Biology subjects

Zhang, F.

Publications and source records attributed to Zhang, F..

At least 127 records · Page 7Linked to original sources

bioRxiv2025

Cerebellin-2 positive (Cbln2+) Neurons in the Parafascicular Thalamic Nucleus Regulate Self-Grooming Behavior

Self-grooming represents a stereotyped repetitive behavior which is commonly observed in various neuropsychiatric disorders. Deep brain stimulation (DBS) targeting the centromedian / parafascicular complex (CM/PF) has been shown to alleviate obsessive-compulsive disorder/behavior (OCD/OCB) symptoms in human. However, little is known about the neural circuits of the PF that are involved in the regulation of self-grooming behavior in rodents. Here, we report that cerebellin-2 positive (Cbln2+) neurons in PF bidirectionally encode self-grooming. Chemogenetic activation of Cbln2+ PF neurons significantly reduces both physiological and pathological self-grooming, and chemogenetic inhibition of Cbln2+ PF neurons increases physiological self-grooming. Moreover, activating synaptic excitatory inputs to Cbln2+ PF neurons inhibits self-grooming, while synaptic inhibitory inputs enhance it. Cbln2+ PF neurons independently transmit neural signals to the dorsal and ventral striatum. Activation of PF-dorsal striatum and PF-ventral striatum pathways both inhibit self-grooming behavior. Most excitingly, we alleviate excessive grooming in OCD mice by the activation of PF neurons using non-invasive ultrasound stimulation. Together, these data reveal that Cbln2+ PF neurons are integral components of a brain network involved in self-grooming behavior and highly promising targets in the non-invasive therapy of OCD.

neuroscience↗

bioRxiv2025

Resilience and vulnerabilities of tumor cells under purine shortage stress

Purine metabolism is a promising therapeutic target in cancer; however how cancer cells respond to purine shortage,particularly their adaptation and vulnerabilities, remains unclear. Using the recently developed purine shortage-inducing prodrug DRP-104 and genetic approaches, we investigated these responses in prostate, lung and glioma cancer models. We demonstrate that when de novo purine biosynthesis is compromised, cancer cells employ microtubules to assemble purinosomes, multi-protein complexes of de novo purine biosynthesis enzymes that enhance purine biosynthesis efficiency. While this process enables tumor cells to adapt to purine shortage stress, it also renders them more susceptible to the microtubule-stabilizing chemotherapeutic drug Docetaxel. Furthermore, we show that although cancer cells primarily rely on de novo purine biosynthesis, they also exploit Methylthioadenosine Phosphorylase (MTAP)-mediated purine salvage as a crucial alternative source of purine supply, especially under purine shortage stress. In support of this finding, combining DRP-104 with an MTAP inhibitor significantly enhances tumor suppression in prostate cancer (PCa) models in vivo. Finally, despite the resilience of the purine supply machinery, purine shortage-stressed tumor cells exhibit increased DNA damage and activation of the cGAS-STING pathway, which may contribute to impaired immunoevasion and provide a molecular basis of the previously observed DRP-104-induced anti-tumor immunity. Together, these findings reveal purinosome assembly and purine salvage as key mechanisms of cancer cell adaptation and resilience to purine shortage while identifying microtubules, MTAP, and immunoevasion deficits as therapeutic vulnerabilities.

cancer biology↗

bioRxiv2025

Towards an informed choice of diffusion MRI image contrasts for cerebellar segmentation

