Biology subjects

Hu, Q.

Publications and source records attributed to Hu, Q..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Red yeast rice-derived MKA ameliorates cardiac hypertrophy in hypertensive rats by inhibiting ERK1/2/c-Fos pathway

BackgroundCardiac hypertrophy is a key pathological process in hypertensive heart failure, yet current antihypertensive therapies do not directly target it. Red yeast rice (RYR), rich in monacolin K {beta}-hydroxy acid (MKA), is known for lipid-lowering effects, but its potential to ameliorate cardiac hypertrophy is unreported. PurposeTo investigate the effects of RYR-derived MKA on cardiac hypertrophy in spontaneously hypertensive rats (SHR) and elucidate its molecular mechanisms. MethodsSpontaneously hypertensive rats (SHR) were treated with 0.6% red yeast rice for 8 weeks to assess its effects on blood pressure, cardiac function (echocardiography), cardiac hypertrophy and fibrosis (histopathology), and multi-organ toxicity (histopathology). A multigenerational study was conducted to evaluate protective effects in offspring. Network pharmacology and transcriptomic analysis were integrated to predict molecular targets, which were subsequently validated by molecular docking and experiments. ResultsEight-week RYR treatment significantly reduced blood pressure, inhibited cardiac hypertrophy and fibrosis, and improved cardiac function without gender differences. No pulmonary, hepatic, or renal toxicity was observed. Offspring from treated parents exhibited further reduced hypertrophy upon continued treatment. Mechanistically, MKA bound ERK1/2 with high affinity, inhibiting its phosphorylation and downstream c-Fos expression, thereby downregulating hypertrophy markers. ConclusionRed yeast rice improves hypertensive cardiac hypertrophy via MKA-mediated inhibition of the ERK1/2/c-Fos pathway. Its multi-organ safety and transgenerational effects offer a novel dual-therapy strategy for hypertension and cardiac hypertrophy. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=139 HEIGHT=200 SRC="FIGDIR/small/710945v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@8225acorg.highwire.dtl.DTLVardef@775f43org.highwire.dtl.DTLVardef@10f5201org.highwire.dtl.DTLVardef@d0cd88_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2026

A T-cell engager platform induces deep plasma cell depletion in autoimmune dry eye

Bispecific T cell engagers (TCEs) have transformed targeted immunotherapy but remain constrained by ex vivo manufacturing and systemic delivery, limiting their application in autoimmune disorders. Here, we introduce the first in vivo TCE platform, utilizing a T cell-targeted integration-deficient lentiviral vector (T-IDLE) to reprogram endogenous T cells into engineered T cells secreting BCMAxCD3 TCEs (STCEs) directly in vivo. In murine Sjogrens syndrome-associated dry eye model, a single intravenous administration of T-IDLESTCE achieved deep and sustained depletion of plasmablasts and plasma cells, reversing lacrimal gland inflammation and restoring tear production without detectable off-target transduction. Longitudinal studies in cynomolgus monkeys confirmed translational promise, demonstrating potent plasma cell clearance and favorable safety profiles over 12 weeks post-vector dosing. Mechanistic analyses demonstrated attenuation of Th1/Th17-mediated inflammation and controlled cytokine responses, accompanied by negligible vector integration in non-T cell lineages, thereby substantiating the safety profile of the integration-deficient design. This first-in-class in vivo TCE approach represents a manufacturing-free, scalable immunotherapy strategy for autoantibody-mediated diseases.

bioengineering↗

bioRxiv2026

Radiosynthesis and Evaluation of Novel Cholesterol 24-Hydroxylase Positron Emission Tomography Tracers

