Biology subjects

Tang, K.

Publications and source records attributed to Tang, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Engineered CTLA-4 targeting chimeras for targeted protein degradation and immune synapse reprogramming

Despite the success of immune checkpoint blockade in cancer therapy, overcoming the immunosuppressive tumor microenvironment remains a major barrier to durable responses. Here, we introduce cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4)-targeting chimeras (cTACs), a class of synthetic molecules that harness CTLA-4-mediated trans-endocytosis (TE) for targeted protein degradation across cellular boundaries. The lead candidate, cTAC4.0, is composed of the extracellular domain of CD80 fused to an anti-programmed death-ligand 1 (PD-L1) fragment via a human immunoglobulin G4 (IgG4) heavy chain. Unlike existing protein degraders that function within cancer cells via house-keeping pathways, cTAC4.0 operates in trans between immune and tumor cells. It redirects the natural CD80-CTLA-4 internalization to capture and degrade PD-L1 from tumor cells into T cells, while simultaneously converting PD-L1-mediated suppression into CD28-driven co-stimulation, depending on the relative availability of CTLA-4 or CD28. Mechanistically, we demonstrate that cTAC4.0 promotes CTLA-4-dependent internalization and lysosomal degradation of both membrane-bound and soluble PD-L1, reduces surface PD-L1 levels on target cells, and activates T cells in a CD28-dependent manner. Functionally, cTAC4.0 enhances the cytotoxicity of both bispecific T cell engagers and T cell receptor (TCR)-engineered T cells in vitro, and when combined with these engineered T cells, it suppresses tumor growth in vivo in a glioblastoma xenograft model. By coupling immune checkpoint degradation with T cell activation in an immune synapse-enriched manner, cTACs establish a paradigm for intercellular protein degradation systems that simultaneously deplete suppressive ligands and enhance T cell function to overcome resistance in cancer immunotherapy.

immunology↗

bioRxiv2026

LUstiGE, Light responsive Ustilago maydis Gene Expression: Optogenetic control of morphogenesis and pathogenesis in the corn fungal pathogen Ustilago maydis

The basidiomycete Ustilago maydis is a well-characterized model organism for studying pathogen-host interactions and of great interest for a broad spectrum of biotechnological applications. We set here to develop light inducible molecular tools to enable dynamic studies on signaling networks and fungi-host communication, and for metabolic engineering approaches. In particular, light-controlled, optogenetic switches provide quantitative, spatio-temporal control capabilities, are minimal invasive and reversible. We engineered two blue light-inducible LOV-domain-based gene expression switches, to up- (Blue-ON) and down-regulate (Blue-OFF) gene expression, and performed a functional characterization in sporidia and hyphae of U. maydis. Profiting from the dynamic control ranges and rapid kinetics, we implemented the optoswitches to control cell morphology by initiating the transition from a haploid sporidial cellular morphotype to filaments upon regulation of the levels of the polarity factor Rac1 and its constitutive active mutant Q61L. In addition to showing how expression level of effectors can be precisely regulated as an approach to understand fungi-plants interaction, we show in two proof-of-principle applications targeted control over U. maydis filamentous fungal invasion of plant tissue and the mechanisms of tumor formation. For this we placed under Blue-ON and Blue-OFF control two U. maydis effectors, See1 (Seedling efficient effector 1) and TIN2 (Tumor inducing 2), and tumor formation was assayed on maize leaves. Taken together, this study established blue-light switches as effective tools to control morphogenesis and pathogenesis in U. maydis.

synthetic biology↗

bioRxiv2026

Evolutionary Histories and Environments Shape Ugandan and Global Oral Microbiomes