The fine-grained segmentation of cerebellar structures is an essential step towards supplying increasingly accurate anatomically informed analyses, including, for example, white matter diffusion magnetic resonance imaging (MRI) tractography. Cerebellar tissue segmentation is typically performed on structural magnetic resonance imaging data, such as T1-weighted data, while connectivity between segmented regions is mapped using diffusion MRI tractography data. Small deviations in structural to diffusion MRI data co-registration may negatively impact connectivity analyses. Reliable segmentation of brain tissue performed directly on diffusion MRI data helps to circumvent such inaccuracies. Diffusion MRI enables the computation of many image contrasts, including a variety of tissue microstructure maps. While multiple methods have been proposed for the segmentation of cerebellar structures using diffusion MRI, little attention has been paid to the systematic evaluation of the performance of different available input image contrasts for the segmentation task. In this work, we evaluate and compare the segmentation performance of diffusion MRI-derived contrasts on the cerebellar segmentation task. Specifically, we include spherical mean (diffusion-weighted image average) and b0 (non-diffusion-weighted image average) contrasts, local signal parameterization contrasts (diffusion tensor and kurtosis fit maps), and the structural T1-weighted MRI contrast that is most commonly employed for the task. We train a popular deep-learning architecture using a publicly available dataset (HCP-YA), leveraging cerebellar region labels from the atlas-based SUIT cerebellar segmentation pipeline. By training and testing using many diffusion-MRI-derived image inputs, we find that the spherical mean image computed from b=1000 s/mm2 shell data provides stable performance across different metrics and significantly outperforms the tissue microstructure contrasts that are traditionally used in machine learning segmentation methods for diffusion MRI. Key pointsO_LIWe provide evidence about the performance of different dMRI contrasts for cerebellar structure segmentation using a deep learning neural network. C_LIO_LIThe diffusion MRI spherical mean provides improved and stable cerebellar structure segmentation performance. C_LIO_LIThe spherical mean is easy to compute and can be used for cerebellar structure segmentation on retrospective clinical diffusion MRI data. C_LI

neuroscience↗

bioRxiv2025

MSMCE: A Novel Representation Module for Classification of Raw Mass Spectrometry Data

Mass spectrometry (MS) analysis plays a crucial role in the biomedical field; however, the high dimensionality and complexity of MS data pose significant challenges for feature extraction and classification. Deep learning has become a dominant approach in data analysis, and while some deep learning methods have achieved progress in MS classification, their feature representation capabilities remain limited. Most existing methods rely on single-channel representations, which struggle to effectively capture the structural information within MS data. To address these limitations, we propose a Multi-Channel Embedding Representation Module (MSMCE), which focuses on modeling inter-channel dependencies to generate multi-channel representations of raw MS data. Additionally, we introduce a "residual" connection along the channel dimension, significantly enhancing the classification performance of subsequent models. Experimental results on four public datasets demonstrate that the proposed MSMCE module not only achieves substantial improvements in classification performance but also reduces computational resource consumption and enhances training efficiency, highlighting its effectiveness and generalizability in raw MS data classification.

bioengineering↗

bioRxiv2025

Developmental regulators enable rapid and efficient soybean transformation and CRISPR-mediated genome editing

Soybean transformation remains challenging and has not kept pace with the rapid advancement of genetic engineering technologies due to low efficiency, lengthy timelines, and genotype dependency. Here, we developed a streamlined transformation method by leveraging developmental regulators (DRs) to promote de novo shoot regeneration directly from growing soybean plants. By evaluating multiple DR combinations, our results showed that co-expression of WUSCHEL2 (WUS2) and isopentenyltransferase (IPT) achieved higher transformation efficiencies (15.2% to 22.3%) in Williams 82 and Bert varieties than individual DRs without requiring exogenous hormones or selection agents. Moreover, this method produces heritable transgenic events within 9-11 weeks and successfully delivers CRISPR-Cas9 components, generating heritable mutations with 20% efficiency. The temporal transcriptomic and gene regulatory network analyses revealed that WUS2/IPT synergistically modulates stress responses and activates developmental pathways, orchestrating a transition from initial stress adaptation to regenerative programming. Together, our findings demonstrate that this DR-enabled approach significantly enhances soybean transformation efficiency, reduces tissue culture requirements, and offers a promising genome editing platform for soybean improvement.

plant biology↗

bioRxiv2025

A single amino acid variant in the variable region I of AAV capsid confers liver detargeting

