Biology subjects

Long, Y.

Publications and source records attributed to Long, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Artemisinin-Conjugated Phototheranostic Probe for Golgi-Targeted H2O2 Detection and Side Effect Attenuation

Understanding the mechanism by which Artemisinin conjugated with phototheranostic probes for reducing side effects (AC-Pr-RSE) will guide endeavor to improve the therapeutic outcomes. Herein, the first report describes the application of a cytotoxic artemisinin derivative and N-methyliminodiacetic acid (MIDA) boronates conjugated with a naphthalimide fluorophore for the detection of H2O2 in the Golgi apparatus. A comparative analysis of the localization signals from the fluorescent artemisinin derivative and organelle-specific dyes uncovered that the Golgi apparatus functions as the primary site of its accumulation, which could efficiently enhance the intracellular reactive oxygen species (ROS) level and induce cell apoptosis. This work highlights the potential of AC-Pr-RSE study, which enables us to optimize the design of theranostic probes to improve their biological activities during oxidative stress.

biochemistry↗

bioRxiv2025

SPARP-seq reveals poly(A) tail shortening as a hallmark of the active human translatome

The poly(A) tail at the 3 end of mRNAs plays a critical role in the synthesis of proteins, but its dynamic relationship with the translation process has been unclear. Here, using a novel single-molecule approach SPARP-seq that integrates poly(A) assessment with ribosome profiling, we demonstrate that poly(A) tail shortening is a universal hallmark of active translation in human cells. We find that most mRNAs begin translation on single ribosome (monosome) with long tails that progressively shorten as additional ribosomes are recruited. In contrast, highly expressed housekeeping genes are translated efficiently on monosomes and possess intrinsically short poly(A) tails. Furthermore, we find that translationally repressed transcripts, such as those retaining introns, are trapped on monosomes with long tails. Pharmacological inhibition of translation induces global poly(A) tail lengthening, confirming that deadenylation is coupled to ongoing translation. These findings reveal a dynamic system where the poly(A) tail integrates mRNA status with translational fate, defining the monosome as a central hub for post-transcriptional control.

molecular biology↗

bioRxiv2025

A dual-metric framework for quantifying the biological fidelity of scRNA-seq pipelines

Single-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptional profiling, but early-stage processing pipelines differ markedly in barcode recovery, UMI correction, and read assignment-variations that can propagate and bias downstream analyses. We present a reproducible, parameter-aware benchmarking framework to quantify the biological fidelity of four primary pipelines-STARsolo, Cell Ranger, Kallisto|Bustools, and Alevin-fry-across simulated ground-truth datasets and complex biological contexts, including a Huntingtons disease (HD) mouse model. Our approach introduces two complementary metrics: the Cluster Annotation Score (CAS), assessing concordance between direct cell-level and cluster-level consensus labels, and the Marker Concordance Score (MCS), measuring cohesion of de novo marker genes per cell type. By systematically varying highly variable gene (n_HVG) and principal component (n_PC) settings, we map how upstream quantification interacts with downstream parameter choice. Simulations show STARsolo and Kallisto|Bustools deliver high technical accuracy, but only STARsolo consistently preserves stable cell identities and coherent marker expression across parameter regimes. In empirical datasets, alignment-based pipelines (STARsolo, Cell Ranger) yield higher CAS/MCS values and more biologically faithful annotations, while alignment-free methods show reduced signal fidelity despite faster runtimes. Differences introduced during primary processing persist after batch correction and integration, altering disease-associated cell type detection. Our open-source CAS-MCS-Scoring toolkit enables transparent evaluation of pipeline performance, providing a practical guide for selecting analysis strategies that maximize reproducibility and biological interpretability in scRNA-seq studies.

bioinformatics↗

bioRxiv2025

Dynamically Assembling Biological Intelligence to Predict Novel Cellular Phenotypes

