Biology subjects

Gutova, M.

Publications and source records attributed to Gutova, M..

11 recordsLinked to original sources

bioRxiv2026

Intranasal L-MYC Neural Stem Cells Restore Neurovascular and Neuroimmune Homeostasis After TBI via CX3CL1 Upregulation and ICAM-1 Downregulation

Traumatic brain injury (TBI) triggers persistent neuroimmune and neurovascular dysfunction that contributes to secondary brain injury and limits recovery. Here, we investigated whether a single intranasal dose of LMNSC01 could reprogram neuroinflammatory and neurovascular responses post TBI and whether these effects were conserved across biological sex. Immunocompetent male and female rats underwent a controlled cortical impact (CCI) or Sham surgery and 7 days later received a single bolus dose of LMNSC01 (6 x10^6 cells/dose) or vehicle. Integrated histological and transcriptomic analyses and multiplex tissue ELISA were used to characterize injury- and treatment-associated responses. CCI induced persistent but sex-dependent transcriptional changes. Male rats exhibited prominent innate immune, adhesion, extracellular matrix, and tissue remodeling responses, whereas females demonstrated broader activation of microglial, interferon, complement, MHC/antigen presentation, and humoral/adaptive immune pathways. LMNSC01 treatment reprogrammed these distinct injury states through partially sex-specific mechanisms. In males, LMNSC01 increased endogenous immune-regulatory mediators, including Il1rn, and reduced pathways associated with adhesion and immune cell trafficking. In females, treatment decreased expression of genes related to microglial, leukocyte, MHC/antigen presentation, cytokine, and adhesion pathways, and reduced macrophage- and CD45 positive cell signatures. Despite these sex-dependent transcriptional changes, LMNSC01 produced conserved protein changes in both sexes, reducing injury associated ICAM-1 and restoring CX3CL1 proteins, proposing a novel molecular mechanism of LMNSCs modulating endothelial activation and neuron-microglia communication post injury. Our study provides further insight into sex differences in responses to injury and treatment after TBI and highlights candidate repair mechanisms, supporting further development of LMNSC01 as a minimally invasive therapy for TBI.

neuroscience↗

bioRxiv2026

Patient-Derived Glioma Models Preserve Tumor Heterogeneity and Identify Stearoyl-CoA Desaturase1 (SCD1) as a Candidate Biomarker for Precision Immunotherapy

Background: Pediatric and adult brain tumors, including glioblastoma, astrocytoma, ependymoma, and medulloblastoma, remain associated with poor prognosis despite advances in surgery, radiation, and chemotherapy. Therapeutic resistance, tumor heterogeneity, and treatment-related toxicity highlight the need for clinically relevant models that enable precision medicine and immunotherapy development. Methods: Freshly dispersed tumors (FDTs), low-passage patient-derived brain tumor (PBT) spheroid lines, and matched patient-derived xenograft (PDX) models were established from patients with primary brain tumors. Models were characterized using single-cell and bulk RNA sequencing, whole-exome sequencing, multiparameter flow cytometry, and immunohistochemistry. PBTs were compared with matched FDTs to evaluate model fidelity. Results: PBT lines were established in approximately 68% of cases and retained key patient-specific genomic alterations, including IDH1, MGMT, TP53, and PTEN, with expression of therapeutically relevant targets, including IL13R2, EGFR, HER2, WNT1, JAK1/2, and NOTCH1-4. Gene expression profiles of PBTs closely correlated with matched FDTs (R = 0.38, P = 0.0038). PBT and PDX models preserved intratumoral heterogeneity and non-clonal populations, enabling identification of therapy-resistant subclones during in vitro selection. Molecular analyses identified Stearoyl-CoA Desaturase1 (SCD1) as an overexpressed biomarker across glioma PBTs and matched patient tumors. Ingenuity Pathway Analysis identified SCD1 as an upstream regulator of EGFR-, TP53-, and CYCS-associated signaling networks implicated in tumor progression and immune suppression. Conclusions: Clinically relevant PBT and matched PDX models recapitulate molecular, transcriptional, and histopathological characteristics of primary brain tumors. These platforms provide tools for biomarker discovery, therapeutic testing, and precision immunotherapy development, while identifying SCD1 as a biomarker and therapeutic target in glioma.

