Biology subjects

Deng, Q.

Publications and source records attributed to Deng, Q..

At least 19 recordsLinked to original sources

bioRxiv2026

Targeting Cardiac Fibrosis with Chimeric Antigen Receptor Neutrophils Derived from Human Pluripotent Stem Cells

Heart failure remains a leading cause of global morbidity and mortality, with cardiac fibrosis contributing to disease progression. Following myocardial injury, activated cardiac fibroblasts orchestrate adverse extracellular matrix (ECM) remodeling, which leads to tissue stiffening and compromised contractility. Despite the clinical importance of cardiac fibrosis, current pharmacological interventions modulate disease progression without directly eliminating activated fibrogenic cells. To target these cells directly, we engineered human pluripotent stem cell (hPSC)-derived neutrophils expressing a chimeric antigen receptor (CAR) directed against fibroblast activation protein (FAP). In vitro, FAP CAR neutrophils exhibited increased reactive oxygen species-associated fluorescence during fibroblast co-culture and dose-dependent cytotoxicity in 2D cultures, while reducing fibroblast-associated fluorescence in 3D cardiac organoids without a significant reduction in cardiomyocyte-associated cardiac troponin T staining. FAP CAR neutrophils also showed increased iNOS and decreased Arginase-1 expression compared with unmodified neutrophils, consistent with a shift toward an N1-associated polarization profile. Analysis of public cardiac single-cell and single-nucleus RNA-sequencing datasets identified temporal patterns of immune-associated expression and fibroblast activation that informed treatment timing. In a murine myocardial infarction (MI) model, mice treated systemically with FAP CAR neutrophils showed higher day 7 left ventricular ejection fraction than PBS-treated mice in an exploratory comparison. Together, these findings support further evaluation of hPSC-derived FAP CAR neutrophils as an immunotherapeutic approach to cardiac fibrosis.

bioengineering↗

bioRxiv2026

PeTFlow: Flow-Matching Based All-Atom Peptide Design through Side-Chain State Tokenization

Peptide offers a powerful modality for targeting protein-protein interfaces, yet their computational design remains challenging because sequence identity, backbone geometry and side-chain packing must be optimized simultaneously within a three-dimensional pocket constrained environment. Here we present PeTFlow, a pocket-conditioned all-atom peptide co-design framework that rationalizes this constrained design problem through discrete side-chain state tokenization. In PeTFlow, each residue is represented as a chemically compatible state token, enabling unified modeling of peptide sequence, conformation and all-atom interface geometry. PeTFlow achieved superior generation quality on peptide-design benchmarks and can further be applied to therapeutically relevant peptide drug design.

biochemistry↗

bioRxiv2026

Biophysical modeling of excitation/inhibition imbalance in Alzheimer's disease

INTRODUCTION: Alzheimer's disease (AD) involves early sensory and perceptual changes that can precede memory decline. We developed a two-layer rate-based neural model of early and higher visual areas with feedforward, feedback, and recurrent connections to examine how excitatory/inhibitory (E/I) balance shapes visual motion perception in AD. METHODS: Using Motion-Induced Position Shift and Altered Motion Repulsion stimuli, we factorially manipulated excitatory (E) and inhibitory (I) subfield power and connectivity to examine their effects on illusion representation across visual layers. RESULTS: Increased E, decreased I, and increased E/I reduced illusion representation, consistent with reduced illusion susceptibility in AD, whereas the converse increased illusion representation, reflecting neurotypical processing. Higher-level layer 2 representations were more resistant to E/I perturbations than lower-level layer 1. Illusion representation was less affected by E/I increases in intra-areal (recurrent) connectivity than in inter-areal (feedforward/feedback) connectivity. DISCUSSION: This mechanistic framework links E/I balance and connectivity to altered visual motion illusion representations in AD and suggests that E/I and connectivity measures may provide probes of early AD neurobiology. The framework may also generalize beyond vision.

neuroscience↗

bioRxiv2026

DECIPHER integrates disentangled representation learning and prototype-based cell-type deconvolution across molecular modalities

