Biology subjects

Xia, Z.

Publications and source records attributed to Xia, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Creator 1.0: Phylogeny-Guided Stagewise Pretraining for Plant Genome Foundation Models across 1,000 Species

Genomic language models have advanced DNA sequence analysis, but their applicability to plant-specific tasks and representation changes during pretraining remain poorly characterized. Here, we present Creator 1.0, a 2-billion-parameter parameter plant genome foundation model pretrained on 1,000 plant genomes using phylogeny-guided stagewise pretraining (PGSP). Training comprised 83 consecutive stages and produced 52 cumulative checkpoints associated with distinct plant orders. On GENEB, the stage 83 checkpoint ranked first among 46 models across 10 plant-related tasks, with a mean Matthews correlation coefficient (MCC) of 0.565, and fourth across all 100 tasks. Evaluation of 52 checkpoints on six plant lncRNA tasks identified different optimal stages under full-data, 10-shot and 1-shot settings, corresponding to stages 71, 72 and 29, respectively. Checkpoint performance varied across tasks, without consistent advantages for corresponding plant orders. These findings demonstrate the competitive performance of Creator 1.0 and highlight the potential of PGSP to provide task-adaptable checkpoints for plant genomic applications.

genomics↗

bioRxiv2026

Loss of DNA Demethylase Enhances Arsenic Resistance Via Reducing of PIN2 Antisense Long Noncoding RNA

DNA methylation dynamics regulate diverse plant stress responses, but the mechanisms governing arsenic (As) tolerance through epigenetic pathways remain largely unknown. Here we report that Arabidopsis mutants lacking the DNA demethylase, REPRESSOR OF SILENCING (ROS1), show substantially enhanced As resistance. Integrating transcriptome and methylome data, we found that PIN-FORMED 2 (PIN2), encoding an auxin efflux carrier that also transports As out of cells, represents a key ROS1 target. In ros1, we observed increased methylation in the last exon of PIN2, a region functioning as the promoter for an antisense long non-coding RNA (lncRNA). Hypermethylation of the promoter suppresses the antisense lncRNA, which follows reduction of sense PIN2 transcription. As a result, PIN2 expression increases in ros1 mutants during As exposure, enhancing As tolerance. Analysis of ros1 pin2 double mutants confirmed that PIN2 acts downstream of ROS1 in As tolerance. Our study demonstrates an epigenetic pathway in which ROS1 regulates As tolerance by controlling PIN2 expression through antisense lncRNA-mediated transcriptional interference, offering new epigenetic strategies using plants in heavy metal contaminated environments.

plant biology↗

bioRxiv2026

ACCREDIT: A Quality-Aware Agentic Engine for Cell-resolved Cross-modal Image Registration with Dynamic Iterative Tuning

Spatial omics across complementary modalities is transforming our understanding of tissue architecture. Realizing this potential requires accurate and robust registration of cross-platform molecular images with hematoxylin-and-eosin (H&E) sections, the primary morphological reference for pathology. Existing methods, however, often fail silently when image orientation is unknown, image contrast is inverted, or tissue overlap is incomplete, producing erroneous registrations without alerting users or attempting recovery. Here, we present ACCREDIT, a quality-aware agentic framework that redefines cross-modal registration as an adaptive decision-making process rather than a one-shot computation. ACCREDIT combines deterministic registration pipelines with a reference-free composite quality score that automatically evaluates registration quality and rejects plausible but biologically incorrect registrations. When registration quality is insufficient, a large language model (LLM)-based rescue agent autonomously diagnoses failure modes and selects targeted recovery strategies, while an optional strategy-learning module captures expert-validated corrections for future reuse. Across Xenium, CODEX, cell-boundary, and IHC-to-H&E registration tasks, ACCREDIT outperformed competing methods by detecting registration failures and improving alignment quality through automated recovery and rescue. Ultimately, ACCREDIT enables robust integration of histology and spatial molecular profiling, providing a foundation for translating spatial omics into routine H&E-based pathology workflows.

bioinformatics↗

bioRxiv2026

PlantAI: A Multi-Agent System for Plant Functional Genomics Analysis and Biological Knowledge Interpretation

