Biology subjects

Li, P.

Publications and source records attributed to Li, P..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Global O-GlcNAcome Identifies O-GlcNAcylated Proteins Regulating Oligodendrocyte Precursor Cells Differentiation

Oligodendrocyte precursor cells (OPCs) differentiate into myelinating oligodendrocytes to form the myelin sheaths essential for neural function, a process frequently compromised during aging and in diseases like multiple sclerosis. However, the regulatory landscape of myelination remains incompletely understood. Here, we perform a comprehensive proteomic analysis of the O-GlcNAcome in postnatal day 0 mouse OPCs, identifying 165 O-GlcNAcylation sites across 118 proteins. These proteins are mostly nuclear and characterized by single-site modifications. Comparative analysis identifies 74 novel sites and 22 proteins uniquely O-GlcNAcylated in OPCs. Molecular docking suggestes that O-GlcNAcylation might act as a dynamic regulator of OPC differentiation. Notably, global O-GlcNAcylation levels increase during the OPC-to-oligodendrocyte transition. By integrating our findings with multiple sclerosis-related transcriptomic and proteomic datasets, we identify candidate proteins linking O-GlcNAc dysregulation to demyelination. Among these, Vimentin (Vim) exhibits a pronounced age-dependent discrepancy between decreased protein levels and elevated mRNA expression during differentiation. We validate Threonine 35 and 63 as key O-GlcNAcylation sites on Vimentin. Functional assays confirm that blocking Vimentin O-GlcNAcylation enhance the OPC differentiation. Collectively, our study establishes the first O-GlcNAc landscape for OPCs and suggests O-GlcNAcylation as a pivotal regulator for OPC differentiation.

neuroscience↗

bioRxiv2026

Genomic selection for seed yield enhances flax breeding efficiency

Genomic selection (GS) is a promising strategy to improve breeding efficiency for complex traits such as seed yield by enabling early selection and reducing reliance on extensive field testing. However, practical deployment of GS remains challenging due to limited training populations sizes and reduced predictive ability when models are applied to true breeding germplasm. In this study, we evaluated GS for flax (Linum usitatissimum L.) seed yield under realistic breeding scenarios, with a focus on across-population prediction (APP) and breeding decision support rather than model benchmarking. Using historical germplasm collections and a newly developed breeding-oriented population as training sets, GS performance was assessed across multiple independent test populations representing contemporary breeding lines evaluated in replicated yield trials. APP predictive abilities ranged from r = 0.67 to 0.84 depending on population relatedness when training and test populations were genetically aligned, supporting routine breeding deployment. Training population composition emerged as a key determinant of prediction success, with breeding-oriented populations consistently outperforming broad germplasm collections for predicting true breeding lines. Check-based selection analyses showed that GS reliably reproduced phenotypic advancement decisions while eliminating 61-91% of low-performing lines, resulting in 48-78% reduction in field evaluation costs for a typical cohort of 300 lines. Marker subsampling analyses further indicated that moderate-density genotyping-by-sequencing panels ([~]2,500-3,000 SNPs) are sufficient to achieve stable predictive abilities. Overall, these results demonstrate that GS for seed yield in flax is ready for routine integration into breeding programs, offering a practical pathway to reduce costs, accelerate breeding cycles, and enhance selection efficiency.

genomics↗

bioRxiv2026

Synthetic reconstitution of planar polarity initiation reveals collective migration as a symmetry-breaking cue

Planar cell polarity (PCP) organizes cellular behaviors along tissue axes, yet how symmetry is broken to initiate polarity across initially symmetric tissues remains unknown. We reconstituted tissue-scale polarity emergence ex vivo using an engineered epithelial system. A collective migration cue induces front-back enrichment of CELSR junctions, establishing aligned multicellular polarity that tracks migration direction and speed. A migration-onset wave consistently precedes the polarity-initiation wave, indicating that individual cells directly interpret the global cue rather than acquire polarity through intercellular relay. Strikingly, CELSR polarity arises without reciprocal VANGL-FZD interactions, identifying CELSR as the driver for planar polarization. These findings reveal how a global migratory cue can break symmetry and coordinate PCP across molecular, cellular, and tissue scales, providing a framework for engineering polarized tissues.