The growing availability of single-cell resources creates new opportunities to extract cell-type-resolved information from the vast body of existing bulk omics data through cell-type deconvolution. Conventional deconvolution methods often rely on linear mixture models or specific probabilistic assumptions and can be sensitive to batch effects, whereas many deep-learning approaches are modality-specific or lack a unified end-to-end learning framework. Here, we developed DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that can be applied across multiple molecular modalities. DECIPHER learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation. By integrating nonlinear representation learning with differentiable non-negative least-squares optimization, DECIPHER estimates cell-type proportions from Zc. Across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets, and multiple molecular modalities, DECIPHER showed robust and competitive cell-type deconvolution performance. Beyond cell-type proportion estimation, the learned Zc supported chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma, demonstrating that DECIPHER can transform high-dimensional bulk omics data into low-dimensional, biologically informative representations. DECIPHER thus broadens the utility of existing bulk omics resources for biological discovery and clinical research.

bioinformatics↗

bioRxiv2026

AI-Guided Multi-Objective Engineering of Glucoamylase Enables Acidification-Free Starch Saccharification

Glucoamylase is essential for industrial starch saccharification, but the limited thermostability and near-neutral pH tolerance of fungal glucoamylases necessitate cooling and acidification of liquefied starch. Here, we developed an artificial intelligence-guided strategy to simultaneously improve the thermostability, pH tolerance, and catalytic activity of glucoamylase from Penicillium oxalicum (PoGA). Two property-specific machine-learning models, CASPE-T and CASPE-A, identified substitutions associated with thermostability and pH tolerance, respectively. Experimental screening identified beneficial substitutions in 11 of 21 CASPE-T and 12 of 22 CASPE-A candidates. Folding-energy-guided recombination integrated the two traits while maintaining structural compatibility. The optimal variant, PoGA T513E/Q305N, exhibited 2.21-fold higher specific activity than the wild type, with half-life extended from 22.3 to 57.9 min at 60 degrees C and from 16.6 to 64.7 min at pH 8.0. Molecular dynamics simulations attributed these improvements to reinforcement of high-occupancy hydrogen-bonding networks, suppression of conformational fluctuations in the linker and carbohydrate-binding module, enhanced long-range dynamic coordination, and preservation of a compact catalytic architecture. At 60 degrees C and pH 6.5 without acidification, PoGA T513E/Q305N produced 219.9 g/L glucose and achieved 89.1% starch conversion, 31.4% higher than the wild type. This work provides an efficient framework for multi-objective enzyme engineering and sustainable starch biorefining.

synthetic biology↗

bioRxiv2026

A therapeutic vulnerability linking MNK1/2 inhibition and G1/S cyclin-dependent kinase blockade in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a clinically challenging disease with limited therapeutic options. MAP kinase-interacting kinases 1 and 2 (MNK1/2)-mediated phosphorylation of eukaryotic initiation factor 4E (eIF4E) promotes oncogenic translation and represents a potential therapeutic target. We previously showed that loss of eIF4E phosphorylation suppresses metastasis but not primary tumor growth. Here, an shRNA screen surveying druggable genes unveiled cyclin-dependent kinase 4 (CDK4) as a genetic vulnerability to MNK1/2 inhibition in MDA-MB-231 TNBC cells. Although CDK4/6 inhibitors are approved for hormone receptor (HR)-positive, HER2-negative breast cancer, they are not approved for TNBC. We demonstrate that MNK1/2 inhibition synergizes with CDK4/6 blockade suppresses TNBC cell growth, a synergy also observed in another independent TNBC cell line (SUM159) using a broader G1/S CDK (CDK2/4/6) inhibitor. Integrated RNA sequencing and ribosome profiling revealed combination-specific changes in translation associated with mitotic checkpoint control and DNA repair. Our findings document MNK1/2 inhibition as a strategy to sensitize TNBC to G1/S CDK inhibition and provide a new avenue for therapeutic combination.

cancer biology↗

bioRxiv2026

Dual-Mode Bio-CM2: Multimodal Computational Miniature Mesoscope for Fluorescence and Label-Free Imaging