Industrialization has been identified as the single biggest factor driving global microbiome diversity. While many studies examining gut microbiomes attribute these shifts to dietary increases in fat and reductions in protein, oral microbiome responses to industrialization remains debated. The oral microbiome is more resilient due to long-standing coevolution with host tissues and biofilm stability. However, limited geographic and historical representation has constrained our understanding of how these transitions unfolded globally in the oral microbiomes. Here, we investigate oral microbiome variation in Batwa rainforest hunter-gatherers and neighboring Bakiga subsistence farmers from southwestern Uganda, comparing them with publicly available data from Tanzanian, Venezuelan, and industrialized populations from North America, Europe, and Australia. Using 16S rRNA gene sequencing, we characterized salivary microbiota and evaluated differences in local and global diversity, composition, and differential abundance. Ugandan populations contained significant compositional differences but similar levels of diversity, suggesting that shared environments and dietary overlap may shape microbial assemblages despite distinct cultural histories. Globally, strong continental and industrialization effects were observed in the oral microbiome, with all industrial populations clustering separately from people living in other locations. African populations also clustered separately from non-African groups. Oral microbiome diversity was highest in Ugandan individuals and lowest in industrialized populations, mirroring patterns previously observed in the gut microbiome. Together, these findings demonstrate that both geography and subsistence strategy structure global oral microbiome variation. They also clarify the position that oral microbial communities record biocultural transitions and highlight the need to better understand the industrial mechanisms that shape microbial diversity in the oral cavity.

evolutionary biology↗

bioRxiv2026

RNABag: A Generalizable Transcriptome Foundation Model for Precision Oncology across Biopsy Modalities

Transcriptomic data is highly sensitive to cancer state and progression, making transcriptome-based foundation models a great promise for diverse clinical ontological inference. However, analyses of transcriptome are conventionally hindered by technical batch effects and limited generalization across platforms. Here, we introduce RNABag, a foundation model designed to generalize well to external datasets. In particular, the model only focuses on highly variable genes to reduce noise; and extensive data augmentation was utilized to pretrain RNABag to learn robust representations, invariant to batch variations. We demonstrate that RNABag achieves superior performance in pan-cancer tissue-of-origin classification and cancer detection in internal validation sets, as well as in zero-shot generalization to external cohorts and in-house clinical samples. Furthermore, RNABag, after specialized finetuning, exhibits strong capabilities in a wide range of clinical applications. The model effectively stratifies patient survival and predicts relapse risks, highlighting key molecular pathways driving tumor progression. Crucially, we extend RNABags utility to liquid biopsies, achieving high diagnostic accuracy in plasma cfRNA and tumor-educated platelets (TEPs), thereby supporting its application in non-invasive cancer monitoring. Interpretability analysis revealed pivotal role of tumor immune escape in the cancer induced plasma cfRNA signals. In summary, our study indicates that cancer states and progression may be monitored in details and precision via comprehensive modeling of transcriptome across biopsy modalities.

bioinformatics↗

bioRxiv2026

Fruit trichome density outweighs cuticle thickness as the dominant barrier to postharvest water loss in tomato

Fruit cuticle thickness and biochemical composition have traditionally been regarded as the primary determinants of postharvest water loss in fleshy fruits. However, several reports indicate that some tomato mutants with thinner fruit cuticles or less cutin and waxes do not always show increased transpiration, suggesting that additional surface features influence postharvest water loss. Here, we s how that fruit trichome density is a previously underappreciated determinant of postharvest water loss in tomatoes. Using two independent mutants, cr-slhdziv7 and cr-slhdziv9, which exhibit reduced fruit trichome density, we found that both mutants displayed reduced water loss rates and extended shelf life during postharvest storage despite having thinner cuticles and reduced levels of key cutin monomers. Further molecular analyses, including RNA-seq, yeast one-hybrid (Y1H), and dual-luciferase reporter assays, revealed that these two HD-ZIP IV proteins not only regulate fruit trichome formation but also directly or indirectly modulate the expression of multiple cutin biosynthesis genes. Collectively, our results demonstrate that the benefit of reducing trichome-associated microchannels can outweigh the negative effects of cuticle thinning on postharvest water loss. This study establishes fruit trichome density as an important and previously underestimated target for improving postharvest fruit quality and shelf life. HighlightO_LIFruit trichome density controls tomato postharvest water loss and dominates over cuticle thickness barrier. C_LIO_LILess trichomes reduce microchannels, lowering transpiration despite thin cuticles and extending tomato fruit shelf life. C_LI

plant biology↗

bioRxiv2026

Joint Learning of Drug-Drug Combination and Drug-DrugInteraction via Coupled Tensor-Tensor Factorization with SideInformation

