Biology subjects

Xia, H.

Publications and source records attributed to Xia, H..

At least 19 recordsLinked to original sources

bioRxiv2026

A lactate-HIF1-VDR positive feedback loop drives protective SPP1+ macrophage differentiation to inhibit schistosomiasis induced liver fibrosis

Hepatic fibrosis remains a major cause of morbidity and mortality in patients with schistosomiasis without effective therapy. Here we show that vitamin D receptor (VDR) signaling in macrophages is essential for limiting fibrosis, acting through a previously unrecognized metabolic epigenetic circuit. Hepatic macrophages exhibit the highest VDR expression among liver resident cells, and pharmacological VDR activation with paricalcitol selectively expands a protective SPP1+ macrophage subset derived from circulating monocytes. Myeloid specific VDR knockout exacerbates fibrosis and abrogates paricalcitol's hepatoprotective effects, whereas SPP1+ macrophage depletion worsens disease. Mechanistically, VDR activation synergizes with hypoxia and lactate to drive SPP1 expression via glycolytic reprogramming. Moreover, lactate and HIF1alpha cooperatively induce VDR transcription by binding to the Vdr promoter and enhancing histone lactylation, forming a positive feedback loop that amplifies the antifibrotic response. Our findings establish the lactate-HIF1alpha-VDR-SPP1 axis as an endogenous defense mechanism and identify macrophage VDR as a promising therapeutic target for fibrotic liver diseases.

immunology↗

bioRxiv2026

A Practice on Antibody Hydrophobic Interaction Chromatography Retention Time Prediction using Pre-Trained Large Language Model Fine-Tuning

Hydrophobicity is a critical property associated with the risk of non-specific binding, and it is commonly assessed using hydrophobic interaction chromatography retention time. Several computational approaches have been developed to predict antibody developability based on pre-trained language models. Such models can be fine-tuned with limited labeled antibody sequences and, in principle, do not require structural information, which is often challenging to obtain. Nevertheless, few studies have achieved strong performance in hydrophobicity prediction without incorporating structural features. Here, we present a case study of fine-tuning the pre-trained model IgBert to predict antibody hydrophobicity. Using Herceptin as a reference, we performed hydrophobic interaction chromatography retention time experiments and generated Herceptin-adjusted datasets. The fine-tuned model achieved a best R2 of 0.916, underscoring the critical role of rigorous data quality control. We also synthesized and validated 20 commercially available antibody sequences, and the results showed that the predicted hydrophobic properties were correctly reflected. Our findings provide practical guidance and highlight considerations for future applications of fine-tuned pre-trained language models in antibody hydrophobicity prediction.

bioinformatics↗

bioRxiv2026

An allosteric pocket in KV1.3 defines a distinct chemical space for immunomodulator design

KV1.3 is a validated autoimmune drug target, yet all disclosed inhibitors converge on a narrow set of conserved binding sites, suffering from poor selectivity or clinical failure. Whether KV1.3 harbors a distinctive, druggable pocket amenable to selective targeting has remained unknown. Here we report MPiN, a highly selective, state-dependent KV1.3 inhibitor with in vivo efficacy in a mouse psoriasis model, which engages a previously uncharacterized extracellular allosteric pocket framed by the PP1-PP2 turret loops, the pore helix and the outer S5/S6 helices, acting through a bidirectional pore-to-sensor coupling that simultaneously constricts the selectivity filter and facilitates the voltage sensor toward activation. Unexpectedly, despite the extensive structural conservation of this pocket across KV1 paralogs, subtype selectivity is dictated by the peripheral residues G427 and H451, which define pocket geometry without directly contacting the ligand, thereby establishing a geometry-driven "non-contact selectivity" mechanism. By opening an unrecognized, structurally distinct chemical space on KV1.3 and redefining how selectivity is achieved within a conserved channel family, this work lays the structural and conceptual foundation for rational, structure-guided design of next-generation KV1.3 immunomodulators.

biochemistry↗

bioRxiv2026

Diurnal rhythms of choice: a novel state-dependent drift diffusion model uncovers time-dependent changes in rat decision making