Plant functional genomics requires the integration of sequence, expression, evolutionary, regulatory and literature evidence. However, the corresponding analyses are often distributed across disparate programs, scripts and databases, creating substantial barriers to task organization and result interpretation. Here, we present PlantAI, a multi-agent system that integrates bioinformatics analysis, project-level process tracking and knowledge-assisted interpretation. A Main Agent coordinates two complementary routes: an analysis route that invokes bioinformatics tools for RNA-seq and gene-family analyses, and a knowledge route that uses PlantAI-RAG for knowledge retrieval and evidence synthesis. PlantAI-RAG currently contains 31,207 plant-science literature records, comprising approximately 3.82 million normalized entities and 8.25 million literature-supported relation assertions. In an evaluation using plant-science questions, it achieved a Gold evidence-assertion recall of 86.7%, while strict accuracy ranged from 77% to 82% across three independent evaluator models. We further demonstrate an end-to-end task using 24 rice RNA-seq libraries collected under salt stress, spanning transcriptome analysis, candidate-family screening, HXK/HKL family analysis and knowledge-assisted interpretation, and prioritize OsHXK8 for experimental validation. By preserving analysis artifacts, run manifests, logs and environment records, PlantAI supports result verification and repeat execution while linking project-derived results to traceable literature evidence. Together, these capabilities provide an integrated and auditable framework to support plant functional genomics research.

bioinformatics↗

bioRxiv2026

Performance Attribution in pLM-Based Biological Relation Prediction

Protein language models (pLMs) have enabled strong performance in biological relation-prediction benchmarks, yet aggregate predictive metrics do not reveal which sources of information support that performance. We examined three pLM-based case studies--MetaESI, Deep-GNHV, and SAGEPhos--using frozen pLM-derived full-input baselines, component-restricted controls, train-derived endpoint or site priors, and selector-matched random restrictions. Generic tabular learners trained on frozen pLM-derived inputs achieved strong task-specific benchmark references, reaching AUROC/AUPRC values of 0.827/0.703 for the MetaESI-derived full-input rerun, 0.922/0.708 for DeepGNHV, and 0.896/0.893 for SAGEPhos. Across tasks, substantial predictive signal remained available from restricted endpoint, site/window, motif, or train-derived prior features. In the MetaESI-derived row-level evaluation, a self-label-excluding endpoint-prior control reached AUROC/AUPRC of 0.846/0.676 without sequence embeddings, numerically close to the frozen full-input reference. In a separate frozen-pooling experiment, GARD-selected positions did not outperform count-matched random token pooling. Endpoint-cold diagnostics further showed substantial performance degradation when one or both endpoint types were unseen during training. These findings do not diagnose leakage, imply memorization, exclude biological learning, or invalidate the evaluated models. Rather, they show that strong benchmark performance can reflect multiple overlapping information sources and that aggregate scores alone cannot establish pair-specific learning, the incremental value of task-specific architectures, selector-specific information content, or mechanistic biological interpretation. We therefore propose that such claims require matched attribution controls, representation-matched baselines, and evaluation protocols aligned with the intended generalization setting.

bioinformatics↗

bioRxiv2026

Anthocyanin-associated cellular programs underlying terroir variation in Cabernet Sauvignon grape berry revealed by SEED-based deconvolution

Plant tissues consist of diverse cell populations that collectively contribute to development, metabolism, environmental responses, and phenotype formation. Although single-cell and single-nucleus RNA sequencing have greatly advanced the study of plant cellular heterogeneity, their application to large sample cohorts remains limited by cost, technical complexity, tissue dissociation constraints, and throughput. In contrast, bulk RNA-seq datasets have accumulated extensively across plant species, tissues, developmental stages, and environmental conditions, yet the celltype-level information embedded in these datasets remains difficult to resolve because plant-oriented deconvolution frameworks are still lacking. Existing deconvolution methods have largely been developed in mammalian systems and have not been systematically optimized for plant transcriptomic features, leaving their applicability under plant-specific constraints unclear. Here, we present SEED, an adaptive deconvolution framework optimized for plant transcriptomic data. SEED integrates candidate reference-template construction with seven deconvolution strategies and automatically identifies an optimal combination for a given dataset. In grapevine simulated benchmarking, SEED showed its clearest advantage under low-replication conditions and remained broadly competitive, rather than uniformly dominant, when larger pseudo-bulk sample sizes were evaluated. SEED further performed robustly in public Arabidopsis thaliana and Nicotiana tabacum datasets. Finally, we applied SEED to bulk RNA-seq data generated in this study from Vitis vinifera cv. Cabernet Sauvignon berries collected from Yinchuan and Yantai, identifying terroir-associated cell subtypes and coordinated celltype interaction patterns. Together, these results establish SEED as a practical framework for plant transcriptome deconvolution and provide a new tool for dissecting cellular heterogeneity associated with environmental adaptation and phenotype formation in plants.

