Biology subjects

Bhat, R.

Publications and source records attributed to Bhat, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Predicting Protein-Protein Interactions Using Vectorized Sequence Descriptors and Machine Learning Classifiers

Accurate prediction of protein-protein interactions (PPIs) is fundamental for elucidating cellular mechanisms and accelerating drug discovery. Although experimental techniques provide extensive interaction maps, their high cost and time requirements necessitate robust computational alternatives. In this study, we present an interpretable machine-learning framework that integrates sequence-derived descriptors, specifically 120 Auto-Covariance (AC) features, 686 Conjoint Triad (CT) features, and 1,820 Local Descriptors (LD) features, with classical classification algorithms, including Support Vector Machines (SVM), Random Forests (RF), and XGBoost (XGB). Evaluated on a gold-standard sequence-based PPI benchmark dataset using stratified five-fold cross-validation, the models achieved robust classification accuracies: 86.92% (SVM), 97.75% (RF), and a peak cross-validation accuracy of 97.97% with XGBoost (Rec: 98.09%, Pr: 98.10%, F1: 98.09%). Furthermore, rigorous evaluation on an independent unseen (Human-PPI) test set demonstrated exceptional generalization capabilities. The Random Forest model emerged as the top-performing architecture, yielding an outstanding accuracy of 99.51%, a precision of 99.72%, a recall of 99.29%, an F1-score of 99.50%, an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.9990, and an Area Under the Precision-Recall Curve (AUPRC) of 0.9989 (closely followed by XGBoost with 98.54% accuracy, AUROC 0.9979, and AUPRC 0.9974). Feature-importance analysis revealed that short-range physicochemical correlations (such as polarity and hydrophobicity lags in Protein 2), regional amino-acid compositions, and specific triad patterns (e.g., A/G/V-M/S/T/YM/S/T/Y) drive accurate PPI discrimination. These findings demonstrate the effectiveness of domain-informed, handcrafted sequence features combined with machine-learning classifiers for PPI prediction, providing biologically interpretable insights with minimal computational overhead and enabling efficient, scalable interactome screening.

bioinformatics↗

bioRxiv2026

HMVA-DDI: A Hierarchical Multi-View Attention Network for Drug-Drug Interaction and Severity Prediction

Background and Objective: Prescribing multiple drugs simultaneously, known as polypharmacy, is increasingly common in managing chronic diseases such as diabetes and cardiovascular conditions. Studies show that roughly 40% of elderly patients are on five or more medications, putting them at significant risk of adverse drug-drug interactions (DDIs) [1]. While computational methods based on Graph Neural Networks (GNNs) have shown strong potential for predicting these interactions from a drugs molecular structure, most existing models are limited in two important ways: they rely on attention mechanisms that cannot adapt to the unique context of each atom in a molecule, and they only predict whether an interaction exists rather than how dangerous it might be. Methods: We propose HMVA-DDI, a two-stage deep learning framework that addresses both shortcomings. In the first stage, our model predicts whether two drugs will interact. If it detects an interaction, the second stage classifies it into one of three clinical severity levels: minor, moderate, or major. To better capture molecular structure, we replace the standard attention mechanism with a more powerful version called Graph Attention Network v2 (GATv2), which considers each atoms specific context when learning its representation. We also introduce a technique to prevent information loss in deeper network layers, and we tailor the input features separately for each prediction task. Results: Evaluated on two standard benchmarks, DrugBank 5.0.3 [2] and DDInter [3], HMVA-DDI achieves an AUROC of 0.9819 and AUPRC of 0.9759 on binary interaction detection, a marked improvement over the baseline. For severity classification, our best model variant (qcSvr4) reaches an overall AUROC of 0.9444, with a particularly large improvement in detecting low-severity (Minor) interactions, a historically difficult class. Conclusion: HMVA-DDI bridges the gap between computational drug research and clinical usefulness by not only predicting interactions accurately but also indicating interaction severity. This two-level system can help healthcare professionals prioritize warnings, reducing the alert fatigue problem where doctors are overwhelmed by too many non-critical notifications.

bioinformatics↗

bioRxiv2026

Structure-aware graph attention based hierarchical transformer framework for drug-target binding affinity prediction