AAV capsid serotypes isolated from nature have been widely used in gene delivery and gene therapy. Recently, more than 1,000 distinct AAV capsids were identified from human clinical samples by high-throughput, long-read DNA sequencing. In this study, we tap into this broad natural biodiversity of AAV capsids to develop liver-tropic AAV capsids. We initially screened a subset of variants derived from AAV8 (n=159) for packaging efficiency. The high-yielding variants were subjected to a barcoded vector library screen in mice and ferrets for their ability to mediate liver gene transfer. Although no variant surpassed AAV8 for liver targeting, several exhibited a liver detargeting phenotype. Among these, we focused on the N271D variant (AAV8 VP1 numbering), located in the variable region I (VR-1), which has been previously implicated in influencing liver tropism. The liver detargeting phenotype of AAV8.N271D was confirmed by single vector administration in mice. Additionally, we grafted the N271D variant onto AAV9 and MyoAAV capsids (N270D by AAV9 VP1 numbering). The AAV9.N270D and MyoAAV.N270D vectors showed a similar liver-detargeting phenotype, although muscle targeting was moderately reduced. Although we did not identify any capsid variants that outperform AAV8 in liver transduction, this study reinforces the important role of VR-1 in modulating liver tropism and highlights the potential of engineering VR-1 residues to reduce liver gene transfer and associated toxicity observed in several gene therapy studies.

molecular biology↗

bioRxiv2025

Rapaprotin is Activated by an Endopeptidase to Disassemble 26S Proteasome

The 19S regulatory particle (RP) associates with the 20S core particle (CP) to form the 26S proteasome, an evolutionarily conserved holoenzyme that plays key roles in both physiological and pathological processes. Proteasome inhibitors that target the catalytic subunits within the 20S have proven to be valuable research tools and therapeutics for various cancers. Herein we report the discovery of rapaprotin, a 26S proteasome assembly inhibitor from our natural product-inspired hybrid macrocycle rapafucin library. Rapaprotin induces apoptosis in both myeloma and leukemia cell lines. Genome-wide CRISPR-Cas9 screen identified a cytosolic enzyme, prolyl endopeptidase (PREP) that is required for the pro-apoptotic activity of rapaprotin. Further mechanistic studies revealed that rapaprotin acts as a molecular transformer, changing from an inactive cyclic form into an active linear form, rapaprotin-L, upon PREP cleavage, to block 26S proteasome activity. Time-resolved cryogenic electron microscopy (cryo-EM) revealed that rapaprotin-L induces dissociation of the 19S RP from the 26S holoenzyme, which was verified in cells. Furthermore, rapaprotin exhibits a marked synergistic effect with FDA-approved proteasome inhibitors and resensitizes drug-resistant multiple myeloma cells from patients to bortezomib. Taken together, these results suggest that rapaprotin is a new chemical tool to probe the dynamics of the 26S proteasome assembly and a promising anticancer drug lead.

pharmacology and toxicology↗

bioRxiv2025

Intracellular Trafficking SNARE Protein, Syntaxin-6, is a Modifier of Prion and Tau Pathogenesis in vivo and in Cellular Models

Syntaxin-6, a SNARE protein involved in intracellular protein trafficking, is a proposed risk factor for sporadic prion disease, progressive supranuclear palsy and Alzheimers disease. However, no study has validated its functional role in these diseases, explored the disease stage at which it is acting nor its mechanism of action. Here, we show that syntaxin-6 acts at early stages of prion disease in experimental mice by increasing disease transmission risk following inoculation with low prion doses. Conversely, syntaxin-6 does not affect prion propagation kinetics or toxicity during established disease. Syntaxin-6 manipulation in cellular models profoundly alters the subcellular distribution and morphologies of disease-related PrP and modifies prion export. Furthermore, syntaxin-6 knockout in a transgenic tauopathy mouse model exerts protective effects on numerous physiological, behavioural and neuropathological outcome measures. Therefore, our studies firmly establish syntaxin-6 as a modifier of prion and tau pathogenesis, providing key insights into a fundamental mechanism of neurodegeneration.

neuroscience↗

bioRxiv2025

STEAM: Spatial Transcriptomics Evaluation Algorithm and Metric for clustering performance