Cholesterol 24-hydroxylase (CH24H or CYP46A1) is a pivotal enzyme in brain cholesterol metabolism and has emerged as a therapeutic and imaging target in neurodegenerative disorders. Although [18F]Cholestify ([18F]CHL-2205) has shown promise as a positron emission tomography (PET) tracer for imaging of CYP46A1, the impact of cyclopropyl moiety conformation on binding and imaging performance remains unexplored. Here, we report the rational design and preliminary evaluation of novel CYP46A1 PET tracers, in which the left-side cyclopropyl group was modified into bridged, spirocyclic, and fused bicyclic architectures to probe steric and conformational effects. All compounds 9-11 exhibited high CYP46A1 affinity (IC50 = 0.19-0.28 nM). Radiosynthesis of [18F]9-11 was achieved via copper-mediated [18F]fluorination, providing practical non-decay-corrected radiochemical yields of 10-34% with excellent radiochemical purity (>98%). In vitro autoradiography in rat brain sections demonstrated specific and regionally selective binding, comparable to that observed for [18F]CHL-2205. These cyclopropyl-derived scaffolds establish a scaffold-driven strategy for PET tracer development, providing a robust framework for further structure-activity relationship studies and the rational optimization of CYP46A1 PET tracers.

pharmacology and toxicology↗

bioRxiv2026

Hidden stop codons orchestrate mRNA fate by ambushing ribosomal frameshifting associated with codon usage

In eukaryotes, the mRNA stability is generally shaped by codon usage bias, the uneven preferences for synonymous codons, in a translation-dependent manner. However, the conserved mechanism linking codon to mRNA decay remains elusive. Hidden stop codons (HSCs), defined as stop codons located in the +1 or -1 frame relative to canonical ORF, are widespread across all genomes but largely uncharacterized. Here, we demonstrate that in both fungi and human cells, HSCs play an conserved and mechanistic role in promoting mRNA decay by rapidly terminating out-of-frame translation promoted by nonoptimal codons, primarily through +1 ribosomal frameshifting. In the filamentous fungus Neurospora crassa, partially deleting HSCs via synonymous substitutions in the clock gene frequency increases mRNA stability and disrupts circadian rhythmicity. In human cells, acute depletion of translation termination factor eRF1 impairs recognition of HSCs, leading to global stabilization of mRNAs enriched in nonoptimal codons and HSCs. In both Neurospora and humans, these mRNAs are in part degraded through the NMD pathway via UPF1. Collectively, these findings suggest that in eukaryotes, HSCs serve as surveillance elements to monitor ribosomal frameshifting and out-of-translation promoted by nonoptimal codons, thereby initiating mRNA decay through NMD.

molecular biology↗

bioRxiv2026

Structural insights into the recognition and catalysis of tRNA by human NSUN2

The human RNA m5C methyltransferase NSUN2 catalyzes site-specific cytosine methylation across diverse RNA substrates and thereby regulates a wide range of biological and physiological processes. However, the molecular basis by which NSUN2 achieves broad substrate recognition while maintaining catalytic specificity has remained unclear. Here we determine structures of human NSUN2 in both substrate-free and substrate-bound states using X-ray crystallography and cryo-electron microscopy. Structures of NSUN2 in complex with multiple tRNA substrates reveal a structure-first, sequence-tolerant strategy in which NSUN2 actively remodels tRNA architecture, exposing the buried target cytosine and positioning it within the catalytic pocket for methyl transfer. This recognition strategy enables NSUN2 to accommodate diverse tRNA substrates through a largely conserved interaction interface. Guided by these structural insights, we identify a small-molecule inhibitor that suppresses NSUN2 activity and cancer cell proliferation. Together, our findings define the molecular principles underlying NSUN2-mediated RNA m5C modification and provide a structural foundation for targeting NSUN2 in cancers.

