Biology subjects

Bull, R.

Publications and source records attributed to Bull, R..

4 recordsLinked to original sources

bioRxiv2025

Between- and within-host mutation of dengue virus

RNA viruses exhibit high mutation rates due to error-prone polymerases, leading to a diverse pool of viral haplotypes (also referred to as quasi-species) within infected hosts. While haplotypes have been well studied in chronic infections like HIV and HCV, diversity remains under-explored in acute infections like dengue (DENV), which are constrained by a short viremic phase. This study aimed to characterise the mutation hotspots in DENV genomes at both consensus and haplotype levels. Near full length DENV genomes were sequenced using Oxford Nanopore Technology (ONT) from the plasma of Sri Lankan patients with dengue fever recruited between 2017 -2020. Consensus sequences were mapped with Minimap-2, and haplotypes were reconstructed with Nano-Q, a tool designed for estimation of RNA virus haplotypes and their relative abundance. The genomic variability of DENV genomes was assessed by calculating Shannon Entropy (SE). Codons undergoing diversifying selection were identified with three phylogenetics-based algorithms (FEL, MEME, FUBAR) implemented within the Datamonkey suite. From 150 samples tested, both consensus and haplotype sequences were characterised in 90 samples (DENV1: 8, DENV2: 51, DENV3: 31). The genomic variability of consensus sequences measured by SE was higher in DENV2 compared to DENV3, and the reverse was true for haplotypes. At the consensus level, the NS2A gene had the greatest number of mutable sites when adjusted for gene length across all serotypes, while at the haplotype level the NS1 gene had the same. Overall, the haplotypes sequences revealed more sites with high mutability and codons under diversifying selection than those visible at consensus level. This provides proof-in-principle that in acute RNA viruses also have high mutability in haplotypes, which may be inapparent with a consensus-level analysis.

bioinformatics↗

bioRxiv2023

HCV E1 influences the fitness landscape of E2 and may enhance escape from E2-specific antibodies

The Hepatitis C virus (HCV) envelope glycoprotein E1 forms a noncovalent heterodimer with E2, the main target of neutralizing antibodies. How E1-E2 interactions influence viral fitness and contribute to resistance to E2-specific antibodies remains largely unknown. We investigate this problem using a combination of fitness landscape and evolutionary modelling. Our analysis indicates that E1 and E2 proteins collectively mediate viral fitness, and suggests that fitness-compensating E1 mutations may accelerate escape from E2-targeting antibodies. Our analysis also identifies a set of E2-specific human monoclonal antibodies that are predicted to be especially resilient to escape via genetic variation in both E1 and E2, providing directions for robust HCV vaccine development.

bioinformatics↗

bioRxiv2021

Single cell multi-omics reveals early elevated function and multiple fates within human progenitor exhausted CD8+ T cells

T-cell exhaustion is a hallmark of hepatitis C virus (HCV) infection and limits protective immunity in chronic viral infections and cancer. Limited knowledge exists of the initial viral and immune dynamics that characterise exhaustion in humans. We studied longitudinal blood samples from a unique cohort of subjects with primary infection using single cell multi-omics to identify the functions and phenotypes of HCV-specific CD8+ T cells. Early elevated IFN-{gamma} response against the transmitted virus was associated with the rate of immune escape, larger clonal expansion, and early onset of exhaustion. Irrespective of disease outcome we discovered progenitors of early-exhaustion with intermediate expression of PD-1. Intra clonal analysis revealed distinct trajectories with multiple fates suggesting evolutionary plasticity of precursor cells. These findings challenge current paradigm on the contribution of CD8+ T cells to HCV disease outcome and provide data for future studies on T-cell differentiation in human infections. One sentence summaryProgenitors of T-cell exhaustion in acute HCV infection

immunology↗

bioRxiv2017

Impact Of Sequencing Depth And Read Length On Single Cell RNA Sequencing Data: Lessons From T Cells

Single cell RNA sequencing (scRNA-seq) has shown great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant sub-populations of T cells, and notably the identification of the full length T cell receptor (TCR{beta}), which defines the specificity against cognate antigens. Several factors, such as RNA library capture, cell quality, and sequencing output have been suggested to affect the quality of scRNA-seq data, but these factors have not been systematically examined.\n\nWe studied the effect of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCR{beta} reconstruction, utilising 1,305 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (<50 bp), but these featured higher technical variability compared to profiles from longer reads. TCR{beta} were detected in 1,027 cells (79%), with a success rate between 81% and 100% for datasets with at least 250,000 (PE) reads of length >50 bp.\n\nSufficient read length and sequencing depth can control technical noise to enable accurate identification of TCR{beta} and gene expression profiles from scRNA-seq data of T cells.

bioinformatics↗