MotivationSpatial transcriptomic technologies allow researchers to explore the diversity and specificity of gene expression within their original tissue structure. Accurately identifying regions that are spatially coherent in both gene expression and physical tissue structures is an emerging topic, but challenging due to the lack of ground truth labels which renders complicating validation of clustering consistency and reproducibility. This highlights a need for a computational evaluation framework to rigorously and unbiasedly assess clustering performance. ResultsTo address this gap, we propose STEAM (Spatial Transcriptomics Evaluation Algorithm and Metric), a user-friendly computational pipeline designed to evaluate the consistency and reliability of clustering results by leveraging machine learning classification and prediction methods, with the goal of maintaining the spatial proximity and gene expression patterns within clusters. We benchmarked STEAM on various public datasets, spanning multi-cell to single-cell resolution, as well as spatial transcriptomics and proteomics. The results highlighted its robustness and generalizability through comprehensive statistical evaluation metrics, such as Kappa score, F1 score, accuracy, and adjusted rand index. Notably, STEAM supports multi-sample training, enabling cross-replicate clustering consistency assessment. Moreover, STEAM provides practical guidance by comparing clustering results across multiple approaches; here, we evaluated four different methods, including spatial-aware and spatial-ignorant approaches. In summary, we believe that STEAM provides researchers a promising tool for evaluating clustering robustness and benchmarking clustering performance for spatial omics data, offering valuable insights to drive reproducible discoveries in spatial biology. Availability and implementationSource code and the R software tool STEAM are available from https://github.com/fanzhanglab/STEAM.

bioinformatics↗

bioRxiv2025

Investigating Gene Expression Noise Reduction by MicroRNAs and MiRISC Reinforcement by Self-Feedback Regulation of mRNA Degradation

The microRNA (miRNA) induced silencing complex (miRISC) is the targeting apparatus and arguably the rate-limiting step of the miRNA-mediated regulatory subsystem - a major noise reducing, though metabolically costly, mechanism. Recently, we reported that miRISC channels miRNA-mediated regulatory activity back onto its own mRNAs to form negative self-feedback loops, a noise-reduction technique in engineering and synthetic/systems biology. In this paper, our mathematical modeling predicts that mRNA expression noise exhibits a negative correlation with the degradation rate (Kdeg) and is attenuated by self-feedback control of degradation. We also calculated Kdeg and expression noise of mRNAs detected in a total-RNA single-cell RNA-seq (scRNA-seq) dataset. As predicted, miRNA-targeted mRNAs exhibited higher Kdeg values accompanied by reduced inter-cell expression noise, confirming the operational trade-off between noise suppression and the increased metabolic/energetic costs associated with producing these mRNAs subjected to accelerated degradation and translational inhibition. Moreover, consistent with the Kdeg self-feedback control model, miRISC mRNAs (AGO1/2/3 and TNRC6A/B/C) exhibited further reduced expression noise. In summary, mathematical-modeling and total-RNA scRNA-seq data-analyses provide evidence that negative self-feedback regulation of mRNA degradation reinforces miRISC, the core machinery of the miRNA-mediated noise-reduction subsystem. To our knowledge, this is the first study to concurrently analyze mRNA degradation dynamics and expression noise, and to demonstrate noise reduction by self-feedback regulation of mRNA degradation.

systems biology↗

bioRxiv2025

Gene Editing for ATXN3 Inactivation in Machado-Joseph disease: CRISPR-Cas9 as a Therapeutic Alternative to TALEN-Induced Toxicity