developmental biology↗

bioRxiv2026

Foundation Models Improve Perturbation Response Prediction

Predicting cellular responses to genetic or chemical perturbations has been a long-standing goal in biology. Recent applications of foundation models to this task have yielded contradictory results regarding their superiority over simple baselines. We conducted an extensive analysis of over 600 different models across various prediction tasks and evaluation metrics, demonstrating that while some foundation models fail to outperform simple baselines, others significantly improve predictions for both genetic and chemical perturbations. Furthermore, we developed and evaluated methods for integrating multiple foundation models for perturbation prediction. Our results show that with sufficient data, these models approach fundamental performance limits, confirming that foundation models can improve cellular response simulations. Code and Data: https://github.com/genbio-ai/foundation-models-perturbation

bioinformatics↗

bioRxiv2026

Fine-tuning STEAP1 protein expression and purification to preserve its conformation and function

Six-transmembrane Epithelial Antigen of the Prostate 1 (STEAP1) has emerged as a promising therapeutic target for prostate cancer. We have optimized the expression and purification conditions of human STEAP1 to maximize the production of its homotrimeric form, which is crucial for metal ion reduction and maintaining cellular redox balance. Proteins obtained from these optimized conditions were complexed with both heme and flavin-adenine dinucleotide (FAD), two cofactors that are fundamental to STEAP functionality, suggesting native folding and interactions of the protein. In addition, we compared the impact of stable and transient expression systems on the protein quality of STEAP1. We found that stable expression promoted heme incorporation, improved expression homogeneity, and ensured correct protein orientation on cell surfaces. Our findings present effective strategies for optimizing the recombinant production of STEAP1, with potential applicability to other STEAP family proteins to facilitate therapeutic discovery.

biochemistry↗

bioRxiv2026

Z-RNA-ZBP1 axis drives Tau-mediated neurodegeneration

Although the causes of Tau aggregates vary, once Tau aggregates are formed, their neurotoxicity significantly contributes to neuronal death and cognitive decline in tauopathies, with Alzheimers disease (AD) being the most well-known example. Despite its central pathogenic role, however, effective therapeutic strategies targeting neurotoxicity of Tau remain poor. Here we demonstrate the pathogenic role of neuronal cell death in Tau-related neurodegeneration. Tau-expressing neurons undergo cell death through Z-DNA-binding protein 1 (ZBP1) activation triggered by endogenous Z-RNAs. These Z-RNAs are derived from reactivated transposable elements (TEs) that are typically silenced within heterochromatin. Tau aggregates show a strong affinity for H3K9me3-modified chromatin, effectively sequestering these epigenetic marks from Heterochromatin Protein 1 (HP1), thereby disrupting the condensation of constitutive heterochromatin. Clinically, an inverse correlation between ZBP1 expression levels in excitatory neurons and cognitive performance in AD patients was observed. Importantly, Zbp1 haploinsufficiency significantly ameliorated cognitive deficits in aged Tau-transgenic mice (24-month-old), highlighting the therapeutic potential of ZBP1 inhibition to strive against neurodegeneration in tauopathies.