cancer biology↗

bioRxiv2026

Extracellular Vesicles Derived from L-MYC Neural Stem Cells Mediate Neuroprotection in 3D Models of Chemotherapy- and Radiation-Induced Neurotoxicity

Background/Objectives: Cancer survivors frequently experience long-term neurocognitive impairments following chemotherapy and cranial irradiation, yet experimental models that enable mechanistic investigation of therapy-induced neurotoxicity at the transcriptional level remain limited. This study aimed to develop a human three-dimensional (3D) neural tissue model derived from L-Myc immortalized neural stem cells (LMNSCs) and use transcriptomic profiling to identify molecular pathways underlying chemotherapy- and radiation-induced neural injury and extracellular vesicle (EV)-mediated recovery. Methods: LMNSCs were differentiated in a 3D, methylcellulose-based culture to generate neural tissue containing neurons, astrocytes, and oligodendrocytes. Cultures were exposed to methotrexate (MTX) or ionizing radiation to induce neural injury and subsequently treated with LMNSC-derived EVs. Neural injury and repair mechanisms were evaluated by immunocytochemistry and bulk transcriptomics. Results: MTX and irradiation induced dose-dependent injury, exhibited by loss of neuronal complexity and reduced glial populations. LMNSC-EV treatment promoted recovery of neuronal and glial populations following MTX- and irradiation-induced injury. Transcriptomic analysis of irradiated cultures revealed activation of inflammation, DNA damage, and stress-response pathways, which were attenuated after treatment with LMNSC-EVs. Conclusions: LMNSC-based 3D neural tissue provides a human-relevant platform for modeling cancer therapy-induced neurotoxicity. Furthermore, LMNSC-EVs represent a promising cell-free regenerative therapeutic that restores injury-associated inflammatory, stress, and metabol-ic transcriptional programs after radiation-induced neural injury.

cancer biology↗

bioRxiv2025

Study of combination CAR T-cell treatment for glioblastoma using mathematical modeling

Glioblastoma is a highly aggressive and difficult-to-treat brain cancer that resists conventional therapies. Recent advances in chimeric antigen receptor (CAR) T-cell therapy have shown promising potential for treating glioblastoma; however, achieving optimal efficacy remains challenging due to tumor antigen heterogeneity, the tumor microenvironment, and T-cell exhaustion. In this study, we developed a mathematical model of CAR T-cell therapy for glioblastoma to explore combinations of CAR T-cell treatments that take into account the spatial heterogeneity of antigen expression. Our hybrid model, created using the multicellular modeling platform PhysiCell, couples partial differential equations that describe the tumor microenvironment with agent-based models for glioblastoma and CAR T-cells. The model captures cell-to-cell interactions between the glioblastoma cells and CAR T-cells throughout treatment, focusing on three target antigens: IL-13R2, HER2, and EGFR. We analyze tumor antigen expression heterogeneity informed by expression patterns identified from human tissues and investigate patient-specific combination CAR T-cell treatment strategies. Our model demonstrates that an early intervention is the most effective approach, especially in glioblastoma tumors characterized by mixed antigen expression. However, in tissues with clustered antigen patterns, we find that sequential administration with specific CAR T-cell types can achieve efficacy comparable to simultaneous administration. In addition, spatially targeted delivery of CAR T-cells to specific tumor regions with matching antigen is an effective strategy as well. Our model provides a valuable platform for developing patient-specific CAR T-cell treatment plans with the potential to optimize scheduling and locations of CAR T-cell injections based on individual antigen expression profiles.

systems biology↗

bioRxiv2025

Mathematical Modeling of Neural Stem Cell Migration within Brain using Multi-Fiber Tractography

An agent-based model of therapeutic neural stem cell (NSC) migration is developed and used to predict the migration of NSCs in naive mouse brain. The model utilizes generalized q-sampling imaging which resolves white matter fibers that cross in the brain and is shown to better account for variations in NSC migration patterns as compared to diffusion tensor imaging. In calibrating the model to experimental data, we show that the model is able to reproduce the distribution of NSCs in the mouse brain. In addition, we show that the distribution of NSCs in the mouse brain is sensitive to the location of NSC injection. Persistent distribution of NSCs to the olfactory bulb, consistent with developmental pathways including the rostral migratory stream, suggests that future models of therapeutic NSCs in the naive brain may need to include other factors such as chemotaxis or blood flow to account for variations in NSC migration paths. The results highlight the usefulness of the model in predicting which injection locations may provide the best distribution of NSCs to a given target location.