Machado-Joseph disease (MJD) is an autosomal dominantly-inherited neurodegenerative disorder, caused by an over-repetition of the polyglutamine-codifying region in the ATXN3 gene. Strategies based on the suppression of the deleterious gene products have demonstrated promising results in pre-clinical studies. Nonetheless, these strategies do not target the root cause of the disease. In order to prevent the downstream toxic pathways, our goal was to develop gene editing-based strategies to permanently inactivate the human ATXN3 gene. TALENs and CRISPR-Cas9 systems were designed to target exon 2 of this gene and functional characterization was performed in a human cell line. After the demonstration of TALENs and CRISPR-Cas9 efficiency on gene disruption, a sequence of each system was selected for further in vivo experiments. Although both TALENs and CRISPR-Cas9 systems led to a drastic reduction of ATXN3 aggregates in the striatum of a lentiviral-based mouse model of MJD/SCA3, only CRISPR-Cas9 system allowed the improvement of key neuropathological markers of the disease. Importantly, the administration of the engineered system in YAC-MJD84.2/84.2 mice mediated a delay in disease progression, when compared with non-treated littermates. These data provide the first in vivo evidence of the efficacy of a CRISPR-Cas9-based approach to permanently inactivate the ATXN3 gene in the brain of two mouse models of the disease, supporting its potential as a new therapeutic avenue in the context of MJD/SCA3.

neuroscience↗

bioRxiv2025

PROBind: A Web Server for Prediction, Analysis and Visualization of Protein-Protein and Protein-Nucleic Acid Binding Residues

Protein-protein and protein-nucleic acids interactions are fundamental to numerous cellular functions, yet only a small fraction have been experimentally characterized. Although modern computational methods have been developed for predicting interacting residues in proteins, they are challenging to use due to individual installation and execution requirements, lack of a standardized input or output format, and absence of support for result analysis. Moreover, methods trained using structures of complexes or intrinsically disordered regions, may not perform well on other types. To overcome these challenges, we develop PROBind, a web server for predicting, analyzing, and interactively visualizing protein, DNA and RNA binding residues from both protein sequences and structures. PROBind integrates 12 predictors trained on structural or disordered proteins, and supports the upload of results from external predictors. By normalizing and averaging predictions from multiple predictors targeting the same ligand type, PROBind generates meta-predictions that balance discrepancies among different methods. Furthermore, it provides interactive graphical tools for result analysis and contextualization. Overall, PROBind accommodates diverse ligand types and supports predictions and analysis based on both structure and sequence data, overcoming the limitations of existing tools. PROBind is freely accessible at https://www.csuligroup.com/PROBind.

bioinformatics↗

bioRxiv2025

Multi-lineage transcriptional and cell communication signatures define pathways in individuals at-risk for developing rheumatoid arthritis that initiate and perpetuate disease

Rheumatoid arthritis (RA) is a systemic autoimmune disease arising from loss of tolerance and autoantibody development in at-risk individuals. Targeted therapies yield variable responses and current preventive strategies delay but do not stop disease onset. We propose that a set of transcription factors (TFs) and their downstream pathways regulate inflammatory cell communication networks in at-risk populations and RA. These networks enable multiple pathogenic cell types and mediators and could account for variable responses to targeted agents. To test this hypothesis, we identified anti-citrullinated protein antibody (ACPA)-positive at-risk individuals, patients with early and established RA and healthy controls. Single cell chromatin accessibility and transcriptomic profiles from blood mononuclear r cells were integrated and identified share pathogenic mechanisms, especially SUMOylation, RUNX2, YAP1, NOTCH3, and {beta}-Catenin Pathways. Surprisingly, this signature was found in multiple cell types. Individualized gene expression patterns were then confirmed in RA synovium. Cell communication analysis revealed that multiple lineages can deliver a core set of pro-inflammatory mediators to receiver cells. Longitudinal analysis showed that the signature cell types evolve in individual at-risk participants. Cell-type-specific signature pathways could contribute to the differing clinical responses to targeted therapies. This study describes how a common clinical phenotype could arise from multiple pathogenic mechanisms.

systems biology↗

bioRxiv2025

Shoot at Site: Advancing in planta transformation, regeneration and gene-editing through a cascade of wounding-mediated developmental regulators

