Biology subjects

Dhillon, K.

Publications and source records attributed to Dhillon, K..

5 recordsLinked to original sources

bioRxiv2026

HyperSketch: de Bruijn graph sketching for genomic similarity estimation with Hyperdimensional Computing

The exponential growth of genomic databases necessitates alignment-free methods for comparing genomes. While MinHash-based tools have revolutionized this field by efficiently estimating the Average Nucleotide Identity based on k-mer sets, they inherently discard structural genomic information. We introduce HyperSketch, a novel sketching tool that encodes the de Bruijn graph structure of a genome into a fixed-size, topology-aware vector using Hyperdimensional Computing (HDC). Unlike set-based sketches, HyperSketch encodes the transitions between adjacent k-mers into a superposition of orthogonal hypervectors. To formalize parameter selection, we also propose an analytical framework proving that graph-based sketches fundamentally require a smaller k-mer size than set-based models due to their expanded k+1 biological footprint. We benchmarked HyperSketch against Mash and HyperGen using a dataset of ~26 thousand viral reference genomes from NCBI GenBank. Under optimal parameters, we demonstrate a strong linear correlation (>99%) between the graph-based similarity computed by HyperSketch and standard MinHash distance estimates. Crucially, we show that the mathematical formulation of HyperSketch introduces a distance scaling effect that expands the dynamic range of estimates for closely related strains, providing a higher-resolution metric for sub-lineage clustering than purely compositional estimators. HyperSketch provides a computationally efficient, structure-aware alternative to traditional sketching. By natively encoding genomic syntax, it offers a new dimension of genomic comparison that excels at both high-resolution strain differentiation and deep evolutionary scaling, complementing existing nucleotide identity metrics without requiring sequence alignment.

bioinformatics↗

bioRxiv2026

Extended maturation of the HD10.6 immortalised human dorsal root ganglion cell line enables modelling of nociceptive responses and neural injury

Human sensory neuron models are an important resource for studying pain mechanisms and axon injury and repair. Current systems are limited by accessibility, scalability, or incomplete functional maturation. The HD10.6 human dorsal root ganglion-derived immortalised cell line represents a promising alternative; however, its maturation trajectory and suitability for disease modelling remain incompletely defined. Here, we performed a longitudinal, multi-modal characterisation of HD10.6 cells during differentiation over 28 days. Bulk RNA sequencing revealed progressive transcriptional remodelling, with temporal up-regulation of neuronal and nociceptor-associated gene programmes, including ion channels implicated in pain signalling. Protein-level analyses confirmed increased expression of key nociceptor markers and neuropeptides, including TRPV1, Nav1.7, Nav1.8 and CGRP. Functional assays demonstrated the emergence of sensory neuron-like properties over time. Calcium imaging revealed increasing responsiveness to capsaicin, allyl isothiocyanate, ,{beta}-MeATP, and prostaglandin E2, while patch-clamp electrophysiology at DIV 21 after maturation showed repetitive firing of action potential and, most importantly, exhibited TTX-Resistant sodium currents. These findings establish a temporal relationship between transcriptional changes and functional competence. Finally, we evaluated the utility of HD10.6 neurons for modelling axon degeneration. Treatment with vacor-induced robust neurite degeneration, which was attenuated by pharmacological inhibition of SARM1, demonstrating engagement of conserved axon degeneration pathways. Together, our findings define the progressive maturation of HD10.6 sensory neurons and establish this system as a scalable human platform for studying nociceptor biology and SARM1-dependent axon degeneration.

neuroscience↗

bioRxiv2026

In vivo motor unit decoding and in vitro cellular characterisation of spinal circuits for urination in adult mice

