Biology subjects

Jayaraman, P.

Publications and source records attributed to Jayaraman, P..

6 recordsLinked to original sources

bioRxiv2026

Mechanical Signaling Regulates DNA Methylation to Maintain Muscle Stem Cell Quiescence

Skeletal muscle stem cells (MuSCs) reside within a mechanically dynamic niche where they integrate biophysical and biochemical cues to maintain quiescence. Here, we show that substrate stiffness and RhoA-dependent signaling regulate MuSC fate. MuSCs cultured on soft matrices or depleted of RhoA exhibit altered morphology, diminished actomyosin organization, and undergo premature activation. Loss of RhoA reshapes the DNA methylation landscape, leading to widespread changes in gene expression and alternative splicing. Dnmt3a was among the genes transcriptionally downregulated following loss of RhoA signaling. Mechanistically, RhoA maintains Dnmt3a expression by promoting SP1 occupancy at its promoter. Importantly, loss of Dnmt3a in quiescent MuSCs is sufficient to drive activation, identifying Dnmt3a as a key epigenetic effector downstream of mechanical signaling. Together, these findings define a mechanotransduction-epigenetic axis in which RhoA maintains stem cell quiescence by preserving DNA methylation programs through Dnmt3A.

cell biology↗

bioRxiv2025

Sample-level modeling of single-cell data at scale with tinydenseR

Single-cell studies now routinely encompass hundreds of samples and millions of cells, offering unprecedented opportunities to link sample-level phenotypes with cellular and molecular states. However, current workflows often depend on cell-level inference and rigid clustering, which can distort significance and obscure subtle, continuous variation, in particular for complex experimental designs. Here, we present tinydenseR, a clustering-independent framework that enables robust, scalable, and statistically sensitive detection of differential cell state density, outperforming existing workflows in speed and memory usage. Technology-agnostic at its core, tinydenseR works seamlessly on scRNA-seq, flow, mass and spectral cytometry. Across synthetic benchmarks, a preclinical xenograft model, a publicly available COVID-19 study, two immuno-oncology trials and a multi-study atlas, tinydenseR uncovers disease and treatment history-associated effects, including subtle cell state heterogeneity, while embedding samples in a quantitative manner. Designed to accelerate discovery in clinical, preclinical, and translational research, the open-source package is available at GitHub.com/Novartis/tinydenseR.

bioinformatics↗

bioRxiv2020

Induction of SARS-CoV-2 protein S-specific CD8+ T cells in the lungs of gp96-Ig-S vaccinated mice

Given the aggressive spread of COVID-19-related deaths, there is an urgent public health need to support the development of vaccine candidates to rapidly improve the available control measures against SARS-CoV-2. To meet this need, we are leveraging our existing vaccine platform to target SARS-CoV-2. Here, we generated cellular heat shock chaperone protein, glycoprotein 96 (gp96), to deliver SARS-CoV-2 protein S (spike) to the immune system and to induce cell-mediated immune responses. We showed that our vaccine platform effectively stimulates a robust cellular immune response against protein S. Moreover, we confirmed that gp96-Ig, secreted from allogeneic cells expressing full-length protein S, generates powerful, protein S polyepitope-specific CD4+ and CD8+ T cell responses in both lung interstitium and airways. These findings were further strengthened by the observation that protein-S -specific CD8+ T cells were induced in human leukocyte antigen (HLA)-A2-02-01 transgenic mice thus providing encouraging translational data that the vaccine is likely to work in humans, in the context of SARS-CoV-2 antigen presentation.

immunology↗

bioRxiv2019

AnthOligo: Automating the design of oligonucleotides for capture/enrichment technologies