Drug-target interaction prediction is significant in the hit identification phase of drug discovery, enabling the identification of potential drug candidates for downstream optimization. Traditional computational methods have some drawbacks in their ability to represent 3D structural data for both molecules and target proteins, which is required for the intricate protein-ligand interactions that regulate binding affinity. In this approach, we propose a graph transformer-based model (GTStrDTI) that combines an intragraph attention mechanism with cross-modal attention to enrich the representation of both the drug molecule and target protein. This approach comprehensively models both intramolecular structural features and intermolecular interactions, thereby enhancing binding affinity prediction performance. A thorough evaluation on benchmark datasets such as KIBA, DAVIS, and BindingDB_Kd shows that our approach surpasses the state-of-the-art methods under challenging target cold-start settings. Our analysis found that augmenting graph-based 3D structural protein target (C-alpha contact graphs from PDB with threshold distance of 5[A]) and incorporating molecule adjacency information, boosts predictive performance, thus contributing towards narrowing the gap between computational and experimental research.

bioinformatics↗

bioRxiv2026

Sequence-Driven Drug-Target Affinity Prediction Via Graph Attention Networks and Bidirectional Cross-Attention Fusion

Accurate prediction of drug-target affinity (DTA) is a core challenge in computational drug discovery. Structure-based methods depend on experimentally determined protein coordinates, which are unavailable for most drug-relevant targets. sequence-only approaches, in turn, operate on linear residue representations and lack an explicit mechanism to encode the spatial proximity relationships that govern protein-ligand interactions. We present XAttn-DTA, a sequence-driven framework that addresses both limitations without requiring experimental structural data. Drug molecules are encoded as 2D molecular graphs via multilayer Graph Attention Networks (GATs), capturing atomic topology and bond-level chemistry. Proteins are represented as residue-level graphs constructed from ESM2-predicted contact maps, that captures inter-residue coevolutionary and structural signals embedded within the sequence. The bidirectional cross-attention fusion module projects both embeddings into a shared latent space and applies dual multi-head cross-attention. This enables ligand and protein residue environments to inform one another. On the Davis benchmark, XAttn-DTA achieves a concordance index (CI) of 0.907 and MSE of 0.175, improving CI by 1.8% and reducing MSE by 9.3% over the strongest baseline. On KIBA, it achieves an MSE of 0.121, a 13.6% reduction. Under three strict cold-start settings across Davis, KIBA, and BindingDB, the model yields MSE reductions of up to 79.0% and CI improvements of up to 31.5% over the strongest baseline, demonstrating strong generalization to unseen scaffolds and novel protein families.

bioinformatics↗

bioRxiv2026

Tannic acid fortifies extracellular matrix against dicarbonyl stress

Methylglyoxal (MGO), a highly reactive dicarbonyl metabolite that accumulates in diabetes and aging, causes tissue dyshomeostasis, for which therapeutic interventions are limited. Herein, we investigate the potential of tannic acid (TA) in fortifying organ and organismal health against MGO. Anatomical disruption in vivo of hydra bodies and ex vivo decellularization of murine mesenteries with MGO suggested an impaired interaction between cells and their extracellular matrix (ECM); however, pretreatment of these systems with TA reversed this effect. We confirmed this through subsequent exposure of control and TA-pretreated mammalian cell-secreted endogenous matrix, Collagen I, and basement membrane matrix to MGO. TA prevented loss of ECM biochemical characteristics and restored perturbed cell adhesion and spreading on these substrata induced by MGO. NMR titrations confirmed TA-bound MGO in a 1:5 stoichiometry, potentially quenching its electrophilic properties. Our study posits TA as a novel candidate for protecting organ and organismal architectures against the histopathological effects of dicarbonyl stress.