2Time-of-day severely impacts human decision-making, with real-world consequences. Studying shifts in decision-making strategy requires controlled, long timescale behavioral measurement and analyses that can extract insight from time-varying behavior. We introduce two complementary advances to address this gap: an autonomous 24-hour training facility for continuous behavioral measurement during decision-making and an interpretable modeling framework that captures non-stationary decision dynamics from reaction times and choices. Rats were trained on a visual evidence accumulation task across months, generating over a half million trials spanning the circadian period. Our model revealed latent behavioral states characterized by distinct evidence accumulation parameters, including differences in drift rate, bias, and decision-commitment time. These states recur across days and align with feeding schedules and the light-dark cycle, producing periodic fluctuations in performance over 24 hours. Together, these results demonstrate how continuous behavioral sampling combined with generative modeling uncovers long-timescale structure in decision-making obscured by stationary analyses. 1 HIGHLIGHTSO_LI24-hour live-in operant system allows autonomous training in cognitive tasks across months C_LIO_LI24-hour measurements reveal that rat performance fluctuates with time of day C_LIO_LINovel DDM-HMM framework identifies reaction time and accuracy shifts across multiple timescales C_LIO_LIDDM-HMM captures serial dependence in decisions that classic models ignore C_LI

animal behavior and cognition↗

bioRxiv2026

Integrative Single-Cell and Multi-Cohort Analysis of the Netrin-1 Signaling Pathway Reveals Divergent Prognostic Trends and Sex-Dimorphic Associations in Glioblastoma

BackgroundThe Netrin-1 dependence receptor pathway plays critical roles in neural development, but its expression landscape and prognostic significance in glioblastoma (GBM) remain poorly characterized. MethodsSingle-cell RNA-seq data from 148,019 cells across 34 tumors (Neftel et al., 2019) were analyzed to map Netrin-1 pathway gene expression across GBM cellular states. Differential gene expression and pathway enrichment analyses were performed on NEO1-defined subpopulations. Bulk RNA-seq survival analysis was conducted across three independent GBM cohorts TCGA (n=106), CGGA mRNAseq_325 (n=137), and CGGA mRNAseq_693 (n=237), totaling 480 patients. Primary analysis used continuous Cox regression (per-SD hazard ratios); meta-analysis employed fixed-effects inverse-variance weighting. ResultsIn GBM single-cell data, Netrin-1 pathway genes showed state-specific enrichment --NEO1, DCC, NTN1, and RGMB were predominantly expressed in oligodendrocyte-precursor (OPC) and neural-progenitor (NPC) states. Cells positive for NEO1 were enriched for neural differentiation programs (nervous system development, p=9.6x10-; Axon Guidance, p=2.8x10-), whereas NEO1-negative cells were dominated by ribosomal/translational and immune activation programs. In the 3-cohort survival meta-analysis, NTN1 (Netrin-1 ligand) emerged as the sole gene reaching meta-analytic significance as a risk factor (Meta HR=1.163 per SD, 95% CI 1.056-1.281, p=0.0021, I{superscript 2}=0%, 3/3 cohorts concordant), while DCC and RGMB showed directionally consistent protective trends (DCC: Meta HR=0.938, 95% CI 0.858-1.025, p=0.156; RGMB: Meta HR=0.979, 95% CI 0.881-1.087, p=0.686; both 3/3 cohorts concordant). NEO1 itself did not independently predict survival (Meta HR=1.008, 95% CI 0.885-1.147, p=0.910). After Bonferroni correction for 10 genes tested (threshold p<0.005), only NTN1 met strict significance. In exploratory sex-stratified analysis of a single cohort (CGGA 693, n=237), NEO1 and NTN1 exhibited female-specific risk enhancement (NEO1: HR=1.417, p=0.014; NTN1: HR=1.249, p=0.019), with minimal effects in males. UNC5B showed context-dependent risk in MGMT-unmethylated tumors (HR=1.331, p=0.037). These sex-dimorphic findings require independent validation. ConclusionsThe Netrin-1 pathway exhibits divergent prognostic trends in GBM, with NTN1 as a risk factor and DCC trending toward protection--consistent with the dependence receptor model. These findings, which should be interpreted as hypothesis-generating, nominate NTN1 as a candidate therapeutic target and highlight the potential importance of sex-stratified evaluation in future Netrin-1-directed trials. Independent replication in larger cohorts is warranted.