SummaryA number of methods have been devised to address the need for targeted genomic resequencing. One of these methods, Region-specific extraction (RSE) of DNA is characterized by the capture of long DNA fragments (15-20 kb) by magnetic beads, after enzymatic extension of oligonucleotides hybridized to selected genomic regions. Facilitating the selection of the most optimal capture oligos targeting a region of interest, satisfying the properties of temperature (Tm) and entropy ({Delta}G), while minimizing the formation of primer dimers in a pooled experiment is therefore necessary. Manual design and selection of oligos becomes an extremely arduous task complicated by factors such as length of the target region and number of targeted regions. Here we describe, AnthOligo, a web-based application developed to optimally automate the process of generation of oligo sequences to be used for the targeting and capturing the continuum of large and complex genomic regions. Apart from generating oligos for RSE, this program may have wider applications in the design of customizable internal oligos to be used as baits for gene panel analysis or even probes for large-scale comparative genomic hybridization (CGH) array processes. Implementation and AvailabilityThe application written in Java8 and run on Tomcat9 is a lightweight Java Spring MVC framework that provides the user with a simple interface to upload an input file in BED format and customize parameters for each task. A Redis-like MapReduce framework is implemented to run sub-tasks in parallel to optimize time and system resources alongside a task-queuing system that runs submitted jobs as a server-side background daemon. The task of probe design in AnthOligo commences when a user uploads an input file and concludes with the generation of a result-set containing an optimal set of region-specific oligos. AnthOligo is currently available as a public web application with URL: http://antholigo.chop.edu.

bioinformatics↗

bioRxiv2019

Systematic and comprehensive benchmarking of an exome sequencing based germline copy-number variant detection pipeline in a clinical setting

PurposeDetecting germline copy-number variants (CNVs) from exome sequencing (ES) is not a standard practice in clinical settings owing to several reasons concerning performance. We comprehensively characterized an ES-based CNV pipeline and developed frameworks for minimizing false-positives and assess the reproducibility.\n\nMethodsWe used a cohort of 387 individuals with both clinical chromosomal microarray (CMA) and ES data available to estimate the initial performance by comparing CNVs from both platforms. A modification of the default workflow was performed to reduce the number of false positives and the reproducibility of the CNVs was assessed using an iterative variant calling process.\n\nResultsThe default pipeline was 93% sensitive with a high false-discovery rate of 44%. The modified workflow had a higher sensitivity of 96% while reducing the total number of CNVs identified and improving the false-discovery rate to 11.4%. With the modified workflow, we demonstrated a 100% validation rate for the CNVs identified in the STRC, a challenging gene to ascertain by short-read NGS. The exome-based pipeline was 100% sensitive for clinically-relevant, rare variants (including single exon deletions), and was reproducible.\n\nConclusionWe demonstrate with our modified workflow and the benchmarking data that an exome-based CNV detection pipeline can be reliably used to detect clinically-relevant CNVs.

bioinformatics↗

bioRxiv2018

Rapid interpretation of clinical exomes using Phenoxome: a computational phenotype-driven approach

Clinical exome sequencing (CES) has become the preferred diagnostic platform for complex pediatric disorders with suspected monogenic etiologies, solving up to 20%-50% of cases depending on indication. Despite rapid advancements in CES analysis, the major challenge still resides in identifying the casual variants among the thousands of variants detected during CES testing, and thus establishing a molecular diagnosis. To improve the clinical exome diagnostic efficiency, we developed Phenoxome, a robust phenotype-driven model that adopts a network-based approach to facilitate automated variant prioritization and subsequent classification. Phenoxome dissects the phenotypic manifestation of a patient in conjunction with their genomic profile to filter and then prioritize putative pathogenic variants. To validate our method, we have compiled a clinical cohort of 105 positive patient samples (i.e. at least one reported pathogenic variant) that represent a wide range of genetic heterogeneity from The Childrens Hospital of Philadelphia. Our approach identifies the causative variants within the top 5, 10, or 25 candidates in more than 50%, 71%, or 88% of these patient samples respectively. Furthermore, we show that our method is optimized for clinical testing by yielding superior ranking of the pathogenic variants compared to current state-of-art methods. The web application of Phenoxome is available to the public at http://phenoxome.chop.edu/.

bioinformatics↗