Developing transgenic and/or gene-edited plants largely depends on tedious, lengthy, and costly in vitro regeneration protocols. While plants have remarkable regeneration ability, not all species, genotypes or even explants exhibit the same transformation and regeneration potential under in vitro conditions. To tackle this bottleneck, we have developed a seamless and user-friendly system to induce transgenic and gene-edited de novo meristems via a synthetic cascade comprising a wound-induced regeneration pathway, plant developmental regulators (DRs) and gene-editing reagents. WOUND INDUCED DEDIFFERENTIATION 1 (WIND1) is used as a transcriptional regulator to control the expression of various DR genes through ENHANCER OF SHOOT REGENERATION 1 (ESR1) promoter. This cascade was strategically applied in planta to the non-meristematic internode of N. benthamiana to induce meristematic activity and regenerate de novo shoots with knock-out mutations of the phytoene desaturase (PDS) gene. This synthetic toolkit was further applied successfully to tomato and soybean. This methodology offers a transformative approach to overcome barriers in plant biotechnology, potentially accelerating the generation of transgenic and gene-edited plants without reliance on conventional tissue-culture intermediates.

plant biology↗

bioRxiv2025

Consistent cerebellar pathway-cognition associations across pre-adolescents & young adults: a diffusion MRI study of 9000+ participants

The cerebellum, long implicated in movement, is now recognized as a contributor to higher-order cognition. The cerebellar pathways provide key structural links between the cerebellum and cerebral regions integral to language, memory, and executive function. Here, we present a large-scale, cross-sectional diffusion MRI (dMRI) analysis investigating the relationships between cerebellar pathway microstructure and cognitive performance in over 9,000 participants spanning pre-adolescence (n>8,000 from the ABCD dataset) and young adulthood (n>900 from the HCP-YA dataset). We assessed the microstructure of five cerebellar pathways--the inferior, middle, and superior cerebellar peduncles; the parallel fibers; and input/Purkinje fibers--using three dMRI measures of fractional anisotropy, mean diffusivity, and number of streamlines. Cognitive performance was evaluated using seven NIH Toolbox assessments of language, executive function, and memory. In both datasets, we found numerous significant associations between cerebellar pathway microstructure and cognitive performance. These associations showed a strong correlation across the two datasets (r = 0.47, p < 0.0001), underscoring the reliability of cerebellar dMRI-cognition relationships in pre-adolescents and young adults. In both datasets, the strongest associations were found between the superior cerebellar peduncle and performance on language assessments, suggesting this pathway plays an important role in language function across age groups. In young adults, but not pre-adolescents, parallel fiber microstructure was linked to inhibitory control, suggesting that contributions to attentional processes may emerge or strengthen with maturation. Overall, our findings highlight the important role of cerebellar pathways in cognition and the utility of large-scale datasets for advancing our understanding of brain-cognition relationships. SignificanceThis study provides strong evidence linking cerebellar pathway tissue microstructure to cognition across large populations of preadolescent children and young adults. By leveraging diffusion MRI tractography and cognitive performance data from two major datasets, we identify significant relationships between cerebellar pathway microstructure and cognitive performance. Importantly, findings are significantly correlated across datasets, pointing to the consistency of these relationships, bridging age groups and acquisitions. These results highlight the cerebellar pathways integral role in cognitive functioning and underscore the value of large-scale, population-based studies in advancing our understanding of brain-cognition relationships. Classification 1) Biological Sciences, Neuroscience; 2) Social Sciences, Psychological and Cognitive Sciences

neuroscience↗

bioRxiv2025

CellPhenoX: An eXplainable Cell-specific machine learning method to predict clinical Phenotypes using single-cell multi-omics

Single-cell technologies have enhanced our knowledge of molecular and cellular heterogeneity underlying disease. As the scale of single-cell datasets expands, linking cell-level phenotypic alterations with clinical outcomes becomes increasingly challenging. To address this, we introduce CellPhenoX, an eXplainable machine learning method to identify cell-specific phenotypes that influence clinical outcomes. CellPhenoX integrates classification models, explainable AI techniques, and a statistical framework to generate interpretable, cell-specific scores that uncover cell populations associated with relevant clinical phenotypes and interaction effects. We demonstrated the performance of CellPhenoX across diverse single-cell designs, including simulations, binary disease-control comparisons, and multi-class studies. Notably, CellPhenoX identified an activated monocyte phenotype in COVID-19, with expansion correlated with disease severity after adjusting for covariates and interactive effects. It also uncovered an inflammation-associated gradient in fibroblasts from ulcerative colitis. We anticipate that CellPhenoX holds the potential to detect clinically relevant phenotypic changes in single-cell data with multiple sources of variation, paving the way for translating single-cell findings into clinical impact.