cancer biology↗

bioRxiv2026

Advancing ab initio genome annotation with OrionGeno

The rapid expansion of eukaryotic genome sequencing has created an urgent demand for accurate and scalable genome annotation. Existing ab initio methods often struggle to reconstruct complex gene architectures and generalize across distant lineages, limiting their use for large-scale annotation. Here we present OrionGeno, a phylogeny-aware deep learning model for end-to-end eukaryotic genome annotation. OrionGeno integrates phylogenetic context, long-range sequence modeling and joint prediction of gene structures and repetitive elements to annotate exons, introns, untranslated regions and repeats directly from genomic sequences. Applied to chromosome-level eukaryotic genomes from NCBI that lack annotations, OrionGeno generates annotations for more than 5,300 genomes, substantially expanding public annotation resources. Across diverse eukaryotic lineages, OrionGeno outperforms state-of-the-art methods at the exon, gene, protein-sequence, and protein-structural levels. It also identifies candidate protein-coding loci absent from reference protein-coding annotations in well-curated genomes. Together with a web platform and integrated annotation database, OrionGeno provides a scalable and accessible framework for translating genome assemblies into functional biological resources and supporting large-scale biodiversity initiatives such as the Earth BioGenome Project.

bioinformatics↗

bioRxiv2026

Toxoplasma gondii associates with Benign Prostatic Hyperplasia and induces prostatic hyperplasia and urinary dysfunction in mice

ObjectivesBenign Prostatic Hyperplasia (BPH) is the non-cancerous enlargement of the prostate accompanied by lower urinary tract symptoms, affecting 50% of men by the age of 501,2. Advanced highly symptomatic BPH exhibits large epithelial glandular nodules with microglandular/atypical adenomatous hyperplasia, but how these features form is unknown3. Our lab has reported that the common parasite Toxoplasma gondii can infect the prostate and induce glandular nodule formation in mice3. The objective of this study is to determine if T. gondii exposure in humans correlates to BPH and nodule formation and if it induces urinary dysfunction concurrent in the mouse model. MethodsWe assessed Toxoplasma exposure by serum ELISA in patients with BPH and non-BPH donor controls, and compared seropositivity rates between the groups. We further assessed the histopathology of these patients for the presence of inflammation and epithelial glandular nodule formation and compared Toxoplasma positive and negative samples. We determined voiding function in Toxoplasma-infected mice between 14 and 60 days of infection with void spot with Void Whizzard software. ResultsMen diagnosed with BPH are more likely to be seropositive for Toxoplasma than age-matched undiagnosed donor controls. In addition, BPH patients that are seropositive for Toxoplasma are more likely to exhibit glandular nodule formation with microglandular / adenomous hyperplasia than seronegative BPH patients. In animal studies, Toxoplasma infection results in abnormal void patterns concurrent with microglandular hyperplasia and nodule formation. ConclusionsThese results suggest that Toxoplasma may be contributing to BPH pathology and lower urinary tract dysfunction in both humans and mice, opening new insights into the development of this important disease. The results also serve to further characterize this model of prostatic hyperplasia and define it as a potential urinary dysfunction model.

pathology↗

bioRxiv2026

Phenotype-Guided In Silico Molecular Generation Using Large Language Models

Complex diseases often emerge from coordinated, system-level cellular state changes that are difficult to address with target centric drug discovery. Phenotypic drug discovery offers a principled alternative but remains constrained by the cost and scalability of pathologically relevant assays. Here we present GEMGen, a large language model-based framework that performs in silico phenotypic drug discovery by generating small molecules directly from transcriptomic representations of cellular states. GEMGen encodes desired phenotypic transitions as text-based representations of up- and down-regulated gene sets, enabling transferable modeling across experimental platforms and data modalities. Trained on large scale chemical perturbation data, GEMGen robustly identifies phenotype-oriented compounds and mechanistically related but structurally distinct candidates across multiple benchmarks. Applied to signatures induced by genetic perturbations, GEMGen produces small molecules that phenocopy gene knockdown effects and identifies chemically novel inhibitors, including previously unreported KEAP1 inhibitors that activate NRF2 signaling. Extending this approach to a disease relevant model of fibrosis, GEMGen generates compounds that reverse profibrotic transcriptional programs and cellular phenotypes. These results establish a scalable framework for translating transcriptomic phenotypes into candidate therapeutic molecules, enabling systems-level exploration of vast chemical space and offering a complementary in silico counterpart to physical phenotypic drug screens.

