Biology subjects

Panwar, P.

Publications and source records attributed to Panwar, P..

9 recordsLinked to original sources

bioRxiv2026

CCIDeconv: Hierarchical model for deconvolution of subcellular cell-cell interactions in single-cell data

Cell-cell interaction (CCI) underlies several fundamental biological processes, including development, homeostasis and disease progression. Subcellular spatial transcriptomics (sST) provides an opportunity to examine whether CCI-associated signals show compartment-specific patterns within cells. Assessing CCI at subcellular level can help us gain insights into the distinct pathway activation and signalling patterns. We developed a novel approach that deconvolutes CCI into subcellular CCI (sCCI) information from non-spatial single-cell transcriptomics (scRNA- seq) based CCI using a modified CellChat-derived communication score. By estimating communication scores separately for cytoplasmic and nuclear compartments, we identified compartment-associated sCCI. We then deconvolved whole-cell communication scores into subcellular compartments using a hierarchical classification and regression framework, which we call CCIDeconv. To ensure biological fidelity, we integrated protein localization data from the Human Protein Atlas in our deconvolution model. Across nine publicly available human sST datasets, leave-one- dataset-out validation achieved a median composite score of 0.75, with mean R2 values of 0.87 and 0.80 for cytoplasmic- and nuclear-associated scores, respectively. Performance without spatial features approached that of spatial models as the number of training datasets increased, supporting application to non-spatial scRNA-seq data. This highlighted the potential for prediction of sCCI from scRNA-seq, given a sufficiently large number of training datasets. Overall, our method can attribute whole-cell CCI to its subcellular compartments, allowing researchers to dissect sCCI patterns and gain insights into the underlying biology of healthy and disease tissues. Keywords Cell-Cell Communication, Single Cell RNA-seq, Predictive Modeling, Bioinformatics, Transcriptomics, Machine Learning

bioinformatics↗

bioRxiv2026

Origin-1: a generative AI platform for de novo antibody design against novel epitopes

0Generative artificial intelligence has advanced antibody discovery, yet de novo design of therapeutic antibodies against targets with "zero-prior" epitopes remains a fundamental challenge. We define "zero-prior" epitopes as target sites lacking structural data from any reported antibody-antigen or protein-protein complex involving the target. Here we present Origin-1, a generative AI platform that overcomes this by integrating epitope-conditioned all-atom structure generation, paired complementarity determining region sequence design, and a specialized co-folding-based scoring protocol to select antibody designs predicted to be high-confidence, specific binders with favorable developability. We evaluated Origin-1 on a panel of ten targets selected to have no available protein-protein complex structures and minimal homology ([≤]60% sequence identity) to proteins with known complexes, creating stringent design conditions. In fewer than one hundred design attempts per target, we identified developable, specific antibodies, validated across multiple biophysical and developability assays, for four targets: COL6A3, AZGP1, CHI3L2, and IL36RA, with functional inhibition demonstrated for IL36RA. Cryogenic electron microscopy confirmed the atomic accuracy of our designs, revealing complexes that closely matched the computational models with high structural fidelity (3.0-3.3 [A] resolution; 0.83-0.91 DockQ). Furthermore, we employed AI-guided affinity maturation to optimize a de novo antibody binder against IL36RA, producing functional antagonists with sub-nanomolar affinities and a top EC50 of 12.3 nM. These results demonstrate a framework for targeting epitopes without structural precedent, expanding the programmable therapeutic antibody landscape.

molecular biology↗

bioRxiv2025

ClustSIGNAL identifies cell types and subtypes using an adaptive smoothing approach for scalable spatial clustering

Unsupervised clustering of spatial transcriptomics data is challenging due to sparsity and complex tissue structures. We introduce ClustSIGNAL spatial clustering method that employs neighbourhood complexity-based expression smoothing. ClustSIGNAL uses entropy to measure neighbourhood compositions and generate cell-specific weights that control the effective smoothing radius within neighbourhoods. Simulation analysis demonstrates its robustness to segmentation errors and sparsity. Benchmarking across diverse SRT datasets shows its ability to perform scalable, multi-sample clustering of atlas-level data. By stabilising expression in homogeneous regions and preserving distinct expression in heterogeneous regions, ClustSIGNAL avoids over-smoothing and identifies biologically meaningful spatially-informed cell states. ClustSIGNAL Bioconductor package: bioconductor.org/packages/clustSIGNAL.