molecular biology↗

bioRxiv2025

A novel allele of Sh1 underlines the conversion of waxy corn to wx-sweet corn

AbstractWaxy corn and sweet corn are two major types of fresh-eating corn. Each has significant market value due to its unique sensory and nutritional profiles. Developing a variety that combines both traits would meet growing consumer demand and create new market opportunities. From a fast neutron-mutagenized population of the waxy corn inbred line HB522, we identified a mutant, wx-sweet, whose kernels exhibit both waxy and sweet properties at the filling stage. Using bulked segregant analysis (BSA) and fine mapping, we localized the causal locus to SHRUNKEN1 (Sh1) on chromosome 9, which was confirmed by an allelic test with a known Mu-insertion mutant. PCR and sequencing indicated a putative large structural variation in the second and third exons of Sh1 in wx-sweet, likely disrupting the reading frame. This novel sh1 allele significantly reduced sucrose synthase activity. The sh1 and waxy1 mutant genes act synergistically to remodel carbohydrate metabolism in wx-sweet endosperms. This remodeling, revealed by integrated transcriptomic and metabolomic analyses, drives transcriptional reprogramming and restructures metabolic flux. These changes thereby enhance sucrose and raffinose accumulation, which underlies the unique waxy-sweet texture in fresh-eating maize endosperm. In summary, our work not provides valuable genetic resources for fresh-eating corn breeding, but also elucidates, for the first time, the molecular mechanism underlying the synergistic formation of waxy and sweet texture. Key messageThe novel allele of Sh1 acts synergistically with waxy1 to regulate carbohydrate metabolism, which elucidates the molecular mechanism for the unique waxy-sweet texture in fresh-eating corn endosperm. Our work provides new strategies for sweet-waxy maize breeding.

genetics↗

bioRxiv2025

Similar processing of novelty in rat dorsal and intermediate CA1 despite differences in spatial tuning

Elucidating the fundamental neural mechanisms of hippocampal information processing is necessary for understanding memory formation and related brain disorders. Differences in hippocampal genetics, anatomy, and connectivity across the longitudinal axis suggest functional heterogeneity in this structure. The dorsal pole is suggested to be primarily involved in spatial processing, whereas the ventral pole is implicated in emotional processing. Connectivity and genetic studies show this functional segregation is more prominent near their respective poles and weaker toward the intermediate region. The current study compares the firing properties of CA1 cells in the dorsal and intermediate regions of the hippocampus during spatial or social/odor novelty. We measured basic spatial properties, firing rate response, and remapping to the spatial re-configuration of a linear track or the social/odor presentation of a novel male conspecific, female bedding, or coyote urine. Behaviorally, the average rat exhibited slower maze running latencies during the novel spatial manipulation and spent more time adjacent to the chamber containing the novel social/odor stimulus. As previously shown, dorsal cells had fewer, smaller place fields, and higher spatial information content than intermediate cells in the hippocampus. Despite the differences in place field characteristics, cells in both regions responded similarly to spatial and social/odor manipulations. Taken together, these data support the differentiation of some functions, together with an overlap of other functions progressing along the longitudinal axis, which may facilitate the integration of information throughout the hippocampus.

neuroscience↗

bioRxiv2025

Geometry-Constrained Prediction of Catalytically Competent Kinase Domains Across the Human Kinome

Understanding cellular signaling networks benefits from accurate structural modeling of protein kinases across their different functional states. However, most human kinases lack experimentally determined active-state structures, and existing computational methods often fail to reliably predict catalytically competent conformations. Here, we present a geometry-constrained modeling framework based on AFEXplorer (AFEX) that enables robust prediction of active, ATP- and magnesium-bound states for 436 catalytic kinase domains across the human kinome. By explicitly enforcing key biophysical criteria, AFEX overcomes the template dependence and conformational bias inherent in AlphaFold2 and AlphaFold3. Benchmarking against 156 kinase-substrate complexes shows that AFEX achieves sub-2{square}[A] backbone RMSD in 99% of cases and accurately recapitulates critical catalytic features with precise ATP coordination. Notably, AFEX correctly predicted active conformations for EGFR and CDK2, where AlphaFold3 failed. These improvements are consistent across diverse kinase families, including both well-characterized kinases and those underrepresented or absent from AlphaFolds training data. AFEX bridges a critical gap in kinase biology by providing a generalizable framework for studying active conformations and dynamics in kinases and other signaling proteins.

biophysics↗

bioRxiv2025

A novel 18F-labeled brain penetrant PET ligand for imaging poly(ADP-ribose) polymerase-1

