Biology subjects

Mukhopadhyay, C.

Publications and source records attributed to Mukhopadhyay, C..

4 recordsLinked to original sources

bioRxiv2026

Integrated Clinical and Proteomic Precision Subgrouping for Severe Dengue Endotype Signature

BackgroundSevere dengue remains difficult to predict because patients with different clinical trajectories may present with overlapping features, and conventional severity classifications may not fully capture underlying biological heterogeneity. In this study, we applied an integrated clinical and proteomic endotyping approach to dissect dengue disease heterogeneity and identify molecular signatures associated with severity. MethodsPlasma proteomic profiles were analyzed together with detailed clinical, biochemical, hematological, coagulation, and immunological parameters from healthy controls and dengue patients classified according to WHO 2009 severity criteria. High-throughput proteomic analysis, unsupervised clustering, pathway enrichment, and machine-learning-based classification were used to identify dengue endotypes and define molecular features associated with predicted severe disease. ResultsIncreasing dengue severity was associated with progressive abnormalities in liver function, coagulation parameters, hematological indices, and inflammatory mediators, including IL-6, IL-15, HGF, and MUC-16. However, proteomic profiling revealed substantial overlap across conventional severity categories, indicating that clinical classification alone does not fully resolve dengue host-response heterogeneity. Integrated clinical-proteomic clustering identified distinct dengue endotypes, including a predicted severe endotype enriched for inflammatory, antiviral, and cytotoxic lymphocyte-associated pathways. This high-risk endotype was characterized by elevated IL-15, IFN-{gamma}, and granzymes, consistent with coordinated activation of cytotoxic lymphocyte-associated antiviral responses. Machine-learning analysis further showed that proteomic features were strong discriminators of this endotype, supporting their potential utility as biomarkers of severe host-response states. ConclusionIntegrated clinical-proteomic endotyping provides molecular resolution beyond conventional severity grading and identifies immune pathways associated with severe dengue. This framework may improve biological understanding of dengue progression and support future risk stratification and biomarker development.

systems biology↗

bioRxiv2022

A preliminary description of the ecological characteristics of wild waterbird Japanese encephalitis virus hosts in high risk landscapes in India

Wild reservoirs of Japanese encephalitis virus are under-studied globally, which presents critical knowledge gaps for JEV infection ecology despite decades of received wisdom regarding this high-impact mosquito-borne virus. As a result, ardeid birds, generally understood to be the primary reservoirs for JEV, as well as other waterbirds occupying landscapes at high risk for spillover to humans, are frequently ignored by current surveillance mechanisms and infrastructure. This is particularly true in India, which experiences a high annual burden of human outbreaks. Incorporating wild reservoirs into surveillance of human and livestock populations is therefore essential but will first require a data-driven approach to target individual host species. The current study sought to define a preliminary ecological profile of JEV hosts based on 1) species ecological traits, and 2) species presence and abundance adjusted for the biotic constraints of sympatry. Optimal host species tended to be generalists and demonstrate regionally-increasing populations. While ardeid bird species richness, abundance, and relative abundance did demonstrate the strongest and most consistent associations with the distribution of human JEV outbreaks, this study also identified several individual species among two other bird families in these landscapes, the Anatidae and the Rallidae, which also exhibited an optimal host profile and were strongly associated with the distribution of outbreaks. The findings from this work provide the first data-driven evidence base to inform wildlife sampling for the monitoring of JEV circulation in outbreak hotspots in India and thus identify good preliminary targets for the development of One Health wildlife JEV surveillance.

ecology↗

bioRxiv2021

Low mammalian species richness increases Kyasanur Forest Disease outbreak risk in deforested landscapes in the Western Ghats, India

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 worlds 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. HighlightsO_LILocal mammalian species richness is estimated across the entire Western Ghats region C_LIO_LILow species richness is associated with high KFDV risk in deforested landscapes C_LIO_LIThis work identifies key landscapes for wildlife disease surveillance development C_LI

ecology↗

bioRxiv2018

Unraveling the implications of environmental, host and pathogen-specific determinants on clinical presentations and disease progression among Indian melioidosis patients.

BackgroundMelioidosis is gaining recognition as an emerging infectious disease with diverse clinical manifestations and high-case fatality rates,worldwide. However, the molecular epidemiology of the disease outside the endemic regions such as,Thailand and Northern Australia remains unclear.\n\nMethodsClinical data and B. pseudomallei (Bps) isolates obtained from 199 culture-confirmed cases of melioidosis, diagnosed during 2006-2016 inSouth India were used to elucidate the host and pathogen-specific variable virulence determinants associated with clinical presentations and disease progression. Further, we determined the temporal variations and the influence of ecological factors on Bps Lipopolysaccharide (LPS) genotypes causing infections.\n\nResultSevere forms of the disease were observed amongst 169 (85%) patients. Renal dysfunction and infection due to Bps harboring Bim-ABm variant had significant associations with severe forms of the disease. Diabetes mellitus, septicemic melioidosis and infection due to LPS-B genotype were independent risk factors for mortality. LPS-B (74%) and LPS-A (20.6%) were the prevalent genotypes causing infections. Both genotypes demonstrated temporal variations and had significant correlations with rainfall and humidity.\n\nConclusionOur study findings suggest that the pathogen-specific virulence traits, under the influence of ecological factors are the key drivers for geographical variations in the molecular epidemiology of melioidosis.

microbiology↗