Biology subjects

Tang, K.-W.

Publications and source records attributed to Tang, K.-W..

3 recordsLinked to original sources

bioRxiv2021

Epstein-Barr virus long non-coding RNA RPMS1 full-length spliceome in transformed epithelial tissue

Epstein-Barr virus is associated with two types of epithelial neoplasms, nasopharyngeal carcinoma and gastric adenocarcinoma. The viral long non-coding RNA RPMS1 is the most abundantly expressed poly-adenylated viral RNA in these malignant tissues. The RPMS1 gene is known to contain two cassette exons, exon Ia and Ib, and several alternative splicing variants have been described in low-throughput studies. To characterize the entire RPMS1 spliceome we combined long-read sequencing data from the nasopharyngeal cell line C666-1 and a primary gastric adenocarcinoma, with complementary short-read sequencing datasets. We developed FLAME, a Python-based bioinformatics package that can generate complete high resolution characterization of RNA splicing at full-length. Using FLAME, we identified 32 novel exons in the RPMS1 gene, primarily within the large constitutive exons III, V and VII. Two of the novel exons contained retention of the intron between exon III and exon IV, and a novel cassette exon was identified between VI and exon VII. All previously described transcript variants of RPMS1 containing putative ORFs were identified at various levels. Similarly, native transcripts with the potential to form previously reported circular RNA elements were detected. Our work illuminates the multifaceted nature of viral transcriptional repertoires. FLAME provides a comprehensive overview of the relative abundance of alternative splice variants and allows for a wealth of previously unknown splicing events to be unveiled.

bioinformatics↗

bioRxiv2020

A three-dimensional Air-Liquid Interface Culture Model for the Study of Epstein-Barr virus Infection in the Nasopharynx

Epstein-Barr virus (EBV) infection is ubiquitous in humans and is associated with the cancer, nasopharyngeal carcinoma. EBV replicates in the differentiated layers of stratified keratinocytes but whether the other cell types of the airway epithelium are susceptible to EBV is unknown. Here, we demonstrate with primary nasopharyngeal cells grown at the air-liquid interface that the pseudostratified epithelium can be susceptible to EBV infection and we report that susceptible cell types with distinct EBV transcription profiles can be identified by single-cell RNA-sequencing. Although EBV infection in the nasopharynx has evaded detection in asymptomatic carriers, these findings demonstrate that EBV latent and lytic infection can occur in the cells of the nasopharyngeal epithelium.

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↗