In this work, we introduce Bio-AMLM (Biological Adaptive Modular Learning Model), a new framework designed to address out-of-distribution (OOD) generalization challenges in predicting cellular responses. Unlike monolithic deep learning models or simple data retrieval methods, which struggle to predict the effects of novel genetic or chemical perturbations, Bio-AMLM dynamically constructs a bespoke analytical pipeline for each biological query. It leverages a library of pre-trained, functionally specialized biological modules (e.g., for genomic, proteomic, and metabolic analysis). Guided by a biological context encoder, an adaptive inference planner selects, configures, and links these modules to form an optimal analysis chain. In experiments on several challenging bio-simulation benchmarks, including Gene-Edit-Bench, Drug-Response-Bench, and Toxicity-Bench, Bio-AMLM consistently outperformed state-of-the-art approaches, producing more reliable, robust, and interpretable predictions of cellular behavior in complex OOD scenarios.

bioengineering↗

bioRxiv2025

Molecular insights into ligand recognition and signaling of GPR99/OXGR1

GPR99/OXGR1 is a G protein-coupled receptor (GPCR) with two endogenous agonists, the tricarboxylic acid cycle derivative oxoglutarate and the inflammatory lipid mediator leukotriene E4 (LTE4). How GPR99/OXGR1 recognizes two distinct ligands is a biologically important question relevant to therapeutic development due to its established roles in asthma, allergy and inflammation. Here we present cryo-EM structures of GPR99/OXGR1-Gq complex with oxoglutarate and LTE4, respectively. The oxoglutarate-bound structure shows a binding pocket surrounded by the transmembrane domains (TM), with a primary site and an accessory site for simultaneous binding of two oxoglutarate molecules for full activation of the receptor. The TM binding pocket, however, is too small to accommodate the "Y" shaped structure of LTE4, a cysteinyl leukotriene. Alanine substitution of key residues for oxoglutarate binding had little impact on LTE4-induced signaling. An alternative site in between TM3/4/5 just above intracellular loop 2 was identified in the cryo-EM structure of GPR99/OXGR1-Gq complex formed with LTE4, but the densities were less well defined. Alanine substitution of amino acids potentially involved in LTE4 interaction at this site abrogated LTE4-induced receptor activation, with no effect on oxoglutarate-induced signaling. Both ligands activated GPR99/OXGR1 primarily through the Gq pathway, but LTE4 also induced inhibition of cAMP accumulation that was sensitive to pertussis toxin. These findings illustrate the structural basis for GPR99/OXGR1 interaction with oxoglutarate and suggest the presence of a distinct binding site for LTE4.

biochemistry↗

bioRxiv2025

SpaFoundation: a visual foundation model for spatial transcriptomics

Spatial transcriptomics (ST) has revolutionized biological research by enabling the joint profiling of gene expression and spatial context, along with histological images. However, current existing ST technologies remain high-cost and time-consuming, hindering their broader clinical applications. Although computational methods have been developed to infer gene expression directly from histology images, these methods still suffer from limited accuracy and spatial resolution due to insufficient training data and model capacity. Here, we introduce SpaFoundation, a large-scale histology foundation model designed to accurately predict spatial gene expression from tissue images. SpaFoundation employs a teacher-student Vision Transformer (ViT) architecture to learn generalizable histological representations by modeling potential dependencies among image patches. Notably, we incorporated self-distillation and masked image modeling (MIM) jointly to capture high-level semantic representations and fine-grained structural features, enriching spots representations. The model is pretrained on 1.79 million patches spanning 26 tissue types, with 80 million parameters. We validated SpaFoundation using 117 samples, demonstrating its flexibility across different spatial resolutions and superior performance in spatial gene expression prediction, as well as strong transferability to downstream tasks such as tumor detection and spatial domain clustering. Our results highlight the potential of large-scale foundation model to learn informative histological representations and underscore the benefits of domain-specific pretraining in extracting task-relevant representations, paving the way for foundation model-driven spatial gene expression inference. The implementation and pre-trained weights of SpaFoundation are publicly available at https://github.com/NingZhangCSUBio/SpaFoundation.

bioinformatics↗

bioRxiv2025

SpatialMap: A Scalable Deep Learning Method for Cell Typing in Subcellular Spatial Transcriptomics