biochemistry↗

bioRxiv2025

TAK1 is a key regulator of oncogenic signaling and differentiation blockade in rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is a malignant soft tissue sarcoma with a skeletal muscle phenotype, accounting for approximately 50% of all pediatric soft tissue sarcomas and 8% of all childhood cancers. Although RMS cells express myogenic regulatory factors, they fail to undergo terminal differentiation into mature muscle cells. Transforming growth factor {beta}-activated kinase 1 (TAK1) is a major signaling protein that activates multiple intracellular pathways in response to growth factors, cytokines, and microbial products. Emerging evidence suggests that TAK1 is also an important regulator of self-renewal, proliferation, and differentiation of muscle progenitor cells. However, the role and mechanisms of action of TAK1 in RMS remain completely unknown. In this study, we demonstrate that TAK1 expression and activity are markedly elevated in a panel of RMS cell lines and in patient tumor specimens. Reverse phase protein array (RPPA) analyses revealed that TAK1 regulates the expression and activity of many molecules involved in cell cycle control, cell proliferation, and oncogenic signaling. Genetic knockdown or pharmacological inhibition of TAK1 suppresses RMS cell proliferation, migration, and invasiveness, while also promoting terminal myogenic differentiation. TAK1 inhibits differentiation in RMS, at least in part, through up-regulating YAP1 signaling. Our results also demonstrate that inducible knockdown of TAK1 in human RMS xenografts retards tumor growth and enhances myogenic differentiation in vivo. Collectively, these findings uncover a previously unrecognized role for TAK1 in RMS growth and differentiation, and suggest that TAK1 can be a potential therapeutic target for the treatment of RMS.

cancer biology↗

bioRxiv2025

Intermittent attachments form three-dimensional cell aggregates with emergent fluid properties

In living systems across developmental and cancer biology, populations of cells on surfaces organize themselves into aggregates that mediate function and disease. Recent experimental studies have identified that such aggregates can have emergent fluid-like properties such as surface tension, yet the physical origin of these properties is not clear. Here, we develop a minimal cell-based model in which cell-cell and cell-substrate interactions are governed by active intermittent attachments. We explain the transition of cells from a dilute population to a dense aggregate, and quantify the emergent material properties underpinning this transition. We use our model to interpret experiments on dewetting of aggregates of MDA-MDB-231 cancer cells and shape fluctuations of surface-associated OVCAR3 cell aggregates. Finally, we show how spatial heterogeneity in attachments governs collective chemotaxis of cell aggregates. Together, these results reveal how active intermittent attachments generate cell aggregates with emergent material properties, with broad implications for development and cancer.

biophysics↗

bioRxiv2025

Suppression and modulation mechanism underlying the flavonoid-induced inhibition of fibrillation of α-synuclein

The accumulation of -synuclein (Syn) aggregates is a hallmark of synucleinopathies, including Parkinsons disease (PD). However, the progression from harmless monomeric Syn to misfolded oligomers and fibrillar species remains largely unclear. In this study, we examined the effect of Cyanidin, a naturally occurring neuroprotective compound, on the aggregation properties of -syn using a combination of various biophysical tools. Thioflavin T fluorescence measurements revealed the inhibition of -syn fibrillation in the presence of sub-stoichiometric concentration of Cyanidin, indicating its effect on -syn nucleation. Ultracentrifugation and size exclusion chromatographic analyses demonstrated that increasing concentrations of Cyanidin reduced the conversion of monomers to aggregated forms of -syn. Analysis of the aggregation reaction of -syn based on monomer concentration suggested that Cyanidin reduces the in-vitro conversion of monomers to amyloid nuclei at sub-stoichiometric micromolar concentrations. Cyanidin was observed to bind weakly with the unstructured, monomeric -syn, limiting the nucleation step and modulating the pathway to form conformationally restrained, SDS-resistant -helical higher-order oligomers ([~]670 kDa), which are less-hydrophobic. AFM and TEM images show that Cyanidin also possesses strong fibril disaggregation activity. Furthermore, seeding studies reveal that the higher-order oligomers, which are otherwise stable, destabilise upon sonication and attain seeding capability. Steady-state fluorescence spectroscopy and isothermal titration calorimetry (ITC) reveal weak interactions between Cyanidin and Syn, with a dissociation constant in the mM range. Interestingly, Cyanidin-generated oligomers increase the viability of neuroblastoma (SH-SY5Y) cells, thereby protecting the neuronal cells from degeneration. Our study suggests that stabilization of structured oligomers by small molecule modulators like Cyanidin provides a viable strategy to interfere with Syn fibrillization.