cell biology↗

bioRxiv2025

Nuclear N-Glycosylation Redefines the Glycoscape and Directs Cell Identity

Glycans modify nucleic acids, proteins and lipids in fundamental biological processes, but have long been considered confined to the secretory pathway and cell surface. Here, we challenge this view with evidence that N-glycosylation, a ubiquitous glycan modification, occurs within cell nuclei across tissues and species, exhibiting cell type-dependent abundance and localization patterns. Using a multi-faceted approach, we show that numerous classical membrane and secreted glycoproteins localize to the nucleus with N-glycan modifications, exemplified by the cell adhesion molecule L1CAM. Mechanistically, we show that the N-glycosylated L1CAM is transported to the nucleus by KPNB1, where it transcriptionally modulates neuronal differentiation. Moreover, this nuclear N-glycosylation event contributes to neurodevelopmental disorders and is pharmacologically reversible. These findings revise the cellular geography of N-glycosylation and expand the known routes of protein trafficking, and highlight nuclear N-glycosylation pathways as potential therapeutic entry points for developmental disorders.

cell biology↗

bioRxiv2025

LorMe: a streamlined and interoperable R framework for end-to-end microbiome analysis

Microbiome research is rapidly evolving accompanied by increasingly diverse analytical strategies and visualization formats. However, existing R-based tools remain fragmented, differ in data structures, and often require extensive package-specific pre-processing. This increases command-line complexity and limits both interoperability and reproducibility across packages. Here, we present LorMe (Lightweight One-line Resolving Microbial Ecology), an R package that provides a unified, interoperable, and user-friendly framework for microbiome data analysis. LorMe incorporates a standardized S4 object system with fully bidirectional compatibility with phyloseq and microeco, which enables seamless data exchange across major microbial analysis packages. A global configuration system ensures consistent parameter control and visualization standards, and a modular architecture supports both complete end-to-end workflows and flexible execution of individual analytical components. Based on these, LorMe further offers an one-command pipeline that conducts the full spectrum of microbial community analyses, including alpha and beta diversity, differential abundance testing, co-occurrence network inference, and meta-network construction, while archiving all intermediate objects and source data to ensure complete reproducibility. Demonstrations using sample datasets show that LorMe performs comprehensive analyses within minute, produces publication-ready outputs, and maintains methodological transparency. Application to a real rhizosphere dataset revealed biologically coherent patterns across diversity metrics, differential taxa, and network modules, highlighting the capacity of LorMe to support robust ecological interpretation. Collectively, LorMe provides a lightweight, extensible, and flexible solution that reduces technical barriers and enhances reproducibility in microbiome research. LorMe package is freely accessible from both CRAN (https://cran.r-project.org/web/packages/LorMe) and github (https://github.com/wangnq111/LorMe)

bioinformatics↗

bioRxiv2025

Multi-Dimensional Spatiotemporal Attention Neural Network for Next Generation Sequencing Basecalling

Next-generation sequencing (NGS) remains the most used sequencing technique in the field of genomics. Traditional basecall methods face significant challenges in decoding high density sequencing data due to inherent noise in biochemical reactions and limitations of instruments. Here, we present a multi-dimensional deep learning neural network based on spatiotemporal attention mechanism named AICall. The network skips computationally heavy but less effective steps of peak finding and brightness extraction/correction, and directly basecalls from the time sequence of multi-dimensional image stacks obtained in real time. By introducing attention mechanism, it effectively extracts spatial and time-related key information including spatial crosstalk, spectral crosstalk, phasing, base-quenching, and intensity decay, and significantly improves basecall accuracy. We demonstrate that AICall achieves an average error rate less than 0.01% and provides more reliable sequencing results for downstream analysis.

bioinformatics↗

bioRxiv2025

DomDiff: protein family and domain annotation via diffusion model and ESM2 embedding

