Biology subjects

Luo, H.

Publications and source records attributed to Luo, H..

At least 19 recordsLinked to original sources

bioRxiv2026

A brain-behavior generative model links individual brain dynamics to perceptual decisions

Perceptual decision speed varies across individuals and trials. Explaining this variability requires linking individual brain anatomy, neural dynamics, and behavior, yet whole-brain models typically reproduce brain signals without behavior, whereas behavioral models lack anatomical constraints. Here we introduce a brain-behavior generative model in which whole-brain dynamics on each participant's structural connectome generate both single-trial electroencephalography (EEG) and reaction times. Fitted to 17 participants performing motion discrimination, the models reproduced individual event-related potentials and reaction-time distributions. Joint fitting enabled the models to predict whether held-out trials were relatively fast or slow within each stimulus condition. In silico silencing further predicted where, how, and when each person's brain controls decision speed. Sensorimotor regions affected decision speed consistently across most participants, whereas the same parietal region had opposite effects in different participants. These opposite effects reflected whether the region acted mainly through network dynamics or the decision readout. Temporal analyses suggested that early activity largely set decision speed, whereas late EEG mainly tracked the unfolding decision. These results show that brain-behavior generative modeling can link individual brain dynamics to perceptual decisions and yield testable predictions of why decision speed varies across individuals and trials.

neuroscience↗

bioRxiv2026

Towards Robust Automated Mouse Scratching Behavior Detection for Itch Research

Automated detection of mouse scratching behavior could reduce manual annotation and is essential for preclinical itch research. However, robust performance across laboratories remains a challenge for current methods. We present Scratch-AID+, a framework dedicated to automated mouse scratching detection, including a diverse and progressive set of approaches: classic ResNet-based detectors, task-adaptive fine-tuning of video foundation models, and in-context learning with advanced multimodal large language models. To simulate real-world use cases, we benchmark on a public dataset and utilize additional cross-laboratory video recordings for independent testing. We find that classic baselines suffered severe performance degradation on external data under the realistic setting with sparse scratching events. In contrast, fine-tuned video foundation models successfully bridged this generalizability gap, achieving significantly better detection performance. Furthermore, frozen multimodal large language models demonstrated promising out-of-the-box ability using only few-shot demonstrations. We also examined the effects of different video preprocessing strategies. Our findings highlight the improved performance and generalizability of the adapted foundation models for automated scratching behavior analysis and shed light on the real-world deployment of such detectors. Our code is publicly available at https://anonymous.4open.science/r/scratch aid-plus/.

bioinformatics↗

bioRxiv2026

Ruegeria persistens sp. nov., a persistent symbiotic bacterium isolated from coral

A Gram-stain-negative, aerobic, moderately halophilic, non-motile, rod-shaped bacterium, designated MC10-B4T, was isolated from the coral Oulastrea crispata collected from Hong Kong waters. The strain showed the ability to persist in a coral holobiont during an eight-month monitoring period in a field trial, indicating a long-term association with the coral host. Growth occurred at 10-37 {degrees}C (optimum, 28-30 {degrees}C), at NaCl concentrations up to 4.0% (w/v; optimum, 0.5-2.0%), and at pH 7.0-10.0 (optimum, pH 7.0-8.0) under standard laboratory conditions. Colonies on Marine Agar 2216 were round, smooth, convex, white, opaque, and had entire margins. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain MC10-B4T belongs to the genus Ruegeria and shares the highest sequence similarity with Ruegeria conchae TW15T (99.64%), followed by Ruegeria profundi ZGT108T (98.92%), Ruegeria lacuscaerulensis ITI-1157T (98.46%), Ruegeria denitrificans CECT 5091T (98.31%), Ruegeria arenilitoris G-M8T (97.69%), Ruegeria atlantica NBRC 15792T (97.55%), Ruegeria halocynthiae MA1-6 (97.11%), Ruegeria atlantica IAM 14463T (96.98%), Ruegeria meonggei MA-E2-3 (96.83%), and Ruegeria pomeroyi DSS-3T (96.65%). Genome sequencing revealed a genome size of 4.55 Mbp and a DNA G+C content of 57.5 mol%. The average nucleotide identity and digital DNA-DNA hybridization values between strain MC10-B4T and Ruegeria conchae TW15T were 77.6% and 18.2-22.8%, respectively. These values are below the recommended thresholds for species delineation (95% and 70%). Strain MC10-B4T shares the same predominant fatty acid, summed feature 8 (consisting of C18:1 {omega}7c and/or C18:1 {omega}6c), with other Ruegeria strains; however, it also contains C19:0 cyclo {omega}8c, which was not detected in Ruegeria conchae TW15T and Ruegeria pomeroyi DSS-3T. The type strain is MC10-B4T (= GDMCC 1.6889T = KCTC 18785T).