Subcellular imaging transcriptomics has offered unprecedented resolution of spatial mapping of gene expressions at subcellular level. However, accurate and robust cell type annotation at cellular level remains a challenge. Here we present SpatialMap, a deep weakly supervised cell typing framework that combines graph neural networks (GNN) with transfer learning to annotate cellular identities in subcellular spatial transcriptomics (ST). SpatialMap consistently outperforms state-of-the-art annotation methods on both simulated and real-world ST datasets. It demonstrates strong scalability across diverse tissue types and platforms, including NanoString CosMx, MERFISH and 10x Genomics Xenium. When applied to a mouse primary motor cortex dataset, SpatialMap successfully identified Micro, L4/5 IT, and L6 IT Car3 absent from the reference data, showcasing its capability to discover novel cell types. Furthermore, SpatialMap accurately annotated malignant cells in human liver cancer samples. Its design also enables seamless extension to cross-omics cell typing and cross-platform annotation within proteomics.

bioinformatics↗

bioRxiv2025

Macromolecular interactions dictate Polycomb-mediated epigenetic repression

The dynamic regulation of epigenetic states relies on complex macromolecular interactions. PRC2, the methyltransferase complex responsible for depositing H3K27me3, interacts with distinct accessory proteins to form the mutually exclusive subcomplexes PHF1-PRC2.1, MTF2-PRC2.1, PHF19-PRC2.1, and PRC2.2. The functions of these subcomplexes are unclear and thought to be highly redundant. Here we show that PRC2 subcomplexes have distinct roles in epigenetic repression of lineage-specific genes and stem cell differentiation. Using a human pluripotent stem cell model, we engineered a comprehensive set of separation-of-function mutants to dissect the roles of individual protein-protein and DNA-protein interactions. Our results show that PRC2.1 and PRC2.2 deposit H3K27me3 locus-specifically, resulting in opposing outcomes in cardiomyocyte differentiation. We find that MTF2 stimulates PRC2.1-mediated repression in stem cells and cardiac differentiation through its interaction with DNA and H3K36me3, while PHF19 antagonizes it. Furthermore, MTF2-PRC2.1 maintains normal cardiomyocyte function. Together, these results reveal the importance and specificity of individual macromolecular interactions in Polycomb-mediated epigenetic repression in human stem cells and differentiation. HighlightsO_LIThe PRC2.1 and PRC2.2 subcomplexes have distinct specificities for H3K27me3 deposition C_LIO_LIThe three PCL accessory proteins have distinct functions in regulating the PRC2 core complex, with MTF2 and PHF19 antagonizing each other C_LIO_LIInteractions between the PCL proteins and the PRC2 core, DNA, and H3K36me3 dictate PRC2 occupancy and activity at developmental genes C_LIO_LIPRC2.1 and PRC2.2 play opposing roles in stem cell cardiomyocyte differentiation C_LIO_LIMTF2 plays key functions in regulating differentiation timing and action potential rhythm in cardiomyocytes C_LI

molecular biology↗

bioRxiv2025

The neurocomputational mechanisms of hierarchical linguistic predictions during narrative comprehension

Language comprehension requires a listener to predict the upcoming inputs of linguistic units with multiple timescales based on previous contexts, but how the prediction process is hierarchically represented and implemented in the human brain remains unclear. Combining the natural language processing (NLP) approach and functional Magnetic Resonance Imaging (fMRI) in a narrative comprehension task, we first applied the group-based general linear model (gGLM) to identify the neural underpinnings associated with the language prediction on word and sentence. Our results revealed a cortical architecture supporting the prediction, extending from the superior temporal cortices to the regions in the default mode network. Then, we investigated how these adjacent levels interact with each other by testing two rival hypotheses: the continuous updating hypothesis posits that the higher level of the representational hierarchy is continuously updated as inputs unfold over time, while the sparse updating hypothesis states that the higher level is only updated at the end of their preferred timescales of linguistic units. By conducting computational modeling and autocorrelation analysis, we found the sparse model outperformed the continuous model and the updating might occur at the sentence boundaries. Together, our results extend the linguistic prediction from the small timescales such as words to large timescales such as sentences, providing novel insights into the neurocomputational mechanisms of information updating within the linguistic prediction hierarchy.

