Vasylyeva, T. I. | Search bioRxiv

bioRxiv2020

Global prevalence and phylogeny of hepatitis B virus (HBV) drug and vaccine resistance mutations

IntroductionVaccination and anti-viral therapy with nucleos(t)ide analogues (NAs) are key approaches to reducing the morbidity, mortality and transmission of hepatitis B virus (HBV) infection. However, the efficacy of these interventions may be reduced by the emergence of drug resistance-associated mutations (RAMs) and/or vaccine escape mutations (VEMs). We have assimilated data on the global prevalence and distribution of HBV RAMs/VEMs from publicly available data and explored the evolution of these mutations. MethodsWe analysed sequences downloaded from the Hepatitis B Virus Database, and calculated prevalence of 41 RAMs and 38 VEMs catalogued from published studies. We generated maximum likelihood phylogenetic trees and used treeBreaker to investigate the distribution of selected mutations across tree branches. We performed phylogenetic molecular clock analyses using BEAST to estimate the age of mutations. ResultsRAM M204I/V had the highest prevalence, occurring in 3.8% (109/2838) of all HBV sequences in our dataset, and a significantly higher rate in genotype C sequence at 5.4% (60/1102, p=0.0007). VEMs had an overall prevalence of 1.3% (37/2837) and had the highest prevalence in genotype C and in Asia at 2.2% (24/1102; p=0.002) and 1.6% (34/2109; p=0.009) respectively. Phylogenetic analysis suggested that most RAM/VEMs arose independently, however RAMs including A194T, M204V and L180M formed clusters in genotype B. We show evidence that polymorphisms associated with drug and vaccine resistance may have been present in the mid 20th century suggesting that they can arise independently of treatment/ vaccine exposure. DiscussionHBV RAMs/VEMs have been found globally and across genotypes, with the highest prevalence observed in genotype C variants. Screening for the genotype and for resistant mutations may help to improve stratified patient treatment. As NAs and HBV vaccines are increasingly being deployed for HBV prevention and treatment, monitoring for resistance and advocating for better treatment regimens for HBV remains essential.

evolutionary biology↗

bioRxiv2019

Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts

AO_SCPLOWBSTRACTC_SCPLOWBirth-death processes have given biologists a model-based framework to answer questions about changes in the birth and death rates of lineages in a phylogenetic tree. Therefore birth-death models are central to macroevolutionary as well as phylodynamic analyses. Early approaches to studying temporal variation in birth and death rates using birth-death models faced difficulties due to the restrictive choices of birth and death rate curves through time. Sufficiently flexible time-varying birth-death models are still lacking. We use a piecewise-constant birth-death model, combined with both Gaussian Markov random field (GMRF) and horseshoe Markov random field (HSMRF) prior distributions, to approximate arbitrary changes in birth rate through time. We implement these models in the widely used statistical phylogenetic software platform RevBayes, allowing us to jointly estimate birth-death process parameters, phylogeny, and nuisance parameters in a Bayesian framework. We test both GMRF-based and HSMRF-based models on a variety of simulated diversification scenarios, and then apply them to both a macroevolutionary and an epidemiological dataset. We find that both models are capable of inferring variable birth rates and correctly rejecting variable models in favor of effectively constant models. In general the HSMRF-based model has higher precision than its GMRF counterpart, with little to no loss of accuracy. Applied to a macroevolutionary dataset of the Australian gecko family Pygopodidae (where birth rates are interpretable as speciation rates), the GMRF-based model detects a slow decrease whereas the HSMRF-based model detects a rapid speciation-rate decrease in the last 12 million years. Applied to an infectious disease phylodynamic dataset of sequences from HIV subtype A in Russia and Ukraine (where birth rates are interpretable as the rate of accumulation of new infections), our models detect a strongly elevated rate of infection in the 1990s. AO_SCPLOWUTHORC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWSUMMARYC_SCPLOWBoth the growth of groups of species and the spread of infectious diseases through populations can be modeled as birth-death processes. Birth events correspond either to speciation or infection, and death events to extinction or becoming noninfectious. The rates of birth and death may vary over time, and by examining this variation researchers can pinpoint important events in the history of life on Earth or in the course of an outbreak. Time-calibrated phylogenies track the relationships between a set of species (or infections) and the times of all speciation (or infection) events, and can thus be used to infer birth and death rates. We develop two phylogenetic birth-death models with the goal of discerning signal of rate variation from noise due to the stochastic nature of birth-death models. Using a variety of simulated datasets, we show that one of these models can accurately infer slow and rapid rate shifts without sacrificing precision. Using real data, we demonstrate that our new methodology can be used for simultaneous inference of phylogeny and rates through time.

evolutionary biology↗