microbiology↗

bioRxiv2026

Functional spatial transcriptomics uncover LMO7 as a fusion-regulated and clinically relevant driver of metastasis in Ewing sarcoma

Metastatic dissemination represents the major determinant of poor clinical outcome across cancer entities. Yet, how driver oncogenes shape transcriptional programs facilitating metastasis is poorly understood. In Ewing sarcoma (EwS) - a highly aggressive pediatric bone and soft-tissue sarcoma driven by chimeric FET::ETS transcription factors - low activity of the fusion oncoproteins is thought to promote metastasis, but the underlying molecular mechanisms remain largely elusive. Here, using spatially resolved functional transcriptomics in EwS patient tumors, we identify a distinct transcriptional state at the invasive tumor front, that in contrast to the tumor core, is characterized by lower FET::ETS activity and induction of the multifunctional shuttle LIM domain only protein 7 (LMO7). Integrating these data with clinical information reveals that high LMO7 expression is associated with poor outcomes. Gene network analysis of patient tumors and integrated proteomic and transcriptomic profiling of EwS cell lines following inducible LMO7 silencing highlight LMO7 as a central regulatory hub orchestrating epithelial-mesenchymal transition (EMT) and cytoskeletal remodeling in EwS. Functional experiments demonstrate that LMO7 silencing decreases clonogenicity and migratory capacity in vitro and suppresses primary tumor growth and metastatic dissemination in vivo. Collectively, these findings identify LMO7 as a clinically relevant effector of FET::ETS fusions in EwS, and illustrate how integrating functional, spatial and clinical data can uncover oncogene-driven effectors of metastasis.

cancer biology↗

bioRxiv2026

Factorial Knockouts Distinguish Physical Necessity from Numerical Compensation in a PSI-LHCI Transport Model

Mechanistic interpretation of photosynthetic energy-transfer models requires more than reproduction of experimental observables: a model intended to support mechanistic claims should also respond consistently when its proposed functional organizations are removed. Here, we evaluate whether a calibrated PSI-LHCI transport surrogate encodes physically meaningful principles by applying a 2x2 factorial knockout framework that independently removes site-energy heterogeneity and coupling-strength heterogeneity in a 155-pigment network. All perturbations were evaluated using the same Full-model calibration without parameter refitting. Although the calibrated model reproduced a high excitation-trapping yield, eliminating either energetic or coupling heterogeneity unexpectedly improved its apparent transport performance. A strict zero-coupling control confirmed that coupling itself remained necessary for network-mediated reaction-center access, whereas the supplied organization of coupling strengths was not supported by the surrogate. An audit of the model inputs further identified peripheral localization of all lowest-energy states and effective coupling scales far above those used in structure-based chlorophyll Hamiltonians. These findings do not imply that native PSI favors flat energy landscapes or uniform couplings. Instead, they show that endpoint agreement alone does not validate a mechanistic interpretation of a pigment-network model. Factorial knockout analysis provides a falsification-oriented framework for separating physical necessity from proposed organization, diagnosing numerical compensation, and identifying the constraints required for more predictive models of PSI-LHCI energy transfer.

biophysics↗

bioRxiv2026

Multimodal spatial-omics reveal the heterogeneity and intercellular network characteristics of papillary craniopharyngiomas.

Despite significant advancements in microsurgical techniques in recent years, the treatment and prognosis of craniopharyngiomas remain unsatisfactory. As a central nervous system tumor located adjacent to important brain structures such as the hypothalamus-pituitary axis and accompanied by a highly inflammatory microenvironment, the tumor heterogeneity and tumor microenvironment characteristics of papillary craniopharyngiomas (PCPs) remain unclear. In this study, we integrated multimodal single-cell and spatial profiling from PCP tissue and peripheral blood mononuclear cells (PBMCs) to elucidate the tumor heterogeneity and microenvironment characteristics of PCP. Our single-cell and spatial analyses defined four specific tumor cell states in PCP, representing specific transcriptional regulatory programs and spatial heterogeneity characteristics during tumor progression. By constructing a spatial niche composed of tumor, immune, and stromal cells, we analyzed the cellular and spatial ecosystem of PCP at multiple levels to further assess the communication relationships between different tumor cell states and microenvironment cells. This study established a multidimensional molecular atlas of PCP from the perspectives of cell state, spatial structure, and microenvironment interactions, providing a foundation for understanding its biological behavior and exploring new intervention strategies.

