Biology subjects

Rezvani, K.

Publications and source records attributed to Rezvani, K..

12 recordsLinked to original sources

bioRxiv2025

Ser14-phosphorylated Rpn6 Limits Proteostasis Impairment and Pathology in Both Brain and Heart of Tauopathy Mice

Alzheimers disease (AD) patients often display neurobehavioral and cardiac impairments, but the underlying factors remain unclear. Ser14 phosphorylation in RPN6 (p-S14-RPN6) mediates the activation of 26S proteasomes by protein kinase A (PKA). Proteasome priming is implicated in protection by cAMP-PKA against AD, but this remains to be established. Hence, this study was conducted to interrogate homeostatic p-S14-RPN6 in AD. The recently validated Rpn6S14A knock-in (S14A) mice were crossbred with the PS19 tauopathy mice (RRID: IMSR_JAX:008169). The resultant wild type (WT), PS19, and PS19::S14A littermates were compared. Expedited declines in cognitive and motor functions as indicated respectively by significant decreases in object recognition and discrimination indexes and rotarod time were observed in PS19::S14A mice vs. PS19 mice, which is associated with more pronounced synaptic losses, microglial activation, and gliosis in the hippocampus. Compared with WT and PS19 mice, PS19::S14A mice showed exacerbated cardiac malfunction, cardiac hypertrophic responses and fibrosis, and greater increases of total and hyperphosphorylated tau proteins and ubiquitin conjugates in both hippocampi and hearts. These findings demonstrate that genetic blockade of p-S14-RPN6 exacerbates tauopathy in both the brain and heart, which for the first time establishes that homeostatic p-S14-RPN6 promotes proteostasis and protects against pathogenesis in AD.

cell biology↗

bioRxiv2025

Literature-scaled immunological gene set annotation using AI-powered immune cell knowledge graph (ICKG)

Large scale application of single-cell and spatial omics in models and patient samples has led to the discovery of many novel gene sets, particularly those from an immunotherapeutic context. However, the biological meaning of those gene sets has been interpreted anecdotally through over-representation analysis against canonical annotation databases of limited complexity, granularity, and accuracy. Rich functional descriptions of individual genes in an immunological context exist in the literature but are not semantically summarized to perform gene set analysis. To overcome this limitation, we constructed immune cell knowledge graphs (ICKGs) by integrating over 24,000 published abstracts from recent literature using large language models (LLMs). ICKGs effectively integrate knowledge across individual, peer-reviewed studies, enabling accurate, verifiable graph-based reasoning. We validated the quality of ICKGs using functional omics data obtained independently from cytokine stimulation, CRISPR gene knock-out, and protein-protein interaction experiments. Using ICKGs, we achieved rich, holistic, and accurate annotation of immunological gene sets, including those that were unannotated by existing approaches and those that are in use for clinical applications. We created an interactive website (https://kchen-lab.github.io/immune-knowledgegraph.github.io/) to perform ICKG-based gene set annotations and visualize the supporting rationale.

bioinformatics↗

bioRxiv2025

Agent-based modeling of cellular dynamics in adoptive cell therapy

Adoptive cell therapies (ACT) leverage tumor-immune interactions to cure cancer. Despite promising phase I/II clinical trials of chimeric-antigen-receptor natural killer (CAR-NK) cell therapies, molecular mechanisms and cellular properties required to achieve clinical benefits in broad cancer spectra remain underexplored. While in vitro and in vivo experiments are required in this endeavor, they are typically expensive, laborious, and limited to targeted investigations. Here, we present ABMACT (Agent-Based Model for Adoptive Cell Therapy), an in silico approach employing agent-based models (ABM) to simulate the continuous course and dynamics of an evolving tumor-immune ecosystem, consisting of heterogeneous "virtual cells" created based on knowledge and omics data observed in experiments and patients. Applying ABMACT in multiple therapeutic context indicates that to achieve optimal ACT efficacy, it is key to enhance immune cellular proliferation, cytotoxicity, and serial killing capacity. With ABMACT, in silico trials can be performed systematically to inform ACT product development and predict optimal treatment strategies.

bioinformatics↗

bioRxiv2024

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

bioinformatics↗

bioRxiv2023

COP9 Signalosome Promotes Neointimal Hyperplasia via Deneddylation and CSN5-Mediated Nuclear Export