neuroscience↗

bioRxiv2025

AI-Guided Precision in Antibody Humanization: Structural Modeling to Minimize Immunogenicity and Preserve Efficacy

Antibody humanization is an essential part of converting animal-derived antibodies into clinical candidates however conventional CDR grafting techniques often faces challenges, especially when essential antigen-binding sites extend beyond CDR regions. Additionally, the CDR grafting process tends to retain undesired non-human elements within the CDR regions. In this study, we overcame these challenges by using our proprietary AI-predicted antigen-antibody complex algorithm to effectively humanize a murine antibody. During our AI-powered humanization process, in contrast to traditional CDR grafting techniques, only the crucial paratopes identified through our AI-predicted complex structure were precision grafted onto a select human germline. This paratope grafting technique significantly reduced the antibodies immunogenicity risk while maintaining bioactivity. Moreover, all AI-guided deep humanized antibody variants demonstrated favorable developability, including thermal stability, poly-reactivity, and hydrophobicity. These results not only advance antibody humanization techniques but also demonstrate the power of AI in expediting antibody engineering and derisking clinical research.

bioengineering↗

bioRxiv2025

Predicting PROTAC-targeted Degradation and Designing Androgen Receptor Degraders with AiPROTAC

Proteolysis-targeting chimeras (PROTACs), a pioneering class of heterobifunctional ligands, have emerged as transformative tools in combating cancer and immune-related diseases due to their ability to target previously undruggable proteins and overcome drug resistance. Accurate assessment of the degradation potential of PROTACs is essential for advancing their therapeutic applications. However, the development of robust predictive models is hindered by the scarcity of large-scale datasets and domain-specific tools, as well as the underutilization of existing unlabeled data. To address these challenges, we developed an innovative method called AiPROTAC to predict the degradation capacity of PROTACs. In particular, AiPROTAC integrates graph augmentation, message-passing enhanced encoders and cross-attention mechanisms within a contrastive learning framework. Moreover, AiPROTAC leverages a curated specialized dataset combined with the PROTAC-DB dataset to enhance prediction accuracy. Experimental results across two datasets and six evaluation metrics demonstrate that AiPROTAC consistently outperforms state-of-the-art models. Further case studies underline its superior sensitivity and reliability. Notably, AiPROTAC facilitated the design of a novel androgen receptor (AR) degrader, PROTAC GT19, which achieved enhanced in vitro degradation compared to Bavdegalutamide (ARV-110). This advancement highlights our AiPROTACs potential to accelerate PROTAC-based therapeutic development and optimization.

bioinformatics↗

bioRxiv2025

Essential role of CFAP77-CCDC105-TEX43 subcomplex in connecting axonemal A and B tubules for mammalian sperm motility

The assembly and physiological function of cilia and flagella depend on the stable association of A and B tubules, which form axonemal microtubule doublets (DMTs). CFAP77 encodes a core outer junction (OJ) protein within DMTs that is conserved across species and cell types. However, whether and how CFAP77 mediates the connection of B tubules to A tubules of DMTs in mammalian cilia/flagella are unknown. In this study, Cfap77-KO mice were generated to reveal that CFAP77 is essential for sperm progressive motility and male fertility. Loss of CFAP77 led to opened B tubules specifically at the OJ regions of axonemal DMTs as revealed by conventional transmission electron microscopy. Cryo-electron tomography further resolved the in situ structure of sperm axonemal DMTs from Cfap77-KO mice, which exhibited a loss of large filamentous density corresponding to the CFAP77-CCDC105-TEX43 ternary subcomplex at the OJ regions. Additionally, sperm proteomic analysis confirmed that CFAP77 knockout led to the complete loss of this ternary complex. Our work not only explores the physiological role of OJ protein CFAP77 on axonemal A-B tubule connection in mammals but also combines in situ structural biology and knockout mice to reveal the underlying structural/molecular mechanism.

cell biology↗

bioRxiv2025

Non-Visual Light Sensing Enhances Behavioral Memory and Drives Gene Expression in C. elegans