cancer biology↗

bioRxiv2026

Ori-Finder-Arch: An Updated Web Server for the Annotation and Visualization of Archaeal Replication Origins

Archaea are promising chassis organisms in biotechnology, and the accurate annotation of their chromosomal replication origins (oriCs) is the key to unlocking their full potential. However, the existing Ori-Finder 2 web server suffers from low accuracy, slow speed, and limited scalability. In this study, we present Ori-Finder-Arch, an updated web server for high-performance oriC prediction in archaea. This pipeline integrates HMMER-based replication initiation protein (RIP) annotation, refined consensus motif recognition, and GC profile-based DNA unwinding element (DUE) detection. On a benchmark set of experimentally validated oriCs, Ori-Finder-Arch achieved a recall of 95.6% and a precision of 86.0%, substantially outperforming Ori-Finder 2 (62.2% and 63.6%, respectively), while running 4.75 times faster and supporting diverse assembly levels. When applied to the available archaeal assemblies, it successfully annotated 17,472 oriCs. Meanwhile, the web server provides interactive visualizations at different levels. In conclusion, Ori-Finder-Arch offers an efficient, accurate, and user-friendly platform for advanced studies of archaeal DNA replication initiation and synthetic biology applications, and is freely available at https://tubic.org/Ori-Finder-Arch/ and https://tubic.tju.edu.cn/Ori-Finder-Arch/.

bioinformatics↗

bioRxiv2026

BBBP_Atlas: Unified Interpretable Modeling of Blood Brain Barrier Permeability across Small Molecules and Peptides

Accurate prediction of blood-brain barrier permeability (BBBP) is essential for central nervous system drug discovery, yet existing models are often limited by their reliance on predefined physicochemical descriptors, small-molecule-centered training sets, or conformation-dependent representations, which restricts their transferability across chemically diverse modalities especially peptides. In addition, publicly available BBBP datasets remain fragmented, inconsistently standardized, and weakly controlled for molecular redundancy, increasing the risk of data leakage and overestimated model performance. In this study, we propose BBBP-Atlas, a structure-aware BBB permeability prediction model designed for unified modeling of small molecules and peptides with the first cross-modal dataset OmniBBBP. Designed to bypass descriptor and conformation dependencies, our model represents standardized molecular structures as atom-level graphs to capture local atom-bond environments and long-range topological dependencies associated with BBB transport. This design enables direct learning of structure-permeability relationships from molecular topology. For model training and evaluation, we curated a cross-modal, redundancy-filtered database OmniBBBP that seamlessly unifies small molecules and complex peptides, containing 10,218 unique compounds with 9,316 small molecules and 902 peptides. BBBP-Atlas achieved an accuracy of 0.8914 and an MCC of 0.7678 on the independent test set. On a balanced external benchmark of 200 compounds, our model reached an AUC of 0.9108, an accuracy of 0.8500, and an MCC of 0.7000, outperforming LightBBB by an absolute MCC gain of 6%. Case studies further showed that BBBP-Atlas captured clinically meaningful BBB permeability patterns, correctly identifying lorlatinib as BBB-permeable and vancomycin as BBB-impermeable with high confidence. The OmniBBBP-backed BBBP-Atlas offers a versatile and cross-modal approach for single-compound prediction, batch screening, and dataset exploration for CNS drug discovery. BBBP-Atlas is available at https://cadd.drugflow.com/bbbp/.