bioinformatics↗

bioRxiv2026

OryzaG3: A Single-species Genomic Foundation Model Pretrained on Rice Pangenome

While multi-species genomic language models have advanced biological representation learning, high-quality, single-species foundation models for crops remain scarce. Leveraging recently expanded rice pangenome resources, we introduce OryzaG3, a species-specific DNA language model with 700M parameters. OryzaG3 was pretrained on 59.20 Gb of chromosome-level sequences from 149 high-quality rice genomes using a non-overlapping 3-mer tokenization strategy and a causal language modeling objective, featuring context-length variants up to 32k tokens. On the Plants Genomic Benchmark polyA prediction task, OryzaG3 achieves competitive predictive performance against leading multi-species models while delivering a four-fold increase in inference throughput under identical long-context conditions. Ultimately, OryzaG3 demonstrates that lightweight, single-species foundation models trained on high-quality pangenomes can match multi-species benchmarks while significantly reducing computational overhead. This work provides a scalable framework for rice functional genomics, molecular breeding, and targeted crop foundation model development.

bioinformatics↗

bioRxiv2026

Reconstructing True 3D Spatial Omics at Single-Cell Resolution

Capturing the three-dimensional (3D) organization of cells is essential for deciphering complex biological processes, yet comprehensive 3D spatial omics is severely hindered by the destructive nature of physical sectioning and the depth limitations of intact tissue imaging. Current computational methods rely on 2.5D stacking of discrete slices, which inherently disrupts tissue topology and fails to resolve continuous depth-dependent molecular gradients. To bridge this gap, we introduce DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW, an Optimal Transport flow matching framework that models tissue evolution as a continuous dynamic vector field. By solving the underlying probability flow ODEs, DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW enables the direct extraction of uninterrupted, infinitely resolvable tissue states at arbitrary spatial depths. Using Deep STAR/RIBOmap 3D technologies, we demonstrate that DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW achieves improved 3D reconstruction fidelity relative to 2.5D approaches, yielding structures that more closely recapitulate native tissue microenvironments in real-world datasets. Across diverse spatial omics modalities, including spatial proteomics using imaging mass cytometry in human breast cancer and spatial transcriptomics using openST in head and neck squamous cell carcinoma metastatic lymph nodes, DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW produces biologically interpretable and high-fidelity reconstructions across datasets. We evaluated the scalability and robustness of DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW on a large-scale mouse brain dataset, reconstructing a continuous 3D cellular atlas comprising 39 million cells within 41.6 hours. Systematic downstream characterization validated its ability to recapitulate consistent spatial architectures, cell-type distributions, transcriptomic patterns, and microenvironmental structures across brain regions. Collectively, these results demonstrate DO_SCPLOWEEPC_SCPLOWSO_SCPLOWPATIALC_SCPLOW as a generalizable and efficient solution for true 3D spatial reconstruction across scales and modalities. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/721395v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1a19624org.highwire.dtl.DTLVardef@188361forg.highwire.dtl.DTLVardef@199321corg.highwire.dtl.DTLVardef@a8f411_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Seeded aggregation of ANXA11 induces prion-like propagation, TDP-43 co-pathology and nucleocytoplasmic transport defects