Targeted drug therapies offer a promising approach for treating complex diseases, with combinational drug therapies often employed to enhance therapeutic efficacy. However, unintended drug-drug interactions may undermine treatment outcomes or cause adverse side effects. In this work, we propose a novel joint learning framework for the simultaneous prediction of effective drug combinations and drug-drug interactions, based on coupled tensor-tensor factorization. Specifically, we model drug combination therapies and DDI by representing drug-drug-disease associations and drug-drug interaction profiles as coupled three-way tensors. To address the challenges of data incompleteness and sparsity, the proposed model integrates auxiliary drug similarity information, such as chemical structure similarities, drug-specific side effects, drug target profiles, and drug inhibition data on cancer cell lines, within a multi-view learning frame-work. For optimization, we adopt a modified Alternating Direction Method of Multipliers (ADMM) algorithm that ensures convergence while enforcing non-negativity constraints. In addition to standard tensor completion tasks, we further evaluate the proposed method under a more realistic new-drug prediction setting, where all interactions involving a previously unseen drug are withheld. This scenario closely aligns with real-world applications, in which reliable predictions for emerging or under-studied compounds are essential. We evaluate the proposed method on a comprehensive dataset compiled from multiple sources, including DrugBank, CDCDB, SIDER, and PubChem. Our experiments show that SI-ADMM maintains robust performance and achieves the best results comparing to other tensor factorization approaches, with or without auxiliary information, particularly in the new-drug prediction setting. The implementation of our method is publicly available at: https://github.com/Xiaoge-Zhang/SI-ADMM.

bioinformatics↗

bioRxiv2026

UniFacePoint-FM: A Foundation Model for Generalizable 3D Facial Representation Learning and Multi-Attribute Prediction

The human face is a rich medium for biometric, behavioral, and clinical information. However, 2D facial images based technologies lack critical geometric details and are susceptible to pose and illumination interference, while 3D facial deep learning frameworks are hindered by complex annotation, preprocessing, and task-specific designs with poor cross-domain generalization. To address these challenges, we propose UniFacePoint-FM, a 3D facial foundation model built on a self-supervised Point-MAE framework, tailored for high-fidelity point cloud representation learning. The model was pretrained on a self-constructed dataset of high-resolution 3D facial scans, followed by supervised fine-tuning and comprehensive evaluation across three independent datasets for diverse downstream tasks. Experimental results demonstrate that UniFacePoint-FM is both pretraining-efficient and highly generalizable: it achieves state-of-the-art performance on gender classification, age regression, and BMI prediction, and matches the accuracy of the ResMLP model (while outperforming other baselines) in facial expression recognition. Notably, by learning high-quality, fine-grained representations directly from raw point clouds, UniFacePoint-FM delivers robust generalization and transferability across tasks, datasets, and even different face scanning platforms. Overall, our work establishes an effective foundation model paradigm for 3D facial analysis, with promising implications for biometric security, health monitoring, and advanced human-computer interaction systems.