neuroscience↗

bioRxiv2026

A Systematic Benchmark for Peptide Property Prediction

Accurate prediction of peptide physicochemical properties and biological activities is critical for rational peptide design and high-throughput screening. However, current research is often constrained by heterogeneous data sources and inconsistent evaluation standards, which hinder fair comparisons and reliable assessments of model generalization. In this work, we present PPB, a peptide property prediction benchmark designed to evaluate model performance with an emphasis on realistic generalization across both classification and regression tasks. By applying unified biological filtering criteria, we systematically curated and standardized 15 datasets comprising 161,571 unique sequences, spanning a wide range of physicochemical properties and functional activities. We benchmarked seven representative architectures--encompassing traditional machine learning, deep learning, and pre-trained language models--alongside diverse feature encoding schemes. Furthermore, we investigated the impact of random versus homology-based (sequence similarity) data splitting strategies on model robustness. To facilitate community access, we developed the PPB web server (http://ppb.molmatrix.com/index.html), which provides centralized resources for standardized dataset downloads, interactive visualization of benchmark results, and detailed evaluation protocols. Author summaryPeptides are short amino acid chains essential for biological functions and drug discovery. While AI models have accelerated peptide property prediction, the field lacks a unified standard to fairly compare these methods, often leading to inconsistent results and overoptimistic performance estimates. In this study, we introduce the Peptide Property Benchmark (PPB), a comprehensive framework featuring 15 standardized datasets and over 160,000 sequences. We systematically evaluated diverse AI paradigms, including traditional machine learning and advanced protein language models. Our results demonstrate that large-scale pre-trained models--the biological equivalent of large language models--offer superior accuracy and stability, particularly for small or complex datasets. Crucially, our analysis reveals a "clustering bottleneck": standard tools used to group proteins based on similarity often fail when applied to short peptides, causing data to fragment excessively. This suggests that traditional strategies for testing model generalization may be less effective for peptides than previously assumed. To facilitate community progress, we provide an online platform for standardized data and evaluation. This work establishes a rigorous foundation for developing more reliable AI tools for the next generation of peptide-based therapeutics.

bioinformatics↗

bioRxiv2026

Mechanistic tradeoffs between local and long-range signaling activity in natural and synthetic morphogens

Hedgehog family morphogens present an interesting paradox: despite being hydrophobic due to dual-lipid modifications, they form spatial concentration gradients that are highly conserved and essential for many aspects of metazoan development. Using live-cell single-molecule tracking and engineered synthetic signaling ligands, we isolated the distinct contribution of each lipid modification to Hedgehog diffusion and signaling potency. We found that although both lipid modifications enhance signaling potency, they do so through different mechanisms. Palmitate directly promotes receptor engagement, whereas cholesterol topologically confines secreted morphogens on the cell surface, effectively using the lipid membrane as a non-signaling co-receptor that enriches ligands locally at the cost of restricting long-range diffusion. Our results on the function of cholesterol point to an intrinsic tradeoff between signaling potency and gradient formation, with implications for the evolution and mechanism of non-signaling co-receptors.

developmental biology↗

bioRxiv2026

TREM2 agonist antibody rebuilds the resident synovial macrophage lining barrier in rheumatoid arthritis

Rheumatoid arthritis (RA) can be viewed as a disease of barrier failure, in which CX3CR1+/TREM2+ synovial tissue-resident macrophages that form the lining barrier over cartilage and bone become fragmented and disorganized. However, how to therapeutically rebuild this barrier--and how macrophage states transition during repair--remain unclear. We engineered TR-Ab19, a mouse-selective agonistic antibody against TREM2, as a precision tool to initiate and interrogate barrier repair in vivo. TR-Ab19 engages TREM2-linked downstream signaling and redirects synovial macrophages from Clec4d+ inflammatory/proliferative programs toward TREM2+CX3CR1+Aqp1+ barrier-like states, thereby rebuilding the lining barrier. Across collagen-induced arthritis (CIA) and serum-transfer arthritis (STA) models, TR-Ab19 reduces synovitis, preserves cartilage and bone microarchitecture, limits osteoclastogenesis, and attenuates systemic cytokines and B-cell abnormalities. Single-cell RNA-seq with trajectory and cell-cell communication analyses reveal a TREM2-dependent shift toward a barrier-dominant macrophage ecosystem. Together, these findings establish antibody-mediated reprogramming of resident synovial macrophages as a barrier-centered strategy for RA and provide a framework for instructing macrophage niches in chronic inflammation.

bioengineering↗

bioRxiv2026

Using carrier DNA in ultra-low input library preparations for next-generation sequencing