Mutations in ANXA11 cause amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), yet the mechanisms linking ANXA11 dysfunction to neurodegeneration remain poorly defined. Recent cryo-EM studies revealed heteromeric ANXA11-TDP-43 filaments in patient brains, suggesting a direct pathological connection between these two ALS-associated proteins. However, whether ANXA11 possesses intrinsic amyloidogenic properties and how its aggregation relates to TDP-43 proteinopathy remain unknown. Here, we demonstrate that ANXA11 undergoes liquid-liquid phase separation and subsequently matures into amyloid fibrils through a liquid-to-solid phase transition. ANXA11 fibrils exhibit prion-like properties, including self-templating seeding activity and intercellular propagation in human iPSC-derived neurons. Strikingly, ANXA11 fibrils induces pathological conversion of TDP-43, including hyperphosphorylation, accumulation in detergent-insoluble fractions, and formation of cytoplasmic aggregates. TurboID proximity-labeling proteomics further revealed aggregation-dependent enrichment of nuclear pore complex and nucleocytoplasmic transport factors in the ANXA11 aggregate-proximal proteome. Consistently, ANXA11 aggregation was associated with nuclear envelope abnormalities, altered nucleoporin distribution, impaired mRNA export, and progressive neuronal toxicity in iPSC-derived neurons. Together, these findings establish ANXA11 as an intrinsically amyloidogenic, phase-transition-competent protein whose seeded assemblies propagate between cells, induce TDP-43 co-pathology, and are linked to nucleocytoplasmic transport defects and neuronal injury, thereby providing a mechanistic framework for ANXA11-associated ALS/FTD pathogenesis. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/713853v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@1aaad3org.highwire.dtl.DTLVardef@c51d5eorg.highwire.dtl.DTLVardef@10b2891org.highwire.dtl.DTLVardef@1945575_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIANXA11 undergoes liquid-liquid phase separation and matures into amyloid fibrils through a liquid-to-solid phase transition. C_LIO_LIANXA11 fibrils supports homotypic seeding and propagate within SH-SY5Y cells and iPSC-derived neurons. C_LIO_LIANXA11 fibrils induce TDP-43 pathological conversion, including phosphorylation and accumulation in insoluble aggregates. C_LIO_LIANXA11 aggregation is associated with nuclear pore complex remodeling, impaired mRNA export, and neuronal toxicity. C_LI In BriefANXA11 undergoes phase separation and matures into prion-like amyloid fibrils through a liquid-to-solid transition. These seeded assemblies propagate between cells and human neurons, induce TDP-43 pathological conversion, and are associated with remodeling of nuclear pore complexes, impaired mRNA export, and progressive neuronal toxicity. ANXA11 forms seeded amyloid assemblies that spread between cells, induce TDP-43 pathology, and disrupt nucleocytoplasmic transport.

neuroscience↗

bioRxiv2026

Spatial mapping of RNA turnover kinetics and regulatory landscapes of mRNA stability in the mammalian brain

Spatial and activity-dependent gene regulation in the mammalian brain requires coordinated control of RNA synthesis and degradation 1,2, yet spatially resolved measurement of RNA turnover kinetics in complex tissues remains technically challenging 3. Here, we present spatial NT-seq, an approach that integrates transgenesis-free metabolic RNA labeling with in situ chemical recoding to spatially co-map RNA abundance and turnover kinetics in the mouse brain. By distinguishing newly synthesized from pre-existing RNAs, this method reveals spatially resolved transcriptional and post-transcriptional responses to electroconvulsive stimulation (ECS), a treatment for refractory depression. We uncover pronounced spatial heterogeneity in RNA turnover, with the dentate gyrus (DG) exhibiting elevated basal RNA turnover and robust ECS-induced responses. These findings reveal a "kinetics scaling" mechanism of coordinated regulation of RNA synthesis and decay, by which DG cells can rapidly remodel their transcript pools in responses to external stimuli or differentiation signals 4,5. Machine learning applied to in vivo RNA kinetics landscapes further identifies sequence features and post-transcriptional regulators underlying region-and cell-type-specific control of mRNA stability. Together, this integrated experimental and computational framework, in vivo Timescope, enables transcriptome-wide mapping of RNA turnover kinetics and the regulatory architecture of RNA stability across spatial and cellular contexts, providing new insights into the spatiotemporal regulation of RNA dynamics in brain function and disease.

