Integrative Biology

Protein tyrosine phosphatase receptor J (PTPRJ) restrains cell proliferation and migration by dephosphorylating receptor tyrosine kinases (RTKs) including the epidermal growth factor receptor (EGFR). PTPRJ is a purported tumor suppressor, and alterations to its expression and/or function are associated with colorectal, breast, lung, and other cancers. While there is interest in controlling PTPRJ-regulated phenotypes, efforts are limited by the complexity of PTPRJ-mediated signaling. PTPRJ dephosphorylates multiple RTKs, and the degree to which PTPRJ control of signaling and phenotypes depends on local cellular RTK activation profiles is unknown. To probe the context dependence of PTPRJ signaling regulation, we collected signaling measurements across 16 pathway nodes at two time points in a panel of HSC3 carcinoma cells engineered with different PTPRJ expression profiles. Cells were treated with three different RTK ligands, and paired phenotype measurements (viability, wound healing, xCELLigence cell index) were made. Partial least squares regression models were developed to predict relationships between PTPRJ-regulated signaling pathways and cell phenotypes. The model effectively separated contributions to variance arising from the PTPRJ expression background and growth factor context. In testing model predictions, we demonstrated that PTPRJ suppressed MET-induced cell cell proliferation via regulation of a HER3/AKT signaling axis that stabilized PTPRJ expression through an unanticipated feedback mechanism. We also found that PTPRJ regulated HSC3 cell migration via JNK signaling that was preferentially activated by MET. Our results identify new regulatory nodes through which PTPRJ influences cancer cell phenotypes and demonstrates that these processes preferentially occur in the context of distinct RTK activation states.

BackgroundDespite improved cancer outcomes with kinase inhibitors (KIs), their cardiotoxicity remains a significant clinical challenge. Current approaches to predict and prevent KI-induced cardiac adverse events (CAEs) are limited by an incomplete understanding of underlying mechanisms, including the contribution of off-target kinase engagement. ObjectivesTo establish links between kinase inhibition profiles and cardiotoxic phenotypes using empirical proteomic data, and to leverage these profiles in machine learning (ML) models capable of predicting KI cardiotoxicity. MethodsWe curated a database connecting kinome-wide target binding profiles of FDA-approved KIs (n=44) with documented incidence rates of six distinct CAEs. Binding profiles were derived from unbiased chemoproteomics and used to assess associations between KI selectivity, specific kinase targets, and CAEs. Profiles were further used to develop ML models to predict CAE risk, with SHAP-based model interpretation applied to identify cardiotoxicity-associated kinases. ResultsKI promiscuity was not a significant predictor of cardiotoxicity across all six CAEs. Frequency analysis revealed that kinases including RET, PDGFRB, and DDR1 are recur-rently inhibited across CAE-linked compounds, with nearly all identified as off-targets not annotated by the FDA. Network and pathway enrichment analyses supported a systems-level model in which cardiotoxicity arises from coordinated disruption of cardiac-relevant signaling networks. ML models achieved 66-84% cross-validated accuracy (ROC-AUC 0.75-0.8) across CAE endpoints, with SHAP analysis identifying PDGFRB, EGFR, and MEK1/2 among the most predictive kinases. ConclusionsProteomic kinome profiling combined with machine learning provides a mechanistically grounded framework for predicting KI cardiotoxicity and supports off-target-aware drug design to minimize cardiovascular risk.

