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Livestock farming across the world is constantly threatened by evolutionary turnover of foot-and-mouth disease virus (FMDV) strains in endemic systems, the underlying dynamics of which remain to be elucidated. Here, we map the eco-evolutionary landscape of co-circulating FMDV lineages within an important endemic virus pool encompassing Western, Central and parts of Southern Asia, reconstructing the evolutionary history and spatial dynamics over the last 20 years that shapes the current epidemiological situation. We demonstrate that new FMDV variants periodically emerge from Southern Asia, precipitating waves of virus incursions that systematically travel in a westerly direction. We evidence how metapopulation dynamics drive the emergence and extinction of spatially structured virus populations, and how transmission in different host species regulates the evolutionary space of virus serotypes. Our work provides the first integrative framework that defines co-evolutionary signatures of FMDV in regional contexts to help understand the complex interplay between virus phenotypes, host characteristics, and key epidemiological determinants of transmission that drive FMDV evolution in endemic settings.

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BackgroundA phylogenetic network is a generalization of phylogenetic trees that allows the representation of conflicting signals or alternative evolutionary histories in a single diagram. There are several methods for constructing these networks. Some of these methods are based on distances among taxa. In practice, the methods which are based on distance perform faster in comparison with other methods. The Neighbor-Net (N-Net) is a distance-based method. The N-Net produces a circular ordering from a distance matrix, then constructs a collection of weighted splits using circular ordering. The SplitsTree which is a program using these weighted splits makes a phylogenetic network. In general, finding an optimal circular ordering is an NP-hard problem. The N-Net is a heuristic algorithm to find the optimal circular ordering which is based on neighbor-joining algorithm.ResultsIn this paper, we present a heuristic algorithm to find an optimal circular ordering based on the Monte-Carlo method, called MC-Net algorithm. In order to show that MC-Net performs better than N-Net, we apply both algorithms on different data sets. Then we draw phylogenetic networks corresponding to outputs of these algorithms using SplitsTree and compare the results.ConclusionsWe find that the circular ordering produced by the MC-Net is closer to optimal circular ordering than the N-Net. Furthermore, the networks corresponding to outputs of MC-Net made by SplitsTree are simpler than N-Net.

The importance of protein subcellular localization problem is due to the importance of protein’s functions in different cell parts. Moreover, prediction of subcellular locations helps to identify the potential molecular targets for drugs and has an important role in genome annotation. Most of the existing prediction methods assign only one location for each protein. But, since some proteins move between different subcellular locations, they can have multiple locations. In recent years, some multiple location predictors have been introduced. However, their performances are not accurate enough and there is much room for improvement. In this paper, we introduced a method, PMLPR, to predict locations for a protein. PMLPR predicts a list of locations for each protein based on recommender systems and it can properly overcome the multiple location prediction problem. For evaluating the performance of PMLPR, we considered six datasets RAT, FLY, HUMAN, Du et al., DBMLoc and Höglund. The performance of this algorithm is compared with six state-of-the-art algorithms, YLoc, WOLF-PSORT, prediction channel, MDLoc, Du et al. and MultiLoc2-HighRes. The results indicate that our proposed method is significantly superior on RAT and Fly proteins, and decent on HUMAN proteins. Moreover, on the datasets introduced by Du et al., DBMLoc and Höglund, PMLPR has comparable results. For the case study, we applied the algorithms on 8 proteins which are important in cancer research. The results of comparison with other methods indicate the efficiency of PMLPR.

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