bioinformatics↗

bioRxiv2026

Catestatin ameliorates tauopathy and amyloidogenesis via adrenergic inhibition

Neurodegenerative disorders like Alzheimers disease (AD), Corticobasal Degeneration (CBD), and Progressive Supranuclear Palsy (PSP) are characterized by Tau aggregation, synaptic dysfunction, neuroinflammation, and progressive cognitive decline. Although metabolic dysregulation and neuropeptide imbalance have been linked to these disorders, the functional consequences of such imbalance and its potential for therapeutic reversal remain poorly understood. Our previous work identified chromogranin A (CgA), which encodes a pro-hormone for several metabolic peptides, as a key regulator of Tau pathology. Here, we investigate Catestatin (CST), a CgA-derived peptide that is a potent inhibitor of catecholamine release and has been shown to increase insulin sensitivity and lower peripheral blood pressure. We report significant reductions in CST levels in the hippocampus and cortex of AD brains, as well as in the frontal cortex of CBD and the basal ganglia of PSP. Supplementing CST in cortical neuronal cultures and organotypic slice cultures (OTSC) decreased Tau phosphorylation and aggregation. In vivo, CST administration in PS19 Tauopathy mice reduced pathological Tau species, attenuated gliosis, and improved cognitive function. CST treatment also lowered amyloid plaque burden and neuroinflammation in 5xFAD mice. Mechanistically, CST decreased epinephrine (EPI) levels in both PS19 and 5xFAD mice and suppressed downstream protein kinase A (PKA) hyperactivation in PS19 and OTSC. These findings reveal a previously unrecognized neuropeptidergic mechanism linking CST deficiency to elevated adrenergic receptor (ADR)-EPI-PKA stress signaling and Tauopathy-driven neurodegeneration, suggesting CST replacement as a promising therapeutic approach.

neuroscience↗

bioRxiv2026

CAR-T-drug conjugate against solid tumor

Chimeric antigen receptor (CAR)-T cell therapy has been clinically successful in hematologic cancers but faces challenges in solid tumors, primarily due to limited tumor infiltration, immunosuppressive tumor microenvironments (TME), and antigen heterogeneity. While combining CAR-T cell therapy with chemotherapy can enhance antitumor activity, this often leads to substantial systemic toxicity. In this study, we introduce CAR-T-drug conjugate (CAR-T-D-C), a new class of dual-functional therapeutics that effectively addresses these obstacles by integrating click chemistry for precise conjugation of cytotoxic agents onto antigen-specific CAR-T cells. By transforming the hostile TME into an ally, this approach facilitates localized delivery of cytotoxic payload directly to the tumor site, enhancing the overall effectiveness of CAR-T therapy in solid tumors. CAR-T-D-Cs with different CAR-T cell binders exhibit robust antitumor activity across diverse solid tumor models, including both human tumor xenografts and syngeneic models. Spatial transcriptomic studies reveals that CAR-T-D-C achieves improved CAR-T tumor infiltration and functional activation within the TME. Compared to conventional CAR-T therapy, CAR-T-D-C markedly enhances immune cell infiltration, augments effector functions, promotes antigen spreading, amplifies systemic immune responses, and improves overall anti-tumor immunity. CAR-T-D-C represents a versatile therapeutic concept that combines the potency of small molecule drugs and the specificity of CAR-T cells as a 2-in-1 immunochemotherapy for treatment of solid tumors.

immunology↗

bioRxiv2025

A Combinatorial mRNA Therapy for Treating Rheumatoid Arthritis and Osteoarthritis by Inhibiting Inflammation and Promoting Cartilage Regeneration

Rheumatoid arthritis (RA) and osteoarthritis (OA) are debilitating joint disorders with distinct etiologies but share common pathological features of chronic inflammation and progressive cartilage damage. Current therapeutic options are largely palliative and yet fail to achieve desired effect. In this study, we developed a combinatorial mRNA therapy using a rationally engineered lipid nanoparticle (LNP) containing a novel ionizable lipid for enhanced cartilage penetration upon intra-articular administration. This LNP co-delivers mRNAs encoding two complementary therapeutic proteins: interleukin-1 receptor antagonist (IL-1Ra) for inflammation attenuation and a C-terminal truncated derivative of angiopoietin-like 3 (ANL3) for cartilage regeneration. In pre-clinical models including collagen-induced arthritis mice, TNF-transgenic mice with spontaneous RA and destabilization of medial meniscus (DMM) surgical OA model, this "one-shot" combinatorial therapy significantly ameliorated disease severity, reduced synovitis and bone erosion, and potently promoted the regeneration of hyaline-like cartilage. Mechanistically, transcriptomic profiling of joint specimens revealed that the combinatorial therapy concurrently suppressed inflammatory pathways and extracellular matrix degradation pathways, meanwhile upregulating anabolic genes facilitating chondrogenesis. Collectively, our study establishes a versatile and synergistic mRNA therapy that offers a potent disease-modifying immunomodulatory strategy capable of addressing the long-standing unmet needs in arthritis treatment.