genomics↗

bioRxiv2026

Global mRNA 3'UTR lengthening in small-cell neuroendocrine carcinoma

Small-cell neuroendocrine carcinoma (SCNC) is a rare but highly malignant tumor subtype that primarily arises in the lung, also rarely in other organs, and as a consequence of treatment induced lineage transdifferentiation of prostate adenocarcinomas. The molecular convergence of SCNC across diverse tissues enables its identification through conserved SCNC-specific molecular markers, facilitating tumor subtype classification. As a critical post-transcriptional regulatory mechanism, alternative polyadenylation (APA) modulates 3'UTR length and significantly impacts tumor progression. However, its role in SCNC remains largely unclear. Here, we report a global 3'UTR lengthening pattern driven by APA in SCNC. We identified a set of conserved 3'UTR lengthening events across SCNCs of different tissue origins, which are strongly associated with neural development and related signaling pathways. Furthermore, we developed a neural network-based prediction model to classify SCNC by leveraging these specific APA signatures. Our study provides new insights into the post-transcriptional landscape of SCNCs and highlights APA signatures as promising biomarkers for SCNC identification.

cancer biology↗

bioRxiv2026

Periplasmic gatekeeping of phage DNA entry by rSAM enzyme-mediated maturation of an effector with His-X-Ser repeats

In the relentless arms race between bacteria and phages, bacteria have evolved a variety of defense strategies to combat phage infection. However, no bacterial system has been identified that specifically target the phage DNA entry phase. Here, we present a novel bacterial antiphage system, termed HXS, which provides broad-spectrum and robust antiphage activity by interfering with phage DNA entry. The HXS system consists of two radical S-adenosylmethionine (rSAM) enzymes (HxsB and HxsC), a small protein (HxsD), and the effector HxsA with a peptidoglycan-binding domain and five His-Xaa-Ser (HXS) repeats. HxsB/HxsC catalyze rSAM enzyme-dependent maturation of HxsA, including N-terminal processing and a site-specific +8 Da modification, thereby producing a periplasmic effector required for HXS defense. Biochemical evidence supports a model in which the matured effector likely engages incoming DNA electrostatically to arrest entry, establishing an rSAM enzyme-modified protein effector in antiphage defense.

microbiology↗

bioRxiv2025

GNL3 SUMOylation is essential for DNA double-strand break repair by homologous recombination

DNA double-strand break (DSB) repair via homologous recombination (HR) is critical for maintaining genomic integrity and requires proper DNA end resection to generate single-stranded DNA (ssDNA) overhangs. However, the mechanisms governing this critical step in cells remains poorly understood. Here, we report that GNL3, a nucleolar GTP-binding protein, plays a key role in HR repair via regulating DNA end resection dependently on its SUMOylation. Ectopic expression of wild-type, but not the SUMO-defective K196R mutant, GNL3 completely abolished DNA damage response induced by knockdown of endogenous GNL3. GNL3 interacts with the BLM-DNA2 helicase-nuclease complex and is critical for DNA end resection and subsequent loading of RPA and RAD51. This interaction requires SUMOylation and SUMO-interacting motifs (SIMs) in both proteins. We further demonstrate that USP36, a nucleolar deubiquitinating enzyme, functions as a novel SUMO ligase for GNL3, while the SUMO protease SENP3 deSUMOylates GNL3. Notably, several breast cancer-derived GNL3 variants that disrupt its SUMOylation or SIM fail to interact with the BLM-DNA2 complex. Knockdown of GNL3 sensitizes HR-proficient breast cancer cells to etoposide or Olaparib treatment. Together, our results reveal that GNL3 SUMOylation is crucial for HR repair and suggest that targeting GNL3 SUMOylation may induce HR deficiency, thereby sensitizing breast cancers to DNA damage-inducing agents.

cell biology↗

bioRxiv2025

Tumor-intrinsic MHC-II activation in pancreatic ductal adenocarcinoma enhances immune response and treatment efficacy

