Biology subjects

Atkins, K. E.

Publications and source records attributed to Atkins, K. E..

3 recordsLinked to original sources

bioRxiv2024

Biased estimates of phylogenetic branch lengths resulting from the discretised Gamma model of site rate heterogeneity

A standard method in phylogenetic reconstruction for representing variation in substitution rates between sites in the genome is the discrete Gamma model (DGM). Relative rates are assumed to be distributed according to a discretised Gamma distribution, where the probabilities that a site is included in each class are equal. Here, we identify a serious bias in the branch lengths of reconstructed phylogenies when the DGM is used, with the magnitude of the effect varying with the number of sequences in the alignment. We demonstrate the existence of the bias in both simulated dataset and using real HIV-1 sequences; in both cases branch lengths are overestimated. We show that the alternative "FreeRate" model, which assumes no parametric distribution and allows the class probabilities to vary, is not subject to the issue. We further establish that the reason for the behaviour is the equal class probabilities, not the discretisation itself. We explore the mathematics of for the phenomenon, showing how maximum likelihood branch lengths under the DGM may differ from the true ones used to generate the tree, and that branch lengths will be overestimated where there is a long-tail of fast-evolving sites in the true rate distribution, which is the usual situation in real datasets. We recommend that the DGM be retired from general use. While FreeRate is an immediately available replacement, it is known to be difficult to fit, and thus there is scope for innovation in rate heterogeneity models.

evolutionary biology↗

bioRxiv2019

Within-host competition determines vaccine impact on antibiotic resistance

Bacterial vaccines can protect recipients from contracting potentially antibiotic-resistant infections. But by altering the selective balance between sensitive and resistant strains, vaccines may also suppress--or spread--antibiotic resistance among unvaccinated individuals. Predicting the outcome requires knowing what drives selection for resistance in bacterial pathogens, and in particular, what maintains the circulation of both antibiotic-sensitive and resistant strains of bacteria. Using mathematical modelling, we show that the frequency of penicillin resistance in Streptococcus pneumoniae (pneumococcus) across 27 European countries can be explained by between-host diversity in antibiotic use, heritable diversity in pneumococcal carriage duration, or frequency-dependent selection brought about by within-host competition between resistant and sensitive strains. We use our calibrated models to predict the impact of non-serotype-specific pneumococcal vaccination upon the prevalence of carriage, incidence of disease, and frequency of resistance for S. pneumoniae. We find that the relative strength and directionality of competition between resistant and sensitive pneumococcal strains is the most important determinant of whether vaccination promotes, inhibits, or has little effect upon the evolution of antibiotic resistance. Finally, we show that country-specific differences in pathogen transmission substantially alter the predicted impact of vaccination, highlighting that policies for managing resistance with vaccines must be tailored to a specific pathogen and setting. One sentence summaryFrequency-dependent competition and extrinsically-maintained diversity shape selection for antibiotic resistance following vaccination.

evolutionary biology↗

bioRxiv2017

Within-host dynamics explain the coexistence of antibiotic-sensitive and resistant bacteria

The spread of antibiotic resistance, a major threat to human health, is poorly understood. Empirically, resistant strains gradually increase in prevalence as antibiotic consumption increases, but current mathematical models predict a sharp transition between full sensitivity and full resistance. In other words, we do not understand what drives persistent coexistence between resistant and sensitive strains of disease-causing bacteria in host populations. Without knowing what drives patterns of resistance, we cannot accurately predict the impact of potential strategies for managing resistance. Here, we show that within-host dynamics--bacterial growth, strain competition, and host immune responses--promote frequency-dependent selection for resistant strains, explaining patterns of resistance at the population level. By capturing these processes in a parsimonious mathematical framework, we resolve a long-standing conflict between theory and observation. Our models capture widespread coexistence for multiple bacteria-drug combinations across 30 European countries and explain associations between carriage prevalence and resistance prevalence among bacterial subtypes. A mechanistic understanding of resistance evolution is needed to accurately forecast the impact and effectiveness of resistance-management strategies.

epidemiology↗