Accurate identification of conserved protein domain boundaries and their classification are fundamental to genome annotation, but are hindered by ambiguous boundaries, cross-domain interference, and limited samples for rare families. Here, we present DomDiff, a supervised conditional diffusion framework that reformulates the task as a generative process. Taking ESM2 embeddings, secondary structures, and biLSTM priors as inputs, it generates labels from Gaussian noise through iterative denoising, allowing coarse-to-fine optimization. We conducted a series of benchmark analyzes on publicly available protein sequence datasets, showing that DomDiff outperforms existing methods in domain boundary identification and classification, delivering performance gains of 12.6% in boundary detection and 4.2% in classification accuracy compared to other leading models. It excels particularly in annotating rare families, offering a powerful tool for specific applications such as large-scale genome annotation and functional characterization of novel proteins, thus providing a new paradigm for few-shot challenges in bioinformatics.

genomics↗

bioRxiv2025

FlexRibbon: Joint Sequence and Structure Pretraining for Protein Modeling

AO_SCPLOWBSTRACTC_SCPLOWProtein foundation models have advanced rapidly, with most approaches falling into two dominant paradigms. Sequence-based language models (e.g., ESM-2) capture sequence semantics at scale, and a number of recent works incorporate structural signals into sequence encoders. MSA-based predictors (e.g., AlphaFold 2/3) achieve accurate folding by exploiting evolutionary couplings, but their reliance on homologous sequences makes them less reliable in highly mutated or alignment-sparse regimes. We present FlexRibbon{ddagger}, a pretrained protein model that jointly learns from amino acid sequences and three-dimensional structures. Our pretraining strategy combines masked language modeling with diffusion-based denoising, enabling bidirectional sequence-structure learning without requiring MSAs. Trained on both experimentally resolved structures and AlphaFold 2 predictions, FlexRibbon captures global folds as well as flexible conformations critical for biological function. Evaluated across diverse tasks spanning interface design, intermolecular interaction prediction, and protein function prediction, FlexRibbon establishes new state-of-the-art performance on 12 different tasks, with particularly strong gains in mutation-rich settings where MSA-based methods often struggle.

molecular biology↗

bioRxiv2025

Regulation of Decision Threshold by the Locus Coeruleus

A fundamental challenge for decision-making under uncertainty lies in regulating the balance between speed and accuracy. Humans and animals solve this problem by adjusting their decision thresholds -- the criterion that determines how much information is required before committing to a choice. While brain regions associated with this process have been identified, the neural circuits able to directly alter decision thresholds remain to be identified. Here we investigate the role of the locus coeruleus (LC) norepinephrine (NE) system in controlling the balance between speed and accuracy during decision making. Through cell-type specific chemogenetic manipulations, we discovered that LC-NE activation increased decision thresholds. We further demonstrated that this effect is replicated by systemic administration of the 2 adrenergic receptor (2 AR) agonist, clonidine. Notably, 2 AR activation drove changes in decision threshold specifically, without reproducing other LC-NE activation effects such as promoting task engagement. Together, these results suggest that LC-NE regulates decision thresholds via activation of downstream 2 ARs. HighlightsO_LILC-NE activation increases reaction time and accuracy of perceptual decisions C_LIO_LIBehavioral effects are consistent with an elevation of decision thresholds C_LIO_LIHeightened decision thresholds are replicated by selective activation of 2 ARs C_LIO_LIActivation of LC-NE and 2 ARs differently alter task engagement C_LI

animal behavior and cognition↗

bioRxiv2025

Nanosecond pulsed electric field precisely manipulates cytoplasmic Ca2+ oscillations during oocyte activation

Oocyte activation deficiency (OAD) is a primary cause of fertilization failure following intracytoplasmic sperm injection (ICSI), a problem that can potentially be overcome through artificial oocyte activation (AOA). However, concerns persist regarding the safety and efficacy of AOA techniques in clinical practice. In this study, nanosecond pulsed electric field (nsPEF) is proposed as a safe and controllable method for oocyte activation in vitro by precisely manipulating Ca2+ signaling in the ooplasm. Mouse oocytes collected from oviducts were exposed to nsPEF stimulation at varying intensities to induce distinct Ca2+ signaling patterns. Subsequently, these oocytes underwent parthenogenetic activation and were cultured to assess developmental potential up to the blastocyst stage. The sperm-initiated physiological Ca2+ oscillations are successfully mimicked by one series of Ca2+ signals induced by nsPEF at low or medium intensities, and improve activation efficiency and developmental potential compared to calcium ionophore A23187. Low-intensity nsPEF pulses mediated repetitive extracellular Ca2+ influx in a non-invasive, electro-permeable manner. Medium-intensity nsPEF stimulation triggered periodic Ca2+ release from the endoplasmic reticulum (ER) via the PIP2-IP3-IP3R pathway, producing physiological-like Ca2+ oscillations. The non-invasive nsPEF method ensures safe oocyte activation by preserving cellular integrity and minimizing stress responses. The efficacy of nsPEF in precisely manipulating Ca2+ signaling patterns was further validated in human unfertilized oocytes. Our findings present a novel AOA approach with enhanced safety and efficacy, especially for patients experiencing repeated fertilization failures. It is anticipated that nsPEF is emerging as a promising technology for addressing male infertility issues, and may offer a potential alternative fertility treatment, bypassing sperm for initiation of oocyte activation.