The purpose of this study was to evaluate the use of carrier DNA (i.e., exogenous DNA spike-in) for shotgun metagenome sequencing of ultra-low levels (less than 50 picograms) of metagenomic DNA. We hypothesized that carrier DNA would improve the robustness of library preparation for samples with DNA concentrations below detection by providing a tangible amount of known DNA thereby bringing total DNA concentrations closer to recommended input ranges for metagenomic library kits. We employed adaptive PCR cycling using an iconPCR instrument (N6tec) to allow dynamic thermocycling until sufficient library for sequencing was amplified, regardless of input DNA concentration. Libraries were sequenced and mapped to reference genomes of lambda and mock community organisms, and outcome measures included total reads, on-target reads, evenness of coverage across 10 organisms within each mock community, and PCR duplication rate. We demonstrate that libraries can be prepared down to 50 femtograms of input DNA, but that there is a strong correlation between input DNA concentration and PCR duplication rate. The utility of spiking in carrier DNA is equivocal as it mildly negatively impacts the observed distribution of mock communities and serves as a loss of sequencing output. Although the loss of sequencing capacity due to carrier DNA can be partially offset by reduced loss of data from PCR duplication, carrier DNA spike-in is not recommended for routine library preparation of ultra-low input samples. Adaptive cycling allows for appropriate cycling conditions when input DNA concentrations are below detection.

microbiology↗

bioRxiv2026

Fully genetically encoded low-molecular-weight protein tags with defined shapes for direct molecular identification by cryo-electron tomography

Cryo-electron tomography (cryo-ET) enables three-dimensional visualization of cells in near-native states, but direct identification of specific proteins in situ remains challenging due to crowded cellular environments and the low intrinsic contrast of most proteins smaller than [~]500 kDa. Consequently, molecular identification often relies on indirect labeling strategies or bulky probes that can perturb native structures. Here we present a "shape-as-signal" strategy that uses fully genetically encoded protein tags with defined shapes as a molecular signal for direct identification by cryo-ET. We designed two single-chain, monomeric, low-molecular-weight tags: an extended V-shaped tag (62 kDa) and a compact triangular tag (85 kDa). Both adopt rigid geometries validated by cryo-electron microscopy and remain compatible with fluorescence microscopy when fused to fluorescent proteins. Their characteristic shapes are readily recognized and computationally detected in vitro. In cells, the V-shaped tag yields clear, non-disruptive signals at native locations. These results demonstrate that low-molecular-weight protein tags can be unambiguously detected and assigned in situ within crowded cellular environments. This single-step genetic tagging strategy enables seamless dual fluorescence and electron microscopy without exogenous probes, challenging the assumption that small protein tags are unsuitable for direct cryo-ET identification. More broadly, this approach establishes a scalable and minimally perturbative framework for visual proteomics and paves the way for multiplexed, shape-encoded molecular mapping in intact cells.

biophysics↗

bioRxiv2026

MultiGS: A comprehensive and user-friendly genomic prediction platform Integrating statistical, machine learning, and deep learning models for breeders

Genomic selection (GS) is a core strategy in modern breeding programs, yet the rapid expansion of statistical, machine-learning (ML), and deep-learning (DL) models has made systematic evaluation and practical deployment increasingly challenging. To address these issues, we developed MultiGS, a unified and user-friendly framework that integrates linear, ML, DL, hybrid, and ensemble GS models within a standardized and computationally efficient workflow. MultiGS is implemented through two complementary pipelines: MultiGS-R, a Java/R pipeline implementing 12 statistical and ML models, and MultiGS-P, a Python pipeline integrating 17 models including five linear models, three ML approaches, and nine recently developed DL architectures implemented within the framework. We benchmarked MultiGS using wheat, maize, and flax datasets representing contrasting prediction scenarios. Wheat and maize were evaluated using random training-test splits within the same population, reflecting suitable conditions for assessing model capacity and scalability. Under these scenarios, several DL, hybrid, and ensemble models achieved prediction accuracies comparable to RR-BLUP and consistently exceeded those of GBLUP. In contrast, the flax dataset represented a true across-population prediction scenario with limited training set size and strong population structure. In this challenging context, classical linear models provided stable baselines, while a subset of DL architectures--particularly graph-based models and BLUP-integrated hybrids--demonstrated comparatively improved generalization across populations. Comparisons with previously published DL tools showed that MultiGS models achieved comparable or improved prediction accuracies while requiring lower computational costs, enabling routine retraining and large-scale evaluation. Overall, MultiGS informs, scenario-specific model selection and provides a practical platform for deploying genomic prediction under realistic breeding conditions. The software is freely available on GitHub (https://github.com/AAFC-ORDC-Crop-Bioinfomatics/MultiGS).