immunology↗

bioRxiv2025

Catestatin suppresses melanoma progression and drug resistance through multitargeted modulation of signaling pathways

BackgroundDespite advances in targeted and immune-based therapies, melanoma remains one of the most aggressive and treatment-resistant cancers. Resistance to small-molecule inhibitors and immune checkpoint blockade highlights the need for new mechanistically distinct interventions. Catestatin (CST), a Chromogranin A (CgA)- derived peptide with immunomodulatory and reparative properties, has been implicated in tissue protection, but its role in melanoma remains unknown. MethodsCST expression was analyzed across melanoma stages and correlated with disease progression. Functional effects of CST were assessed in patient-derived and established melanoma cell lines, as well as in B16-F10 melanoma-bearing mice. RNA sequencing and pathway analyses were performed to delineate CST-regulated molecular networks. Vemurafenib-resistant A375 cells were used to examine CSTs effects on drug resistance mechanisms. ResultsCST expression declined with advancing tumor stage. CST treatment inhibited proliferation, migration, and invasion, while inducing apoptosis in melanoma cells but not in normal fibroblasts. In vivo, systemic CST administration significantly reduced tumor volume and mass. Transcriptomic profiling revealed coordinated downregulation of hypoxia-inducible, epithelial-mesenchymal transition (EMT), and collagen-remodeling pathways, alongside suppression of oxidative stress-adaptive signaling. In Vemurafenib-resistant A375 cells, CST restored apoptotic sensitivity and repressed multiple MAPK and PI3K-AKT-linked resistance genes. ConclusionsCST acts as a mechanistically distinct peptide modulator that reprograms oncogenic signaling through inhibition of hypoxia, EMT, and survival pathways. These findings identify CST as a promising therapeutic prototype for mitigating melanoma progression and overcoming resistance to targeted therapy.

cancer biology↗

bioRxiv2025

Red-light-only control of protein-protein interactions using a cyanobacteriochrome (UNICYCL)

Most optogenetic tools are controlled by blue light. Red-light responsive tools enable multiwavelength applications and allow greater biological tissue penetration with reduced toxicity. Current red-light tools are primarily based on phytochromes, large dimeric proteins with a structurally complex mode of interaction with their binding partners. Here we introduce a small red-light-only responsive system composed of a GA domain (6 kDa) that binds to a CBCR GAF domain (17 kDa) with a Kd [~]1-5 {micro}M to form a 1:1 complex in the dark. Red light causes dissociation of the complex by causing a >25-fold decrease in binding affinity. The CBCR GAF domain reverts to the dark state with a half-life of [~]1 min and the complex reforms. Structural analysis using NMR measurements combined with molecular docking and dynamics simulations shows that the binder interacts with the GAF domain and senses isomerization of the bilin chromophore at a site that overlaps the critical tongue domain of phytochromes. This system provides a small, simple red-light-only optogenetic tool that can operate to control protein-protein interactions in vitro and in living cells.

biochemistry↗

bioRxiv2025

Colonization of vineyards by non-Saccharomyces yeast species without evolution of copper and sulfite resistance