Pancreatic ductal adenocarcinoma (PDAC) is characterized by an immunosuppressive tumor microenvironment (TME) and poor prognosis. While major histocompatibility complex class II (MHC-II) expression is traditionally associated with professional antigen-presenting cells, its role in PDAC malignant cells remains underexplored. Herein, we utilized single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, bulk RNA sequencing, multiplex immunohistochemistry (mIHC) and ex vivo studies in culture with both human and murine models to investigate the prognostic relevance of MHC-II expression in malignant PDAC cells. Elevated MHC-II expression in malignant cells was strongly associated with increased infiltration of CD4+ T and CD8+ T cells in human PDAC, and pronounced co-localization with plasma cells, indicative of an antigen-activated immune microenvironment. In the KPC mouse model of PDAC, pharmacologic induction of MHC-II expression by cobimetinib treatment in malignant epithelial cells significantly enhanced the therapeutic response to immune checkpoint blockade (ICB). These findings highlight the role of malignant cell- intrinsic MHC-II expression in promoting antigen presentation and fostering an anti-tumor immune microenvironment. Our results position MHC-II as a promising prognostic biomarker and therapeutic target in PDAC, paving the way for novel immunomodulatory strategies. Summary of highlightsSingle-cell and spatial transcriptomic analyses reveal that elevated MHC-II expression in malignant PDAC cells correlates with increased infiltration of CD4 and CD8 T cells. Stimulating MHC-II expression in tumors effectively enhances immunotherapeutic responses to ICB in the PDAC KPC mouse model, including PDAC tumors previously resistant to therapeutic interventions. MHC-II serves as a prognostic biomarker and a promising target for immunotherapy in PDAC.

cancer biology↗

bioRxiv2025

Metabolic response of Brevibacterium epidermidis TRM83610 to NaCl stress and ectoine fermentation optimization

To elucidate the metabolic response of Brevibacterium epidermidis TRM83610 to NaCl stress and facilitate its industrial application, this study employed metabolomics techniques to analyze changes in intracellular metabolites, particularly compatible solutes, under NaCl concentrations of 0, 5%, 10%, and 15%. The ectoine production capacity was further evaluated and optimized using response surface methodology. Results indicated significant metabolic differences among sample groups, with detection of various secondary metabolites associated with antimicrobial activity and plant growth promotion. Six compatible solutes dominated by ectoine were identified. The metabolic response strategies to NaCl stress included osmostress resistance, oxidative stress resistance, and survival competition strategies. Through response surface optimization, ectoine yield reached 440.60 mg/L, representing a 6.22-fold increase over the initial yield of 70.75 mg/L, demonstrating substantial application potential for this strain. This research enriches our understanding of the metabolic profile of B. epidermidis TRM83610, preliminarily reveals its metabolic responses to NaCl stress, and provides a foundational basis for its further development and utilization. IMPORTANCEOur study reports, for the first time, the detection of N{varepsilon}-Acetyl-L-lysine in Brevibacterium epidermidis and the identification of Azetidomonamide A in a microorganism outside of Pseudomonas aeruginosa. It also represents the first elucidation of this strains metabolic response to NaCl stress. This research demonstrates the significant application and research value of B. epidermidis TRM 83610 and reveals that its strategy for coping with NaCl stress is multifaceted. This includes employing multi-pathway synergy to regulate multiple compatible solutes for osmostress resistance, producing antioxidant compounds for oxidative stress resistance, and secreting antimicrobial compounds as part of a survival competition strategy.

microbiology↗

bioRxiv2025

Super-resolution microscopy of alpha-synuclein aggregates in brain samples indicates a subset of cells have disrupted protein homeostasis

Nanoscopic aggregates of alpha-synuclein have been observed in Parkinsons disease (PD). However, the processes that occur in-vivo leading to the formation of these small aggregates are not well understood. We used ultra-sensitive single-molecule methods including SIMOA and super-resolution microscopy to quantify and characterise alpha-synuclein aggregates harvested from human brain samples alongside the Line 61 mouse model using different tissue processing methods. While aggregate numbers did not differ between PD and control samples, larger aggregates were detected in PD brain samples. Moreover, different sub-populations of aggregates were obtained by different extraction methods, with diffusible and membrane-bound aggregates producing a more pronounced difference between disease and control samples. Our data suggests that alpha-synuclein aggregates slowly in the brain, leading to formation of larger aggregates in a sub-set of cells.