bioinformatics↗

bioRxiv2026

In vitro virus-like particle assembly follows two distinct, competing pathways

1.Virus-like particles (VLPs) formed from the murine polyomavirus major capsid protein VP1 are widely used as vaccine antigens and are being explored as nucleic acid and drug delivery vehicles. However, the factors controlling distinct VP1 capsid morphologies remain unclear. We investigated in vitro VP1 assembly with tRNA across NaCl concentrations of 0.15-1.0 M and tRNA mass ratios of 1:1-1:80 (w/w) using SEC-HPLC, transmission electron microscopy, and dynamic light scattering. Two competing assembly pathways were identified. At low ionic strength ([≤]0.15 M NaCl), nucleic acid-templated assembly produced compact, tRNA-filled T=1 VLPs ([~]28-30 nm). Assembly was maximal at tRNA ratios of 1:10-1:20, whereas excess or insufficient tRNA reduced yields. Increasing NaCl to 0.30 M lowered T=1 yields by 68-97%, and no T=1 particles were detected at [≥]0.5 M NaCl. Conversely, high ionic strength ([≥]0.5 M NaCl) promoted template-independent formation of hollow T=7 VLPs ([~]55-60 nm). T=7 assembly was inhibited by tRNA and was highest without nucleic acid. At 1.0 M NaCl, reducing the tRNA ratio from 1:40 to 1:80 increased T=7 yield more than 11-fold, while removing tRNA produced the greatest assembly efficiency. Kinetic analyses further showed that VP1 concentration and ionic strength regulate nucleation and assembly rate in the template-driven pathway. These findings show that electrostatic interactions govern pathway selection between tRNA-templated T=1 assembly and salt-driven T=7 self-assembly, providing practical guidance for controlling capsid morphology and cargo loading in VLP-based applications.

Molecular Biology↗

bioRxiv2026

The free-living wellspring of symbiotic nitrogen fixation in Bradyrhizobium

The evolutionary origin of nitrogen-fixing symbiosis has been a long-standing question. To address this, we focused on Bradyrhizobium, a globally abundant bacterial genus that includes classic symbiotic lineages, which rely on the common Nod factor signaling pathway to form nodules, and close relatives capable of fixing nitrogen in a free-living state. We isolated 88 strains carrying the key genes for nitrogen fixation (nif) from non-legume environments and analyzed them alongside 586 public Bradyrhizobium genomes harboring these genes to reconstruct a robust phylogeny of nif genes. Analysis reveals that the earliest-diverging nif lineages are members capable of free-living nitrogen fixation, establishing this as the ancestral state. The Nod factor-dependent symbiotic lineages are polyphyletic, demonstrating at least three independent origins via horizontal acquisition of symbiosis islands. This evolutionary history is reflected in a genomic dichotomy: lineages capable of free-living nitrogen fixation possess a conserved nif island architecture that consistently includes the oxygen-protective gene glbO, whereas the symbiotic nif-associated regions are highly variable and universally lack glbO. Using both loss-of-function and gain-of-function genetic approaches, we show that glbO contributes significantly to nitrogenase activity under free-living conditions, whereas it is dispensable within the protected nodule environment. This work establishes a new framework for the evolution of symbiosis, identifying free-living ancestors as the source from which nitrogen-fixing symbiosis repeatedly and independently evolved in Bradyrhizobium. SignificanceFor over a century, nitrogen-fixing bacteria called rhizobia have been celebrated for their symbiotic partnership with legumes like soybeans and peas. This study overturns the view that this symbiotic lifestyle was the original, advanced state. By discovering and analyzing diverse Bradyrhizobium bacteria from ordinary soils and non-legume plants, we show that the ancestor was capable of free-living nitrogen fixation. Root nodule nitrogen fixation evolved multiple independent times from free-living ancestors. We identify glbO, an oxygen-protective gene located immediately next to the nitrogen fixing genes, as critical for nitrogen fixation in variable soil conditions but dispensable and lost in Bradyrhizobium specialized inside nodules. This repositions free-living Bradyrhizobium as a major potential nitrogen source beyond legumes, with promise for sustainable agriculture.

microbiology↗

bioRxiv2026

A staircase model for the co-evolution of Bradyrhizobium and legumes dated by non-symbiotic relatives

The transition from free-living bacteria to mutualistic symbionts is a major evolutionary innovation, but its timing and stepwise nature remain unresolved because non-symbiotic ancestors have rarely been sampled. Here we isolated 10 Bradyrhizobium strains from non-legume plants and soils, and discovered three basal clades within the B. japonicum supergroup that completely lack nodulation (nod) genes, which are required for Nod factor synthesis and legume symbiosis. To date the emergence of symbiosis, we employed a mitochondrial endosymbiosis-based molecular clock dating framework that jointly estimates the divergence times of bacterial symbionts and their hosts in a single analysis. Using these non-nodulating lineages as evolutionary anchors, we show that nodulating B. japonicum arose contemporaneously with nodulating legumes most likely around 90-80 million years ago. Excluding these basal clades makes rhizobial nodulation appear to predate nodulating legumes, while the use of the poorly-fitting homogeneous models produces a similar temporal distortion. The inferred patterns are robust to alternative calibration strategies, gene sets, and mixture models. Together, these findings highlight the importance of comprehensive taxon sampling and appropriate substitution models in relaxed molecular clock dating, and support a co-emergence model in the origin of legume-rhizobia mutualism.

