Biology subjects

Bhargava, S.

Publications and source records attributed to Bhargava, S..

6 recordsLinked to original sources

bioRxiv2025

Sequence based prediction of cell type specific microRNA binding and mRNA degradation for therapeutic discovery

MicroRNAs and RNA binding proteins are crucial elements of post-transcriptional gene regulation, which governs the fate of mRNA molecules in the cell. However, the landscape of these regulatory interactions, particularly across different mammalian cell types, remains underexplored. We describe REPRESS, a deep learning model that predicts cell-type-specific microRNA binding and mRNA degradation directly from RNA sequence. REPRESS was trained on AGO2-CLIP, miR-eCLIP and Degradome-Seq data profiling millions of microRNA binding and mRNA degradation sites across multiple cell types in human and mouse. It reveals biology that other state-of-the-art methods did not, such as identifying repressive non-canonical miRNA target sites and decoding the regulatory effects of sequence context and miRNA binding site multiplicity. REPRESS outperforms other advanced methods and neural architectures on a comprehensive suite of seven orthogonal tasks, including identifying genetic variants that affect microRNA binding, predicting out-of-distribution data from massively parallel reporter assays, and predicting canonical and non-canonical miRNA mediated repression. To demonstrate the general utility of REPRESS, we show that it provides insights into novel biology and the design of RNA therapeutics. Code is available at : https://github.com/deepgenomics/repress

genomics↗

bioRxiv2025

Single Nuclei-Derived Molecular Subtypes of Gastrointestinal Stromal Tumors Correlate with Clinicopathologic Features and Predict Clinical Outcomes

Historically, gastrointestinal stromal tumor (GIST) has been subtyped by oncogenic driver mutations. However, tumors with the same mutational profile can have variable biology. To further explore the impact of molecular diversity on GIST biology, we performed single nucleus RNA sequencing on 16 primary GIST and utilized an integrated single cell atlas of the normal GI tract composed from multiple publicly available datasets to identify six distinct GIST cell states. We then statistically estimated the relative abundances of these profiles in bulk transcriptomic data. These were used to define six common GIST molecular subtypes based upon one or two predominant tumor cell states. We found that these molecular subtypes correlate with tumor locations, mutational profiles, and patient outcomes, and validated these subtypes in an independent international cohort. These molecular subtypes have the potential to be used for clinical prognostication for patients with GIST, identifying new therapeutic targets, and studying the cell of transformation of GIST.

cancer biology↗

bioRxiv2025

Early cell cycle genes in cortical organoid progenitors predict interindividual variability in infant brain growth trajectories

Human induced pluripotent stem cell (iPSC) derived cortical organoids (hCOs) model neurogenesis on an individuals genetic background. The degree to which hCO phenotypes recapitulate the brain growth of the participants from which they were derived is not well established. We generated up to 3 iPSC clones from each of 18 participants in the Infant Brain Imaging Study, who have undergone longitudinal brain imaging during infancy. We identified consistent hCO morphology and cortical cell types across clones from the same participant. hCO cross-sectional area and production of cortical hem cells were associated with in vivo cortical growth rates. Cell cycle associated genes expression in early progenitors at the crux of fate decision trajectories were correlated with cortical growth rate from 6-12 months of age, and were enriched in microcephaly and neurodevelopmental disorder genes. Our data suggest the hCOs capture inter-individual variation in cortical cell types influencing infant cortical surface area expansion.

neuroscience↗

bioRxiv2023

An RNA foundation model enables discovery of disease mechanisms and candidate therapeutics

Accurately modeling and predicting RNA biology has been a long-standing challenge, bearing significant clinical ramifications for variant interpretation and the formulation of tailored therapeutics. We describe a foundation model for RNA biology, "BigRNA", which was trained on thousands of genome-matched datasets to predict tissue-specific RNA expression, splicing, microRNA sites, and RNA binding protein specificity from DNA sequence. Unlike approaches that are restricted to missense variants, BigRNA can identify pathogenic non-coding variant effects across diverse mechanisms, including polyadenylation, exon skipping and intron retention. BigRNA accurately predicted the effects of steric blocking oligonucleotides (SBOs) on increasing the expression of 4 out of 4 genes, and on splicing for 18 out of 18 exons across 14 genes, including those involved in Wilson disease and spinal muscular atrophy. We anticipate that BigRNA and foundation models like it will have widespread applications in the field of personalized RNA therapeutics.

bioinformatics↗

bioRxiv2020

New Radiological Classification of Glioma and validation with the survival analysis

Radiology based classification of glioma independent of histological or genetic markers predicting survival of patients is an unmet need. Until now radiology is chasing these markers rather than focussing directly on the clinical outcome. Our study is first of its kind to come up with the independent new radiological classification of gliomas encompassing both low-and high-grade gliomas under single classification system. TCGA-LGG and REMBRANDT public domain dataset of glioma were analyzed as training and testing dataset respectively. Based on MRI images, gliomas were classified into six types in detailed classification & three types in simplified classification system. Survival analysis using Kaplan Meier and Cox regression was done. Secondary objective was to evaluate the sensitivity and specificity of novel signs with existing histological and genetic markers. The study predicted survival in both training and testing dataset independent of genetic or histological information. Novel signs, "Ball on Christmas tree" sign(highly specific), Type-4 lineage sign(highly sensitive) identifies IDH-wild and high-grade gliomas (grade-III and IV) while Type-2 lineage sign showed good specificity in identifying 1p19q non co-deleted IDH-mutated, ATRX del/mutated, Grade-II gliomas. There is a substantial interobserver agreement for the classification and novel signs. New radiological classification of glioma predicts the survival of patients independent of genetic or histological information. This can act as a scaffolding to formulate and streamline the treatment guidelines for glioma patients. This classification has potential of improving the quality of care of glioma patients by predicting the survival without the need of invasive biopsy.

cancer biology↗

bioRxiv2020

Pharmacological inhibition of longevity regulator PAPP-A restrains mesenchymal stromal cell activity

Reducing insulin-like growth factor (IGF) signaling is one of the best conserved and characterized mechanisms to extend longevity. Pregnancy associated plasma protein A (PAPP-A) is a secreted metalloprotease that increases IGF availability by cleaving IGF binding proteins. PAPP-A inhibition reduces local IGF signaling, limits the progression of multiple age-related diseases, and extends lifespan, but the mechanisms behind these pleiotropic effects remains unknown. Here, we developed and utilized a PAPP-A neutralizing antibody to discover that adulthood inhibition of this protease reduced collagen and extracellular matrix (ECM) gene expression in multiple tissues in mice. Using bone marrow to explore this effect, we identified mesenchymal stromal cells (MSCs) as the source of PAPP-A and primary responders to PAPP-A inhibition. Short-term treatment with anti-PAPP-A reduced IGF signaling in MSCs, altered MSC expression of collagen/ECM, and decreased MSC number. This affected MSC-dependent functions, decreasing myelopoiesis and osteogenesis. Our data demonstrate that PAPP-A inhibition reduces the activity and number of IGF-dependent mesenchymal progenitor cells and their differentiated progeny, and that this reduction leads to functional changes at the tissue level. MSC-like cells are present in virtually all tissues, and aberrant collagen and ECM production from mesenchymal cells drives aspects of aging and age-related diseases, thus this may be a mechanism by which PAPP-A deficiency enhances lifespan and healthspan. SummaryInhibition of PAPP-A, a regulator of IGF signaling, decreases multi-tissue collagen and extracellular matrix gene expression and modulates mesenchymal stromal cell activity in murine bone marrow.

physiology↗