Understanding biological function often requires complementary information on molecular identity, morphology, behavior, and physiology acquired simultaneously from the same specimen. Achieving this capability in miniature microscopes remains challenging because integrating multiple imaging contrasts typically increases optical complexity while compromising field of view (FOV), spatial resolution, or form factor. Here we present dual-mode Bio-CM2, a multimodal computational miniature mesoscope that simultaneously captures fluorescence and label-free reflectance through a shared optical architecture based on distributed computational optics. The system enables frame-interleaved, co-registered dual-mode imaging over a [~]7.5 x 10 mm2 FOV with [~]6 m lateral resolution. We demonstrate the versatility of dual-mode Bio-CM2 across diverse biological systems. In freely moving Caenorhabditis elegans, reflectance captures body posture and locomotor behavior that contextualize fluorescently labeled protein aggregates. In freely swimming larval zebrafish, reflectance captures whole-body morphology and swimming behavior, while fluorescence visualizes cardiac activity. In head-fixed mice, simultaneous fluorescence and reflectance imaging across the bilateral dorsal cortex provides complementary measurements of neuronal calcium activity and intrinsic hemodynamic signals. By integrating molecularly specific fluorescence with complementary reflectance contrast, dual-mode Bio-CM2 extends distributed computational optics into a scalable platform for multimodal miniature imaging.

bioengineering↗

bioRxiv2026

Bio-CM{superscript 2}: Distributed computational optics for cortex-widecellular imaging

Understanding distributed biological systems, particularly neural circuits, requires simultaneous cellular-resolution imaging across millimeter-scale fields of view (FOV). Existing miniature microscopes remain fundamentally constrained by trade-offs among FOV, spatial resolution, and optical complexity, limiting their ability to bridge cellular microscopy with cortex-scale imaging. Here we introduce distributed computational optics, a framework that distributes image formation across coordinated optical modules and computationally integrates their measurements into a unified image. We realize this framework in Bio-CM2, a computational miniature mesoscope that partitions the imaging field across four optical modules while converging their measurements onto a common image sensor. This architecture overcomes the aberration-scaling limitations of conventional miniature optics while avoiding the hardware complexity of multi-camera systems and the contrast degradation associated with optical multiplexing. Bio-CM2 achieves a 7.5 x 10 mm2 FOV while enabling cellular-resolution in vivo imaging at video rates. We demonstrate its utility through two complementary imaging modalities in head-fixed mice: cortex-wide functional vascular imaging, enabling simultaneous quantification of pial arteriole vasomotion and mesoscale hemodynamic functional connectivity, and cellular-resolution calcium imaging, resolving the activity of over 3,000 neurons together with mesoscale neuronal functional connectivity. We further demonstrate the versatility of the platform through cellular-resolution imaging of entire coronal mouse brain sections, population-scale imaging of freely behaving Caenorhabditis elegans, and odor-evoked calcium imaging of the main olfactory bulb in head-fixed mice, highlighting its broad applicability across diverse biological systems and imaging modalities. By overcoming the conventional trade-off between FOV and spatial resolution in a compact miniature platform, Bio-CM2 establishes distributed computational optics as a scalable framework for multiscale biological imaging.

bioengineering↗

bioRxiv2026

NNMT Loss Drives Cancer Progression by enhancing SAM availability for mTORC1 Signaling and Chromatin Methylation

Aberrant epigenetic reprogramming together with dysregulated mTOR signaling are hallmarks of cancer, where altered chromatin methylation and nutrient-sensing pathways cooperate to drive tumor progression. S-adenosylmethionine (SAM), the universal methyl donor, is essential for these processes, yet how tumors sustain elevated SAM availability to support oncogenic transmethylation reactions remains poorly defined. Here, using prostate cancer (PCa) as a model system, we identify nicotinamide N-methyltransferase (NNMT) as a critical metabolic-epigenetic regulator and tumor suppressor. Using a prostate-specific Nnmt knockout mouse model, we demonstrate that NNMT loss accelerates PCa progression, particularly in the context of Pten deletion, resulting in infiltrating carcinoma and reduced survival. Mechanistically, NNMT functions as a "SAM-sink," and its loss increases intracellular SAM abundance, thereby activating mTORC1 signaling through SAMTOR-dependent sensing and broadly enhancing chromatin methylation. In human PCa, recurrent genomic deletions of NNMT occur in up to 7% of cases, and NNMT protein expression is largely absent in primary tumors and metastases. NNMT-deficient PCa cells exhibit elevated SAM:SAH ratios, increased histone methylation, and heightened mTORC1 activity, enabling sustained tumor growth even under dietary methionine-restriction (MR). Notably, combined MR and pharmacologic mTORC1 inhibition synergistically suppresses the growth of NNMT-deficient tumors, revealing a previously unrecognized therapeutic vulnerability. Collectively, these findings establish NNMT as a key tumor suppressor that constrains SAM-driven epigenetic and signaling programs in PCa and suggest a rational, diet-based therapeutic strategy for advanced cancers with NNMT loss.