IntroductionCancer progression is driven not only by tumor cells but also by interactions between the extracellular matrix (ECM), stromal cells, and immune cells within the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are major drivers of ECM remodeling, assembling ECM with aberrant organization. Extra domain A fibronectin (EDA-FN), a cellular FN containing an extra type III domain, is upregulated in the TME. EDA-FN regulates cellular behavior and has been associated with poor patient prognosis. Macrophages are among the most abundant immune cells within the TME, where they contribute to TME remodeling and inflammation to promote cancer cell invasion and metastasis. However, how tumor-associated matrix-specific cues regulate macrophage behavior remains largely understudied. PurposeHere, we developed a fibroblast-derived matrix platform that captures the structural imprint of tumor-associated EDA-enriched matrices and investigated how matrix-specific cues regulate macrophage behavior in the absence of ongoing soluble factor cues. MethodHuman mammary fibroblasts (HMFs) preconditioned in incubated low-serum media (lNC, or control) and MDA-MB231 metastatic breast cancer cell-conditioned media (mTCM) were cultured on polyacrylamide gels of 2 kPa and 20 kPa, respectively, followed by decellularization. Matrix organization, including fiber alignment, width, and intrafibrillar spacing, was quantified from confocal images. Decellularized EDA-FN-enriched matrices were subsequently reseeded with macrophages to assess macrophage morphology, phenotype, and matrix interactions. ResultsThe combined effects of tumor-derived soluble factors and pathological stiffness induced a CAF-like phenotype in HMFs, accompanied by cytoskeletal reorganization and microarchitectural alterations of EDA-FN-enriched matrices. Tumor-associated matrices exhibited increased alignment, narrower fiber width, and enlarged intrafibrillar spacing compared to control matrices. These aberrant, tumor-associated matrix-derived features were associated with altered macrophage behavior, including heterogeneous morphology, enhanced localized EDA-FN matrix loss beneath the cell body, and a hybrid phenotype with a shift toward a CD206-dominant profile. ConclusionsThese findings demonstrate the feasibility of obtaining EDA-FN-enriched matrices to isolate matrix-specific cues for investigating macrophage-ECM interactions. Furthermore, this platform can be leveraged to identify matrix-targeting therapeutic approaches for modulating macrophage function within the TME.

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor involved in xenobiotic sensing, as well as development, immunity, and tissue homeostasis. AhR signaling can proceed through a canonical and non-canonical pathway; the present study focuses on the canonical pathway. While ligand-dependent differences in binding affinities and direct ligand degradation kinetics are well known, and subtle differences in ligand binding can shape downstream signaling, it is still unclear which biochemical reaction steps within the canonical pathway are responsible for distinct ligand-specific transcriptional responses. Here, we developed a mechanistic ordinary differential equation model of the canonical AhR pathway. We calibrated the model to time-resolved qPCR measurements of Cyp1a1 and Ahrr mRNA in mouse bone-marrow-derived macrophages exposed to structurally diverse, environmentally relevant ligands with known immunomodulatory activity (3-methylcholanthrene, indolo[3,2-b]carbazole, and bisphenol A) using global optimization under a heteroskedastic likelihood. To dissect ligand specificity, we evaluated 528 candidate models that allow one or two ligand-involving reaction rate constants to vary. Akaike-based model selection reveals a dominant dynamical regime governed by promoter occupancy and target-gene mRNA synthesis, indicating that ligand-specific transcriptional responses are primarily encoded at the level of transcriptional regulation rather than upstream signaling events. The resulting model is made available in SBML and PEtab formats for reproducibility, and to enable further research into whether ligand-specific effects are conserved or rewired across cell types.

Tissue engineered constructs are increasingly used for both modeling organs and disease in vitro as well as for therapeutic intervention. In addition to collagen, these constructs commonly include native extracellular matrix proteins (ECM), such as fibronectin and laminin. Given the critical role of inflammatory pathways in disease and in response to implanted materials, it is important to understand the role these proteins play in regulating the inflammatory environment. Fibronectin and laminin influence neutrophil function and endothelial activation in 2D, but their regulation of the inflammatory environment in 3D engineered constructs is not clear. For this study, we used an inflammation-on-a-chip device that includes a model blood vessel surrounded by a collagen I hydrogel with fibronectin and/or laminin. We investigated the additive effects of both proteins and a range of concentrations for each protein to determine concentration dependence. Both fibronectin and laminin have concertation dependent effects on neutrophils and the endothelium. High concentrations (50 {micro}g/mL) of fibronectin reduced neutrophil migration, while 20 {micro}g/mL laminin reduced neutrophil extravasation and migration, potentially due to lower ICAM-1 expression by the endothelium. Interestingly, 50 {micro}g/mL of laminin significantly disrupted endothelial vessel formation and reduced ICAM-1 and VE-cadherin expression, likely due to significant changes in the collagen architecture. The inclusion of fibronectin and laminin, even at physiological levels, results in significant effects on neutrophil behavior, endothelial vessel formation, and collagen architecture. These proteins impact the inflammatory environment and thus need to be considered when modeling diseases and designing therapeutics, especially when neutrophils or an endothelium are involved. Translational Impact StatementThis work uses an inflammation-on-a-chip device to study how fibronectin and laminin impact neutrophil behavior and vascular inflammation as these proteins are commonly used in engineered constructs. We found that fibronectin impairs neutrophil migration, while laminin decreases neutrophil extravasation and migration and at higher concentrations also prevents endothelial vessel formation. Therefore, researchers should be aware that these proteins will alter the inflammatory environment when including them in engineered constructs.