biophysics↗

bioRxiv2025

Breast cancer cell migration is potentiated by associated fibroblasts through a laminin-511-driven cortical localization of Arp2/3

Invading cancer cells are intermingled with fibroblasts that interact with them to regulate their migration, although the precise motility-driving signaling mechanisms initiated upon such interactions remain ill-understood. We constitute time-lapse-tractable 3D pathotypic cultures with breast cancer cell lines with fibroblasts enriched from invasive ductal carcinoma biopsies. Herein, cancer-associated fibroblasts (CAFs) enhanced breast cancer migration relative to non-cancerous fibroblasts (NFs), also observed through greater infiltration of orthotopically injected cancer cells within murine inguinal lymph nodes. The enhancement was also seen in cancer cells exposed to CAF-secreted medium. CAFs phenocopied laminin-rich matrix in compacting cancer cell clusters. A subsequent immunocytochemical screen identified laminins 5, -{beta}1, and -{gamma}1 to be highly expressed in enriched CAFs populations as well as in stromal areas of breast cancer sections. Depleting laminin-5, -{beta}1, and -{gamma}1 in CAFs downregulated 3D cancer cell migration. Cancer cells cultivated on Laminin-511 substrata showed increased adhesion, faster and persistent migration, enhanced shape polarization, and deformability of cancer cells relative to Laminin-211 controls. Observation of higher invadopodial F-actin dynamics on Laminin-511 led us to assay for and demonstrate anisotropically corticalized Arp 2/3 in cancer cells. An integrin antibody screen showed Integrin 6{beta}1 inhibition specifically nullified Laminin-511-driven enhancement of morphomigrational traits of cancer cells, similar to Arp2/3 inhibition. Moreover, cells on Laminin-511 showed enhanced Integrin 6 localization to their invadopodia. We propose that activated fibroblasts use Laminin-511 to localize cognate integrin receptors, and Arp2/3 of cancer cells to their invadopodia, resulting in higher Arp2/3-driven actin remodeling and enhanced cell migration.

cancer biology↗

bioRxiv2025

Stromal LOX/FAK/beta catenin pathway locks mammary fibroblasts into a tumor-promoting myCAF state

BackgroundCancer-associated fibroblasts (CAFs) sustain tumor progression, yet the soluble cues that maintain their myofibroblast (myCAF) state are poorly defined. Transforming growth factor beta (TGF-{beta}) is a canonical CAF activator. This study aims to identify TGF-{beta}-induced secreted mediators that reinforce the myCAF phenotype in breast cancer and map the downstream signaling cascade. Methods and ResultsSecretome profiling of primary patient-derived myCAFs and human mammary fibroblasts (HMF3s) engineered to over-express TGF-{beta}1 revealed 20 extracellular-matrix remodelers shared exclusively by both activated states; lysyl oxidase (LOX) was the top-ranked hit. LOX knockdown abrogated TGF-{beta}-driven -smooth-muscle actin (-SMA) induction, collagen-gel contraction and migration in HMF3s, and reduced constitutive -SMA and {beta}-catenin in myCAFs. Mechanistically, TGF-{beta} upregulated LOX, which activated focal-adhesion kinase (FAK), leading to p38 MAPK- and Akt-mediated Ser9 phosphorylation (inactivation) of GSK3{beta} and consequent {beta}-catenin stabilization. In HCC1806-luciferase orthotopic xenografts, CAFs accelerated tumor growth, whereas LOX-deficient CAFs lost this pro-tumoral effect. ConclusionLOX is a pivotal autocrine effector of TGF-{beta} that locks breast CAFs into a pro-tumoral myCAF state through a LOX/FAK/GSK3{beta}/{beta}-catenin axis. Targeting stromal LOX may disrupt CAF activation and curb breast cancer progression.