Humans have generated ecological and environmental disturbances, such as vineyards, across the globe. Disturbed environments create widespread and repeated selective pressures that can drive colonization and local adaptation in microbial species. We investigated the distribution of fermentative yeast species in vineyards compared to nearby arboreal habitats and measured their resistance to two commonly used vineyard antimicrobials, copper and sulfite. We analyzed 4,101 strains, representing 70 species, collected from grapevine- and oak-associated substrates at 17 vineyard and 20 non-vineyard sites in the USA and Slovenia. Species frequency varied with geography and substrate, but the majority of species commonly present in vineyards were also found in non-vineyard arboreal environments, representing a potential source for vineyard colonization and exploitation of sugar from grapes. Species varied in both copper and sulfite resistance, but only Saccharomyces cerevisiae showed elevated resistance in vineyard compared to non-vineyard samples. Our results indicate that S. cerevisiae has uniquely taken advantage of vineyard environments through adaptations that appear either unnecessary or inaccessible to other yeast species present in vineyards.

evolutionary biology↗

bioRxiv2025

O-mannosylation and protein maturation check-points represent therapeutic opportunities in BRAF fusion protein oncogenesis

Fusions between protein-coding genes are common oncogenic drivers across cancers, typically pairing a proto-oncogene with partner that does not independently drive cancer. In all therapeutically actionable fusions, the proto-oncogene is the drug target, the contributions to oncogenicity of the fusion partner have largely been ignored. We studied the role of BRAF fusion partners and found that they are necessary for transformation. In the setting of KIAA1549::BRAF, the most common fusion protein across brain tumors, we found that KIAA1549 is necessary for the oncogenicity of KIAA1549::BRAF and engenders a striking and specific dependency on the protein O-mannosyltransferase complex (POMT1/2). Specifically, we show that genetic silencing or pharmacologic inhibition of the protein O-mannosyltransferase complex (POMT1/2) reverses fusion-induced transformation, thereby representing a novel and MAPK independent therapeutic target. Furthermore, POMT1/2 is required to glycosylate and enable maturation of the K::B fusion protein. These findings represent a proof-of-concept for targeting the partners in oncogenic fusions as a potential cancer therapeutic strategy.

cancer biology↗

bioRxiv2025

A machine-learning-guided hydrogen-bonded organic framework for long-term, ultrasound-triggered pain therapy

Effective treatment of chronic pain remains hindered by the lack of drug delivery systems that simultaneously achieve long-term stability, high spatial precision, and non-invasiveness1,2,3. Here, we utilize a programmable, ultrasound-responsive drug delivery platform based on hydrogen-bonded organic framework (HOF) nanoparticles, enabling on-demand anesthetic release with long-term and durable analgesic efficacy. A machine learning (ML)-guided screening pipeline was developed to evaluate about 250 FDA-approved drugs, spanning both hydrophilic and lipophilic agents, and identified bupivacaine (lipophilic) and lidocaine hydrochloride (hydrophilic) as optimal candidates. These agents were efficiently encapsulated into HOF nanoparticles via diffusion and double-solvent methods. Ultrasound-triggered drug release in vitro transiently suppressed calcium signaling in adeno-associated virus (AAV)-transfected neurons. In vivo, ultrasound-activated release of bupivacaine or lidocaine HCl at the sciatic nerve site significantly elevated mechanical nociceptive thresholds for up to seven days, reduced autotomy (self-mutilaion) behavior, and improved motor function in a rat model of chronic pain. Notably, the machine learning-identified top candidates not only exhibited high loading efficiency, but also demonstrated superior therapeutic outcomes in vivo, establishing a direct link between computational prediction and biological efficacy. This non-invasive, programmable HOF-based system provides a clinically translatable platform for on-demand, spatiotemporally precise pain management.

bioengineering↗

bioRxiv2025

DrugLM: A Unified Framework to Enhance Drug-Target Interaction Predictions by Incorporating Textual Embeddings via Language Models