Cells communicate via extracellular ligands, such as hormones, which bind to plasma membrane receptors and trigger intracellular signaling cascades. G Protein-Coupled Receptors (GPCRs) exemplify this mechanism by initiating signaling both at the cell surface and, from intracellular compartments such as endosomes. The kinetics and spatial localization of these signals are critical determinants of cellular responses, yet receptor trafficking-including internalization, endosomal sorting, and recycling-remains a pivotal but often overlooked component of theoretical GPCR models. In this study, we present a mathematical framework that integrates receptor trafficking and signaling compartmentalization into generic GPCR dynamic models. Using a compartmentalized approach based on systems of ordinary differential equations (Chemical Reaction Networks), we analyze how receptor internalization and recycling modulate ligand-induced responses. Our results show that the balance between plasma membrane and endosomal signaling can significantly enhance or diminish ligand efficacy. Calibrated with high-throughput kinetic data, our model offers a refined tool for ligand pharmacological characterization and advances the understanding of GPCR signaling spatial organization.

BackgroundCytokines are immunomodulatory proteins that play central roles in regulating immune responses and represent attractive targets for cancer therapy. However, as single agents, cytokines have shown limited clinical benefit due to systemic toxicities and a short in vivo half-life. Our group has focused on engineering fusion cytokines (fusokines) that couple two cytokines into a single biologic to reprogram immune cell responses by enforcing non-canonical receptor engagement and signaling. A chimeric IL-6/IL-1{beta} fusokine was engineered to test the hypothesis that enforced co-engagement of IL-6 and IL-1{beta} signaling pathways would confer a gain-of-function phenotype in T cells and promote robust anti-tumor immunity. Here, we describe the immunomodulatory properties of IL6/1 fusokine and a method to deliver this fusokine to produce inhibition of ovarian tumor growth in a pre-clinical mouse model. MethodsLentiviral vectors encoding murine or human IL6/1 were designed using Vector Builder and expressed in either HEK293, CHO or ID8-F3 (p53-/-) cells depending on the downstream experiment to be conducted. IL6/1 expression was validated by ELISA and flow cytometry. Effects of human IL6/1 (hIL6/1) on T cell function (proliferation, memory phenotype, activation induced apoptosis) were monitored by flow cytometry. For in vivo studies, ID8-F3 murine ovarian cancer cells expressing mouse IL6/1 (mIL6/1) were administered intraperitoneally (I.P.) as a cell-based therapy to C57BL/6 female mice bearing established ID8-F3 luciferase tumors. Tumor progression was monitored by bioluminescence (BLI) imaging, and overall survival was evaluated. ResultshIL6/1 significantly enhanced T cell survival and selectively promoted activation and expansion of CD45RO memory T cells. mIL6/1 expressing ID8-F3 cells (ID8IL6/1) demonstrated stable transduction and sustained cytokine secretion. In vivo, ID8IL6/1 cell therapy significantly reduced tumor growth and improved overall survival compared to control groups, with 2 of 8 mice achieving complete tumor clearance. ConclusionThese findings indicate that IL6/1 fusokine enhances T cell survival and proliferation while promoting memory responses. Engineered cancer cells (ID8-F3) expressing mIL6/1 fusokine induced a strong anti-tumor response when delivered as a therapeutic vaccine in ovarian cancer mouse model. What is already known on this topicO_LIFusokines are a class of bifunctional proteins designed to achieve synergistic immune modulation. Previous studies in our lab have shown fusokine exhibit gain-of-function immunomodulating activity. Individually, IL-6 and IL-1{beta} are recognized for their roles in promoting T-cell proliferation and effector function. However, the potential for a fused IL-6/1 fusokine to reprogram the immune system and elicit a superior anti-tumor response in vivo in ovarian cancer model is not yet studied. C_LI What this study addsO_LIThis study develops a novel fusion cytokine (fusokine), combining IL-6 and IL-1{beta}, and demonstrate robust activation of T cells. In a preclinical ovarian cancer model, engineered cancer cells expressing IL6/1 used as a therapeutic vaccine showed significant tumor reduction and improved overall survival. C_LI How this study might affect research, practice or policyO_LIThis study demonstrates that in comparison to individual cytokines, fusokines have greater potential to activate T cell function and when delivered as a cell therapy, achieve clear therapeutic efficacy in an ovarian cancer model. Further translational and clinical studies may enable the development of novel and more effective fusokine cell therapy approaches for patients with ovarian cancer. C_LI

