Biology subjects

Varghese, L.

Publications and source records attributed to Varghese, L..

3 recordsLinked to original sources

bioRxiv2025

TRPM2 is a direct pain transducer

Chronic pain results from maladaptive interaction between the immune and nervous systems. TRPM2 channels in immune cells (immune TRPM2) are believed to facilitate chronic pain by indirectly promoting immune-inflammatory responses. Whereas TRPM2 in sensory neurons (neuronal TRPM2) acts as a warmth sensor critical to sense innocuous warm temperatures. However, neuronal TRPM2 mediates the warmth sensitivity of less than 3.5% of sensory neurons. The functions of the vast majority (42%) of TRPM2+ neurons are unknown. Here we show that neuronal TRPM2 functions as a pain sensor responsible for directly transducing acute and chronic pain independently of immune TRPM2. Both chronic arthritis pain and neuropathic pain were markedly reduced in TRPM2-knockout mice, and the pain deficit was recapitulated by sole deletion of neuronal TRPM2. However, immune and inflammatory responses were largely similar between wild-type and neuronal TRPM2-deficient mice. Moreover, antagonizing joint TRPM2 rapidly reversed chronic arthritis pain without affecting joint inflammation. Mechanistically, TRPM2 is activated by PGE2 and IgG immune complex (IgG-IC) through GoA and Fc{gamma}RI coupling, respectively, independently of conventional signalling messengers. Consistently, acute pain induced by PGE2 and IgG-IC was abolished in TRPM2 mutant mice. We conclude that neuronal TRPM2 is a convergent direct pain transducer independently of inflammation, representing an appealing target for alleviating chronic pain.

neuroscience↗

bioRxiv2025

Integrative proteomics and metabolomics map reveals key sectors of defense metabolism in ginger against Pythium myriotylum

Ginger (Zingiber officinale) cultivation is severely threatened by rhizome rot caused by Pythium myriotylum. Hitherto, detail molecular machineries and metabolic pathways of defense against P. myriotylum in edible ginger are elusive. To elucidate the defense mechanisms, we employed integrative quantitative proteomics (TMT labelling) and high resolution untargeted metabolomics to investigate temporal defense responses of ginger during infection. Proteomics analysis revealed enrichment of cellular detoxification and secondary metabolism pathways indicating co-activation of oxidative stress management and its mediated defense metabolite biosynthesis. Key phenylpropanoid enzymes (PAL, C4H, CAD) and LOX, a regulator of jasmonic acid (JA) biosynthesis, were significantly up-regulated, correlating JA signaling with secondary metabolic defenses. Metabolomics profiling confirmed accumulation of phenylpropanoids, flavonoids, and terpenoids, including known antimicrobial metabolites such as gingerol, shogaol, curcumin, and gingerdione, alongside expression of their key regulatory enzyme was also induced. Notably, machine learning identified the monoterpenoid TGA as the top defense-associated metabolite. Functional assays demonstrated strong anti-P. myriotylum activity of TGA, which inhibited mycelial growth and suppressed expression of key pathogenicity-related genes (NIP1, GHs, cellulase). These findings highlight coordinated activation of detoxification and secondary metabolic pathways in ginger defense and establish TGA as a promising bio-control agent against P. myriotylum.

plant biology↗

bioRxiv2025

Learning a CoNCISE language for small-molecule binding

Rapid advances in deep learning have improved in silico methods for drug-target interaction (DTI) prediction. However, current methods do not scale to the massive catalogs that list millions or billions of commercially-available small molecules. Here, we introduce CoNCISE, a method that accelerates drug-target interaction (DTI) prediction by 2-3 orders of magnitude while maintaining high accuracy. CoNCISE uses a novel vector-quantized codebook approach and a residual-learning based training of hierarchical codes. Strikingly, we find that much of binding-specificity information in the small molecule space can be compressed into just 15 bits of information per compound, characterizing all small molecules into 32,768 hierarchically-organized binding categories. Our DTI architecture, which combines these compact ligand representations with fixed-length protein embeddings in a cross-attention framework, achieves state-of-the-art prediction accuracy at unprecedented speed. We demonstrate CoNCISEs practical utility by indexing 6.4 billion ligands in the Enamine dataset, enabling researchers to query vast chemical libraries against a protein target in seconds. A "CoNCISE + docking" pipeline screened Enamine to propose strong binders (predicted KD {approx} 10-20 {micro}M) of three difficult-to-drug targets, each within two hours. CoNCISEs advance could democratize access to largescale computational drug discovery, potentially enabling rapid identification of promising molecules for therapeutic targets and cellular perturbations.

bioinformatics↗