Visible light influences a range of physiological processes, yet how animals respond to it independently of the visual system remains largely unknown. Here, we uncover a previously undescribed light-induced transcriptional pathway that modulates behavioral plasticity in C. elegans, a roundworm without eyes. We demonstrate that ambient visible light or controlled-intensity visible-spectrum LED activates an effector gene cyp-14A5 in non-neuronal tissues through the bZIP transcription factors ZIP-2 and CEBP-2. Light induction of cyp-14A5 is more prominent at shorter wavelengths but is independent of the known blue light receptors LITE-1 and GUR-3 in C. elegans. This bZIP-dependent genetic pathway in non-neuronal tissues enhances behavioral adaptability and olfactory memory, suggesting a body-brain communication axis. Furthermore, we use the light-responsive cyp-14A5 promoter to drive ectopic gene expression, causing synthetic light-induced sleep and rapid aging phenotypes in C. elegans. These findings advance our understanding of light-responsive mechanisms outside the visual system and offer a new genetic tool for visible light-inducible gene expression in non-neuronal tissues.

physiology↗

bioRxiv2024

Discovery of A Chimeric Polyketide Family as Cancer Immunogenic Chemotherapeutic Leads

Discovery of cancer immunogenic chemotherapeutics represents an emerging, highly promising direction for cancer treatment that uses a chemical drug to achieve the efficacy of both chemotherapy and immunotherapy. Herein we report a high-throughput screening platform and the subsequent discovery of a new class of cancer immunogenic chemotherapeutic leads. Our platform integrates informatics-based activity metabolomics for rapid identification of microbial natural products with both novel structures and potent activities. Additionally, we demonstrate the use of microcrystal electron diffraction (MicroED) for direct structure elucidation of the lead compounds from partially purified mixtures. Using this strategy to screen geographically and phylogenetically diverse microbial metabolites against pseudomyxoma peritonei, a rare and severe cancer, we discovered a new class of leads, aspercyclicins. The aspercyclicins feature an unprecedented tightly packed polycyclic polyketide scaffold that comprises continuous fused, bridged, and spiro rings. The biogenesis of aspercyclicins involves two distinct biosynthetic pathways, leading to formation of chimeric compounds that cannot be predicted by bottom-up approaches mining natural products biosynthetic genes. With comparable potency to some clinically used anticancer drugs, aspercyclicins are active against multiple cancer cell types by inducing immunogenic cell death (ICD), including the release of damage-associated molecular patterns and subsequent phagocytosis of cancer cells. The broad-spectrum ICD-inducing activity of aspercyclicins, combined with their low toxicity to normal cells, represents a new class of potential cancer immunogenic chemotherapeutics and particularly the first drug lead for pseudomyxoma peritonei treatment.

biochemistry↗

bioRxiv2024

LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning

Sequence-function data provides valuable information about the protein functional landscape, but is rarely obtained during directed evolution campaigns. Here, we present Long-read every variant Sequencing (LevSeq), a pipeline that combines a dual barcoding strategy with nanopore sequencing to rapidly generate sequence-function data for entire protein-coding genes. LevSeq integrates into existing protein engineering workflows and comes with open-source software for data analysis and visualization. The pipeline facilitates data-driven protein engineering by consolidating sequence-function data to inform directed evolution and provide the requisite data for machine learning-guided protein engineering (MLPE). LevSeq enables quality control of mutagenesis libraries prior to screening, which reduces time and resource costs. Simulation studies demonstrate LevSeqs ability to accurately detect variants under various experimental conditions. Finally, we show LevSeqs utility in engineering protoglobins for new-to-nature chemistry. Widespread adoption of LevSeq and sharing of the data will enhance our understanding of protein sequence-function landscapes and empower data-driven directed evolution.

biochemistry↗

bioRxiv2024

DeepSME: De Novo Nanopore Basecalling of Motif-insensitive DNA Methylation and Alignment-free Digital Information Decryptions at Single-Molecule Level