Treatment of advanced head and neck squamous cell carcinoma (HNSCC) often involves radiotherapy combined with chemotherapy, targeted therapy, or immunotherapy. However, due to its anatomical and molecular heterogeneity, identifying the most effective treatment for each patient remains a major clinical challenge. To address this need, we developed a high-throughput organoid-based drug screening platform that uses patient-derived organoids to assess candidate treatment regimens. We validated the platform by establishing bioprinted 3D organoids of human HNSCC cell lines and exposing them to X-ray radiation in combination with various small-molecule drugs and biologics. We quantified viability using ATP release assays and assessed extracellular matrix (ECM) invasion with a machine learning-based brightfield image analysis pipeline. Proof-of-concept experiments with HPV-negative HNSCC lines (HN30 and HN31, established from primary and metastatic disease from the same patient) and HPV-positive HNSCC cells (SCC154) revealed different therapy agents that can radiosensitize each cell line. Image analysis showed that copanlisib, afatinib, and ibrutinib could limit ECM invasion of HN31, while the AKT inhibitor ipatasertib promotes invasion of HN30 cells, consistent with previous studies. Application of the platform to patient-derived HPV+ oropharyngeal tumor organoids showed that they shared sensitivity to several agents while also exhibiting differences against certain therapies. Cetuximab, sorafenib, and nedisertib significantly radiosensitized organoids from two clinical samples. This work demonstrates the feasibility of performing sensitivity screening by integrating bioprinting, conventional viability assays, and advanced image analysis techniques. This platform has the potential to enable a personalized therapeutic pipeline for patients with advanced HNSCC, optimizing responses to radiotherapy and targeted agents to improve clinical outcomes while avoiding modulators that may promote tumor invasion.

Postoperative gastric leak after bariatric surgery is a serious complication associated with prolonged treatment, repeated interventions, and substantial morbidity. Endoscopic internal drainage using double pigtail stents is widely adopted. However, current stents, originally designed for biliary use and often based on simple cylindrical geometries, are not optimized for post-bariatric gastric leak anatomy, mechanical support, or fluid drainage. Here, we present BRIDGE (Biodegradable aRchitected Internal DrainaGE), a stent concept integrating triply periodic minimal surface (TPMS) architectures to control mechanical compliance, kink resistance, and drainage performance. Using computational modeling, mechanical testing, and benchtop flow studies, we evaluate TPMS designs and identify volume fraction as a key parameter balancing flexibility, structural integrity, and hydraulic performance. TPMS-integrated designs tolerated a 7.1-fold smaller bend radius than a commercial stent without kinking and achieved up to a 2-fold increase in drainage. We also developed a stereolithography-printable biodegradable resin and fabricated a prototype lattice-integrated stent. TeaserA biodegradable, 3D-printed stent with an architected lattice design improves flexibility, kink resistance, and abscess drainage while eliminating the need for device removal.

Background and ObjectivesCardiovascular cells differentiated from human induced pluripotent stem cells (iPSCs), including cardiomyocytes, are valuable for evaluating human cardiac pharmacology and toxicity. Early assessment of cardiotoxicity, especially for novel drugs like anticancer agents, is essential for improving drug development efficiency and reducing costs. This study aimed to develop a highly sensitive bioassay system capable of evaluating the physiological function of human cardiac tissue in vitro. MethodsHuman iPSCs were differentiated into cardiovascular cell types (cardiomyocytes, vascular endothelial cells, and vascular mural cells) and assembled into a cardiac tissue model on aligned fiber device. This tissue was cultured dynamically to induce the formation of vascular network-like structure. By combining the fiber device with our previously developed heart-on-a-chip microdevice (HMD), we created a new model of HMD (Aligned Fiber-based HMD; AF-HMD) with improved throughput and stability. Pulsatile force changes induced by drug exposure were quantified by tracking the displacement of fluorescent microbeads within the microchannels. ResultsAF-HMD demonstrated functional responses to known cardiac agonists and toxicants, such as doxorubicin. The device also replicated clinically relevant cardiotoxic events, including the synergistic effects of trastuzumab and doxorubicin, showing marked reductions in contractile force and beat rate, mirroring clinical observations. ConclusionsThe AF-HMD system provides a sensitive and reproducible platform for evaluating cardiotoxicity in drug development. It offers a promising tool for preclinical screening, with potential applications in personalized medicine and predicting cardiotoxic risk in cancer therapy.