Poly(ADP-ribose) polymerase-1 (PARP-1) is a key mediator of DNA repair, and its inhibition has become a validated therapeutic strategy in homologous recombination-deficient cancers. However, tools for non-invasive assessment of PARP-1-specific expression remain limited. Here, we evaluated [18F]AZD9574, a next-generation PARP-1-selective PET radiotracer. [18F]AZD9574 was tested in a panel of breast, glioblastoma, prostate, and pancreatic cancer cell lines. Uptake correlated with PARP-1 expression and was dose-dependently blocked by a variety of clinically relevant PARP inhibitors, confirming its binding specificity. In 22Rv1 xenograft mouse models, the tracer demonstrated significant tumor accumulation that was specific to PARP-1, and ex vivo biodistribution confirmed organ uptake consistent with specific tumor binding and PARP-1 expression. Together, these findings establish [18F]AZD9574 as a promising PARP-1-targeted imaging agent with strong potential for advancing cancer research and therapeutic monitoring.

cancer biology↗

bioRxiv2025

Ganaxolone, an approved therapy for CDKL5-Deficiency Disorder, is an inhibitor of PTP1B

CDKL5-deficiency disorder or CDD, which results in intellectual disability, speech and motor deficits, and seizures that can start as early as six weeks after birth, is caused by de novo mutations in the CDKL5 gene. In early 2022, the FDA approved Ganaxolone (Commercial name: Ztalmy) for treatment of seizures in CDD patients 2 years and older. Ganaxolone has been reported to act as a GABAA receptor agonist that helps reduce neuronal excitability; however, based on its chemical structure we hypothesized that ganaxolone may also act as a PTP1B inhibitor. We observed that ganaxalone was able to inhibit PTP1B activity both in an in vitro assay of enzyme activity and in different cell models, in a comparable way to known inhibitors of PTP1B, including MSI-1436, which has a similar chemical structure. Additionally, inhibition of PTP1B in differentiating SH-SY5Y cells increased TRKB/BDNF signaling. This effect was prominent in CDKL5-KO cells, where inhibition of PTP1B brought TRKB levels and BDNF signaling to levels similar to those of wild-type cells and the observed signaling changes also coincided with restoration of cellular morphology. Finally, loss of CDKL5 function resulted in increased levels of PTP1B. Our results suggest that, like in Rett syndrome, targeting PTP1B may be a beneficial therapeutic strategy for CDD.

molecular biology↗

bioRxiv2025

Synthesis and In Vitro Characterization of OGA-2506 as a High Affinity Radioligand for O-GlcNAcase

O-GlcNAcase (OGA) is a glycoside hydrolase that regulates protein O-GlcNAcylation, a dynamic post-translational modification implicated in numerous cellular processes. Dysregulation of OGA alters cellular O-GlcNAc homeostasis and has been linked to neurodegenerative and other chronic diseases. The development of radioligands targeting OGA, particularly those derived from well-characterized tool compounds, could substantially advance drug discovery in this area. Herein, we report the radiosynthesis of a novel tritium (3H)-labeled radioligand 7 (code name [3H]OGA-2506) derived from a chiral piperidine scaffold, and its preliminary in vitro binding evaluation. Starting from iodine precursor 8, palladium-catalyzed tritiation afforded [3H]7 with excellent molar activity (1822 GBq/mmol) and high radiochemical purity (>99%). Saturation binding studies revealed that [3H]7 binds OGA with high affinity to rat striatum homogenates (Kd = 3.56 nM; Bmax = 42.62 nM). Competition assays yielded an IC50 of 5.55 nM, and preliminary autoradiography demonstrated heterogeneous regional distribution in the brain with specific binding. These findings highlight [3H]7 as a valuable tool for OGA binding studies and provide a foundation for the future development of novel positron emission tomography (PET) ligands to probe O-GlcNAc signaling in the brain.

pharmacology and toxicology↗

bioRxiv2025

Macropinocytosis-Mediated Recyclable LYTACs (McR-TACs) for Membrane and Extracellular Protein Degradation