MotivationAccurate prediction of drug-target interactions (DTIs) is central to computational drug discovery, offering the potential to reduce experimental costs and accelerate development timelines. While existing deep learning approaches such as Graph Neural Networks and Transformers have shown promise, they often overlook the rich semantic information embedded in textual descriptions of drugs and targets. These descriptions encode critical biomedical knowledge, including mechanisms of action, biological pathways involved, and therapeutic effects of drugs, which can enhance DTI prediction performance. ResultsWe introduce DrugLM, a unified framework that integrates embeddings derived from large language models (LLMs) into DTI-specific model architectures. DrugLM leverages textual descriptions of drugs and targets to generate semantic embeddings using a range of pretrained LLMs. These embeddings can be seamlessly incorporated into existing DTI models. We systematically evaluate multiple LLMs on benchmark DTI datasets and demonstrate strong performance even without fine-tuning. Moreover, supervised parameter-efficient fine-tuning of the LLMs further improves embedding quality, leading to enhanced prediction accuracy. Notably, a simple multilayer perceptron (MLP) using only LLM-derived embeddings surpasses several established DTI methods, underscoring the power of semantic features. Our findings highlight the practical value of integrating LLMs into DTI pipelines and offer a straightforward recipe for improved drug discovery: LLM embeddings of drugs and targets are both effective and easy to use. AvailabilityOur code and dataset are available at https://github.com/ShPhoebus/DrugLM

bioinformatics↗

bioRxiv2025

Artemisia Database: A Comprehensive Resource for Gene Expression and Functional Insights in Artemisia annua

Artemisia annua is renowned for producing artemisinin, a compound that revolutionized malaria treatment and holds therapeutic promise for other diseases, including cancer and diabetes. However, low natural yields of artemisinin remain a major bottleneck, necessitating a deeper understanding of the genetic and regulatory networks involved in its biosynthesis. Although several transcriptomic studies on A. annua exist, they are often limited in scope, and a comprehensive, tissue-resolved gene expression resource has been lacking. Here, we present the Artemisia Database (Artemisia-DB)--a high-resolution expression atlas constructed from an extensive integration of publicly available RNA-seq datasets. The database provides transcript- and gene-level abundance estimates across major tissues and includes functional annotations such as Gene Ontology (GO) terms, KEGG pathways, and InterPro domains. As a case study, we investigated the coexpression profile of HMGR (3-hydroxy-3-methylglutaryl- CoA reductase), a key enzyme in the mevalonate pathway and an early step in artemisinin biosynthesis. Coexpression analysis in leaf tissue revealed a subset of Auxin Response Factor (ARF) transcription factors strongly correlated to HMGR. This finding suggests a potential regulatory link between auxin signaling and artemisinin biosynthesis, providing new hypotheses for functional validation. Artemisia-DB is freely accessible at https://artemisia-db.com and offers an interactive interface for exploring expression data, functional annotations, transcription factors, CRISPR targets, and more. By combining high-quality transcriptome data with regulatory and functional insights, Artemisia-DB serves as a valuable resource for the plant research community and facilitates deeper investigation into the transcriptional dynamics and specialized metabolism of A. annua.

bioinformatics↗

bioRxiv2025

CCN1 forms a complex with GPC4 and heparin to fine-tune signaling activities for embryonic cortical neural stem cell maintenance

Radial glial cells (RGCs) are the neural stem cells (NSCs) in the developing brain, capable of self-renewing and generating neurons and glia in order. The developmental behaviors of NSCs are determined by extrinsic factors from the NSC niche, together with intrinsic factors. Here, we show that Cellular Communication Network 1 (CCN1), a secreted matricellular protein, is an autocrine factor derived from RGCs in the embryonic telencephalon. Loss of Ccn1 in RGCs leads to accelerated lineage progression of RGCs, and causes premature cell cycle exit and neuronal differentiation, resulting in a reduction in NSC pool size, and in turn, at the expense of the perinatal gliogenesis. Mechanistically, CCN1 interacts with an RGC-expressed membrane protein GPC4, one of the heparan sulfate proteoglycans (HSPGs), and is required for GPC4 to maintain NSCs through the Sonic Hedgehog (Shh) signaling pathway, depending on the binding of heparin to CCN1. Our work provides a potential mechanism for the dynamic and sophisticated interconnections of niche components that meet the needs of context-dependent maintenance, differentiation, and fate determination of NSCs.

developmental biology↗