T cell receptors (TCRs) are critical for immune surveillance and successful adaptive immune response against foreign antigens. TCRs drive this key arm of the immune system through recognition of peptide epitopes presented on MHC complexes. However, they are limited due to their stochastic nature and generation via genetic recombination. In silico design of functional TCRs that target defined peptide epitopes would be of considerable utility but has up until now been unsuccessful. Here, we develop an artificial intelligence (AI)-powered approach using a hybrid physics-based simulation and generative AI that successfully engineers TCRs against defined epitopes presented by MHC-I. We use this approach to design TCRs against two cancer antigens, a HERC1 neoantigen and an immunogenic neoepitope in mutant EGFR. We engineer multiple TCRs against the HERC1 neoantigen which activate T cells in response to exposure to peptide-MHC I and kill cancer cells more effectively than a patient-derived TCR. In addition, we used generative AI to design functional TCRs that target the EGFR T790M neoantigen, engineering greater specificity against the mutant sequence. We present an AI-based approach to TCR design with broad utility for efforts to engineer TCRs and for the development of new cell therapies. One sentence summaryArtificial intelligence-based approach enables the directed engineering of functional TCRs with enhanced features that target cancer neoantigens.

Chimeric antigen receptor (CAR) T-cell therapy has demonstrated transformative efficacy in hematologic malignancies, but its broader use remains constrained by complex ex vivo manufacturing, prolonged production timelines, high cost, and dependence on lymphodepleting chemotherapy. Emerging in vivo CAR-T generation strategies aim to address these limitations, but they introduce additional safety concerns associated with systemic delivery of gene-modifying vectors, including off-target transduction and insertional mutagenesis. This paper describes a novel antigen-driven CAR T-cell expansion platform (adCAR-T) based on co-culture of CAR T cells with engineered target cells expressing defined antigen density and lacking the inhibitory checkpoint ligand PD-L1. This system induces immediate activation, rapid proliferation, and sustained cytotoxic differentiation of CAR T cells without reliance on artificial CD3/CD28 bead stimulation or exogenous cytokine-driven expansion. In contrast to conventional methods, the platform eliminates the lag phase of CAR T-cell expansion and enables rapid scaling to clinically relevant doses (108-109 cells) within several days, depending on the initial cell input. Mechanistically, antigen-driven CAR engagement and target-cell lysis trigger cytokine release and amplification of CAR T cells in a physiologically relevant manner. This process promotes coordinated expansion of both directly antigen-engaged and non-engaged CAR T cells. The platform preserves "functional fitness", minimizes exhaustion, and avoids systemic exposure to gene-delivery vectors. Taken together, this strategy defines a hybrid manufacturing paradigm that bridges the control of ex vivo production with the physiological logic of in vivo activation. Proposed method has a potential to reduce manufacturing complexity, improve safety, and possibly decrease or eliminate the need for lymphodepleting conditioning. This work presents a potential alternative to both standard ex vivo manufacturing and emerging in vivo CAR-T generation approaches, with important implications for improving the accessibility, safety, and cost-effectiveness of CAR T-cell therapies.

Developing parsimonious, mechanism-aware quantitative models that predict how biological effectiveness changes with different modifiers remains, in general, an unsolved problem. Advances in radiobiological research have created a large knowledge base of first-principles mechanistic models of radiation response that, in principle, could accurately predict radiosensitivity across different experimental and clinical conditions. However, in practice these mechanistic models come with an overabundance of parameters, the majority of which are practically unidentifiable and, moreover, likely unnecessary if one simply wishes to predict how radiosensitivity changes for some specific modifier of interest. Nevertheless, determining which few details in the full mechanistic model are relevant for a given purpose, as well as how to remove any other extraneous details, remains a highly non-trivial task. In this study, we demonstrate the potential of model reduction, starting from a detailed mechanistic description, as a systematic strategy for deriving parsimonious, experimentally falsifiable radiobiological descriptors. As a proof-of-concept demonstration, we apply the Manifold Boundary Approximation Method (MBAM) to a Mechanistic Model of DNA Repair and Survival (MEDRAS), for the problem of cell survival prediction following an acute exposure. Our findings reveal that the complete MEDRAS model for an arbitrary mixed-quality exposure can be structurally simplified to a reduced three-parameter model for an effective uniform-quality, named MEDRAS-LPL. Additional MBAM analysis on MEDRAS-LPL identifies two boundaries in parameter space, corresponding to sparsely ionizing and densely ionizing radiation. Mapping of MEDRAS-LPL parameter space on to effective LQ space further demonstrates that parameters close to the sparsely ionizing boundary line up with expectations from the theory of dual radiation, while parameters close to the densely ionizing boundary line up with expectations from a purely linear model based on a target-theory description. Moreover, our formalism predicts enhanced synergistic interactions between sparsely ionizing and densely ionizing radiation beyond the Zaider Rossi model (ZRM) paradigm, in line with empirical observations. The results highlight the potential for using reduced-order models not only for predictive applications but also for generating novel hypotheses that can inform future experimental designs and optimization strategies in radiobiology.