bioinformatics↗

bioRxiv2025

Mapping Core Components of Membrane-less Organelles in Living Cells by Phase-APEX2 Proximity Labeling

Membrane-less organelles (MLOs), formed via liquid-liquid phase separation, are crucial for organization of biomacromolecules and cellular processes, yet their dynamic nature challenges compositional analysis. Conventional proximity labeling methods lack the specificity to overcome high background and systematic biases inherent in studying these condensates. Here we introduce Phase-APEX2, a three-input AND-gate strategy that dramatically enhances labeling specificity. By integrating a split-APEX2 system with a light-controlled H2O2 generator, we developed Phase-APEX2 platforms for two application contexts: Phase-APEX2-MS for proteomics and Phase-APEX2-seq for genomics. Our analyses revealed not only the core interactomes of the nucleolus and stress granules with high accuracy but also uncovered distinct "molecular grammars" governing their assembly and a surprising link between nucleolar dynamics and the transcriptional regulation of mitosis-related genes. This highly specific and adaptable platform opens new avenues for exploring the composition and function of diverse biomolecular condensates.

cell biology↗

bioRxiv2025

IDR-dependent MORC1 bodies limit chromatin accessibility to tune transposon repression

Germline-specific MORC1-mediated transposon repression intersects with the MIWI2-piRNA pathway and involves H3K9me3 deposition, yet the underlying mechanism remains unclear. Here, we show that MORC1 assembles nuclear bodies in gonocytes. Using a doxycycline-inducible MORC1 expression system in NIH3T3 cells, we find that condensate formation is confined to the nucleus via an IDR containing a bipartite NLS, and that TRIM28, a corepressor that recruits the H3K9 methyltransferase SETDB1, is enriched in these bodies through protein-protein interactions. An IDR-deficient MORC1 mutant fails to form nuclear condensates but retains TRIM28 binding. ChIP-seq analysis reveals that this mutant exhibits higher occupancy at heterochromatin-embedded transposons than wild-type MORC1, with a pronounced preference for evolutionarily young transposon subfamilies that strongly overlap endogenous MORC1 targets in gonocytes. Wild-type MORC1 shows weaker enrichment at these elements and instead exhibits a greater tendency to occupy open chromatin. Condensates formed by exogenous MORC1 expression in NIH3T3 cells are substantially larger than those observed in gonocytes. These findings support a model in which MORC1-dependent transposon repression is governed by physical engagement with TRIM28 and by IDR-dependent, yet quantitatively restrained, nuclear body formation, which limits MORC1 chromatin accessibility, curtails off-target binding, and concentrates repression on bona fide transposon targets.

molecular biology↗

bioRxiv2025

Core-shell microparticle encapsulation for pH-responsive and targeted delivery of lactoferrin and ferrous sulfate

Microgel beads of amidated low methoxy pectin and bovine lactoferrin were formed by external gelation of a water in oil emulsion with ferrous sulfate. The stability of the lactoferrin to gastric digestion and proteolysis by pepsin was determined by gel electrophoresis. The microparticles were then dispersed in chitosan and the resulting mixture was spray dried to form a shell that is insoluble at neutral pH conditions. The iron content of the microparticles without chitosan was 34 mg g-1 and with chitosan was 27 mg g-1. The addition of chitosan lead to reduced iron release at pH 7 (30%) compared to 60% iron release without chitosan, but did not prevent iron from releasing in acidic conditions (pH 1). The core shell microparticle system shows promise as an iron fortificant in food applications.

biochemistry↗

bioRxiv2025

A sequence-based classifier distinguishes phenotype-associated genes from other gene models in plants