neuroscience↗

bioRxiv2025

Novel mouse model reveals neurodevelopmental origin of PMM2-CDG brain pathology

Congenital disorders of glycosylation (CDG) are a group of neurogenetic conditions resulting from disruptions in the cellular glycosylation machinery. The majority of CDG patients have compound heterozygous pathogenic variants in the phosphomannomutase 2 (PMM2) gene. Individuals with PMM2-CDG exhibit multi-systemic symptoms, prominently featuring neurological deficits with nearly all patients exhibiting cerebellar hypoplasia and ataxia. To overcome embryonic lethality caused by whole body knock-out of Pmm2 and mimic patient-related compound heterozygous pathogenic variants, we paired a Pmm2 flox allele (Pmm2fl) with a catalytically inactive knock-in allele (Pmm2R137H), commonly present in PMM2-CDG patients. Mice with post-mitotic loss of PMM2 from neurons or astrocytes are indistinguishable from unaffected littermates, including in a broad battery of neurological assessments. In contrast, removal of PMM2 from embryonic neural precursor cells leads to cerebellar hypoplasia, ataxia, seizures, and early lethality. Comprehensive multi-omics profiling, including metabolomics, glycomics, single-cell transcriptomics, proteomics, and glycoproteomics, reveal widespread molecular disturbances throughout the brain, with the cerebellum showing the most pronounced disruption. These findings highlight the heightened dependency of the developing cerebellum on intact N-glycosylation, aligning with clinical observations in PMM2-CDG patients. Importantly, glycoproteomic alterations identified in our mouse model are corroborated in PMM2-CDG patient post-mortem cerebellar tissue, underscoring the translational relevance of our findings and implicating impaired synaptic transmission as a key pathogenic mechanism.

neuroscience↗

bioRxiv2025

PIN1 Drives Cellular Plasticity and Immune Modulation in Chronic Pancreatitis

Background and AimsChronic pancreatitis (CP) is characterized by inflammation, fibrosis, and acinar-to-ductal metaplasia (ADM). PIN1, known to drive oncogenic signaling and cellular plasticity in cancer, has an unexplored role in CP. This study investigates PIN1s expression and function in CP pathogenesis using human tissues and mouse models. MethodsPIN1 expression was assessed in human CP tissue microarrays (TMAs) via immunohistochemistry (IHC) and cyclic immunofluorescence (CyCIF). Acute and chronic pancreatitis were induced in wild-type (WT) and PIN1 knockout (PIN1KO) mice using caerulein. Disease progression was monitored histologically, and immune profiling was conducted using flow cytometry. Pharmacological inhibition was performed using a small molecule PIN1 inhibitor-Sulfopin, and effects were evaluated by histology, qPCR, and cytokine analysis. Single-cell RNA sequencing (scRNA-seq) was performed on pancreatic tissues to perform pathway analysis and intercellular communication. ResultsPIN1 expression was elevated in human CP tissues, correlating with disease severity and ADM. In mice, both acute and chronic pancreatitis increased PIN1 expression, but only in our chronic PIN1KO mice displayed reduced pancreatic injury, fibrosis, ADM, and modulated immune infiltration. Pharmacological PIN1 inhibition mimicked the protective effects of genetic knockout, dampening inflammatory pathways. scRNA-seq revealed that PIN1 inhibition altered the intercellular communication networks between epithelial, immune, and stromal cells. ConclusionPIN1 drives cellular plasticity, immune modulation, and disease progression in CP. Targeting PIN1 may offer a therapeutic strategy to mitigate CP. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=184 HEIGHT=200 SRC="FIGDIR/small/653850v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@67f9fcorg.highwire.dtl.DTLVardef@4d4c1forg.highwire.dtl.DTLVardef@c099d7org.highwire.dtl.DTLVardef@b41aa5_HPS_FORMAT_FIGEXP M_FIG C_FIG Created in BioRender. Shah, V. (2025) https://BioRender.com/undefined

cell biology↗