Immune checkpoint inhibitors (ICI) have revolutionized cancer therapy, yet response rates remain suboptimal across many solid tumors, and resistance mechanisms, particularly those involving glycans, are not fully understood. Recent studies have identified sialic acid-containing glycans and their interactions with Siglec receptors on tumor-associated macrophages as an important contributor to immune suppression within the tumor microenvironment (TME). Targeting this sialic acid-Siglec axis by glycan engineering with sialidases and other glycosidases has shown therapeutic potential in preclinical models. However, safe and effective delivery of sialidases to tumors remains a challenge. Here, we present a novel approach using adeno-associated virus (AAV)-mediated therapy to deliver sialidases (AAVSia) and other glycosidases, including fucosidase, directly to the TME. Intratumoral administration of AAVSia in mouse models resulted in significant tumor growth reduction, enhanced survival, and robust systemic antitumor immunity through improved cross-presentation and dendritic cell activation. Furthermore, combining local sialidase expression with fucosidase treatment and classical PD-1 blockade allowed a synergistic effect, amplifying antitumor response. Our findings highlight the therapeutic promise of glycoengineering the TME using local delivery systems and support the development of combination strategies to overcome glycan-mediated resistance in cancer immunotherapy. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/720097v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@dc9d72org.highwire.dtl.DTLVardef@1e4e455org.highwire.dtl.DTLVardef@4a8f93org.highwire.dtl.DTLVardef@11813a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

Cardiac rhythm is a critical clinical indicator for cardiac arrhythmias and adverse events during drug toxicity studies. In vivo, cardiomyocyte responses to pharmacological agents occur within minutes and are strongly influenced by dynamic drug delivery through blood flow. However, conventional 2D and 3D static culture systems fail to replicate these fluid flow kinetics, limiting their physiological relevance for assessing beat rate responses. Here, we present Mera, an advanced microphysiological system (MPS) developed by Hooke Bio, designed for high-throughput, long-term culture and functional analysis of 3D cardiac spheroids composed of human induced pluripotent stem cell-derived cardiomyocytes and cardiac fibroblasts. Mera enables dynamic perfusion, allowing investigation of cardiomyocyte beat rates under physiologically relevant flow conditions. The platform supports up to 640 spheroids per run and integrates automated imaging, fluid handling, and user-friendly software, operating under controlled physiological conditions (37{degrees}C, 5% CO2). Flow rates are tunable between 0 and 12.5 mL/min to mimic in vivo environments. Pharmacological testing with verapamil, isoproterenol, calcium chloride, and propranolol demonstrated real-time, reversible modulation of beat rate under flow, including recovery following drug-induced suppression. System variability was comparable to a temperature-controlled reference platform, supporting robust statistical analysis. Dose-response studies yielded IC values consistent with literature, confirming physiological relevance. Collectively, these results demonstrate that Mera provides a reproducible, scalable, and human-relevant platform for cardiac drug testing. By enabling dynamic drug exposure and automated analysis, Mera represents a powerful new approach methodology (NAM) for improving the predictive assessment of cardiac safety and beat-rate modulation drug responses.