evolutionary biology↗

bioRxiv2026

Transformed reactivation of latent working memory enables hierarchical language processing

Human language comprehension requires tracking words across intervening material to construct grammatical structures, yet two fundamental questions remain unresolved: whether maintenance of earlier words relies on sustained neural activity or activity-silent working memory mechanisms, and whether memory retrieval during integration engages domain-general or language-selective networks. Using magnetoencephalography with time-resolved decoding, we tracked main-clause subjects as participants heard sentences with embedded clauses (e.g., "The dog, who chases the cat, jumps over the mud."). The subject representation ("dog") decayed to baseline during the embedded clause but reactivated after the main-clause verb ("jumps"), with transformed rather than reinstated neural codes. Critically, reactivation emerged first in right dorsolateral prefrontal cortex before engaging frontotemporal language regions, and reactivation strength was modulated by syntactic structure and predicted comprehension accuracy. These findings demonstrate activity-silent working memory maintenance, structure-dependent retrieval, and cooperative function of domain-general and language-selective networks during hierarchical language processing.

neuroscience↗

bioRxiv2026

Cell membrane proteome profiling uncovers molecular signatures and GPCR regulators underlying diet-induced obesity and exercise-mediated adaptation

Cell membrane proteins (CMPs), notably G protein-coupled receptors (GPCRs) and receptor tyrosine kinases (RTKs), represent the largest class of druggable targets. Despite their therapeutic importance, how the CMP landscape is systemically remodeled during metabolic disease progression and in response to exercise remains poorly understood. Here, we constructed the most comprehensive CMP proteome atlas to date across 22 male mouse tissues and established the first multi-tissue map of GPCR-associated protein hormones/prohormones. Integrated CMP proteomic and transcriptomic analysis revealed that diet-induced obesity and its progression to type 2 diabetes remodel the CMP repertoire in patterns distinct from those induced by exercise. We found that numerous GPCRs and RTKs critical for energy homeostasis are dysregulated during obesity in a tissue-specific manner, and these alterations are reversed to varying extents by exercise training. Importantly, our CMP-centric omics study identified two parathyroid hormone receptors (PTHRs) in the hypothalamus as negative regulators of feeding and body weight. Genetically silencing these hypothalamic PTHRs led to severe obesity even on a chow diet and markedly blunted exercise-induced weight loss. Collectively, our study provides not only new insights into systematic CMP regulation underlying metabolic deterioration versus exercise adaptation, but also nominates potential receptor targets for obesity management.

biochemistry↗

bioRxiv2026

A sequence-based proactive intelligence for influenza antigenic profiling improves vaccine strain selection

Seasonal influenza viruses accumulate antigenic changes, eroding population immunity and necessitating recurrent vaccine updates. Hemagglutination inhibition (HI) assays are the standard for measuring antigenic relationships between circulating and vaccine strains; however, their limited throughput constrains the scale and timeliness of surveillance. Here, we present fluProfiler, a foundation-model-based framework that learns a stable mapping from viral sequences to antigenic space and uses this representation to support influenza antigenic prediction, vaccine strain evaluation, and diversity-driven sampling. fluAgPredictor aligns hemagglutinin (HA) and neuraminidase (NA) sequence representations with HI-derived antigenicity, enabling accurate and consistent inference of pairwise antigenic distances across surveillance-aligned evaluation settings. Without prior annotation of antigenic sites, it identifies key residues in immunodominant epitopes and reveals the cooperative contributions of HA and NA to antigenic variation. Building on this antigenic-space representation, fluVacSelector provides antigenic coverage scores that are concordant with World Health Organization (WHO) vaccine recommendations while also flagging potential candidates that may offer broader coverage ahead of formal consultations. fluAgEnhancer further leverages the same representation to prioritize antigenically informative and diverse strains for experimental characterization, achieving comparable predictive accuracy with approximately 25% fewer HI measurements than random sampling. Together, these modules provide a high-throughput and interpretable complement to HI testing, converting routine genomic surveillance into a more proactive, data-driven support system for antigenic monitoring and vaccine strain selection.