cancer biology↗

bioRxiv2026

A multi-omics map of diabetic nephropathy links c-Jun activation to tubular injury and metabolic stress

Diabetic nephropathy (DN) is a major cause of end-stage renal disease, yet the molecular mechanisms driving tubular injury and fibrosis remain poorly defined. Here, we integrated single-cell multiplexed protein imaging, spatial transcriptomics, single-nucleus and single-cell RNA sequencing and chromatin accessibility profiling to comprehensively characterize human DN pathology. Our multi-modal analysis precisely maps kidney cell types and their spatial distributions, immune-fibrotic interactions, and key transcriptional regulators. We identified eight distinct cellular neighborhoods defining the immune-fibrotic microenvironment and uncovered molecular networks driving tubular injury and fibrosis. JUN (encoding c-Jun) emerged as a central regulator of transcriptional reprogramming during tubular injury and fibrogenic remodeling. In a diabetic mouse model, c-Jun is activated in injured proximal tubules. Using an inducible c-Jun mouse model, we demonstrated that tubular-specific c-Jun activation alone is sufficient to induce tubular injury, chronic inflammation, progressive fibrosis, and systemic metabolic alterations, including impaired glucose homeostasis. We also observed reduced expression of SLC4A4, a bicarbonate transporter essential for proximal tubular function, in injured tubules. Together, our findings establish a spatially resolved framework for understanding DN pathogenesis and identify c-Jun as a key mediator of tubular injury and fibrosis in diabetic kidney disease.

pathology↗

bioRxiv2026

Harnessing Human Immune Organoid model for Systemic and Comparative Analysis of Clinically Vaccine Adjuvants

Effective vaccine adjuvants are crucial for eliciting strong, specific, and long-lasting adaptive immune responses. However, traditional vaccine and adjuvant development is often hindered by prolonged animal testing, reliance on singular evaluation metrics, and batch variability, limiting the comparative analysis of immune responses induced by different adjuvants. To our knowledge, systematic adjuvant-focused analysis in human immune organoids remains limited, particularly across distinct antigen-conditioned immune baselines. Here, we established a matrix supported human tonsil immune organoid (M-hTIO) platform, derived from tonsil tissue for systemic adjuvant evaluation. Within 2 days, the platform replicated key germinal center (GC) features, with the potential for antigen-specific antibody production and plasmablast differentiation. Dynamic GC formation and T-B cell interactions were tracked using light-field imaging. Notably, IL-21 significantly accelerates GC-like remodeling and enhanced immune responses. Employing bulk cell transcriptomics we systematically analyzed early immune signatures and prolonged adaptive responses induced by five clinical-grade adjuvants (Alum, MnJ{beta}, AddaSO3, R848, ODN 1018) paired with influenza HA and SARS-CoV-2 RBD antigens across only adjuvant, weak and strong memory backgrounds. By integrating early and prolonged response phases, M-hTIO captured complementary dimensions of adjuvant activity, distinguishing rapid immune activation from sustained adaptive remodeling. Importantly, early activation signatures and prolonged adaptive remodeling provided complementary information, highlighting the value of temporally resolved adjuvant evaluation. AddaSO3 showed a consistent activity pattern across early and prolonged response phases, supporting both rapid immune activation and sustained adaptive remodeling. Moreover, our findings reveal shared/exclusive signaling pathways and gene networks. These results represent systematic and comparative analysis of clinically tested vaccine adjuvants in a human model, paving the way for high-throughput systems vaccinology research.

