Biology subjects

Gupta, R. M.

Publications and source records attributed to Gupta, R. M..

6 recordsLinked to original sources

bioRxiv2025

Tubulin isotypes contribute opposing properties to balance anaphase spindle morphogenesis

Faithful chromosome segregation requires proper function of the mitotic spindle, which is built from, and depends on, the coordinated regulation of many microtubules and the activities of molecular motors and MAPs. In addition, microtubules themselves are assembled from multiple variants, or isotypes of - and {beta}-tubulin, yet whether they mediate the activities of motors and MAPs required for proper spindle function remains poorly understood. Here, we use budding yeast to reveal that -tubulin isotypes regulate opposing outward- and inward-directed forces in the spindle midzone that facilitate optimal spindle elongation and length control. Moreover, we show that the isotypes mediate balanced spindle forces by differentially localizing the antagonistic force generators Cin8 (kinesin-5) and Kar3 (kinesin-14) to interpolar microtubules. Our results reveal new roles for tubulin isotypes in orchestrating motor and MAP activities and provide insights into how forces in the spindle are properly calibrated to ensure proper mitotic spindle morphogenesis.

cell biology↗

bioRxiv2025

NERINE reveals rare variant associations in gene networks across multiple phenotypes and implicates an SNCA-PRL-LRRK2 subnetwork in Parkinson's disease

There are two primary approaches to study the genetic basis of human phenotypes. Experiments in model systems generate interpretable gene networks but, in isolation, do not establish relevance to the human condition. Statistical genetics identifies relevant association signals at the variant or gene level but lacks tools to test specific mechanistic models, as existing methods do not incorporate the topology of gene-gene interactions. We bridge these two strategies by introducing a method that competitively tests network hypotheses with rare variant associations. A hierarchical model-based association test NERINE for the first time incorporates gene network topology while remaining resilient to network inaccuracies. We demonstrate NERINEs ability to test network hypotheses derived from both canonical pathway databases and model system screens. Comprehensive database-wide search of pathway networks with NERINE uncovers compelling associations for breast cancer, cardiovascular diseases, and type II diabetes, which are undetected by single-gene tests. Testing bespoke networks from experimental screens targeting key PD pathologies: dopaminergic neuron survival and -synuclein pathobiology, NERINE highlights rare variant burden in gene modules related to autophagy, vesicle traiicking, and protein homeostasis. Genome-scale CRISPRi-screening of -synuclein toxicity modifiers in human neurons and NERINE converge on PRL, revealing an intraneuronal -synuclein/prolactin stress response that may impact resilience to PD pathologies.

genetics↗

bioRxiv2023

HiDDEN: A machine learning label refinement method for detection of disease-relevant populations in case-control single-cell transcriptomics

In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, but only a small fraction of them may actually be perturbed. Here, using simulations, we demonstrate that the standard approach to single cell analysis fails to isolate the subset of affected case cells and their markers when either the affected subset is small, or when the strength of the perturbation is mild. To address this fundamental limitation, we introduce HiDDEN, a computational method that refines the case-control labels to accurately reflect the perturbation status of each cell. We show HiDDENs superior ability to recover biological signals missed by the standard analysis workflow in simulated ground truth datasets of cell type mixtures. When applied to a dataset of human multiple myeloma precursor conditions, HiDDEN recapitulates the expert manual annotation and discovers malignancy in previously considered healthy early stage samples. When applied to a mouse model of demyelination, HiDDEN identifies an endothelial subpopulation playing a role in early stage blood-brain barrier dysfunction. We anticipate that HiDDEN should find a wide usage in contexts which require the detection of subtle changes in cell types across conditions.

bioinformatics↗

bioRxiv2022

Mapping the convergence of genes for coronary artery disease onto endothelial cell programs

Genome-wide association studies (GWAS) have discovered thousands of risk loci for common, complex diseases, each of which could point to genes and gene programs that influence disease. For some diseases, it has been observed that GWAS signals converge on a smaller number of biological programs, and that this convergence can help to identify causal genes1-6. However, identifying such convergence remains challenging: each GWAS locus can have many candidate genes, each gene might act in one or more possible programs, and it remains unclear which programs might influence disease risk. Here, we developed a new approach to address this challenge, by creating unbiased maps to link disease variants to genes to programs (V2G2P) in a given cell type. We applied this approach to study the role of endothelial cells in the genetics of coronary artery disease (CAD). To link variants to genes, we constructed enhancer-gene maps using the Activity-by-Contact model7,8. To link genes to programs, we applied CRISPRi-Perturb-seq9-12 to knock down all expressed genes within {+/-}500 Kb of 306 CAD GWAS signals13,14 and identify their effects on gene expression programs using single-cell RNA-sequencing. By combining these variant-to-gene and gene-to-program maps, we find that 43 of 306 CAD GWAS signals converge onto 5 gene programs linked to the cerebral cavernous malformations (CCM) pathway--which is known to coordinate transcriptional responses in endothelial cells15, but has not been previously linked to CAD risk. The strongest regulator of these programs is TLNRD1, which we show is a new CAD gene and novel regulator of the CCM pathway. TLNRD1 loss-of-function alters actin organization and barrier function in endothelial cells in vitro, and heart development in zebrafish in vivo. Together, our study identifies convergence of CAD risk loci into prioritized gene programs in endothelial cells, nominates new genes of potential therapeutic relevance for CAD, and demonstrates a generalizable strategy to connect disease variants to functions.