The development of clinically effective CAR-T cell therapies for solid tumors requires careful optimization of receptor design, functional fitness, and manufacturability. While advancing low-affinity HER2-targeting CAR-T cells toward clinical application, we found that the candidate with the strongest in vivo antitumor activity--comprising a CD8 hinge and transmembrane region and a 4-1BB co-stimulatory domain--exhibited measurable tonic signaling. This basal antigen-independent signaling, likely driven by high CAR surface expression, was associated with increased apoptosis and reduced ex vivo expansion under research-grade manufacturing conditions. Modification of the transmembrane domain reduced CAR surface expression but did not alleviate tonic signaling and instead impaired antitumor activity. By contrast, transient pharmacologic inhibition of CAR signaling with dasatinib rescued expansion and reduced apoptosis in small-scale research cultures. Notably, these tonic-signaling-associated defects were largely absent during large-scale, GMP-compliant manufacturing, which enabled robust CAR-T cell expansion without additional benefit from dasatinib supplementation. Together, these findings show that tonic signaling is not inherently detrimental to CAR-T cell performance and that its functional consequences are highly dependent on manufacturing context. Our study underscores the importance of evaluating CAR candidates within clinically relevant production platforms and supports the advancement of this 4-1BB-based HER2-specific CAR-T cell product toward clinical testing.

Hepatocyte transplantation is a promising alternative to liver transplantation; however, it currently serves only as a temporary treatment until a donor organ becomes available. In contrast, animal studies have demonstrated "liver repopulation", a phenomenon in which transplanted hepatocytes progressively replace host hepatocytes. Despite extensive documentation, the mechanisms driving this process remain poorly understood. More fundamentally, it remains unclear whether liver repopulation is driven by active cell-cell interactions between host and transplanted hepatocytes that induce host cell death, or whether it can be explained solely by intrinsic differences in proliferation and survival between these populations. To address this, we performed computational simulations using an agent-based model constrained by experimental data from repopulation in uninjured rat livers. Our analysis reveals that host hepatocyte death rate is the dominant determinant of repopulation kinetics, whereas variations in proliferation rate have only a limited impact. Notably, experimentally observed repopulation dynamics were only reproduced when cell- cell interactions were incorporated, or alternatively when host hepatocyte lifespan was set to unrealistically short values (approximately 25 days). These findings support a model in which cell- cell interactions play a critical role in efficient liver repopulation. More broadly, this study establishes a conceptual and computational framework for evaluating the requirement for cell-cell interactions in tissue replacement, even in the absence of a defined molecular mechanism.

Breast cancer is the most common malignancy in women, with triple-negative breast cancer (TNBC) representing the most aggressive subtype and carrying a poor metastatic prognosis. Metastasis requires tumor cells to cross the endothelial barrier, a process facilitated by tumor-derived extracellular vesicles (EVs), which can disrupt vascular integrity. Fluid shear stress (FSS), generated by blood flow, shapes endothelial physiology and may influence EV uptake, yet the mechanisms underlying TNBC-derived small EV (sEV) internalization remain unclear. Here, we investigated TNBC sEV-endothelial interactions using combined in silico and in vitro approaches. Human umbilical vein endothelial cells (HUVECs) were cultured under static or FSS conditions (20 dyn/cm{superscript 2}), followed by proteomic profiling and protein-protein interaction analyses with sEV proteomes. Uptake assays employed pharmacological inhibition (Dynasore, M{beta}CD, Pitstop2), Caveolin-1 (CAV-1) and Clathrin Heavy Chain (CLHC), siRNA-mediated knockdown, and junctional interaction analyses via confocal microscopy and co-immunoprecipitation. FSS downregulated proliferation- and angiogenesis-associated proteins while upregulating adhesion and cytoskeletal regulators assessed by proteomics. Network analysis identified clathrin- and caveolin-mediated endocytosis (CME and CavME), integrins, and early endosomes as central mediators of sEV uptake. Functionally, uptake was reduced by Pitstop2, M{beta}CD, and CAV-1/CLHC knockdown under static conditions, but silencing paradoxically enhanced uptake under FSS, suggesting compensatory flow-dependent pathways. Notably, under FSS, sEVs accumulated at endothelial junctions, colocalizing with VE-CAD and associating with CLDN5, indicating a potential disruption mechanism of adherens and tight junctions and consequent endothelial permeability. These findings identify CME and CavME as key uptake routes while underscoring FSS as a critical determinant of endothelial-tumor EV interactions. By revealing junctional targeting of sEVs, this work provides new mechanistic insight into vascular remodeling during metastasis and highlights EV pathways as potential therapeutic targets in TNBC. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/721946v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@f91c5org.highwire.dtl.DTLVardef@2b4dc8org.highwire.dtl.DTLVardef@ff94f1org.highwire.dtl.DTLVardef@18b714b_HPS_FORMAT_FIGEXP M_FIG C_FIG Uptake and localization of sEVs on HUVEC under (a) static and (b) fluid shear-stress conditions. sEVs: Small Extracellular Vesicles. CME: Clathrin-mediated Endocytosis. CavME: Caveolin-mediated Endocytosis. CLDN5: Claudin-5. VE-CAD: Vascular Endothelial Cadherin. FSS: Fluid shear-stress.

