Dataset Information

Identifying geographic hot spots of reassortment in a multipartite plant virus.

ABSTRACT: Reassortment between different species or strains plays a key role in the evolution of multipartite plant viruses and can have important epidemiological implications. Identifying geographic locations where reassortant lineages are most likely to emerge could be a valuable strategy for informing disease management and surveillance efforts. We developed a predictive framework to identify potential geographic hot spots of reassortment based upon spatially explicit analyses of genome constellation diversity. To demonstrate the utility of this approach, we examined spatial variation in the potential for reassortment among Cardamom bushy dwarf virus (CBDV; Nanoviridae, Babuvirus) isolates in Northeast India. Using sequence data corresponding to six discrete genome components for 163 CBDV isolates, a quantitative measure of genome constellation diversity was obtained for locations across the sampling region. Two key areas were identified where viruses with highly distinct genome constellations cocirculate, and these locations were designated as possible geographic hot spots of reassortment, where novel reassortant lineages could emerge. Our study demonstrates that the potential for reassortment can be spatially dependent in multipartite plant viruses and highlights the use of evolutionary analyses to identify locations which could be actively managed to facilitate the prevention of outbreaks involving novel reassortant strains.

SUBMITTER: Savory FR

PROVIDER: S-EPMC4055178 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Identifying geographic hot spots of reassortment in a multipartite plant virus.

Savory Fiona R FR Varma Varun V Ramakrishnan Uma U

Evolutionary applications 20140409 5

Reassortment between different species or strains plays a key role in the evolution of multipartite plant viruses and can have important epidemiological implications. Identifying geographic locations where reassortant lineages are most likely to emerge could be a valuable strategy for informing disease management and surveillance efforts. We developed a predictive framework to identify potential geographic hot spots of reassortment based upon spatially explicit analyses of genome constellation d ...[more]

PMID: 24944570

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Project description:HIV-1 infection is characterized by the rapid generation of genetic diversity that facilitates viral escape from immune selection and antiretroviral therapy. Despite recombination's crucial role in viral diversity and evolution, little is known about the genomic factors that influence recombination between highly similar genomes. In this study, we use a minimally modified full-length HIV-1 genome and high-throughput sequence analysis to study recombination in gag and pol in T cells. We find that recombination is favored at a number of recombination hot spots, where recombination occurs six times more frequently than at corresponding cold spots. Interestingly, these hot spots occur near important features of the HIV-1 genome but do not occur at sites immediately around protease inhibitor or reverse transcriptase inhibitor drug resistance mutations. We show that the recombination hot and cold spots are consistent across five blood donors and are independent of coreceptor-mediated entry. Finally, we check common experimental confounders and find that these are not driving the location of recombination hot spots. This is the first study to identify the location of recombination hot spots between two similar viral genomes with great statistical power and under conditions that closely reflect natural recombination events among HIV-1 quasispecies.The ability of HIV-1 to evade the immune system and antiretroviral therapy depends on genetic diversity within the viral quasispecies. Retroviral recombination is an important mechanism that helps to generate and maintain this genetic diversity, but little is known about how recombination rates vary within the HIV-1 genome. We measured recombination rates in gag and pol and identified recombination hot and cold spots, demonstrating that recombination is not random but depends on the underlying gene sequence. The strength and location of these recombination hot and cold spots can be used to improve models of viral dynamics and evolution, which will be useful for the design of robust antiretroviral therapies.

Project description:BackgroundProtein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots, which account for most of the interface binding free energy. Accurate prediction of hot spots is critical to understand the principle of protein-DNA interactions. There are already some computational methods that can accurately and efficiently predict a large number of hot residues. However, the insufficiency of experimentally validated hot-spot residues in protein-DNA complexes and the low diversity of the employed features limit the performance of existing methods.ResultsHere, we report a new computational method for effectively predicting hot spots in protein-DNA binding interfaces. This method, called PreHots (the abbreviation of Predicting Hotspots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features selected by a sequential backward feature selection algorithm. To this end, we constructed two new and reliable datasets (one benchmark for model training and one independent dataset for validation), which totally consist of 123 hot spots and 137 non-hot spots from 89 protein-DNA complexes. The data were manually collected from the literature and existing databases with a strict process of redundancy removal. Our method achieves a sensitivity of 0.813 and an AUC score of 0.868 in 10-fold cross-validation on the benchmark dataset, and a sensitivity of 0.818 and an AUC score of 0.820 on the independent test dataset. The results show that our approach outperforms the existing ones.ConclusionsPreHots, which is based on stack ensemble of boosting algorithms, can reliably predict hot spots at the protein-DNA binding interface on a large scale. Compared with the existing methods, PreHots can achieve better prediction performance. Both the webserver of PreHots and the datasets are freely available at: http://dmb.tongji.edu.cn/tools/PreHots/ .

Dataset Information

Identifying geographic hot spots of reassortment in a multipartite plant virus.

Publications

Identifying geographic hot spots of reassortment in a multipartite plant virus.

Similar Datasets

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