Biology subjects

Barve, R.

Publications and source records attributed to Barve, R..

8 recordsLinked to original sources

bioRxiv2025

SnakeAltPromoter Facilitates Differential Alternative Promoter Analysis

BackgroundAlternative promoter usage regulates isoform diversity in mammals, playing critical roles in development, disease, and cellular reprogramming. While Cap Analysis of Gene Expression (CAGE) enables precise transcription start site mapping, its high cost and limited coverage hinder scalability. In contrast, RNA-seq is abundant across biological contexts; several algorithms (ProActiv, Salmon, DEXSeq) infer promoter activity from these data, yet no unified, reproducible framework exists to execute, benchmark, and compare them or to scale alternative promoter analyses across large compendia. ResultsWe developed snakeAltPromoter, an end-to-end Snakemake workflow that ingests raw FASTQ files, performs quality control and alignment, quantifies promoter activity using three complementary strategies (junction-based, transcript-based, and first-exon-based), classifies promoters into major, minor/alternative, and inactive categories, and conducts both differential promoter activity and usage analysis. Crucially, snakeAltPromoter integrates a systematic benchmarking module against matched CAGE profiles to reveal method-specific strengths and limitations. ProActiv showed the highest concordance with CAGE in promoter classification, activity and differential analysis, Salmon was robust at low coverage and intronless cases. Overall, the complete workflow recovered a majority of CAGE-validated active promoters and processed a 50 M-read RNA-seq sample in around 2h on a 32-core node, demonstrating both accuracy and scalability. ConclusionssnakeAltPromoter is, to our knowledge, the first reproducible framework that pairs comparative method evaluation with scalable differential alternative promoter analysis. It provides concrete guidance for method selection under different experimental scenarios and enables high-throughput mining of promoter-level regulation from public RNA-seq repositories. Code and example data are freely available at https://github.com/YidanSunResearchLab/SnakeAltPromoter.git.

bioinformatics↗

bioRxiv2024

SIRT6 loss causes intervertebral disc degeneration in mice by promoting senescence and SASP status

Intervertebral disc degeneration is a major risk factor contributing to chronic low back and neck pain. While the etiological factors for disc degeneration vary, age is still one of the most important risk factors. Recent studies have shown the promising role of SIRT6 in mammalian aging and skeletal tissue health, however its role in the intervertebral disc health remains unexplored. We investigated the contribution of SIRT6 to disc health by studying the age-dependent spinal phenotype of mice with conditional deletion of Sirt6 in the disc (AcanCreERT2; Sirt6fl/fl). Histological studies showed a degenerative phenotype in knockout mice compared to Sirt6fl/fl control mice at 12 months which became pronounced at 24 months. RNA-Seq analysis of NP and AF tissues, quantitative histone analysis, and in vitro multiomics employing RNA-seq with ATAC-seq revealed that SIRT6-loss resulted in changes in acetylation and methylation status of specific Histone 3 lysine residues, thereby affecting DNA accessibility and transcriptomic landscape. A decrease in autophagy and an increase in DNA damage were also noted in Sirt6-deficient cells. Further mechanistic insights revealed that loss of SIRT6 increased senescence and SASP burden in the disc characterized by increased p21, {gamma}H2AX, IL-6, and TGF-{beta} abundance. Taken together our study highlights the contribution of SIRT6 in modulating DNA damage, autophagy and cell senescence, and its importance in maintaining disc health during aging thereby underscoring it as a potential therapeutic target to treat intervertebral disc degeneration.

molecular biology↗

bioRxiv2023

Increased HIF-2α Activity in the Nucleus Pulposus Causes Intervertebral Disc Degeneration in the Aging Mouse Spine

Hypoxia-inducible factors (HIFs) are essential to the homeostasis of hypoxic tissues. Although HIF-2, is expressed in nucleus pulposus (NP) cells, consequences of elevated HIF-2 activity on disc health remains unknown. We expressed HIF-2 with proline to alanine substitutions (P405A;P531A) in the Oxygen-dependent degradation domain (HIF-2dPA) in the NP tissue using an inducible, nucleus pulposus-specific K19CreERT allele to study HIF-2 function in the adult intervertebral disc. Expression of HIF-2 in NP impacted disc morphology, as evident from small but significantly higher scores of degeneration in NP of 24-month-old K19CreERT; HIF-2dPA (K19-dPA) mice. Noteworthy, comparisons of grades within each genotype between 14 months and 24 months indicated that HIF-2 overexpression contributed to more pronounced changes than aging alone. The annulus fibrosus (AF) compartment in the 14-month-old K19-dPA mice exhibited lower collagen turnover and Fourier transform-infrared (FTIR) spectroscopic imaging analyses showed changes in the biochemical composition of the 14-and 24-month-old K19-dPA mice. Moreover, there were changes in aggrecan, chondroitin sulfate, and COMP abundance without alterations in NP phenotypic marker CA3, suggesting the overexpression of HIF-2 had some impact on matrix composition but not the cell phenotype. Mechanistically, the global transcriptomic analysis showed enrichment of differentially expressed genes in themes closely related to NP cell function such as cilia, SLIT/ROBO pathway, and HIF/Hypoxia signaling at both 14- and 24-months. Together, these findings underscore the role of HIF-2 in the pathogenesis of disc degeneration in the aged spine.

cell biology↗

bioRxiv2022

Embryonic Vitamin D Deficiency Programs Hematopoietic Stem Cells to Induce Type 2 Diabetes