systems biology↗

bioRxiv2025

Modeling cerebral development in vitro with L-MYC-immortalized human neural stem cell-derived organoids

A promising advance for ex vivo studies of human brain development and formulation of therapeutic strategies has been the adoption of brain organoids that, to a greater extent than monolayer or spheroid cultures, recapitulate to varying extents the patterns of tissue development and cell differentiation of human brain. Previously, such studies been hampered by limited access to relevant human tissue, inadequate human in vitro models, and the necessity of using rodent models that imperfectly reproduce human brain physiology. Here we present a novel organoid-based research platform utilizing L-MYC-immortalized human fetal neural stem cells (LMNSC01) grown in a physiological 4% oxygen environment. We visualized developmental processes in LMNSC01 brain organoids for over 120 days in vitro by immunofluorescence and NanoString gene expression profiling. Gene expression patterns revealed by NanoString profiling were quantitatively compared to those occurring during normal brain development (BrainSpan database) using the Singscore method. We observe similar developmental patterns in LMNSC01 organoids and developing cortex for genes characterizing neurons, astrocytes, and oligodendrocytes, and multiple pathways including those involved in apoptosis, neuronal cytoskeleton, neurotransmission, and metabolism. Notable properties of this LMNSC01 platform are its initiation with immortalized authentic human neural stem cells, growth in a physiological oxygen environment, the consistency of the organoids produced, and favorable comparison of their gene expression patterns with those reported for normal cortical development. SUMMARYEx vivo studies of human brain development has been advanced by adoption of organoids recapitulating to varying extents in utero patterns of tissue development and cell differentiation. We here present an organoid-based human cortical development platform employing immortalized fetal neural stem cells (LMNSC01) grown in a physiological (4% oxygen) environment. Characterizing LMNSC01 organoids for over 120 days in vitro by immunofluorescence and expression profiling (using NanoString), and then comparing these profiles to those of normal cortical development (BrainSpan database), revealed similar developmental patterns for neurons, astrocytes and oligodendrocytes. Notable properties of this platform are its initiation with immortalized authentic human NSCs, growth at physiological oxygen concentration, and subsequent favorable comparison of their gene expression patterns with those observed during cortical development.

neuroscience↗

bioRxiv2023

Structural and practical identifiability of contrast transport models for DCE-MRI

Compartment models are widely used to quantify blood flow and transport in dynamic contrast-enhanced magnetic resonance imaging. These models analyze the time course of the contrast agent concentration, providing diagnostic and prognostic value for many biological systems. Thus, ensuring accuracy and repeatability of the model parameter estimation is a fundamental concern. In this work, we analyze the structural and practical identifiability of a class of nested compartment models pervasively used in analysis of MRI data. We combine artificial and real data to study the role of noise in model parameter estimation. We observe that although all the models are structurally identifiable, practical identifiability strongly depends on the data characteristics. We analyze the impact of increasing data noise on parameter identifiability and show how the latter can be recovered with increased data quality. To complete the analysis, we show that the results do not depend on specific tissue characteristics or the type of enhancement patterns of contrast agent signal.

systems biology↗

bioRxiv2023

Non-invasive measurement of intra-tumoral fluid dynamics with localized convolutional function regression

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to non-invasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that it will contribute to the better understanding of cancer progression and therapeutic response. One-Sentence SummaryA physics-informed computational method enables accurate and efficient measurement of fluid dynamics in individual patient tumors and demonstrates differences between tissues.

bioengineering↗

bioRxiv2021

Destabilization of CAR T-cell treatment efficacy in the presence of dexamethasone