Urinary dysfunction affects billions of individuals worldwide; however, the fundamental cellular and circuit properties that govern perineal motor control remain largely unknown, serving as a functional "black box". Here, we describe several methods that, when used in concert, characterise cellular, synaptic, and motor unit properties underlying the control of urination in adult mice. High-density electromyography combined with real-time cystometry were used to study external urethral sphincter (EUS) motor units, which follow a hierarchical ("onion skin") recruitment pattern during bladder filling. The transition to the voiding phase is marked by inhibition, followed by synchronised bursts. Furthermore, through concurrent recordings of ischiocavernosus (IC) muscles, the relationship between IC and EUS motor units could be studied to look for shared common inputs that could shed light on circuitry. Whole-cell patch-clamp recordings from retrogradely identified neurons revealed a fundamental biophysical divergence: urinary parasympathetic preganglionic neurons (PPGN) are significantly smaller and more excitable than somatic EUS and IC motoneurons and lack the recurrent excitatory and inhibitory circuits present in both EUS and IC motor pools. Finally, using a novel pressure-clamp preparation, we showed that acute tibial nerve stimulation (a widely used treatment for urinary dysfunction) evokes short-latency inhibition of EUS motor units. Collectively, these methods can be used to delineate patterns of motor unit recruitment, local recurrent microcircuit architecture, and distinct biophysical properties of the perineal motor system, providing mechanistic insights into urinary function.

neuroscience↗

bioRxiv2025

Predicting the toxicity of chemical compounds via Hyperdimensional Computing

Accurately and efficiently assessing the potential toxicity of chemical compounds is critical given their wide application across pharmaceutical, industrial, and environmental domains. Traditional toxicological evaluations, which predominantly rely on intensive in vitro and in vivo assays, are frequently slow and expensive processes. Here, we introduce a novel application of Hyperdimensional Computing (HDC), an emerging computational paradigm inspired by the way the human brain works in encoding information, for the efficient classification of chemical compounds as either toxic or non-toxic. Our methodology employs Simplified Molecular Input Line Entry System (SMILES) representations of compounds, drawing data from the comprehensive Tox21 dataset. We delineate a pipeline wherein these chemical structures are encoded into high-dimensional binary vectors, which subsequently serve as the foundation for training and classification within the HDC framework. This approach leverages HDCs inherent advantages, including its resilience to noise, parallel processing capabilities, and efficacy in identifying intricate patterns. This work demonstrates the viability of HDC as a promising alternative for large-scale toxicity prediction, offering a computationally efficient and scalable solution. This research significantly contributes to the field of cheminformatics by validating HDCs potential in chemical property prediction, thereby facilitating accelerated identification of hazardous substances and mitigating the reliance on intensive laboratory experimentations.

bioinformatics↗

bioRxiv2025

A novel Vector-Symbolic Architecture for graph encoding and its application to viral pangenome-based species classification

Viral species classification is crucial for understanding viral evolution, epidemiology, and developing effective diagnostics and treatments. Traditional methods often rely on sequence similarity, which can be challenging for rapidly evolving viruses. Pangenomes, offering a comprehensive representation of species genomic diversity, provide a richer perspective, but their analysis often requires advanced computational methods. We investigate the use of Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architecture (VSA), an emerging computing paradigm that relies on vectors in high-dimensional spaces to encode a multi-species viral pangenome. We develop a new method for encoding graph-structured viral pangenomes using high-dimensional vectors. Pangenomes are represented as weighted de Bruijn graphs constructed using sequences of consecutive k-mers from the genomes, while information about the genome species (their class) is encoded as specific weights on the edges of the graph. The weighted de Bruijn graph representation is encoded into a single high-dimensional vector. We tested three classification strategies: a flat model at the species level, a flat model at the genus level, and a two-step hierarchical model. We applied our method to a pangenome comprising 542 viral species from NCBI GenBank. Our results reveal a complex relationship between model architecture and classification accuracy. The flat species-level model achieved the highest accuracy, correctly classifying 87.08% of test genomes. Counter-intuitively, simplifying the problem to the genus level or using a hierarchical approach degraded performance, with accuracies dropping to 60.51% and 33.57% respectively. These outcomes highlight critical challenges in alignment-free classification, such as signal dilution in overly broad taxonomic groups and error propagation in multi-step models. The models reconstruction rate proved to be a reliable measure of confidence, rather than a direct predictor of correctness. This novel approach offers a promising new direction for viral classification, not only for its predictive power but its ability to reveal underlying challenges in genomic taxonomy.

bioinformatics↗