Only a small fraction of annotated plant genes possesses experimentally validated associations with specific phenotypes. Phenotype associated genes have distinct structural, molecular, and evolutionary characteristics compared to non-validated gene models. Here, we developed a simple classifier that uses sequence and evolutionary features which can be generated for any species with an annotated reference genome assembly, to accurately distinguish phenotype associated genes from both the overall population of annotated gene models and a specific set of genes identified as being tolerant of loss of function mutations. A model trained solely on genes from maize (Zea mays) identified and prioritized rice (Oryza sativa) and arabidopsis (Arabidopsis thaliana) genes that were highly enriched in genes with experimentally validated links to phenotypes in both of these evolutionarily distant species. Gene models predicted to have a higher probability of being linked to phenotypes displayed patterns consistent with known biological properties of phenotype associated genes. Notably, the sets of genes predicted to have a high probability of being linked to phenotype variation did not consist exclusively of well-characterized gene families, but included many uncharacterized gene families carrying domains of unknown function. The quantitative scores generated by this model offer a valuable resource for prioritizing and exploring the vast number of uncharacterized gene models in plants, reducing the risk of failure in future reverse genetics efforts and potentially accelerating gene discovery and functional annotation in crops. Significance StatementAnnotation of an organisms genome produces tens of thousands of predicted genes, termed gene models. However, only a few of these have been associated with phenotypes. We built a simple algorithm that uses DNA sequences and evolutionary conservation-based information to predict which genes are most likely to change a plants phenotype when disrupted. In maize, it separates known phenotype-associated genes from other gene models; it also works in rice and arabidopsis. High-scoring genes show signals of biological importance and include many with no known function. This algorithm provides a shortlist of candidate for gene characterization and crop improvement.

plant biology↗

bioRxiv2025

Global Mapping of circRNA-Target RNA Interactions Reveals P-Body-Mediated Translational Repression

Circular RNAs (circRNAs) are primarily produced through pre-mRNA back-splicing, yet their target repertoire and functional mechanisms remain elusive. Here, we present circTargetMap, a computational framework for globally mapping circRNA targets using RNA-RNA interactomes obtained via RNA in situ conformation sequencing (RIC-seq) in the hippocampus and ten human cell lines. This approach identified 117,163 high-confidence circRNA-target RNA interactions, with 83% of target mRNAs bound by multiple circRNAs. Functionally, CDR1as and circRMST repress target mRNA translation by sequestering them into processing bodies (P-bodies)--membraneless granules--through sequence-specific base-pairing, probably independent of AGO2, DICER, and miRNAs. To directly capture granule-associated interactions, we developed granule RIC-seq (GRIC-seq) method, revealing the broad role of circRNA-target RNA interactions in translational repression. Moreover, pathogenic variants are significantly enriched around circRNA-target RNA interaction sites, suggesting potential roles in disease. Our study provides valuable resources for circRNA functional exploration and a framework for investigating RNA-RNA interactions within membraneless organelles.

molecular biology↗

bioRxiv2025

LabOS: The AI Co-Scientist That Sees and Works With Humans

Modern science advances fastest when thought meets action. LabOS represents the first AI co-scientist that unites computational reasoning with physical experimentation through multimodal perception, self-evolving agents, and XR-enabled, embodied human-AI collaboration. To empower agentic AI with embodied intelligence, we built LabSuperVision, the first vision-language-model (VLM) benchmark spanning biomedical and materials science laboratories, designed to evaluate scientific perception and reasoning. This endowed LabOS with visual reasoning capabilities that surpass those of general-purpose models on laboratory-specific tasks. By connecting multi-model AI agents, smart glasses, and human-AI-robot collaboration, LabOS allows AI to see what scientists see, understand experimental context, and participate in real-time execution through immersive XR interaction and robotic autonomation. Across applications-from cancer immunotherapy target discovery to cell engineering-LabOS shows that AI can move beyond computational design to participation, turning the laboratory into an intelligent, collaborative environment where human and machine discovery evolve together.

bioengineering↗