The degradation of cell membrane and extracellular proteins with lysosome-targeting chimeras (LYTACs) is hampered by non-recyclable and lysosome-shuttling receptor-dependent mechanisms, limiting the wide application of this emerging technology. Here, we developed macropinocytosis-mediated recyclable LYTACs (McR-TACs) that could durably degrade cell membrane and extracellular proteins in a receptor-independent manner. This McR-TAC platform demonstrated the feasibility of harnessing endogenous transport pathways for developing next-generation recyclable protein degraders with broad applicability.

biochemistry↗

bioRxiv2025

Benchmarking of proximity-dependent biotinylation enzymes across cellular compartments and time windows

Proximity-dependent biotinylation has become a powerful approach for mapping protein interactions and subcellular organization in living cells. Although a growing number of engineered biotin ligases have been introduced, their performance has not been systematically evaluated across diverse cellular contexts. Here, we benchmark ten proximity ligases spanning three bacterial lineages using standardized proteomic workflows across multiple labeling durations, subcellular compartments, and two human cell types. While all enzymes efficiently detect proximal associations, they differ in labeling kinetics, background activity, and spatial specificity. TurboID exhibits the highest overall activity but generates substantial background in standard media. miniTurbo and ultraID support rapid, biotin-dependent labeling with low background, making them better suited for dynamic and time-resolved applications. However, miniTurbo showed aberrant mitochondrial localization with two cytoskeletal baits (VASP and PFN1). Across 15 diverse baits, ultraID consistently provides an excellent combination of specificity, efficiency, and spatial compatibility--including unique recovery of Golgi-resident glycosyltransferases. This study serves as a comparative resource, offering guidance for enzyme selection and experimental design in proximity proteomics.

systems biology↗

bioRxiv2025

An Objective Detection Method for Tinnitus using Memristors

Approximately 10-20% of the world population experiences tinnitus, which is increased with age and comorbid with frustration, annoyance, anxiety, depression, cognitive dysfunction, insomnia and stress. The underlying mechanisms of tinnitus remain largely unknown and there are no quantifiable biomarkers, making this prevalent disorder a global burden. There are no FDA-approved therapeutics and the current assessment mainly relies on self-reports (e.g., questionnaires), which underscore the need for diagnostic tools for tinnitus. Memristors, nanoscale electronic components mimicking synaptic plasticity, can monitor and control neuronal activity, hold transformative potential for detecting and characterizing tinnitus objectively. Here, we harvested the blood from human and mice with tinnitus, and engineered memristors via magnetron sputtering and made the functional layer with serum. Its core mechanism lies in regulating the reversible formation and rupture of conductive filaments which is formed by utilizing abnormally elevated glutamate concentrations in serum assisted Ag+ ions. This serum-based memristor achieves unprecedented discrimination between tinnitus and non-tinnitus individuals. Furthermore, this platform provides mechanistic insights into tinnitus pathogenesis, with potential implications in other neurological disorders.

pathology↗

bioRxiv2025

Machine-learning strategy for high error tolerance in image-based digital molecular assays

There is a significant global health need to translate more in vitro diagnostic tests (IVDs) from clinical laboratories to field-based applications, including point-of-care (POC) and self-administered test formats. These applications typically require smaller sample sizes, limit the extent of sample processing and measurement capabilities, and introduce greater handling variability. Error tolerance is one of the most critical factors for successful field-based assay design. Here, we examine machine-learning (ML) strategies to enhance the error tolerance of image-based nanoparticle immunoassays. Random dispersions of nanoparticles were imaged in microliter sample volumes, and images were processed to determine analyte concentrations based on nanoparticle appearance. Assay performance was characterized using two common blood diagnostics: C-reactive protein (CRP) and S.CoV-2 IgG. We compare the results from a conventional image analysis, a hybrid ML-conventional approach based on pixel segmentation, and a full end-to-end image regression using a targeted regularization strategy. Training images for the full image regression approach required only a single label for training - the analyte concentration - eliminating the need for labor-intensive pixel-level labeling. Ultimately, the fully ML-based analysis significantly improved dynamic range, sensitivity, and reproducibility in high-error settings, including direct measurements performed in whole blood.