immunology↗

bioRxiv2026

A Critical Sites-Driven and Light-weighted Protein Engineering Platform

Protein large language models (PLMs) provide a novel computational paradigm for the deep mining of sequence co-evolutionary information, significantly accelerating the generation of functional proteins for biotechnological and medical applications. However, the misalignment between zero-shot predicted evolutionary fitness and industrial application requirements leads to a limited success rate in acquiring beneficial mutations, while the high training cost presents another drawback of large models. Here, we developed CASPE (Critical Amino acids Streamline Protein Evolution), a lightweight protein engineering platform for the precise localization and adaptation of critical residues, consisting of the CAS (Critical amino acid sites) and APCNet (Amino acid Point Cloud Classification Network). CAS utilizes gradient activation mapping and multi-layer attention matrices to directly extract key information determining target properties from PLMs and transform it into explicit site importance indicators, without relying on additional structural information or prior knowledge. Working in tandem with APCNet, CASPE establishes a workflow encompassing the entire trajectory from site localization to residue prediction. CASPE achieved remarkable hit rates in identifying beneficial variants for thermostability (31.3-60%) and pH tolerance (40-80%), further uncovering the potential mechanisms of action at these key target sites. Directed evolution of phytase further validated the generalizability of CASPE. CASPET-Phytase achieved a 33.3% success rate in obtaining beneficial mutants, which was significantly better than FoldX (6.7%) and ESM2-t33 (13.3%). CASPE guides enzyme evolution towards precise, site-targeted optimization, providing an efficient computational framework for developing industrial enzymes.

bioengineering↗

bioRxiv2026

Cell type-resolved transcriptomic map of skeletal muscle in women with polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is associated with skeletal muscle insulin resistance, fibrosis, and lipotoxicity, yet the cellular origins remain unknown. Here, we present a comprehensive cellular atlas of skeletal muscle from hyperinsulinemic and hyperandrogenic women with PCOS and controls of similar age, weight, and BMI. Analysis of 72,247 nuclei from 19 biopsies revealed cell-type-specific dysregulation in PCOS, with fiber-type-specific metabolic impairment, converging with pro-fibrotic reprogramming of fibro-adipogenic progenitors (FAPs) and enhanced FAP-myofiber crosstalk, characterized by enhanced collagen and laminin signaling. Metformin intervention for 16-weeks selectively reversed PCOS-associated transcriptional dysregulation in FAPs, revealing heterogeneous cellular responses in skeletal muscle. In vitro, PCOS myotubes retained metabolic dysfunction, yet show normalized glucose responsiveness, indicating plasticity despite metabolic memory. Systemic hyperinsulinemia and hyperandrogenemia correlated with transcriptional signatures in muscle fibers and FAPs, linking endocrine imbalance to pro-fibrotic remodeling. These findings identify novel therapeutic targets beyond conventional insulin-sensitizing approaches. Highlights- First single-nuclei atlas of human skeletal muscle in women with PCOS - FAPs subpopulations display activated pro-fibrotic phenotype - Metformin selectively targets distinct cell populations - Hyperinsulinemia and hyperandrogenemia correlate with transcriptional signatures of skeletal muscle fibers and FAPs

physiology↗

bioRxiv2026

Implantation failure linked to altered uterine NK cells in PCOS-like mice

Uterine NK (uNK) cells are essential for reproductive function, yet little is known how they are affected in polycystic ovary syndrome (PCOS), despite the strong association between PCOS and reproductive complications. We demonstrate that implantation failure coincides with distinct phenotypic alterations of uNK cells in a PCOS-like mouse model. Hyperandrogenism caused an increased influx of conventional NK (cNK) cells into the uterus, which contributed to an augmented uNK cell population, while tissue-resident NK (trNK) cells remained unchanged. Notably, CD69+ trNK cells were reduced and seemingly compensated by an upregulated expression of CD69 on cNK cells in the uterus. The maturation of uNK cells was disrupted and plausibly linked to an inability of cNK cells to convert into trNK cells. The inhibited maturation was associated with a reduced expression of the inhibitory receptor NKG2A, demonstrating an impaired education of uNK cells. This disruption of uNK cells could contribute to endometrial dysfunction and may be an underlying factor to reproductive comorbidities such as implantation failure, miscarriage and pre-eclampsia in PCOS.

immunology↗

bioRxiv2026

GARD: Genomic Data based Drug Repurposing in Head and Neck Cancer with Large Language Model Validation