BACKGROUNDNeointimal hyperplasia (NH) is a common pathological response to vascular injury and mediated primarily by vascular smooth muscle cell (VSMC) migration and proliferation. The COP9 signalosome (CSN) is formed by 8 canonical subunits (CSN1 through CSN8) with its deneddylation activity residing in CSN5. Each or some of CSN subunits may have deneddylation-independent function. Despite strong evidence linking the CSN to cell cycle regulation in cancer cells, the role of the CSN in vascular biology remains obscure. METHODSNeointimal CSN5 expression in the lung tissue of pulmonary hypertension (PAH) patients was assessed with immunohistochemistry. Adult mice with smooth muscle cell-restricted CSN5 knockout (CSN5-SMKO) or CSN8 hypomorphism (CSN8-hypo) and cultured mouse VSMCs were studied to determine the role and governing mechanisms of the CSN in NH. NH was induced by ligation of the left common carotid artery (LCCA) and PDGF-BB stimulation was used to mimic the vascular injury in cell cultures. RESULTSRemarkably higher CSN5 levels were detected in the neointimal VSMCs of the pulmonary arteries of human PAH. LCCA ligation induced NH and significantly increased the mRNA and protein levels of CSN subunits in the LCCA wall of adult wild type mice. CSN5-SMKO impaired Cullin deneddylation and the nuclear export of p27 in vessel walls and markedly inhibited VSMC proliferation in mice. On the contrary, CSN8-hypo significantly exacerbated NH and VSMC proliferation in vivo and in cellulo. Cytoplasmic CSN5 mini-complexes and the nuclear export of p27 were significantly increased in CSN8-hypo mouse vessels and cultured CSN8-hypo VSMCs. Nuclear export inhibition with leptomycin attenuated the PDGF-BB-induced increases in VSMC proliferation in both CSN8-hypo and control VSMCs. Further, genetically disabling CSN5 nuclear export but not disabling CSN5 deneddylase activity suppressed the hyperproliferation and restored p27 nuclear localization in CSN8 hypomorphic VSMCs. Interestingly, CSN deneddylase inhibition by CSN5i-3 did not alter the hyperproliferation of cultured CSN8-hypo VSMCs but suppressed wild type VSMC proliferation in cellulo and in vivo and blocked neointimal formation in wild type mice. CONCLUSIONThe CSN promotes VSMC proliferation and NH in injured vessels through deneddylation activity and CSN5-mediated nuclear export.

cell biology↗

bioRxiv2022

Characterizing metabolism from bulk and single-cell RNA-seq data using METAFlux

Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the TME (Tumor Microenvironment) is of emerging importance in ongoing cancer research and therapy development. Recent developments in mass spectrometry (MS)-based technologies allow simultaneous characterization of metabolic features of tumor, stroma, and immune cells in the TME. However, they only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and have recently been extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable the results are, particularly in the context of tissue TME characterization. To investigate this question and fill the analytical gaps, we developed a computational program METAFlux (METAbolic Flux balance analysis), which extends the FBA framework to infer metabolic fluxes from either bulk or single-cell transcriptomic TME data. We benchmarked the prediction accuracy of METAFlux using the exometabolomics data generated on the NCI-60 cell lines and observed significant improvement over existing approaches. We tested METAFlux in bulk RNA-seq data obtained from various tumor types including those in the TCGA. We validated previous knowledge, e.g., lung squamous cell carcinoma (LUSC) has higher glucose uptake than lung adenocarcinoma (LUAD). We also found a novel subset of LUAD samples with unique metabolic profiles and distinct survival outcome. We further examined METAFlux on scRNA-seq data obtained from coculturing tumor cells with CAR-NK cells and observed high consistency between the predicted and the experimental (i.e., Seahorse extracellular) flux measurements. Throughout our investigation, we discovered various modes of metabolic cooperation and competition between various cell-types in TMEs, which could lead to further target discovery and development.

bioinformatics↗

bioRxiv2021

ANTIBODY-INDEPENDENT ANTITUMOR EFFECTS OF CD32A-CHIMERIC RECEPTOR T CELLS: IMPLICATIONS FOR BREAST CANCER PROGNOSIS AND TREATMENT.