cancer biology↗

bioRxiv2025

A PKM2-YAP reciprocal repartitioning modulates invasion of breast cancer cells

Invasive cancer cells exhibit distinct morphomigrational and metabolic traits when confronted with biophysically variant matrix microenvironments enroute metastasis. Whether dynamical shifts in such traits are interlinked through a common molecular program remains ill-understood. Using triple negative breast cancer cell lines on Collagen I substrata coated hydrogels recapitulating stiffness values of non-cancerous breast tissue and the desmoplasia of tumors, we observed greater cell shape polarization and migration in the latter. Associated lower lactate and pyruvate levels in such conditions motivated us to examine their pyruvate kinase M2 expression, which showed nuclear and cytoplasmic localization in softer and stiffer environments respectively. Pharmacologically impairing PKM2 activity in stiffer substrata decreased migration and shape polarization of cancer cells while increasing lactate and pyruvate levels. In contrast, increasing its activity on softer substrata attenuated cancer cell migration and elongation. We assayed for localization of the mechanosensory protein YAP upon PKM2 activity modulation: PKM2 activation increased nuclear YAP localization on soft substrata. Pharmacologically inhibiting YAP on stiff substrata not just decreased migration but also increased nuclear localization of PKM2 and lactate and pyruvate levels. We propose that a reciprocal repartitioning of PKM2-YAP interlinks the cognate metabolic and migrational states of cancer cells; targeting such positive feedback may hold the key to future therapeutic strategies.

cancer biology↗

bioRxiv2025

C2-α-dicarbonyls selectively impair morphodynamic interactions between cells and extracellular matrix

Ageing and diabetes, risk factors for cardiovascular diseases and cancer, are characterized by abnormally high tissue levels of several dicarbonyl species. Although all dicarbonyls disrupt biomolecular structure and function by forming advanced glycation end products, understanding the relative toxicity of each can help design targeted control strategies. Although known for cells, the comparative effects of distinct dicarbonyls on extracellular matrices (ECM) have yet to be benchmarked. Here, we investigate how glycation by three well known dicarbonyls: the C2--dicarbonyls methylglyoxal (MGO) and glyoxal (GO), and the C6--dicarbonyl 3-deoxyglucosone (3-DG), affects two ECMs, Collagen I and laminin-rich basement membrane (lrBM), and alters their morphodynamic interactions with two human cells: untransformed endothelial TeloHAEC and aggressive triple negative breast cancer MDA-MB-231. On dicarbonyl-treated lrBM, endothelial and cancer cell adhesion, collective cord morphogenesis, cancer cell motility, shape polarity and its entropy are perturbed at low MGO levels, high GO levels, and are unchanged for 3-DG. Alcian Blue staining of lrBM shows dissolution of matrix at low MGO and high GO levels with no effect on 3-DG exposure. On collagen I substrata, adhesion for both cells is disrupted at low MGO levels, at high GO levels but is unchanged for 3-DG. Interestingly at high concentrations of MGO and GO, the latter decreases motility and increases elongation entropy more than the former; 3-DG has no effect. Picrosirius staining suggests that at high levels GO-driven collagen hyperpolymerization is similar to MGO with no effect of 3-DG. Our observations therefore suggest qualitative and quantitative distinctions in the effect of C2- and C-6--dicarbonyls on ECM homoeostasis and ECM-driven cellular morphodynamics.

cell biology↗

bioRxiv2025

Multiscale modeling predicts dependence of mesenchymally transitioned tumor niche fitness on cell-cell and cell-matrix adhesions

Invasion of cancer cells is often characterized by a transition in phenotype of cells or their niches from an epithelial to a mesenchymal state (EMT). Under what conditions do transitioned niches acquire greater fitness than, and outcompete, their parental un-transitioned niches, is not well-understood. Here, we use a Cellular Potts model-based multiscale computational framework to investigate this question. Inducing an EMT in a single cell at the edge of an early-growing tumor surrounded by a fibrillar extracellular matrix (ECM) allows us to temporally trace inter-niche competitions. We observe that the transitioned niche dominates the population it arises from and invades better when surrounded by dense ECM. An increase in cell-ECM adhesion by itself drives domination at 50% probability, such that the transitioned population invades faster and contributes further to collective invasion of the whole tumor. Decrease in inter- and intra-niche cell-cell adhesion by itself is not sufficient to achieve domination. However, added to increased cell-ECM adhesion, loss of intra-niche (but not inter-niche adhesion) restores the probability, but not the extent, with which domination by the transitioned niche is achieved by attenuating its confinement by its parental population. Our simulations reveal the forces regulating such confinement and how cell-cell and cell-ECM adhesion, stochastic invasion dynamics, and ECM density contribute nuancedly to distinct aspects of inter-niche competitions within tumor populations and their fitness.

cancer biology↗

bioRxiv2025

Isolation of Secretory Immunoglobulin A (sIgA) from nasal secretions collected in saline and Viral Transport Medium.