Background/ObjectivesHead and neck cancer (HNC) represents the seventh most common cancer diagnosis globally, yet current treatments, including surgery, radiation, and immunotherapy, have shown limited improvement in outcomes. Drug repurposing offers a cost-effective strategy to identify new therapeutic options by leveraging existing medications with known safety profiles. Within this study we developed the GARD pipeline (Genomic Alteration-based Repurposing for Drugs), designed to uncover repurposing candidates for HNC using genomic and network-based approaches. MethodsGARD integrates multi-omics data from The Cancer Genome Atlas (TCGA), including copy number variation (CNV) and somatic mutations (SOM). The cohort was stratified by human papillomavirus (HPV) status. Risk-associated genes were identified and then expanded via high-confidence protein-protein interaction (PPI) networks. Top candidate genes were filtered through comprehensive analysis of publicly available literature data in PubMed using LLM to validate the relationship between the identified genes and HNC. The validated top risk genes and their network-expanded neighbors were mapped against DrugBank, and through statistical significance testing, established significant drug-gene associations. ResultsSignificant genes associated with HNC, inferred by genomics alteration, were identified across HPV-positive and HPV-negative subgroups, such as PIK3CA, SOX2, TP53, EIF4G1, TLR7, CLDN1, PRKCI, and EPHA2. Further expansion through the PPI network identified other targetable genes such as EGFR, ERBB2, and the FGFRs. Literature based validation efforts ensured provided confidence in the gene-disease association. Drug-gene mapping revealed candidates spanning already in clinical trials for HNC (e.g. Afatinib, Cabozantinib, Dasatinib, Brigatinib, Lenvatinib, Capivasertib, Erdafitinib) and emerging or repurposing candidates (Amuvatinib, XL765 (Voxtalisib), Golotimod, Artenimol, Quercetin, and Acetylsalicylic Acid), offering opportunities for precision repurposing. ConclusionsThe GARD pipeline demonstrates a genomics-driven, network-informed framework for systematic drug repurposing in HNC. HPV stratification enhances precision, literature-based validation strengthens confidence, and integrated drug mapping enables refinement of existing therapies and discovery of novel candidates for personalized treatment strategies. Code AvailabilityThe full implementation of the GARD pipeline, including preprocessing scripts, statistical analysis modules, and visualization tools, is publicly available on GitHub at: https://github.com/pvtanike/Genomic-Landscape-Based-Drug-Repurposing.git Simple SummaryHead and neck cancer (HNC) is among the most prevalent and challenging cancers world-wide. Developing new drugs is expensive and time-consuming, so this study explored a faster, cost-effective approach utilizing existing medications with established safety profiles: drug repurposing. We developed the GARD pipeline (Genomic Alteration-based Repurposing for Drugs), which utilizes large-scale genomic data from The Cancer Genome Atlas (TCGA) to identify key genomics changes in HNC. These genes are expanded through protein-protein interaction networks to capture related pathways and then validated using evidence from thousands of PubMed articles extracted by large language model (LLM) tools. Finally, validated genes are matched with drugs using the DrugBank database. This approach uncovered both known cancer drugs and promising new candidates. These included targeted therapies such as Fostamatinib, Nintedanib, Brigatinib, Regorafenib, and Lenvatinib, as well as emerging compounds like Artenimol, Quercetin, and Acetylsalicylic Acid (Aspirin). Through a combination of genomic analysis, network expansion, and literature validation, the GARD pipeline offers a powerful way to accelerate personalized cancer treatments while reducing cost and development time.

bioinformatics↗

bioRxiv2025

HypoxamicroRNA-210 protects against hepatic steatosis by inhibiting CIDEC expression