Environmental factors may alter the fetal genome to cause metabolic diseases. It is unknown whether embryonic immune cell programming impacts the risk of type 2 diabetes in later life. We demonstrate that transplantation of fetal hematopoietic stem cells (HSCs) made vitamin D deficient in utero induces diabetes in vitamin D-sufficient mice. Vitamin D deficiency epigenetically suppresses Jarid2 expression and activates the Mef2/PGC1a pathway in HSCs, which persists in recipient bone marrow, resulting in adipose macrophage infiltration. These macrophages secrete miR106-5p, which promotes adipose insulin resistance by repressing PIK3 catalytic subunit alpha and AKT signaling. Vitamin D-deficient monocytes from human cord blood have comparable Jarid2/Mef2/PGC1a expression changes and secrete miR-106b-5p, causing adipocyte insulin resistance. These findings suggest that vitamin D deficiency during development has epigenetic consequences impacting the systemic metabolic milieu.

physiology↗

bioRxiv2021

A literature-derived knowledge graph augments the interpretation of single cell RNA-seq datasets

Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have rapidly advanced in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type defining genes, while the application of scalable natural language processing (NLP) methods to enhance analysis workflows has not been adequately explored. Here we deployed an NLP framework to objectively quantify associations between a comprehensive set of over 20,000 human protein-coding genes and over 500 cell type terms across over 26 million biomedical documents. The resultant gene-cell type associations (GCAs) are significantly stronger between a curated set of matched cell type-marker pairs than the complementary set of mismatched pairs (Mann Whitney p < 6.15x10-76, r = 0.24; cohens D = 2.6). Building on this, we developed an augmented annotation algorithm that leverages GCAs to categorize cell clusters identified in scRNA-seq datasets, and we tested its ability to predict the cellular identity of 185 clusters in 13 datasets from human blood, pancreas, lung, liver, kidney, retina, and placenta. With the optimized settings, the true cellular identity matched the top prediction in 66% of tested clusters and was present among the top five predictions for 94% of clusters. Further, contextualization of differential expression analyses with these GCAs highlights poorly characterized markers of established cell types, such as CLIC6 and DNASE1L3 in retinal pigment epithelial cells and endothelial cells, respectively. Taken together, this study illustrates for the first time how the systematic application of a literature derived knowledge graph can expedite and enhance the annotation and interpretation of scRNA-seq data.

genomics↗

bioRxiv2020

Real-time biomedical knowledge synthesis of the exponentially growing world wide web using unsupervised neural networks

Decoding disease mechanisms for addressing unmet clinical need demands the rapid assimilation of the exponentially growing biomedical knowledge. These are either inherently unstructured and non-conducive to current computing paradigms or siloed into structured databases requiring specialized bioinformatics. Despite the recent renaissance in unsupervised neural networks for deciphering unstructured natural languages and the availability of numerous bioinformatics resources, a holistic platform for real-time synthesis of the scientific literature and seamless triangulation with deep omic insights and real-world evidence has not been advanced. Here, we introduce the nferX platform that makes the highly unstructured biomedical knowledge computable and supports the seamless visual triangulation with statistical inference from diverse structured databases. The nferX platform will accelerate and amplify the research potential of subject-matter experts as well as non-experts across the life science ecosystem (https://academia.nferx.com/).

bioinformatics↗

bioRxiv2020

Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors

The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission. Despite the recent renaissance in unsupervised neural networks for decoding unstructured natural languages, a platform for the real-time synthesis of the exponentially growing biomedical literature and its comprehensive triangulation with deep omic insights is not available. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations extracted from unstructured biomedical text, and their triangulation with Single Cell RNA-sequencing based insights from over 25 tissues. Using this platform, we identify intersections between the pathologic manifestations of COVID-19 and the comprehensive expression profile of the SARS-CoV-2 receptor ACE2. We find that tongue keratinocytes, airway club cells, and ciliated cells are likely underappreciated targets of SARS-CoV-2 infection, in addition to type II pneumocytes and olfactory epithelial cells. We further identify mature small intestinal enterocytes as a possible hotspot of COVID-19 fecal-oral transmission, where an intriguing maturation-correlated transcriptional signature is shared between ACE2 and the other coronavirus receptors DPP4 (MERS-CoV) and ANPEP (-coronavirus). This study demonstrates how a holistic data science platform can leverage unprecedented quantities of structured and unstructured publicly available data to accelerate the generation of impactful biological insights and hypotheses. The nferX Platform Single-cell resource - https://academia.nferx.com/

genomics↗

bioRxiv2018

Single cell RNAseq uncovers a robust transcriptional response to morphine by oligodendrocytes

Molecular and behavioral responses to opioids are thought to be primarily mediated by neurons, although there is accumulating evidence that other cell types also play a role in drug addiction. To investigate cell-type-specific opioid responses, we performed single-cell RNA sequencing of the nucleus accumbens of mice following acute morphine treatment. Differential expression analysis uncovered robust morphine-dependent changes in gene expression in oligodendrocytes. We examined the expression of selected genes, including Cdkn1a and Sgk1, by FISH, confirming their induction by morphine in oligodendrocytes. Further analysis using RNAseq of FACS-purified oligodendrocytes revealed a large cohort of morphine-regulated genes. Importantly, the affected genes are enriched for roles in cellular pathways intimately linked to oligodendrocyte maturation and myelination, including the unfolded protein response. Altogether, our data shed light on a novel, morphine-dependent transcriptional response by oligodendrocytes that may contribute to the myelination defects observed in human opioid addicts.

neuroscience↗