Secretory immunoglobulin A (sIgA) is the major immunoglobulin found on all mucosal surfaces, including the nasal cavity and the lining of the gut. It is the first line of defence against pathogens which enter through the nasal cavity or mouth. sIgA1 binds to a lectin isolated from the tropical jackfruit, known as jacalin. Jacalin binds specifically to the D-galactose moiety of sIgA1 and not to sIgA2 which has different sugars. Here we describe a simple and inexpensive method to isolate jacalin from jackfruit seeds, prepare jacalin-Sepharose conjugated beads and isolate sIgA1 from nasal secretion collected in saline as well as in Viral Transport Medium (VTM). The sIgA identity was confirmed by Western blotting using specific antibodies against sIgA heavy chain. Our approach has potential implications as studying sIgA in nasal secretions provides a direct measure of the effectiveness of nasal vaccines at the site of pathogen entry, making it a critical biomarker for both vaccine development and evaluation. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

immunology↗

bioRxiv2025

Fractal Measures as Predictors of Histopathological Complexity in Breast Carcinoma Mammograms

Breast carcinoma remains the most commonly diagnosed malignancy and a leading cause of cancer-related mortality among women worldwide. While mammography is the gold standard for early detection, challenges such as high breast density often obscure malignancies, reducing diagnostic sensitivity. Conventional parenchymal texture analysis methods have limitations due to struggles with spatial interpretation and noise sensitivity. This study investigates the efficacy of fractal-based global texture features for distinguishing between malignant and normal mammograms and assessing their potential for molecular subtype differentiation. Digital mammograms were analyzed using standardized preprocessing techniques, and fractal measures were computed to capture complexity and connectivity properties within breast tissue structures. Fractal dimension, multifractality strength, and succolarity reservoir were found to effectively characterize specific features of mammographic texture; however, their incorporation into machine learning models yielded moderate discriminatory performance between categories. We introduced succolarity reservoir as a novel parameter accounting for tissues latent connectivity. In addition, while succolarity reservoir exhibits potential for differentiating Luminal B from other molecular subtypes, its overall discriminative power remains limited. This proof-of-concept study underscores the potential of fractal-based texture analysis as a non-invasive biomarker in breast carcinoma diagnosis.

biophysics↗

bioRxiv2024

Collective amoeboid dynamics drives colonization of drug-resistant ovarian cancer cells

Epithelial ovarian cancer (EOC) is characterized by resistance to platinum-based therapy, resulting in rapid progression and poor survival. Here, we ask whether drug resistance and invasiveness coevolve to drive metastasis. Selection experiments involving pulsed carboplatin exposure established isogenic chemoresistant variants of lines, which typify high-grade serous ovarian carcinoma (HGSOC), the most aggressive type of EOC. Time-lapse imaging showed enhanced migration of resistant single cells and their collectives. Resistant cell spheroids spread faster on Collagen I substrata than sensitive controls. The resistant OVCAR-3 transcriptome was ontologically enriched for migration and showed overlap with previously reported markers of resistance in EOC patients and other evolved lines. Gene set enrichment predicted transition between epithelial, mesenchymal, and amoeboid states is higher in resistance compared to control lines. Lower matrix adhesion, weak focal adhesion, and highly deformable and translatory dynamics of cell collectives indicated that resistant cancer cells displayed a unique collective amoeboid-like migration. When injected intraperitoneally into immunodeficient mice, resistant cells colonized to a greater extent on parietal mucosae. Ex vivo, suspended resistant cells formed moruloids associated with quicker peritoneal adhesion, clearing human coelomic mesothelial monolayers with higher efficiency. Knockdown in resistant OVCAR-3 cells of two upregulated proteins, E-cadherin and LGALS3BP, had distinct consequences. E-cadherin knockdown partially restored sensitivity to carboplatin but did not affect invasion. In contrast, silencing LGALS3BP decreased invasion but not resistance. Our results suggest that drug resistance and invasiveness could coevolve through the upregulation of distinct trait drivers in EOC.