Background and AimsMetabolic dysfunction-associated steatotic liver disease (MASLD) is a major global health burden. Although hypoxia is known to contribute to MASLD pathogenesis, the role of hypoxia signaling remains poorly defined. We investigated whether HypoxamicroRNA-210 (miR-210), a key hypoxia-inducible microRNA, regulates hepatic lipid metabolism and MASLD development. MethodsSerum miR-210 levels were quantified in MASLD patients and matched controls. Human hepatic spheroids and HepG2 cells were exposed to fatty acids to assess miR-210 induction and lipid accumulation. miR-210 knockout mice were fed a Western diet to evaluate hepatic steatosis and transcriptomic changes using RNA sequencing. RNA pull-down and 3UTR-driven luciferase reporter assays were employed to identify miR-210 targets. Functional effects of miR-210 mimic were examined in knockout mice, db/db mice, and in vitro human hepatic spheroid models. ResultsSerum miR-210 levels were significantly reduced in MASLD patients compared with matched controls. Consistently, human hepatic spheroids did not appropriately increase miR-210 expression in response to fatty acid-induced intracellular hypoxia. This blunted miR-210 response contributed to hepatic lipid accumulation, as loss of miR-210 in a mouse model of MASLD led to increased hepatic lipid deposition and activation of lipid metabolic pathways. We identified CIDEC as a direct miR-210 target mediating its inhibitory effects on hepatic lipid accumulation, and restoring miR-210 expression suppressed CIDEC and reduced hepatic lipid content in knockout mice on a Western diet. Moreover, miR-210 attenuated lipid accumulation in both in vitro human hepatic spheroids and in vivo db/db mice models of MASLD. ConclusionsmiR-210 protects against hepatic steatosis by inhibiting CIDEC expression, suggesting miR-210-CIDEC axis as a promising therapeutic target for reducing hepatic lipid accumulation and preventing MASLD progression. Impact and ImplicationsThis study addresses a critical gap in understanding how hypoxia signaling shapes MASLD and uncovers a novel pathogenic mechanism of hepatic steatosis arising from fatty acids-induced dysregulation of miR-210. Impaired hypoxia responses, via blunted miR-210 induction, contribute to hepatic lipid accumulation through upregulation of CIDEC, a newly identified target of miR-210. These findings establish miR-210 as a novel regulator of hepatic lipid homeostasis and underscore its therapeutic potential. Interventions aimed at restoring miR-210 function in the liver may offer a promising strategy to ameliorate hepatic steatosis and prevent MASLD progression.

molecular biology↗

bioRxiv2025

Primate-specific adaptation of Ku protects transcriptomic integrity by suppressing Alu-mediated alternative splicing

Accurate pre-mRNA splicing is essential for the transfer of genetic information but faces unique challenge in higher primates due to the massive expansion of intronic Alu elements1-3. While studying Ku, the Ku70/Ku80 heterodimer best known for initiating non-homologous end-joining (NHEJ) by encircling DNA ends4, we discovered that Ku expression increased markedly during primate evolution in parallel with Alu expansion5. Ku binds double-stranded RNA (dsRNA) stem-loops, including those at the antisense Alu (asAlu) elements within introns5. Here, we show that Ku-depletion in human cells has a broad impact on splicing largely independent of cell-cycle states, NHEJ, or innate immune signaling, significantly affecting ~8-10% of quantifiable alternative splicing events. Mechanistically, Ku directly binds exonic asAlu to prevent their aberrant inclusion and binds asAlu within inverted-repeat Alu (irAlu) pairs flanking canonical exons to prevent exon skipping6. Among human tissues, Ku expression in the brain is consistently ~50% lower, correlating with more permissive expression of Alu-derived splice variants, particularly those encoding mitochondrial proteins and RNA-binding factors. Correspondingly, heterozygous Ku loss in patient causes developmental delay, neurological dysfunction, and acidosis. Together our findings identified Ku as a critical suppressor of Alu-associated alternative splicing co-opted during evolution with implications for primate brain function and human disease.

molecular biology↗

bioRxiv2025

SynPROTAC: synthesizable PROTACs design through synthesis constrained generative model and reinforcement learning

Protein hydrolysis targeting chimeric (PROTAC) has emerged as a promising technology in degrading disease-related proteins for drug design. Recent deep generative models can accelerate PROTAC design, but the generated molecules are often difficult to synthesize. Here we develop SynPROTAC model, which employs Graphormer encoded warhead or E3 ligand as input, and autoregressively samples reaction templates and building blocks through transformer based decoder for PROTAC construction. The model is also fine-tuned via reinforcement learning for generating PROTACs with favorable binding properties. The comprehensive evaluations indicated that SynPROTAC is capable of generating novel PROTACs with feasible synthetic routes, reasonable physico-chemical and binding related properties.

bioinformatics↗