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BackgroundDengue is a mosquito-borne viral disease posing a significant threat to public health. Dengue virus (DENV) evolution is often characterized by lineage turnover, which, along with ecological and immunological factors, has been linked to changes in dengue phenotype affecting epidemic dynamics. Utilizing epidemiologic and virologic data from long-term population-based studies (the Nicaraguan Pediatric Dengue Cohort Study and Nicaraguan Dengue Hospital-based Study), we describe a lineage turnover of DENV serotype 2 (DENV-2) prior to a large dengue epidemic in 2019. Prior to this epidemic, Nicaragua had experienced relatively low levels of DENV transmission from 2014 to 2019, a period dominated by chikungunya in 2014/15 and Zika in 2016.ResultsOur phylogenetic analyses confirmed that all Nicaraguan DENV-2 isolates from 2018 to 2019 formed their own clade within the Nicaraguan lineage of the Asian/American genotype. The emergence of the new DENV-2 lineage reflects a replacement of the formerly dominant clade presiding from 2005 to 2009, a lineage turnover marked by several shared derived amino acid substitutions throughout the genome. To elucidate evolutionary drivers of lineage turnover, we performed selection pressure analysis and reconstructed the demographic history of DENV-2. We found evidence of adaptive evolution by natural selection at the codon level as well as in branch formation.ConclusionsThe timing of its emergence, along with a statistical signal of adaptive evolution and distinctive amino acid substitutions, the latest in the NS5 gene, suggest that this lineage may have increased fitness relative to the prior dominant DENV-2 strains. This may have contributed to the intensity of the 2019 DENV-2 epidemic, in addition to previously identified immunological factors associated with pre-existing Zika virus immunity.

Effective population size characterizes the genetic variability in a population and is a parameter of paramount importance in population genetics and evolutionary biology. Kingman's coalescent process enables inference of past population dynamics directly from molecular sequence data, and researchers have developed a number of flexible coalescent-based models for Bayesian nonparametric estimation of the effective population size as a function of time. Major goals of demographic reconstruction include identifying driving factors of effective population size, and understanding the association between the effective population size and such factors. Building upon Bayesian nonparametric coalescent-based approaches, we introduce a flexible framework that incorporates time-varying covariates that exploit Gaussian Markov random fields to achieve temporal smoothing of effective population size trajectories. To approximate the posterior distribution, we adapt efficient Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. Incorporating covariates into the demographic inference framework enables the modeling of associations between the effective population size and covariates while accounting for uncertainty in population histories. Furthermore, it can lead to more precise estimates of population dynamics. We apply our model to four examples. We reconstruct the demographic history of raccoon rabies in North America and find a significant association with the spatiotemporal spread of the outbreak. Next, we examine the effective population size trajectory of the DENV-4 virus in Puerto Rico along with viral isolate count data and find similar cyclic patterns. We compare the population history of the HIV-1 CRF02_AG clade in Cameroon with HIV incidence and prevalence data and find that the effective population size is more reflective of incidence rate. Finally, we explore the hypothesis that the population dynamics of musk ox during the Late Quaternary