Biology subjects

Vyas, N.

Publications and source records attributed to Vyas, N..

6 recordsLinked to original sources

bioRxiv2026

Three-dimensional mapping of human lung fibrosis identifies spatial coupling between tissue microarchitecture and maladaptive repair states

Human lung fibrosis progressively destroys three-dimensional tissue architecture, yet how the structural microenvironment shapes cellular states remains unclear. Integrating 3D X-ray histology with spatial omics, we generate a volumetric multimodal atlas of human lung fibrosis that enables direct interrogation of relationships between tissue microarchitecture and spatial molecular programmes. We identify collapse of the alveolar-capillary unit - the coupled loss of alveolar type 1 epithelial cells, capillary endothelial cells, and basement membrane integrity - as a structural transition associated with early fibrogenesis. Surrounding transitional zones exhibit aberrant epithelial differentiation and proteolytic matrix remodelling, with epithelium shifting toward bronchiolised, airway-like states. Stromal activation converges on spatially restricted fibrogenic cores, sites of maximal collagen maturation and matrix stabilisation that function as signalling hubs within surrounding perifocal and vascular microenvironments. Molecular signatures of atlas-defined tissue states were associated with lung function and mortality across independent patient cohorts. Together, these data support a model in which fibrosis emerges as a spatial tissue-state process, with alveolar-capillary collapse redirecting repair towards mechanically stabilised niches that reinforce pathological remodelling.

systems biology↗

bioRxiv2024

scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery

PurposeSingle-cell RNA sequencing (scRNA-seq) is producing vast amounts of individual cell profiling data. Analysis of such datasets presents a significant challenge in accurately annotating cell types and their associated biomarkers. scRNA-seq datasets analysis will help us understand diseases such as Alzheimers, Cancer, Diabetes, Coronavirus disease 2019 (COVID-19), Systemic Lupus Ery-thematosus (SLE), etc. Recently different pipelines based on machine learning (ML) and Deep Neural Network (DNN) methods have been employed to tackle these issues utilizing scRNA-seq datasets. These pipelines have arisen as a promising resource and are capable of extracting meaningful and concise features from noisy, diverse, and high-dimensional data to enhance annotations and subsequent analysis. Existing tools require high computational resources to execute large sample datasets. MethodsWe have developed a cutting-edge platform known as scaLR (Single Cell Analysis using Low Resource) that efficiently processes data in batches, and reduces the required resources for processing large datasets and running NN models. scaLR is equipped with data processing, feature extraction, training, evaluation, and downstream analysis. The data processing module consists of sample-wise & standard scaler normalization and splitting of data. Its novel feature extraction algorithm, first trains the model on a feature subset and stores feature importance for all the features in that subset. At the end of this process, top K features are selected based on their importance. The model is trained on top K features, its performance evaluation and associated downstream analysis provide significant biomarkers for different cell types and diseases/traits. ResultsTo showcase the capabilities of scaLR, we utilized several scRNA-seq datasets of Peripheral Blood Mononuclear Cells (PBMCs), Alzheimers patients, and large datasets from human and mouse embryonic development. Our findings indicate that scaLR offers comparable prediction accuracy and requires less model training time and compute resources than existing Python-based pipelines and frameworks. Moreover, scaLR efficiently handles large sample datasets (>11.4 million cells) with minimal resource usage (29GB RAM, 12GB GPU, and 8 CPUs) while maintaining high prediction accuracy and being capable of ranking the biomarker association with specific cell types and diseases. ConclusionWe present scaLR a Python-based platform, engineered to utilize minimal computational resources while maintaining comparable execution times to existing frameworks. It is highly scalable and capable of efficiently handling datasets containing millions of cell samples and providing their classification and important biomarkers.

bioinformatics↗

bioRxiv2023

MORPHOGENIC VERSUS MITOGENIC ROLES OF SHH ARE SEGREGATED ON DISTINCT EXOSOMES REGULATED BY CELLULAR RAB7 LEVELS

The secreted signaling molecule, Sonic hedgehog (Shh) is involved in patterning and growth of various embryonic tissues across species. This lipid-anchored protein is secreted on extracellular vesicles to activate signaling. Shh has two functions during spinal cord development, acting as a morphogen to pattern the ventral neural tube and a mitogen to maintain neural progenitors. Here, we find that these activities are segregated on to two distinct pools of exosomes. Shh secreted on a classical exosomal pool (Shh-P150) is able to activate ventral threshold targets in the neural tube. In contrast, the mitogenic activity, is elicited by a lighter exosomal pool (Shh-P450). We further show that cellular environment plays a major role in regulating the biogenesis of P150 versus P450 by modulating Rab7 levels. We find that active Rab7 is necessary for ventral neural tube patterning through the regulation of Shh-P150 secretion. The cellular mechanisms involved in packaging and secretion of Shh with different partners on P150 or P450 pools may be more general, enabling the separation of functional activities of signaling molecules during development and disease.

developmental 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↗

bioRxiv2019

The primary cilium dampens proliferative signaling and represses a G2/M transcriptional network in quiescent myoblasts

Reversible cell cycle arrest (quiescence/G0) is characteristic of adult stem cells and is actively controlled at multiple levels. G0 cells extend a primary cilium, which functions as a signaling hub, but how it controls the quiescence program is not clear. Here, we report that primary cilia distinguish different states of cell cycle exit: quiescent myoblasts elaborate a primary cilium in vivo and in vitro, but terminally differentiated myofibers do not. Myoblasts where ciliogenesis is ablated using RNAi against a key ciliary assembly protein (IFT88) can exit the cell cycle but display an altered quiescence program and impaired self-renewal. Specifically, the G0 transcriptome in IFT88 knockdown cells is aberrantly enriched for G2/M regulators, suggesting a focused repression of this network by the cilium. Cilium-ablated cells also exhibit features of activation including enhanced activity of Wnt and mitogen signaling, and elevated protein synthesis via inactivation of the translational repressor 4EBP1. Taken together, our results show that the primary cilium integrates and dampens proliferative signaling, represses translation and G2/M genes, and is integral to the establishment of the quiescence program.\n\nSummary statementThe primary cilium contributes to reversible arrest (quiescence) in skeletal muscle myoblasts, by coordinating and dampening mitogenic signaling focused on a G2/M transcriptional program and protein synthesis.

cell biology↗