bioengineering↗

bioRxiv2025

Single-cell multiomic human brain atlas reveals regulatory drivers of cortical regionality

Distinct regional functionality of the human cortex is orchestrated by diverse cellular and molecular processes, yet the underlying regulatory mechanisms remain poorly understood. We performed multiomic single-cell and spatial characterization of nine regions of the human cortex to define the gene regulatory networks and transcription factors that govern cell-type and region specificity. With the combined data of over three million cells, two striking patterns of cortical neuron specialization were uncovered: a rostral-caudal gradient of calcium regulatory machinery, and subunit switching of multiple signaling receptor families across the transmodal-sensory axis. Gene regulatory network analysis revealed putative transcriptional regulators of cortical neuron specialization with cell-type- and region-specific gene regulation patterns. While regionalization was observed in gene expression, chromatin accessibility, and spatial distributions, these modalities exhibited distinct cortical patterns. Our findings illuminate critical neuronal pathways that vary throughout the cortex and the gene regulatory networks that establish cortical regionalization in the human brain.

neuroscience↗

bioRxiv2025

Phenotypic scoring of Canola Blackleg severity using machine learning image analysis

Canola blackleg is a fungal disease that causes significant yield loss and plant death of infected canola (Brassica napus L., Brassica rapa L., Brassica juncea L.) fields worldwide. One of the most effective methods for controlling blackleg is through the cultivation of resistant varieties. Consequently, scoring blackleg disease severity of infected plants is a key metric for identifying and selecting resistant varieties. Traditionally, blackleg severity is scored by expert raters who evaluate disease in stem cross sections using established rating scales and reference images; however, human raters are expensive and inconsistent in their scoring. Here, we introduce a machine learning algorithm based on deep learning models that can score blackleg severity from cross-section images of infected plants. We find that expert ratings are largely inconsistent across raters and across years for the same rater, creating substantial noise in susceptibility ratings. Meanwhile, our trained machine learning model performs more consistently than the median rater while maintaining a similar heritability as expert raters for the blackleg susceptibility trait. This model can be used to standardize blackleg susceptibility scoring across locations and years to improve canola breeding outcomes across affected regions. Core IdeasO_LICanola Blackleg is a fungal disease affecting yield of canola, and accurate scoring of Blackleg severity is important for tracking disease and breeding for resistant varieties. C_LIO_LIThe standard practice of utilizing expert raters is expensive, and scores assigned are inconsistent across raters and years. C_LIO_LIOur deep learning model for assigning blackleg severity scores is more accurate than the median expert rater, opening the door for improved breeding of new resistant varieties. C_LI

plant biology↗

bioRxiv2025

Learning the native-like codons with a 5'UTR and secondary RNA structure aided species-informed transformer model

Efficient protein expression across heterologous hosts remains a major challenge in synthetic biology, largely due to species-specific differences in codon usage and regulatory sequence context. A key difficulty lies in reconstructing the codon landscape of the target expression system within a foreign host with a native-like codon preference. To address this, we present TransCodon, a Transformer-based deep learning model that leverages both 5 untranslated regions (5UTRs) and coding sequences (CDS), along with explicit species identifiers and RNA secondary structure information, to learn nuanced codon usage patterns across diverse organisms. By incorporating multisource genomic data and modeling sequence dependencies in a masked language modeling paradigm, TransCodon effectively captures both local and global determinants of codon preference. Our experiments demonstrate that integrating species-level information during training significantly improves the models ability to predict optimal synonymous codons when considering different evaluation metrics. More importantly it identifies native-like codons with less divergence from natural sequences compared to other methods. Besides, TransCodon could capture more low-frequency codons which are often omitted by other deep learning-based methods. The results thus indicate that TransCodon as a robust codon language model has the potential for generating native-like CDS with high translational efficiency in target hosts.

bioinformatics↗