Biology subjects

Kar, N.

Publications and source records attributed to Kar, N..

4 recordsLinked to original sources

bioRxiv2026

Serotonin Receptor Signaling Promotes Amoeboid Migration of Melanoma Cells Through cAMP/PKA Activation

Melanoma cells frequently utilize bleb-driven amoeboid migration to navigate confined microenvironments during metastasis. Through a morphology-based drug repurposing screen, we identified the anti-migraine medication dihydroergotamine (DHE) as a potent inhibitor of this behavior. We show that DHE suppresses amoeboid motility by targeting serotonin receptors, primarily 5-HTR7. Trace levels of peripheral serotonin activates 5-HTR7, which in turn stimulates cAMP/PKA and ROCK signaling. This signaling cascade increases the cortical contractility necessary to sustain blebbing. Accordingly, DHE treatment or PKA inhibition significantly impairs confined migration without altering cell proliferation. Clinically, elevated levels of serotonin-degrading enzymes, such as MAOB, correlate with favorable patient prognoses and are reduced in metastatic lesions. Ultimately, these findings reveal an unanticipated role for peripheral serotonin in melanoma biomechanics and highlight serotonin receptors as highly actionable targets to limit metastatic dissemination. Significance StatementMetastasis is the leading cause of melanoma-related mortality, driven in part by the ability of tumor cells to adopt a highly contractile amoeboid mode of migration that enables movement through spatially confined tissues. This study uncovers a previously unrecognized mechanobiological role for peripheral serotonin in promoting this invasive behavior. We show that low concentrations of serotonin activate cAMP/PKA signaling to generate the actomyosin contractility required for amoeboid migration. Importantly, the FDA-approved migraine drug dihydroergotamine (DHE) blocks this pathway and significantly inhibits cancer cell motility. Supported by patient data linking reduced serotonin degradation with increased metastatic progression, these findings identify serotonin signaling as a novel driver of melanoma dissemination and provide a compelling rationale for repurposing serotonin-targeting therapeutics to prevent metastatic spread.

cancer biology↗

bioRxiv2025

Targeting Cholesterol-Dependent Piezo1 Activation Impairs Amoeboid Migration in Melanoma Cells

Bleb-based migration enables cancer cells to navigate the heterogeneous tumor microenvironment. Here, we report a phenotypic screen identifying drugs that inhibit bleb formation, a driver of amoeboid migration. Statins, including Fluvastatin and Pitavastatin, suppress amoeboid migration of melanoma cells in confined environments by reducing intracellular cholesterol. This disrupts plasma membrane tension sensing by Piezo1, lowering intracellular Ca2+ levels. Both cholesterol supplementation and Piezo1 activation rescue migration in confined environments, confirming their functional link. Notably, high cholesterol biosynthesis enzyme levels correlate with reduced patient survival in melanoma. These findings reveal that cholesterol is essential for confinement sensing through Piezo1, identifying cholesterol biosynthesis or uptake as rational therapeutic targets against metastasis. Significance StatementThis study builds on a phenotypic drug screen that identified statins as inhibitors of bleb-based migration, a key mode of cancer cell movement through confined spaces. We show that statins reduce membrane cholesterol, disrupting the function of the mechanosensitive channel Piezo1 and impairing melanoma cell migration. Restoring cholesterol or activating Piezo1 rescues this effect, revealing a functional link between cholesterol and confinement sensing. Our findings highlight cholesterol biosynthesis as essential for invasive cell behavior and identify it as a therapeutic vulnerability. Importantly, elevated cholesterol pathway activity correlates with reduced survival in melanoma patients, underscoring the clinical relevance of targeting this pathway to limit metastasis.

cancer biology↗

bioRxiv2021

Ethanol pre-exposure enhances alcohol-seeking behavior at cellular level by chemoattraction and exhibits bleb-driven cellular stress response in uniform ethanol concentration

This study investigates the effect of ethanol and its pre-exposure on cell migration. Here, fibroblast cells were first pre-treated with ethanol, and their migratory behavior was tested in both chemotaxis and chemokinesis setup. 1% ethanol was taken as a potential chemotactic agent. The study reveals that in presence of ethanol gradient cells display migration towards ethanol, and pre-exposure further augments this migratory behavior by altering their chemotactic responsiveness. In uniform ethanol concentration, cells first undergo three staged adaptations to the new environment: shrinking, blebbing, and recovery, where cells use bleb-driven cell protection machinery to adapt. Thus, migration is initially stalled. But once the cells resume locomotion, no significant difference in migratory parameters is observed. Overall, this study establishes ethanol as a chemoattractant for fibroblasts, with cells showing enhanced alcohol-seeking behavior upon pre-exposure. Such behavior is reminiscent of seeking and tolerance exhibited by alcohol-dependent addictive behavior in higher organisms including humans.

cell biology↗

bioRxiv2019

Bayesian semi-nonnegative matrix tri-factorization to identify pathways associated with cancer phenotypes

Accurate identification of pathways associated with cancer phenotypes (e.g., cancer sub-types and treatment outcome) could lead to discovering reliable prognostic and/or predictive biomarkers for better patients stratification and treatment guidance. In our previous work, we have shown that non-negative matrix tri-factorization (NMTF) can be successfully applied to identify pathways associated with specific cancer types or disease classes as a prognostic and predictive biomarker. However, one key limitation of non-negative factorization methods, including various non-negative bi-factorization methods, is their lack of ability to handle non-negative input data. For example, many molecular data that consist of real-values containing both positive and negative values (e.g., normalized/log transformed gene expression data where negative value represents down-regulated expression of genes) are not suitable input for these algorithms. In addition, most previous methods provide just a single point estimate and hence cannot deal with uncertainty effectively.\n\nTo address these limitations, we propose a Bayesian semi-nonnegative matrix trifactorization method to identify pathways associated with cancer phenotypes from a realvalued input matrix, e.g., gene expression values. Motivated by semi-nonnegative factorization, we allow one of the factor matrices, the centroid matrix, to be real-valued so that each centroid can express either the up- or down-regulation of the member genes in a pathway. In addition, we place structured spike-and-slab priors (which are encoded with the pathways and a gene-gene interaction (GGI) network) on the centroid matrix so that even a set of genes that is not initially contained in the pathways (due to the incompleteness of the current pathway database) can be involved in the factorization in a stochastic way specifically, if those genes are connected to the member genes of the pathways on the GGI network. We also present update rules for the posterior distributions in the framework of variational inference. As a full Bayesian method, our proposed method has several advantages over the current NMTF methods which are demonstrated using synthetic datasets in experiments. Using the The Cancer Genome Atlas (TCGA) gastric cancer and metastatic gastric cancer immunotherapy clinical-trial datasets, we show that our method could identify biologically and clinically relevant pathways associated with the molecular sub-types and immunotherapy response, respectively. Finally, we show that those pathways identified by the proposed method could be used as prognostic biomarkers to stratify patients with distinct survival outcome in two independent validation datasets. Additional information and codes can be found at https://github.com/parks-cs-ccf/BayesianSNMTF.

bioinformatics↗