developmental biology↗

bioRxiv2025

Three-photon population imaging of subcortical brain regions

Recording activity from large cell populations in deep neural circuits is essential for understanding brain function. Three-photon (3P) imaging is an emerging technology that allows for imaging of structure and function in subcortical brain structures. However, increased tissue heating, as well as the low repetition rate sources inherent to 3P imaging, have limited the fields of view (FOV) to areas of [≤] 0.3 mm2. Here we present a Large Imaging Field of view Three-photon (LIFT) microscope with a FOV of >3 mm2. LIFT combines high numerical aperture (NA) optimized sampling, using a custom scanning module, with deep learning-based denoising, to enable population imaging in deep brain regions. We demonstrate non-invasive calcium imaging in the mouse brain from >1500 cells across CA1, the surrounding white matter, and adjacent deep layers of the cortex, and show population imaging with high signal-to-noise in the rat cortex at a depth of 1.2 mm. The LIFT microscope was built with all off-the-shelf components and allows for a flexible choice of imaging scale and rate.

neuroscience↗

bioRxiv2025

In situ generated vaccine-like pyroptosome for personalized cancer immunotherapy

The efficacy of in situ cancer vaccination has been hampered by poor spatiotemporal orchestration of multiple key steps of cancer-immunity cycle in most tumours and systemic toxicity related to therapeutic strategies. Here, we report a systemic injectable and pyroptosis-enabled nanoadjuvant (SPEN) that precisely evokes the secretion of vaccine-like pyroptosome in tumour area for eliciting robust anti-tumour immunity. SPEN induces vigorous immunogenic pyroptosis, triggering the efficient release of tumour antigen-rich pyroptosomes, DAMPs, and proinflammatory cytokines. Upon the activatable release of a TLR7/8 agonist into pyroptosome, the generated pyroptosome functions as in situ cancer vaccine for cooperatively activating the cancer-immunity cycle while avoiding systemic toxicity. This in situ vaccine boosts both innate and adaptive immune response, facilitating the eradication of primary tumour and long-lasting cancer prevention. Our findings provide new insights into the rational design of pyroptosis-inducing nanomedicines for boosting the cancer-immunity cycle, thus advancing personalized cancer immunotherapy.

cancer biology↗

bioRxiv2025

High-speed mapping of whole-mouse peripheral nerves at subcellular resolution

In contrast to the rapid advancements in mesoscale connectomic mapping of the mammalian brain, similar mapping the peripheral nervous system has remained challenging due to the bodys size and complexity. Here, we present a high-speed blockface volumetric imaging system with an optimized workflow of whole-body clearing, capable of imaging the entire adult mouse at micrometer resolution within 40 hours. Three-dimensional reconstruction of individual spinal fibers in Thy1-EGFP mice reveals distinct morphological features of sensory and motor projections along the ventral and dorsal rami. Immunostaining facilitates body-wide mapping of sympathetic nerves and their branches, highlighting their perivascular patterns in limb muscles, bones, and most visceral organs. Viral tracing elucidates the fine architecture of vagus nerves and individual vagal fibers, revealing unexpected projection routes to various organs. Our approach offers an effective means to achieve a holistic understanding of cellular-level interactions among different systems that underlie body physiology and disease. HighlightsO_LIImaging of whole adult mouse at uniform subcellular resolution within 40 hours C_LIO_LIReconstruction of 191 thoracic spinal neuron projections for Thy1-EGFP mice C_LIO_LIMapping of immunolabeled sympathetic projections and their perivascular patterns C_LIO_LIResolving virus-labeled architecture of vagus nerves and complex single-fiber routes C_LI

neuroscience↗