bioinformatics↗

bioRxiv2025

Avoidance of fratricide and improved efficacy of CD33-MSLN CAR-iNK cells in treating acute myeloid leukemia

Acute myeloid leukemia (AML) patients are often older, which brings challenges of endurance and persistent efficacy of autologous CAR-T cell therapies. Allogenic CAR-NK cell therapies may offer reduced toxicities and enhanced anti-leukemic potential against AML. In this study, we designed a novel CD33-mesothelin loop CAR (Loop CAR) and evaluated its anti-tumor efficacy in human umbilical cord blood-derived NK (UCB-NK) cells and human pluripotent stem cell-derived NK (hPSC-iNK) cells. The Loop CAR exhibited superior cytotoxicity against dual-antigen-positive tumor cell lines and primary AML cells. To further avoid fratricide caused by endogenous CD33 expression in NK cells, we established a hPSC-derived cell line via knockout of CD33 gene (CD33KO) and engineered Loop CAR. Rather than enforced expression of exogenous CD16, we generated abundant mature CD33KO-Loop CAR-iNK cells highly expressing endogenous CD16 via an organoid induction approach. This innovative strategy effectively mitigated NK cell fratricide and significantly enhanced CD33 and mesothelin-mediated specific cytotoxicity. Moreover, the CD33KO-Loop CAR-iNK cells demonstrated superior tumor-killing activity in AML xenograft mice and significantly prolonged survival. hPSC-derived CD33KO-Loop CAR-iNK cells possess unique advantages and translational potential for treating AML.

cancer biology↗

bioRxiv2025

Engineered CRO-CD7 CAR-NK cells derived from pluripotent stem cells avoid fratricide and efficiently suppress human T-cell malignancies

BackgroundT cell malignancies are highly aggressive hematological tumors with limited effective treatment options. CAR-NK cell therapy targeting CD7 has emerged as a promising approach for treating T-cell malignancies. However, conventional CAR-NK cell therapy faces the challenges of cell fratricide due to CD7 expression on both malignant cells and normal NK cells. Additionally, engineering CARs into human tissue-derived NK cells demonstrates heterogeneity, low transduction efficiency, and high manufacturing costs. MethodsThe human pluripotent stem cells (hPSCs) were genetically modified by knocking out the CD7 gene and introducing the CD7 CAR expression cassette to generate CD7 KO-CD7 CAR-hPSCs. These modified hPSCs were subsequently differentiated into CD7 KO-CD7 CAR-iNK cells using an efficient organoid induction method. The cytotoxicity of CD7 KO-CD7 CAR-iNK cells against CD7+ tumor cells was evaluated. Furthermore, we overexpressed the CXCR4 gene in CD7 KO-CD7 CAR-hPSCs and derived CXCR4-expressing CD7 KO-CD7 CAR-iNK (CRO-CD7 CAR-iNK) cells. The dynamics of CRO-CD7 CAR-iNK cells in vivo were tracked, and their therapeutic efficacy was assessed using human T-cell acute lymphoblastic leukemia (T-ALL) xenograft models. ResultsThe CD7 KO-CD7 CAR-iNK cells derived from CD7 KO-CD7 CAR-hPSCs effectively avoided fratricide, demonstrated normal expansion, and exhibited potent and specific anti-tumor activity against CD7+ T-cell tumor cell lines and primary T-ALL cells. CXCR4 overexpression in CRO-CD7 CAR-iNK cells improved their homing capacity and extended their persistence in vivo. The CRO-CD7 CAR iNK cells significantly suppressed tumor growth and prolonged the survival of T-ALL tumor-bearing mice. ConclusionsOur study provides a reliable strategy for the large-scale generation of fratricide-resistant CD7 CAR-iNK cells with robust anti-tumor effects from hPSCs, offering a promising cell product to treat T-cell malignancy.

cancer biology↗