Biology subjects

Illingworth, C.

Publications and source records attributed to Illingworth, C..

6 recordsLinked to original sources

bioRxiv2026

CD8scape: an accessible, command-line tool for predicting viral escape from the CD8+ T cell response

The CD8+ T cell response is a critical component of antiviral immunity, particularly in hosts who are immunocompromised or undergoing B cell-depleting therapy, such as rituximab. As viral evolution can lead to escape from CD8+ T cell recognition, tools that predict such escape are increasingly relevant. Here, we present CD8scape, an accessible command-line tool designed to predict viral escape from the CD8+ T cell response based on within-host sequence variation and HLA class I genotype. CD8scape is primarily a Julia wrapper for NetMHCpan v4.2, a neural network-based predictor trained on mass spectrometry-derived peptide presentation data. CD8scape integrates variant data and viral reading frames to identify all overlapping 8-11mer peptides at variant sites in both ancestral and derived states. These peptides are evaluated using NetMHCpan, which outputs eluted ligand (EL) scores as allele-specific percentile ranks to account for differences in MHC binding fastidiousness, and these are passed back to CD8scape itself. For each variant, the best-ranking peptide across all alleles is identified, and a harmonic mean is used to summarize presentation likelihood across the hosts HLA genotype. A fold-change between ancestral and derived harmonic means quantifies the likelihood of immune escape, with values >1 indicating reduced predicted presentation, and therefore a potential escape from the CD8+ T cell response. This is converted to a log2 value of this fold-change so that the metric is symmetric around 0, with positive values representing predicted escape. CD8scape can operate with known HLA genotypes or a representative HLA supertype panel for generalizable predictions. We demonstrate our method by application to within-host SARS-CoV-2 evolution in a rituximab-treated patient and discuss its implications for population-level CD8+ T cell escape.

bioinformatics↗

bioRxiv2024

Epidemiological inference at the threshold of data availability: an influenza A(H1N2)v spillover event in the United Kingdom

Viruses which infect animals regularly spill over into the human population, but individual events may lead to anything from a single case to a novel pandemic. Rapidly gaining an understanding of a spillover event is critical to calibrating a public health response. We here propose a novel method, using likelihood free rejection sampling to evaluate the properties of an outbreak of swine-origin influenza A(H1N2)v in the United Kingdom, detected in November 2023. From the limited data available we generate historical estimates of the probability that the outbreak had died out in the days following the detection of the first case. Our method suggests that the outbreak could have been said to be over with 95% certainty between 19 and 29 days after the first case was detected, depending upon the probability of a case being detected. We further estimate the number of undetected cases conditional upon the outbreak still being live, the epidemiological parameter R0, and the date on which the spillover event itself occurred. Our method requires minimal data to be effective. While our calculations were performed after the event, the real-time application of our method has potential value for public health responses to cases of emerging viral infection.

microbiology↗

bioRxiv2023

The airborne transmission of viruses causes tight transmission bottlenecks

The transmission bottleneck describes the number of viral particles that found an infection in a new host. Previous studies have used genome sequence data to suggest that transmission bottlenecks for influenza and SARS-CoV-2 involve few viral particles, but the general principles underlying these bottlenecks are not fully understood. Here we show that, across a broad range of circumstances, tight transmission bottlenecks arise as a consequence of the physical process underlying airborne viral transmission. We use a mathematical model to describe the process of infectious particles being emitted by an infected individual and inhaled by others nearby. The extent to which exposure to particles translates into infection is determined by an effective viral load, which is calculated as a function of the epidemiological parameter R0. Across multiple scenarios, including those present at a superspreading event, our model suggests that the great majority of transmission bottlenecks involve few viral particles, with a high proportion of infections being caused by a single viral particle. Our results provide a physical explanation for previous inferences of bottleneck size and predict that tight transmission bottlenecks prevail more generally in respiratory virus transmission.

evolutionary biology↗

bioRxiv2023

Genetic consequences of effective and suboptimal dosing with mutagenic drugs in a hamster model of SARS-CoV-2 infection

Mutagenic antiviral drugs have shown promising results against multiple viruses, yet concerns have been raised about whether their use might promote the emergence of new and harmful viral variants. Here, we examine the genetic consequences of effective and suboptimal dosing of favipiravir and molnupiravir in the treatment of SARS-CoV-2 infection in a hamster model. We identify a dose-dependent effect upon the mutational load in a viral population, with molnupiravir having a greater potency than favipiravir per mg/kg of treatment. The emergence of de novo variants was largely driven by stochastic processes, with evidence of compensatory adaptation but not of the emergence of drug resistance or novel immune phenotypes. Effective doses for favipiravir and molunpiravir correspond to similar levels of mutational load. Combining both drugs had an increased impact on both efficacy and mutational load. Our results suggest the potential for mutational load to provide a marker for clinical efficacy.