Fc{gamma} RIIA (CD32A) and their ligands, including the immunoglobulin Fc fragment and pentraxins, are key players in a variety of innate immune responses. Still unclear is whether additional ligands of CD32A do exist. The objective of this study is to demonstrate that CD32A-chimeric receptor (CR) can be utilized for the identification of CD32A cell surface ligand(s). Among fifteen cancer cell lines tested, CD32A-CR T cells recognized three of breast cancer (BC) including the MDA-MB-468 and one colorectal carcinoma (HT29) in the absence of targeting antibodies. Conjugation of sensitive BC cells with CD32A-CR T cells induced CD32A polarization and down-regulation, CD107 release, and mutual cell elimination in vitro. Conversely, normal fibroblasts and myoblasts were not affected while normal HUVEC cells promoted CD32A down-regulation. CD32A-CR T cell activity was not inhibited by human IgGs or human serum, but; it was rather enhanced by cetuximab antibody. RNAseq analysis of sensitive vs resistant BC cells identified a fingerprint of 42 genes predicting the sensitivity of BC cells to CD32A-CR T cells and their association with favorable prognostic significance in advanced BC patients. Our data also identify ICAM 1 as a major regulator of CD32A-CR T cell-mediated cytotoxicity. Finally, CD32A-CR T cell administration protected immunodeficient mice from subcutaneous growth of MDA-MB-468 cells in the absence of tumor-specific antibodies. These data indicate that CD32A-CR can be utilized for the identification of (1) cell surface CD32A ligand(s); (2) rational therapeutic strategies to target BC; and (3) novel transcriptomic signatures prognostically relevant for advanced BC patients.

immunology↗

bioRxiv2020

Unbiased integration of single cell multi-omics data

Acquiring accurate single-cell multiomics profiles often requires performing unbiased in silico integration of data matrices generated by different single-cell technologies from the same biological sample. However, both the rows and the columns can represent different entities in different data matrices, making such integration a computational challenge that has only been solved approximately by existing approaches. Here, we present bindSC, a single-cell data integration tool that realizes simultaneous alignment of the rows and the columns between data matrices without making approximations. Using datasets produced by multiomics technologies as gold standard, we show that bindSC generates accurate multimodal co-embeddings that are substantially more accurate than those generated by existing approaches. Particularly, bindSC effectively integrated single cell RNA sequencing (scRNA-seq) and single cell chromatin accessibility sequencing (scATAC-seq) data towards discovering key regulatory elements in cancer cell-lines and mouse cells. It achieved accurate integration of both common and rare cell types (<0.25% abundance) in a novel mouse retina cell atlas generated using the 10x Genomics Multiome ATAC+RNA kit. Further, it achieves unbiased integration of scRNA-seq and 10x Visium spatial transcriptomics data derived from mouse brain cortex samples. Lastly, it demonstrated efficacy in delineating immune cell types via integrating single-cell RNA and protein data. Thus, bindSC, available at https://github.com/KChen-lab/bindSC, can be applied in a broad variety of context to accelerate discovery of complex cellular and biological identities and associated molecular underpinnings in diseases and developing organisms.

bioinformatics↗

bioRxiv2020

Generation of glucocorticoid resistant SARS-CoV-2 T-cells for adoptive cell therapy

Adoptive cell therapy with viral-specific T cells has been successfully used to treat life-threatening viral infections, supporting the application of this approach against COVID-19. We expanded SARS-CoV-2 T-cells from the peripheral blood of COVID-19-recovered donors and non-exposed controls using different culture conditions. We observed that the choice of cytokines modulates the expansion, phenotype and hierarchy of antigenic recognition by SARS-CoV-2 T-cells. Culture with IL-2/4/7 but not other cytokine-driven conditions resulted in >1000 fold expansion in SARS-CoV-2 T-cells with a retained phenotype, function and hierarchy of antigenic recognition when compared to baseline (pre-expansion) samples. Expanded CTLs were directed against structural SARS-CoV-2 proteins, including the receptor-binding domain of Spike. SARS-CoV-2 T-cells could not be efficiently expanded from the peripheral blood of non-exposed controls. Since corticosteroids are used for the management of severe COVID-19, we developed an efficient strategy to inactivate the glucocorticoid receptor gene (NR3C1) in SARS-CoV-2 CTLs using CRISPR-Cas9 gene editing.

