Biology subjects

Lumby, C. K.

Publications and source records attributed to Lumby, C. K..

4 recordsLinked to original sources

bioRxiv2022

Accurate simultaneous sequencing of genetic and epigenetic bases in DNA

DNA comprises molecular information stored via genetic bases (G, C, T, A) and also epigenetic bases, principally 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC). Both genetic and epigenetic information are vital to our understanding of biology and disease states. Most DNA sequencing approaches address either genetics or epigenetics and thus capture incomplete information. Methods widely used to detect epigenetic DNA bases typically fail to capture common C-to-T mutations or distinguish 5mC from 5hmC. Here, we present a single-base-resolution sequencing methodology that will simultaneously sequence complete genetics and complete epigenetics in a single workflow. The approach is non-destructive to DNA and provides a digital readout of bases, which we exemplify by simultaneous sequencing of G, C, T, A, 5mC and 5hmC; 6-Letter sequencing. We demonstrate sequencing of human genomic DNA and also cell-free DNA taken from a blood sample of a cancer patient. The approach is accurate, requires low DNA input and has a simple workflow and analysis pipeline. We envisage it will be versatile across many applications in life sciences.

genomics↗

bioRxiv2020

A large effective population size for within-host influenza virus infection

Strains of the influenza virus form coherent global populations, yet exist at the level of single infections in individual hosts. The relationship between these scales is a critical topic for understanding viral evolution. Here we investigate the within-host relationship between selection and the stochastic effects of genetic drift, estimating an effective population size of infection Ne for influenza infection. Examining whole-genome sequence data describing a chronic case of influenza B in a severely immunocompromised child we infer an Ne of 2.5 x 107 (95% confidence range 1.0 x 107 to 9.0 x 107) suggesting the importance of genetic drift to be minimal. Our result, supported by the analysis of data from influenza A infection, suggests that positive selection during within-host infection is primarily limited by the typically short period of infection. Atypically long infections may have a disproportionate influence upon global patterns of viral evolution.

evolutionary biology↗

bioRxiv2020

Inferring transmission bottleneck size from viral sequence data using a novel haplotype reconstruction method

The transmission bottleneck is defined as the number of viral particles transmitted from one host to another. Genome sequence data has been used to evaluate the size of the transmission bottleneck between humans infected with the influenza virus, however, the methods used to make these estimates have some limitations. Specifically, approaches using viral allele frequency data may not fully capture a process which involves the transmission of entire viral genomes. Here we set out a novel approach for inferring viral transmission bottlenecks; our method combines haplotype reconstruction, a method for inferring the composition of genomes in a viral population, with two maximum likelihood methods for bottleneck inference, tailored for small and large bottleneck sizes respectively. Our method allows for rapid calculation, and performs well when applied to data from simulated transmission events, being robust to errors in the haplotype reconstruction process. Applied to data from a previous household transmission study of influenza A infection we confirm the result that the majority of transmission events involve a small number of viruses, albeit with slightly looser bottlenecks being inferred, with between 1 and 13 particles transmitted in the majority of cases. While influenza A transmission involves a tight population bottleneck, the bottleneck is not so tight as to universally prevent the transmission of within-host viral diversity.

evolutionary biology↗

bioRxiv2018

A novel framework for inferring parameters of transmission from viral sequence data

Transmission between hosts is a critical part of the viral lifecycle. Recent studies of viral transmission have used genome sequence data to evaluate the number of particles transmitted between hosts, and the role of selection as it operates during the transmission process. However, the interpretation of sequence data describing transmission events is a challenging task. We here present a novel and comprehensive framework for using short-read sequence data to understand viral transmission events. Our model describes transmission as an event involving whole viruses, rather than independent alleles. We demonstrate how selection and noisy sequence data may each affect inferences of the population bottleneck, and identify circumstances in which selection for increased viral transmission may or may not be identified. Applying our model to data from a previous experimental transmission study, we show that our approach grants a more quantitative insight into viral transmission, inferring that between 2 to 6 viruses initiated infection, and allowing for a more informed interpretation of transmission events. While our model is here applied to influenza transmission, the framework we present is highly generalisable to other systems. Our work provides new opportunities for studying viral transmission.

evolutionary biology↗