Chimeric antigen receptor (CAR) T-cell therapy is potentially an effective targeted immunotherapy for glioblastoma, yet there is presently little known about the efficacy of CAR T-cell treatment when combined with the widely used anti-inflammatory and immunosuppressant glucocorticoid, dexamethasone. Here we present a mathematical model-based analysis of three patient-derived glioblastoma cell lines treated in vitro with CAR T-cells and dexamethasone. Advanced in vitro experimental cell killing assay technologies allow for highly resolved temporal dynamics of tumor cells treated with CAR T-cells and dexamethasone, making this a valuable model system for studying the rich dynamics of nonlinear biological processes with translational applications. We model the system as a nonautonomous, two-species predator-prey interaction of tumor cells and CAR T-cells, with explicit time-dependence in the clearance rate of dexamethasone. Using time as a bifurcation parameter, we show that (1) dexamethasone destabilizes coexistence equilibria between CAR T-cells and tumor cells in a dose-dependent manner and (2) as dexamethasone is cleared from the system, a stable coexistence equilibrium returns in the form of a Hopf bifurcation. With the model fit to experimental data, we demonstrate that high concentrations of dexamethasone antagonizes CAR T-cell efficacy by exhausting, or reducing the activity of CAR T-cells, and by promoting tumor cell growth. Finally, we identify a critical threshold in the ratio of CAR T-cell death to CAR T-cell proliferation rates that predicts eventual treatment success or failure that may be used to guide the dose and timing of CAR T-cell therapy in the presence of dexamethasone in patients. Author summaryBioengineering and gene-editing technologies have paved the way for advance immunotherapies that can target patient-specific tumor cells. One of these therapies, chimeric antigen receptor (CAR) T-cell therapy has recently shown promise in treating glioblastoma, an aggressive brain cancer often with poor patient prognosis. Dexamethasone is a commonly prescribed anti-inflammatory medication due to the health complications of tumor associated swelling in the brain. However, the immunosuppressant effects of dexamethasone on the immunotherapeutic CAR T-cells are not well understood. To address this issue, we use mathematical modeling to study in vitro dynamics of dexamethasone and CAR T-cells in three patient-derived glioblastoma cell lines. We find that in each cell line studied there is a threshold of tolerable dexamethasone concentration. Below this threshold, CAR T-cells are successful at eliminating the cancer cells, while above this threshold, dexamethasone critically inhibits CAR T-cell efficacy. Our modeling suggests that in the presence of high dexamethasone reduced CAR T-cell efficacy, or increased exhaustion, can occur and result in CAR T-cell treatment failure.

cancer biology↗

bioRxiv2020

Chlorotoxin Redirects Chimeric Antigen Receptor T Cells for Specific and Effective Targeting of Glioblastoma

While chimeric antigen receptor (CAR) T cells have demonstrated antitumor activity against glioblastoma (GBM), tumor heterogeneity remains a critical challenge. To more effectively target heterogeneous GBMs, we report the development of a novel peptide-based CAR exploiting the GBM-binding potential of chlorotoxin (CLTX). CLTX bound a greater proportion of tumor cells than GBM-associated antigens EGFR, HER2 and IL13R2. CAR T cells bearing CLTX as the targeting domain (CLTX-CAR), mediated potent in vitro and in vivo anti-GBM activity, and efficiently targeted tumors lacking expression of other GBM-associated antigens. Importantly, CLTX-CAR T cells exhibited no observable off-target effector activity against normal cells, or when adoptively transferred into mice. Effective targeting by CLTX-CAR T cells required cell surface expression of matrix metalloproteinase-2 (MMP-2). Our results are the first demonstration of a peptide toxin utilized as a CAR targeting domain, expanding the repertoire of tumor-selective CAR T cells with the potential to reduce antigen escape. One Sentence SummaryChimeric antigen receptors incorporating chlorotoxin as the tumor targeting domain recognize and kill glioblastoma with high specificity and potency.

immunology↗

bioRxiv2019

A mathematical modeling approach to explore kinetics of Chimeric Antigen Receptor (CAR) T-cell Response in glioma: the CARRGO model

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of hematological cancers and is currently being investigated for solid tumors including high-grade glioma brain tumors. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumors. In this work we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor t-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, CAR T-cell proliferation and exhaustion, and CAR T-cell persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyzer to parameterize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted as compared to higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumor growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumor characteristics including tumor growth rate and antigen level to maximize therapeutic benefit. Statement of SignificanceWe utilize a mathematical model to deconvolute the nonlinear contributions of CAR T-cell proliferation and exhaustion to predict therapeutic efficacy and dependence on CAR T-cell dose and target antigen levels.

immunology↗