bioinformatics↗

bioRxiv2025

Orchestrating Spatial Transcriptomics Analysis with Bioconductor

Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

bioinformatics↗

bioRxiv2025

Cathepsin K as a Key SARS-CoV-2 Cell Entry Protease and Dual-Inhibition Target

SARS-CoV-2 relies on host proteases to prime its spike protein for cell entry through either the endosomal or plasma membrane pathway. Although cysteine cathepsins are known to mediate the endosomal route, the identity of the dominant enzyme has remained unclear. Here, we identify human Cathepsin K (hCatK), a lysosomal cysteine protease, as a previously unrecognized yet functionally important mediator of spike activation. While human Cathepsin L (hCatL) has long been regarded as the principal endosomal protease for spike processing, inhibition of hCatK with the selective inhibitor Odanacatib suppressed viral infection in endothelial cells as effectively as the broad-spectrum cysteine protease inhibitor E-64d, implicating hCatK as a key driver of spike processing during the endosomal viral entry. Comprehensive enzymatic profiling demonstrated that hCatK exhibits 24- to 63-fold higher catalytic efficiency toward the Furin-cleavage site (FCS) sequence than hCatL and displays a distinct substrate-recognition pattern at the Omicron FCS relative to the Wuhan variant. We further demonstrate that hCatK is an off-target of Nirmatrelvir, a clinically approved 3CL-Mpro inhibitor, with a sub-micromolar potency (IC50 = 0.6 {+/-} 0.1 {micro}M). A 1.9 [A] crystal structure of the hCatK-Nirmatrelvir complex delineates the molecular basis of inhibitor binding and supports the rational design of dual-acting antivirals. Collectively, these findings redefine the landscape of host proteases involved in SARS-CoV-2 spike activation and establish hCatK as a previously overlooked but strategic target for antiviral intervention.

biochemistry↗

bioRxiv2025

Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data

With the dramatic take-up of spatially resolved transcriptomics biotechnologies, performing spatially-aware analysis of the resulting data is crucial to maximise advances in biological understanding. Dimensionality reduction is a first step in almost any analysis of spatial transcriptomics data, regardless of whether the data is collected at the single-cell or spot level. While common approaches, such as principal component analysis, aim to identify low-dimensional scores that preserve total variances of gene expression features, such variances do not usually correspond to biologically relevant spatial variation. To this end, we have developed weighted sliced inverse regression (wSIR), a sufficient dimension reduction technique that performs dimensionality reduction and retains as much predictive power of the spatial coordinates as possible. As a linear dimensionality reduction approach, wSIR is applicable to multiple distinct spatial transcriptomic datasets, and is extremely scalable due to the algorithm and our Rcpp implementation, with over 100, 000 cells processed in under 2 minutes on a standard laptop. The feature loadings are interpretable, and new non-spatial data can be projected into the wSIR low-dimensional space for further downstream analyses. We examine wSIRs performance through benchmarking and demonstrate its capability of biological discovery through two case studies in breast cancer and early embryonic development.

genomics↗

bioRxiv2024

Spatial mutual nearest neighbors for spatial transcriptomics data

Mutual nearest neighbors (MNN) is a widely used computational tool to perform batch correction for single-cell RNA-sequencing data. However, in applications such as spatial transcriptomics, it fails to take into account the 2D spatial information. Here, we present spatialMNN, an algorithm that integrates multiple spatial transcriptomic samples and identifies spatial domains. Our approach begins by building a k-Nearest Neighbors (kNN) graph based on the spatial coordinates, prunes noisy edges, and identifies niches to act as anchor points for each sample. Next, we construct a MNN graph across the samples to identify similar niches. Finally, the spatialMNN graph can be partitioned using existing algorithms, such as the Louvain algorithm to predict spatial domains across the tissue samples. We demonstrate the performance of spatialMNN using large datasets, including one with N=36 10x Genomics Visium samples. We also evaluate the computing performance of spatialMNN to other popular spatial clustering methods. Our software package is available at (https://github.com/Pixel-Dream/spatialMNN).