Hidden messages in DNA molecules by employing chemical modifications has been suggested for private data storage and transmission at high information density. However, rapidly decoding these "molecular keys" with corresponding basecallers remains challenging. We present DeepSME, a nanopore sequencing and deep-learning based framework towards single-molecule encryption, demonstrated by using 5-hydroxymethylcytosine (5hmC) substitution for individual nucleotide recognition rather than sequential interactions. This non-natural, motif-insensitive methylation disrupts ion current, resulting in a readout failure of 67.2%-100%, concealing the privacy within the DNAs. We further develop an alignment-free DeepSME basecaller as a key to reconstitute the digital information. Our three-stage training pipeline, expands k-mer size from 46 to 49, achieving over 92% precision and recall from scratch. DeepSME deciphers fully 5hmC concealed text and image within 16x coverage depth with an F1-score of 86.4%, surpassing all the state-of-the-art basecallers. Demonstrated on edge computing devices, DeepSME holds supreme potential for DNA-based private communications and broader bioengineering and medical applications.

bioinformatics↗

bioRxiv2024

Endocytic dynamin deficiency impairs the host colonization of phytopathogenic fungi

During host-pathogen interactions, fungal pathogens exploit the endolysosomal trafficking network to fine-tune their responses to host and environmental stimuli, thereby facilitating disease progression. However, the molecular mechanisms underlying the fungal-specific functions of the endolysosomal network require further investigation. Here, we systematically characterized the endolysosomal network in Fusarium graminearum using the dynamin-like GTPase FgVps1 as an entry point. Functional analysis revealed that FgVps1 is essential for the release of retromer- and sorting nexin-associated vesicles from endosomes, thereby facilitating the trafficking of v-SNARE protein FgSnc1 and promoting fungal development and pathogenicity. Building on this, we further discovered that the retromer core subunit FgVps35 interacts with sorting nexin FgSnx4 and identified the corresponding interaction interface, which involves residues FgVps35N383 and FgSnx4E373. In addition, the ESCRT-II component FgVps36 bridges ESCRT-I and -III and interacts with both FgVps35 and FgSnx4, thereby preventing their mislocalization to the vacuole and maintaining endolysosomal trafficking. Notably, we demonstrated that inhibition of FgVps1 function, either by blocking its GTPase activity or by disrupting actin polymerization, effectively impaired endosomal trafficking and attenuates fungal pathogenicity. Altogether, our results uncover key mechanisms underlying the function of fungal endolysosomal network and providing a promising broad-spectrum strategy for controlling phytopathogenic fungi.

microbiology↗

bioRxiv2024

Composite Hedges Nanopores: A High INDEL-Correcting Codec System for Rapid and Portable DNA Data Readout

DNA, as the origin for the genetic information flow, has also been a compelling alternative to non-volatile information storage medium. Reading digital information from this highly dense but lightweighted medium nowadays relied on conventional next-generation sequencing (NGS), which involves wash and read cycles for synchronization and the indel (insertion and deletion) errors rarely occur. However, these time-consuming cycles hinder the future of real-time data retrieval. Nanopore sequencing holds the promise to overcome the efficiency problem, but high indel error rates lead to the requirement of large amount of high-quality data for accurate readout using emerging NGS-based codec systems. Here we introduce Composite Hedges Nanopores (CHN), a nanopore-based codec scheme tailored for real-time data retrieval, capable of handling indel rates up to 15.9% and substitution rates up to 7.8%. The overall information density can be doubled from 0.59 to 1.17 by utilizing a degenerated eight-letter alphabet, where one composite strand will be projected into eight normal strands. We demonstrate that sequencing times of 20 and 120 minutes were sufficient for processing representative text and image files (7 and 115 composite strands), respectively. The time-diminishing deviations are mainly originated from the extremely uneven abundance among the composite strands (cross-group variation) as well as the huge inequality among the normal strands (in-group variation). Moreover, to achieve complete data recovery, it is estimated that text and image data require 4x and 8x physical redundancy (coverage) of composite strands, respectively. Our CHN codec system excels on both molecular design and equalized dictionary usage, laying a solid foundation for nucleic acid-based data retrieval and encoding approaching to real-time, applicable in both cloud and edge computing systems.

bioinformatics↗