evolutionary biology↗

bioRxiv2019

A de novo approach to inferring within-host fitness effects during untreated HIV-1 infection

In the absence of effective antiviral therapy, HIV-1 evolves in response to the within-host environment, of which the immune system is an important aspect. During the earliest stages of infection, this process of evolution is very rapid, driven by a small number of CTL escape mechanisms. As the infection progresses, immune escape variants evolve under reduced magnitudes of selection, while competition between an increasing number of polymorphic alleles (i.e., clonal interference) makes it difficult to quantify the magnitude of selection acting upon specific variant alleles. To tackle this complex problem, we developed a novel multi-locus inference method to evaluate the role of selection during the chronic stage of within-host infection. We applied this method to targeted sequence data from the p24 and gp41 regions of HIV-1 collected from 34 patients with long-term untreated HIV-1 infection. We identify a broad distribution of beneficial fitness effects during infection, with a small number of variants evolving under strong selection and very many variants evolving under weaker selection. The uniquely large number of infections analysed granted a previously unparalleled statistical power to identify loci at which selection could be inferred to act with statistical confidence. Our model makes no prior assumptions about the nature of alleles under selection, such that any synonymous or non-synonymous variant may be inferred to evolve under selection. However, the majority of variants inferred with confidence to be under selection were non-synonymous in nature, and in nearly all cases were associated with either CTL escape in p24 or neutralising antibody escape in gp41. Sites inferred to be under selection in multiple hosts have high within-host and between-host diversity albeit not all sites with high between-host diversity were inferred to be under selection at the within-host level. Our identification of selection at sites associated with resistance to broadly neutralising antibodies (bNAbs) highlights the need to fully understand the role of selection in untreated individuals when designing bNAb based therapies.\n\nAuthor SummaryDuring the within-host evolution of HIV-1, the diversity of the viral population increases, with many beneficial variants competing against each other. This competition, known as clonal interference, makes the identification of variants under positive selection a challenging task. We here apply a novel method for the inference of selection to targeted within-host sequence data describing changes in the p24 and gp41 genes during HIV-1 infection in 34 patients. Our method adopts a parsimonious approach, assigning selection to the smallest number of variants necessary to explain the evolution of the system. The large size of our dataset allows for the confident identification of variants under selection, alleles at certain loci being repeatedly inferred as under selection within multiple individuals. While early CTL escape mutations have been identified to evolve under strong positive selection, we identify a distribution of beneficial fitness effects in which a large number of mutations are under weak selection. Variants that were confidently identified under selection were primarily found to be associated with either CTL escape in p24 or neutralising antibody escape in gp41, including sites associated with escape from broadly neutralising antibodies. We find that the most frequently selected loci have high diversity both within-host and at the between-host level.

evolutionary biology↗

bioRxiv2018

Building a mechanistic mathematical model of hepatitis C virus entry

The mechanism by which hepatitis C virus (HCV) gains entry into cells is a complex one, involving a broad range of host proteins. Entry is a critical phase of the viral lifecycle, and a potential target for therapeutic or vaccine-mediated intervention. However, the mechanics of HCV entry remain poorly understood. Here we describe a novel computational model of viral entry, encompassing the relationship between HCV and the key host receptors CD81 and SR-B1. We conduct experiments to thoroughly quantify the influence of an increase or decrease in receptor availability upon the extent of viral entry. We use these data to build and parameterise a mathematical model, which we then validate by further experiments. Our results are consistent with sequential HCV-receptor interactions, whereby initial interaction between the HCV E2 glycoprotein and SR-B1 facilitates the accumulation CD81 receptors, leading to viral entry. However, we also demonstrate that a small minority of virus can achieve entry in the absence of SR-B1. Our model estimates the impact of the different obstacles that viruses must surmount to achieve entry; among virus particles attaching to the cell surface, 20-35% accumulate sufficient CD81 receptors, of these 4-8% then complete the subsequent steps to achieve productive infection. Furthermore, we make estimates of receptor stoichiometry; between 3 and 6 CD81 receptors are likely to be required to achieve viral entry. Our model provides a tool to investigate the entry characteristics of HCV variants and outlines a framework for future quantitative studies of the multi-receptor dynamics of HCV entry.

pathology↗