bioinformatics↗

bioRxiv2021

Bone innervation and vascularization regulated by osteoclasts contribute to refractive pain-related behavior in the collagen antibody-induced arthritis model

ObjectiveRheumatoid arthritis is often characterized by eroded joints and chronic pain that outlasts disease activity. Whilst several reports show strong associations between bone resorption and nociception, the underlying mechanisms remain to be unraveled. Here, we used the collagen antibody-induced arthritis (CAIA) model to examine the contribution of osteoclasts in pain regulation. The antinociceptive effects of osteoclasts inhibitors and their mechanisms of actions involving bone vascularization and innervation were also explored. MethodsBALB/c female mice were subjected to CAIA by intravenous injection of a collagen type-II antibody cocktail, followed by intraperitoneal injection of lipopolysaccharide. Degree of arthritis, bone resorption, mechanical hypersensitivity, vascularization and innervation in the ankle joint were assessed. Animals were treated with osteoclast inhibitors, zoledronate and cathepsin K inhibitor (T06), and netrin-1 neutralizing antibody. Potential pronociceptive factors were examined in primary osteoclast cultures. ResultsCAIA induced local bone loss in the calcaneus with ongoing increased osteoclast activity during the inflammatory phase of the model, but not after inflammation has resolved. Mechanical hypersensitivity was reversed by zoledronate in late but not inflammatory phase CAIA. This effect was coupled to the ability of osteoclasts to modulate bone vascularization and innervation, which was inhibited by osteoclast inhibitors. CAIA-induced hypersensitivity in the late phase was also reversed by anti-netrin-1 antibody. ConclusionOsteoclasts induce pain-like behavior in the CAIA model independent of inflammation via effects on bone vascularization and innervation. Key messagesWhat is already known about this subject? O_LIPain and residual signs of erosive lesions are frequently present in rheumatoid arthritis (RA) patients with good disease control C_LIO_LIOsteoclasts can induce nociceptive signaling but the exact mechanism with respect to RA-induced pain is not clear C_LI What does this study add? O_LIThe pronociceptive actions of osteoclasts extend beyond flares of joint inflammation and erosive activity by increasing bone innervation, bone vascularization and netrin-1 release C_LIO_LIOsteoclast inhibitors and neutralizing netrin-1 antibodies reverse refractive pain-related behaviors in the collagen antibody-induced arthritis model C_LI How might this impact on clinical practice or future developments? O_LIThis study provides insights to the potential of osteoclast inhibition as a therapeutic strategy for persistent pain in RA C_LI

neuroscience↗

bioRxiv2019

Lead optimisation of dehydroemetine for repositioned use in malaria

Drug repositioning offers an effective alternative to de novo drug design to tackle the urgent need for novel anti-malarial treatments. The anti-amoebic compound, emetine dihydrochloride, has been identified as a potent in-vitro inhibitor of the multi-drug resistant strain K1 of Plasmodium falciparum (IC50: 47 nM + 2.1 nM). 2,3-dehydroemetine, a synthetic analogue of emetine dihydrochloride has been claimed to have less cardiotoxic effects than emetine. The structures of two diastereoisomers of 2,3-dehydroemetine were modelled on the reported emetine binding site on cryo-EM structure 3J7A and it was found that (-)-R,S-dehydroemetine mimicked the bound pose of emetine more closely than (-)-S,S-dehydroisoemetine. (-)-R,S-dehydroemetine was also found to be highly potent against the multi-drug resistant K1 strain of P. falciparum in comparison with (-)-S,S-dehydroisoemetine, which loses its potency due to the change of configuration at C-1. In addition to its effect on the asexual erythrocytic stages of P. falciparum, the compounds exhibited gametocidal properties with no cross-resistance against any of the multi-drug resistant strains tested. Drug interaction studies showed (-)-R,S-dehydroemetine to have synergistic antimalarial activity with atovaquone and proguanil. Emetine dihydrochloride, and (-)-R,S-dehydroemetine failed to show any inhibition of the hERG potassium channel and displayed atovoquone-like activity on the mitochondrial membrane potential.

microbiology↗