genomics↗

bioRxiv2020

Phylogenomic analysis of SARS-CoV-2 genomes from western India reveals unique linked mutations

India has become the third worst-hit nation by the COVID-19 pandemic caused by the SARS-CoV-2 virus. Here, we investigated the molecular, phylogenomic, and evolutionary dynamics of SARS-CoV-2 in western India, the most affected region of the country. A total of 90 genomes were sequenced. Four nucleotide variants, namely C241T, C3037T, C14408T (Pro4715Leu), and A23403G (Asp614Gly), located at 5UTR, Orf1a, Orf1b, and Spike protein regions of the genome, respectively, were predominant and ubiquitous (90%). Phylogenetic analysis of the genomes revealed four distinct clusters, formed owing to different variants. The major cluster (cluster 4) is distinguished by mutations C313T, C5700A, G28881A are unique patterns and observed in 45% of samples. We thus report a newly emerging pattern of linked mutations. The predominance of these linked mutations suggests that they are likely a part of the viral fitness landscape. A novel and distinct pattern of mutations in the viral strains of each of the districts was observed. The Satara district viral strains showed mutations primarily at the 3' end of the genome, while Nashik district viral strains displayed mutations at the 5' end of the genome. Characterization of Pune strains showed that a novel variant has overtaken the other strains. Examination of the frequency of three mutations i.e., C313T, C5700A, G28881A in symptomatic versus asymptomatic patients indicated an increased occurrence in symptomatic cases, which is more prominent in females. The age-wise specific pattern of mutation is observed. Mutations C18877T, G20326A, G24794T, G25563T, G26152T, and C26735T are found in more than 30% study samples in the age group of 10-25. Intriguingly, these mutations are not detected in the higher age range 61-80. These findings portray the prevalence of unique linked mutations in SARS-CoV-2 in western India and their prevalence in symptomatic patients. ImportanceElucidation of the SARS-CoV-2 mutational landscape within a specific geographical location, and its relationship with age and symptoms, is essential to understand its local transmission dynamics and control. Here we present the first comprehensive study on genome and mutation pattern analysis of SARS-CoV-2 from the western part of India, the worst affected region by the pandemic. Our analysis revealed three unique linked mutations, which are prevalent in most of the sequences studied. These may serve as a molecular marker to track the spread of this viral variant to different places.

genomics↗

bioRxiv2020

Deep learning enables genetic analysis of the human thoracic aorta

The aorta is the largest blood vessel in the body, and enlargement or aneurysm of the aorta can predispose to dissection, an important cause of sudden death. While rare syndromes have been identified that predispose to aortic aneurysm, the common genetic basis for the size of the aorta remains largely unknown. By leveraging a deep learning architecture that was originally developed to recognize natural images, we trained a model to evaluate the dimensions of the ascending and descending thoracic aorta in cardiac magnetic resonance imaging. After manual annotation of just 116 samples, we applied this model to 3,840,140 images from the UK Biobank. We then conducted a genome-wide association study in 33,420 individuals, revealing 68 loci associated with ascending and 35 with descending thoracic aortic diameter, of which 10 loci overlapped. Integration of common variation with transcriptome-wide analyses, rare-variant burden tests, and single nucleus RNA sequencing prioritized SVIL, a gene highly expressed in vascular smooth muscle, that was significantly associated with the diameter of the ascending and descending aorta. A polygenic score for ascending aortic diameter was associated with a diagnosis of thoracic aortic aneurysm in the remaining 391,251 UK Biobank participants who did not undergo imaging (HR = 1.44 per standard deviation; P = 3.7{middle dot}10-12). Defining the genetic basis of the diameter of the aorta may enable the identification of asymptomatic individuals at risk for aneurysm or dissection and facilitate the prioritization of potential therapeutic targets for the prevention or treatment of aortic aneurysm. Finally, our results illustrate the potential for rapidly defining novel quantitative traits derived from a deep learning model, an approach that can be more broadly applied to biomedical imaging data.

genomics↗