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Phylogeography of Kyasanur Forest Disease virus in India (1957-2017) reveals evolution and spread in the Western Ghats region.

ABSTRACT: The Kyasanur Forest Disease (KFD) has become a major public health problem in the State of Karnataka, India where the disease was first identified and in Tamil Nadu, Maharashtra, Kerala, and Goa covering the Western Ghats region of India. The incidence of positive cases and distribution of the Kyasanur Forest Disease virus (KFDV) in different geographical regions raises the need to understand the evolution and spatiotemporal transmission dynamics. Phylogeography analysis based on 48 whole genomes (46 from this study) and additionally 28 E-gene sequences of KFDV isolated from different regions spanning the period 1957-2017 was thus undertaken. The mean evolutionary rates based the E-gene was marginally higher than that based on the whole genomes. A subgroup of KFDV strains (2006-2017) differing from the early Karnataka strains (1957-1972) by ~2.76% in their whole genomes and representing spread to different geographical areas diverged around 1980. Dispersal from Karnataka to Goa and Maharashtra was indicated. Maharashtra represented a new source for transmission of KFDV since ~2013. Significant evidence of adaptive evolution at site 123?A/T located in the vicinity of the envelope protein dimer interface may have functional implications. The findings indicate the need to curtail the spread of KFDV by surveillance measures and improved vaccination strategies.

SUBMITTER: Yadav PD

PROVIDER: S-EPMC7005018 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Phylogeography of Kyasanur Forest Disease virus in India (1957-2017) reveals evolution and spread in the Western Ghats region.

Yadav Pragya D PD Patil Savita S Jadhav Santoshkumar M SM Nyayanit Dimpal A DA Kumar Vimal V Jain Shilpi S Sampath Jagadish J Mourya Devendra T DT Cherian Sarah S SS

Scientific reports 20200206 1

The Kyasanur Forest Disease (KFD) has become a major public health problem in the State of Karnataka, India where the disease was first identified and in Tamil Nadu, Maharashtra, Kerala, and Goa covering the Western Ghats region of India. The incidence of positive cases and distribution of the Kyasanur Forest Disease virus (KFDV) in different geographical regions raises the need to understand the evolution and spatiotemporal transmission dynamics. Phylogeography analysis based on 48 whole genome ...[more]

PMID: 32029759

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Project description:Kyasanur forest disease virus (KFDV) is a rapidly expanding tick-borne zoonotic virus with natural foci in the forested region of the Western Ghats of South India. The Western Ghats is one of the world's most important biodiversity hotspots and, like many such areas of high biodiversity, is under significant pressure from anthropogenic landscape change. The current study sought to quantify mammalian species richness using ensemble models of the distributions of a sample of species extant in the Western Ghats and to explore its association with KFDV outbreaks, as well as the modifying effects of deforestation on this association. Species richness was quantified as a composite of individual species' distributions, as derived from ensembles of boosted regression tree, random forest, and generalised additive models. Species richness was further adjusted for the potential biotic constraints of sympatric species. Both species richness and forest loss demonstrated strong positive associations with KFDV outbreaks, however forest loss substantially modified the association between species richness and outbreaks. High species richness was associated with increased KFDV risk but only in areas of low forest loss. In contrast, lower species richness was associated with increased KFDV risk in areas of greater forest loss. This relationship persisted when species richness was adjusted for biotic constraints at the taluk-level. In addition, the taluk-level species abundances of three monkey species (Macaca radiata, Semnopithecus hypoleucus, and Semnopithecus priam) were also associated with outbreaks. These results suggest that increased monitoring of wildlife in areas of significant habitat fragmentation may add considerably to critical knowledge gaps in KFDV epidemiology and infection ecology and should be incorporated into novel One Health surveillance development for the region. In addition, the inclusion of some primate species as sentinels of KFDV circulation into general wildlife surveillance architecture may add further value.

Project description:The objective of this analysis was to identify topographic and bioclimatic factors that predict occurrence of forest and grassland patches within tropical montane forest-grassland mosaics. We further investigated whether interactions between topography and bioclimate are important in determining vegetation pattern, and assessed the role of spatial scale in determining the relative importance of specific topographic features. Finally, we assessed the role of elevation in determining the relative importance of diverse explanatory factors. The study area consists of the central and southern regions of the Western Ghats of Southern India, a global biodiversity hotspot. Random forests were used to assess prediction accuracy and predictor importance. Conditional inference classification trees were used to interpret predictor effects and examine potential interactions between predictors. GLMs were used to confirm predictor importance and assess the strength of interaction terms. Overall, topographic and bioclimatic predictors classified vegetation pattern with approximately 70% accuracy. Prediction accuracy was higher for grassland than forest, and for mosaics at higher elevations. Elevation was the most important predictor, with mosaics above 2000 m dominated largely by grassland. Relative topographic position measured at a local scale (within a 300 m neighbourhood) was another important predictor of vegetation pattern. In high elevation mosaics, northness and concave land surface curvature were important predictors of forest occurrence. Important bioclimatic predictors were: dry quarter precipitation, annual temperature range and the interaction between the two. The results indicate complex interactions between topography and bioclimate and among topographic variables. Elevation and topography have a strong influence on vegetation pattern in these mosaics. There were marked regional differences in the roles of various topographic and bioclimatic predictors across the range of study mosaics, indicating that the same pattern of grass and forest seems to be generated by different sets of mechanisms across the region, depending on spatial scale and elevation.

Dataset Information

Phylogeography of Kyasanur Forest Disease virus in India (1957-2017) reveals evolution and spread in the Western Ghats region.

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Phylogeography of Kyasanur Forest Disease virus in India (1957-2017) reveals evolution and spread in the Western Ghats region.

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