immunology↗

bioRxiv2020

Ab initio Spillover Compensation in CyTOF Data

Signal intensity measured in a mass cytometry (CyTOF) channel can often be affected by the neighboring channels due to technological limitations. Such signal artifacts are known as spillover effects and can substantially limit the accuracy of cell population clustering. Current approaches reduce these effects by using additional beads for normalization purposes known as single-stained controls. While effective in compensating for spillover effects, incorporating single-stained controls can be costly and require customized panel design. This is especially evident when executing large-scale immune profiling studies. We present a novel statistical method, named CytoSpill that independently quantifies and compensates the spillover effects in CyTOF data without requiring the use of single-stained controls. Our method utilizes knowledge-guided modeling and statistical techniques, such as finite mixture modeling and sequential quadratic programming, to achieve optimal error correction. We evaluated our method using five publicly available CyTOF datasets obtained from human peripheral blood mononuclear cells (PBMCs), C57BL/6J mouse bone marrow, healthy human bone marrow, chronic lymphocytic leukemia patient, and healthy human cord blood samples. In the PBMCs with known ground truth, our method achieved comparable results to experiments that incorporated single-stained controls. In datasets without ground-truth, our method not only reduced spillover on likely affected markers, but also led to the discovery of potentially novel subpopulations expressing functionally meaningful, cluster-specific markers. CytoSpill (developed in R) will greatly enhance the execution of large-scale cellular profiling of tumor immune microenvironment, development of novel immunotherapy, and the discovery of immune-specific biomarkers. The implementation of our method can be found at https://github.com/KChen-lab/CytoSpill.git.

bioinformatics↗

bioRxiv2020

Inhibition of the av integrin-TGF-b axis improves natural killer cell function against glioblastoma stem cells

Glioblastoma, the most aggressive brain cancer, often recurs because glioblastoma stem cells (GSCs) are resistant to all standard therapies. Here, we show that patient-derived GSCs, but not normal astrocytes, are highly sensitive to lysis by healthy allogeneic natural killer (NK) cells in vitro. In contrast, single cell analysis of autologous, tissue infiltrating NK cells isolated from surgical samples of high-grade glioblastoma patient tumors using mass cytometry and single cell RNA sequencing revealed an abnormal phenotype associated with impaired lytic function compared with peripheral blood NK cells from GBM patients or healthy donors. This immunosuppression was attributed to an integrin-TGF-{beta} mechanism, activated by direct cell-cell contact between GSCs and NK cells. Treatment of GSC-engrafted mice with allogeneic NK cells in combination with inhibitors of integrin or TGF-{beta} signaling, or with TGF-{beta} receptor 2 gene-edited NK cells prevented GSC-induced NK cell dysfunction and tumor growth. Collectively, our findings reveal a novel mechanism of NK cell immune evasion by GSCs and implicate the integrin-TGF-{beta} axis as a useful therapeutic target to eliminate GSCs in this devastating tumor.

immunology↗

bioRxiv2020

CIS checkpoint deletion enhances the fitness of cord blood derived natural killer cells transduced with a chimeric antigen receptor

Immune checkpoint therapy has produced remarkable improvements in the outcome for certain cancers. To broaden the clinical impact of checkpoint targeting, we devised a strategy that couples targeting of the cytokine-inducible SH2-containing (CIS) protein, a key negative regulator of interleukin (IL)-15 signaling, with chimeric antigen receptor (CAR) engineering of natural killer (NK) cells. This combined strategy boosted NK cell effector function through enhancing the Akt/mTORC1 axis and c-MYC signaling, resulting in increased aerobic glycolysis. When tested in a lymphoma mouse model, this combined approach improved NK cell anti-tumor activity more than either alteration alone, eradicating lymphoma xenografts without signs of any measurable toxicity. We conclude that combining CIS checkpoint deletion with CAR engineering promotes the metabolic fitness of NK cells in an otherwise suppressive tumor microenvironment. This approach, together with the prolonged survival afforded by CAR modification, represents a promising milestone in the development of the next generation of NK cells for cancer immunotherapy.

immunology↗