New approaches for unravelling reassortment pathways

Abstract

BackgroundEvery year the human population encounters epidemic outbreaks of influenza, and history reveals recurring pandemics that have had devastating consequences. The current work focuses on the development of a robust algorithm for detecting influenza strains that have a composite genomic architecture. These influenza subtypes can be generated through a reassortment process, whereby a virus can inherit gene segments from two different types of influenza particles during replication. Reassortant strains are often not immediately recognised by the adaptive immune system of the hosts and hence may be the source of pandemic outbreaks. Owing to their importance in public health and their infectious ability, it is essential to identify reassortant influenza strains in order to understand the evolution of this virus and describe reassortment pathways that may be biased towards particular viral segments. Phylogenetic methods have been used traditionally to identify reassortant viruses. In many studies up to now, the assumption has been that if two phylogenetic trees differ, it is because reassortment has caused them to be different. While phylogenetic incongruence may be caused by real differences in evolutionary history, it can also be the result of phylogenetic error. Therefore, we wish to develop a method for distinguishing between topological inconsistency that is due to confounding effects and topological inconsistency that is due to reassortment.ResultsThe current work describes the implementation of two approaches for robustly identifying reassortment events. The algorithms rest on the idea of significance of difference between phylogenetic trees or phylogenetic tree sets, and subtree pruning and regrafting operations, which mimic the effect of reassortment on tree topologies. The first method is based on a maximum likelihood (ML) framework (MLreassort) and the second implements a Bayesian approach (Breassort) for reassortment detection. We focus on reassortment events that are found by both methods. We test both methods on a simulated dataset and on a small collection of real viral data isolated in Hong Kong in 1999.ConclusionsThe nature of segmented viral genomes present many challenges with respect to disease. The algorithms developed here can effectively identify reassortment events in small viral datasets and can be applied not only to influenza but also to other segmented viruses. Owing to computational demands of comparing tree topologies, further development in this area is necessary to allow their application to larger datasets.

Du’an Karst of Guangxi: a kingdom of the cavernicolous genus Dongodytes Deuve (Coleoptera, Carabidae, Trechinae)

“…Vigna Taglianti (1997) and Uéno (1998) suggested it may be allied to the European Aphaenops series, contrary to Deuve (1993) who compared Dongodytes with Sinaphaenops Uéno & Wang, 1991. Because recent study based on molecular phylogenetic analysis have clearly demonstrated that the Aphaenops series in the widest and traditional sense are restricted to the Pyrenean lineage (Faille et al 2010, 2013), we agree with Deuve’s opinion. Dongodytes seems to be allied to its Chinese congeners such as Sinaphaenops and Pilosaphaenops Deuve & Tian, 2008.…”

Migration and Interaction in a Contact Zone: mtDNA Variation among Bantu-Speakers in Southern Africa

“…The mtDNA haplogroups L0d and L0k have been convincingly shown to be characteristic of autochthonous populations of southern Africa [18], [24], [35]. They therefore represent an ideal measure for detecting admixture in the maternal line between the immigrating Bantu-speaking groups and these autochthonous populations.…”

The measure of success: geographic isolation promotes diversification in Pachydactylus geckos

“…Trait-associated measurements of diversification rate utilized the BiSSE model. Even though our ingroup phylogeny was comprehensive, the number of taxa in our data set may not have been large enough to avoid inadequacies of the model [69, 71], which is why we refrain from ascribing statistical significance to these results. Alternate methods of trait-dependant diversification (e.g.…”

“…Because hypothesis testing in a BiSSE or MuSSE framework can have a high Type I error rate [68–70] and low statistical power when data sets contain fewer than several hundred terminal taxa [71], we refrain from explicitly testing the statistical significance of character-associated variation in model parameter estimates. Instead, we fit models in which each trait was given individual λ , μ , and q parameters strictly to determine estimates of these model parameters.…”