Cyclophosphamide (CP) is a widely used alkylating agent whose cytotoxic activity depends on hepatic CYP450-mediated bioactivation. While CP-associated neurotoxicity and cognitive impairment are recognized clinically, the mechanisms of secondary organ damage through metabolic cross-talk remain poorly understood due to limitations of conventional monoculture models. Here we employ a multi-organ microphysiological system (MPS) connecting stem cell derived liver and CNS organoids via microfluidic channels to model inter-organ drug metabolism and secondary toxicity. Liver organoids were treated with CP (0-200 {micro}M) for 48 hours, and connected CNS organoids were assessed for secondary damage by confocal Z-stack imaging of DNA damage ({gamma}H2AX), neuronal identity (NeuN), and nuclear content (DAPI). We observe dose-dependent reduction in NeuN expression and {gamma}H2AX signal in connected CNS organoids, consistent with neurotoxic metabolite transfer from liver. Critically, CNS-to-CNS control connections show no comparable damage at equivalent CP concentrations, confirming that hepatic metabolism is required for CNS toxicity. These findings validate the MPS platform for modelling multi-organ drug toxicity and provide direct evidence that liver-derived CP metabolites drive secondary neurotoxicity through inter-organ metabolic communication.

Peripheral artery disease (PAD) is a complex vascular disorder characterized by heterogeneous molecular mechanisms and incomplete functional annotation, limiting systematic biomarker discovery. Network-based learning approaches provide a powerful framework for disease gene prioritization; however, most existing methods produce overconfident predictions without explicitly accounting for model uncertainty or structural novelty. Here, we present an uncertainty-aware framework for PAD biomarker discovery that integrates unsupervised graph representation learning, positive-unlabeled (PU) classification, ensemble prediction, and mechanistic explainability. Node embeddings were learned using multiple unsupervised graph neural network (GNN) objectives and combined with heterogeneous classifiers to generate ensemble-averaged probability estimates and epistemic uncertainty. By jointly modeling predictive confidence and embedding-space novelty, we stratified candidates into high-confidence rediscoveries and structurally novel hypotheses under explicit uncertainty control. Across eight embedding objectives and five classifiers, ensemble aggregation produced stable, well-calibrated predictions and enabled prioritization of 100 candidate PAD-associated proteins. Probability-heavy candidates clustered tightly with known PAD proteins and were enriched for established vascular and hemostatic pathways, including extracellular matrix organization, integrin signaling, coagulation, and fibrinolysis. In contrast, novelty-heavy candidates occupied distinct embedding-space regions and partitioned into multiple coherent clusters enriched for upstream regulatory and signaling processes, including G protein-coupled receptor, ephrin receptor, kinase-driven, and NF-{kappa}B-associated pathways. Five-fold cross-validated comparison with established PU learning baselines demonstrated consistent improvement across all evaluation metrics (AUC 0.916 {+/-} 0.019 vs. 0.821 {+/-} 0.030 for the best baseline), and external validity was confirmed by significant enrichment of top candidates for related cardiovascular disease annotations (5.7x above background). Together, these results demonstrate that integrating uncertainty, novelty, and explainability enables calibrated and biologically grounded biomarker prioritization, with broad applicability to PAD and other complex diseases. Author summaryPeripheral artery disease affects millions of people worldwide but remains underdiagnosed, partly because we lack reliable molecular markers to detect it early. In this study, we developed a computational framework that uses protein interaction network data to predict which proteins may be involved in PAD, even when we only know a small number of confirmed disease-associated proteins. Our approach combines graph neural network embeddings with a machine learning technique called positive-unlabeled learning, which is specifically designed for situations where you have confirmed positives but no confirmed negatives. We also quantify how confident the model is in each prediction and identify candidates that are genuinely novel compared to what is already known. Tested against established methods, our framework consistently found more known disease proteins in cross-validated evaluation. The candidates we identified map to biologically coherent pathways relevant to vascular disease, and our top predictions are enriched for proteins associated with related cardiovascular conditions, providing external validation. This work provides a principled and transparent approach to biomarker discovery that could be applied to other complex diseases with limited molecular annotations.