculated using the leukocyte count and environmental contamination. The choice of bioinformatic method mainly affected the efficiency of pathogen identification, but not the sensitivity of detection. Identification of pathogens require multiple filtering steps including read distribution, sequence diversity and complementary verification of pathogen reads.

microbiology↗