bioinformatics↗

bioRxiv2026

Space-Time Light-Sheet Microscopy

Light-sheet microscopy (LSM) has revolutionized bioimaging by delivering high-contrast volumetric resolution with minimal photodamage. Spatial wavefront shaping, used to gen{-}erate lattice and Airy light-sheets, has been particularly effective in advancing LSM be{-}yond the Rayleigh limit. Despite its broad adoption, most LSM implementations rely on rigid dual-objective geometries that complicate sample handling and impose a trade-off between imaging field of view (FoV) and axial resolution. Here, we introduce space-time light-sheet microscopy (ST-LSM), a single-objective strategy that exploits space-time (ST) correlations for the first time. ST-LSM goes beyond separate spatial or temporal modulation to jointly modulate the spatiotemporal spectral structure of a pulse. This uniquely enabled light-sheets with wavelength-scale thickness over millimeter-scale dis{-}tances. When compared to state-of-the-art approaches, ST-LSM eliminates the dual-objective constraint, expands the sample-accessible volume by 25x, and increases the FoV by 10x without sacrificing sectioning resolution. We demonstrate the versatility of ST-LSM by using a single setup to image specimens across four orders of magnitude in size, from whole roots and developing embryos, down to mammalian cells with sub-cellular axial resolution. These results position ST-LSM as an accessible and high-performance optical microscopy platform at a variety of biological scales, by translating space-time wave-packet physics into a practical imaging modality.

biophysics↗

bioRxiv2026

Automated Knowledge Graph Construction for CAR T Cell Receptor Design via Hybrid Text Mining

Designing next-generation Chimeric Antigen Receptors (CARs) requires a systematic understanding of intracellular signaling domains and their downstream biological effects, yet no comprehensive knowledge resource currently exists for this purpose. Here, we present an automated workflow that integrates multiple natural language processing and large language model tools to extract biomolecular interactions from PubMed literature and assemble them into a CAR T cell signaling knowledge graph. Our pipeline combines REACH, INDRA, and Llama 3 across 15 targeted search queries, yielding a directed multi-relational graph of [~]7,500 unique interactions among [~]1,800 entities, including proteins, biological processes, and chemicals. We further demonstrate that queries incorporating biological process ontology terms retrieve more interaction-rich papers than protein-name-only searches, offering practical guidance for future literature mining efforts. The resulting knowledge base provides a structured foundation for predicting T cell phenotypes and prioritizing intracellular domain candidates for CAR design, with broader applicability to knowledge-driven inference in immunotherapy research.

synthetic biology↗

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

Deep learning framework ChIANet predicts protein-mediated chromatin architecture across functional contexts

The spatial organization of the genome is dynamically shaped by chromatin-binding proteins, yet how protein-mediated three-dimensional (3D) architectures are specified across functional contexts remains incompletely understood. Here we present ChIANet, a multimodal deep learning framework that enables de novo prediction of protein-mediated chromatin contact maps and loops from protein-binding profiles alone, using the reference genome sequence as a prior. By integrating transformer-based long-range modeling with multi-task learning, ChIANet accurately reconstructs protein-mediated 3D chromatin architectures and generalizes across diverse cellular contexts. Systematic application of ChIANet to CTCF, Cohesin and RNAPII across seven human cell types reveals that chromatin architectures follow conserved organizational principles while exhibiting pronounced context-dependent reconfiguration: CTCF- and Cohesin-mediated interactions predominantly support stable structural frameworks, whereas RNAPII-mediated loops display greater variability and are closely coupled to transcriptional programs and regulatory element activity. Functional analyses further uncover distinct regulatory biases and super-enhancer associations among the three proteins. Extending this framework to cancer genomes, ChIANet captures RNAPII-mediated chromatin looping networks associated with extrachromosomal DNA (ecDNA), revealing highly connected, transcription-associated architectures within amplified ecDNA regions across multiple cancer cell types. Together, these results demonstrate that protein-mediated 3D genome organization is not determined by protein identity alone but is flexibly shaped by functional context, regulatory targets and cellular environment, establishing ChIANet as a unified and scalable approach for decoding context-dependent principles of genome folding.

genomics↗