cancer biology↗

bioRxiv2024

Ovarian cancer cells exhibit diverse migration strategies on stiff collagenous substrata

In homoeostasis, the shape of epithelial cells and their sessility, i.e. lack of movement are intricately linked together. Alterations in this relationship as a result of malignant transformation and variations, thereof by the mechanical microenvironments that cancer cells encounter as they migrate are as yet ill-understood. Here we explore the interdependency of such traits in two morphologically distinct but invasive ovarian cancer cell lines (OVCAR-3 and SK-OV-3) under mechanically variant contexts. To do so, we came up with a minimal metric toolkit consisting of velocity, local and global turning angles, persistence of migration, major axis dynamics, morphomigrational angle, and elongation dynamics, and rigorously measured their variation using a Shannon entropic distribution to map heterogeneity in behavior between, and across, trajectories of migration. Two stiffness conditions on polymerized Collagen I with Youngs moduli of 0.5 (soft) and 20 (stiff) kPa were chosen. Both the epithelioid OVCAR-3 and mesenchymal SK-OV-3 cells on soft substrata migrated slowly and in an undirected manner. On stiff substrata, SK-OV-3 showed a persistent directed motion with higher velocity. Surprisingly, OVCAR-3 cells on higher stiffness moved at a velocity higher than SK-OV-3 cells and showed a distinct angular motion. The polarity of SK-OV-3 cells on stiff substrata was well-correlated with their movement, whereas for OVCAR-3, we observed an unusual slip behavior, where the axes of cell shape and movement were poorly correlated. An examination of their deformability showed that OVCAR-3 and SKOV-3 on softer substrata were relatively rigid but showed greater shape variation (especially OVCAR-3) on stiffer substrata. Therefore, a rigorous quantification using the above metric toolkit reveals how an interplay between intrinsic deformability and the mechanical microenvironment on pathotypic matrix substrata allow distinct migratory dynamics of epithelioid and mesenchymal ovarian cancer cells.

cancer biology↗

bioRxiv2024

Dicarbonyl stress enhances tumor intravasation

Metastasis of cancer is a multi-step process that involves the migration of transformed cells from their native organ into a vascular channel, followed by their dissemination to prospective sites of colonization. The entry of tumor cells into blood or lymph, known as intravasation involves their breaching the stromal and endothelial extracellular matrix (ECM) and the endothelial barriers. How the kinetics of these cell-ECM interactions are confounded by chronic inflammatory stresses seen in comorbid risk factors of cancer such as diabetes and aging remain ill-investigated. Here, we construct and deploy a histopathology-motivated, imaging-tractable, microfluidic multi-organ-on-chip platform, that seamlessly integrates two tissue environments: that of a breast tumor and a vascular channel, to study the problem. The former comprises invasive triple-negative MDA-MB-231 breast cancer cells embedded within a three-dimensional fibrillar Collagen I milieu. The latter consists of a monolayer of TeloHAEC, immortalized human aortic endothelial cells arranged on laminin-rich basement membrane ECM, both of which concentrically line a hollow channel, wherein unidirectional fluid flows are implemented. The chip showcases the complexity of intravasation, wherein tumor cells and endothelia cooperate to form anastomotic structures. The formation of such structures is regulated by fluid flow in the vascular channel and is associated with cancer cell migration and entry into the vascular channel. Disseminated cancer cells are observed to enter, get adhered within, and flow through the vascular channel. Exposure to methylglyoxal (MG), a mediator of dicarbonyl stress associated with diabetic circulatory milieu, leads to greater cancer cell intravasation and flow through the vascular channel. This could be driven not just by MG-induced endothelial senescence and shedding, but also by the effect of MG on the chip ECM: we demonstrate it can degrade basement membrane and pathologically crosslink Collagen I, diminishing their cell adhesiveness. Our results thus show how dicarbonyl stress may attenuate homoeostatic barriers to cancer intravasation, exacerbating metastasis.

bioengineering↗