Biology subjects

Zou, X.

Publications and source records attributed to Zou, X..

At least 19 recordsLinked to original sources

bioRxiv2026

NLR DNA copy-number expansion outpaces accumulation of predicted coding-intact copies

Whether nucleotide-binding leucine-rich repeat (NLR) DNA copies and predicted coding-intact copies accumulate proportionally within homologous neighbourhoods remains unresolved. We compared these counts across 173 anchor-defined neighbourhoods in 11 Capsicum genomes. After accounting for neighbourhood and genome effects, predicted coding-intact copies increased less than proportionally with DNA copies. DNA-copy doubling corresponded to 1.48-fold and 1.84-fold increases in expected coding counts under strict and inclusive definitions, respectively. The strict association persisted across sequence and sampling controls, with highly expanded arrays and heterogeneous coding recovery affecting interpretation. An independently defined panel of 17 Arabidopsis thaliana accessions reproduced sublinear scaling using curated gene counts. Matched pepper RNA-seq showed a transcript-support gradient across predicted coding states. At DH06, a near-megabase segmental duplication in C. pubescens contained intact and disrupted family pairs. Copy-specific HiFi reads placed the same F396 coding lesions in both physical blocks. Together, these results reveal a recurrent relationship between NLR array size and coding composition in the surveyed plant systems. Interpreting DNA expansion together with coding state provides a more informative basis for comparing NLR repertoires and prioritizing immune-gene candidates.

evolutionary biology↗

bioRxiv2026

LAND: Latent Aligned Neural-Behavioral Dynamics via Flow Matching forGeneralizable Movement Decoding

Generalizable movement decoding remains a central challenge for invasive brain--computer interfaces (BCIs), as decoders trained under limited calibration conditions often fail to generalize to unseen movement speeds, limbs, and subjects. Existing decoding methods are typically trained on paired data collected under restricted conditions. How to incorporate behavioral structure from unpaired data for robust out-of-distribution (OOD) decoding therefore remains unresolved. To address this, we propose LAND (Latent Aligned Neural-behavioral Dynamics), a framework that aligns latent neural and behavioral dynamics through flow matching. By learning a neural-to-behavioral transport map and using behavioral-dynamics priors from unpaired data to encourage structured neural manifolds, LAND regularizes representation geometry to promote cross-domain generalization. We evaluate LAND on synthetic neural data, epidural BCI recordings from a tetraplegia participant, and multi-electrode array (MEA) recordings from nonhuman primates (NHPs). Across these settings, LAND improves zero-shot generalization to OOD movement speeds and yields speed-modulated manifolds. With limited target-domain fine-tuning, it further improves transfer across limbs and subjects. These results support flow-based neural--behavioral alignment with unpaired kinematic priors as an approach for learning transferable neural representations and robust movement decoding across behavioral and recording domains.

neuroscience↗

bioRxiv2026

Regulation of the human voltage-gated proton channel by membrane sterols

Cholesterol is a key component of eukaryotic cell membranes, promoting membrane stability and modulating the function of many membrane proteins, including ion channels. In our previous work using purified human voltage-gated proton channel proteins, we showed that cholesterol inhibits the hHv1 channel by altering the conformational dynamics of its S4 segment, the key element that senses membrane voltage to control proton permeation. In the present work, we examined the effects of cholesterol analogs and potential sites in the hHv1 channel mediating cholesterol inhibition using site-directed mutagenesis and docking simulations. Our results showed that desmosterol, the immediate precursor of cholesterol, markedly attenuates cholesterol inhibition. Using single-molecule Fluorescence Resonance Energy Transfer (smFRET), we showed that desmosterol attenuates cholesterol inhibition by promoting the intermediate and open state conformations of the S4 segment. Moreover, we identified multiple residues in the hHv1 channel that are critical for cholesterol inhibition, including Y141A in the S2 segment, which reduces cholesterol inhibition by nearly 3-fold. Our smFRET results showed that the Y141A mutation promotes the intermediate conformation in the S4 segment, which underlies the attenuation of cholesterol inhibition. Consistently, docking simulations also revealed multiple residues spanning the transmembrane domain, rather than clustered within a single localized pocket. Our work identified the key molecular determinant in the hHv1 channel that mediates cholesterol inhibition and also provided a mechanism linking the conversion between demosterol and cholesterol by DHCR24 to pH homeostasis in many cells, such as phagocytes, cardiomyocytes, neurons and microglial cells.

biophysics↗

bioRxiv2026

Heart rate dynamics embed a shared representation of individual brain organization and cognitive function

Idiosyncratic brain functional organization shapes intricate cardiac dynamics through central-peripheral autonomic interactions, yet a comprehensive mapping between multi-scale heart rate dynamics and whole-brain functional architecture remains lacking. Here, combining highly comparative time-series analysis with multi-modal neuroimaging and intracranial electrophysiology, we establish heart rate dynamics as a physiological fingerprint that maps onto whole-brain functional architecture. Partial least squares analysis revealed a generalizable latent axis linking reduced heart rate temporal complexity and elevated micro-scale predictability to heightened resting-state functional connectivity across default mode, salience, and sensorimotor networks. This covariance aligns spatially with serotonergic, noradrenergic, and cholinergic neuromodulatory gradients, persists across physiological confound controls and cross-session validations, derives support from human intracranial electrophysiological recordings, and extends to active cognitive states. Furthermore, brain-covarying heart rate signatures underpin the predictive capacity of heart rate dynamics for individual fluid and crystallized intelligence, demonstrating a shared representational substrate. Our findings demonstrate a robust neurovisceral coupling architecture, establishing well-characterized heart rate dynamics as a scalable, neurobiologically anchored window into human brain functional organization and cognitive traits.

neuroscience↗

bioRxiv2026

Physical laws predict methane hotspots in global mountain waters

Accurate accounting of aquatic methane emissions is critical for climate change mitigation, yet current global budgets overlook a key driver: the elevation-regulated atmospheric pressure. Here, we present the first large-scale investigation of methane ebullition across 164 shallow waters spanning elevations from sea level to 4886 meters. We demonstrate that ebullition rate increases with elevation-over four times higher at >3000 m a.s.l. than at sea level-due to two synergistic, pressure-dependent physical mechanisms: a degas effect (enhanced bubble formation) and a trigger effect (facilitated bubble ascent). Independent theoretical prediction of the combined effects shows near-perfect agreement with the empirical elevation trend, quantitatively confirming that these physical mechanisms are the primary drivers of enhanced ebullition at high elevations. Our findings reveal that mountain aquatic ecosystems represent unaccounted methane hotspots that have been systematically underestimated in global inventories.We therefore call for urgent integration of these ecosystems into IPCC assessments and targeted mountain mitigation and sustainable management strategies.

ecology↗

bioRxiv2026

A self-organized geometry sensing oscillator spatially regulates cell division in archaea

Cell division must be tightly regulate in time and space. Many bacteria and eukaryotes employ geometry sensing systems to regulate cell division spatiotemporally. However, how archaea ensure they divide in the right place at the right time is little understood. Here, we report the discovery of a geometry sensing oscillator that determines the division plane in the model haloarcheon Haloferax volcanii, which relies on two tubulin-like proteins, FtsZ1 and FtsZ2, for division. This archaeal oscillator (named as Arco system) is composed of three components, ArcoA, ArcoB and ArcoC, which are a membrane associated Ras superfamily GTPase, a SepF-like protein and an antagonist of FtsZ1, respectively. ArcoA interacts with both ArcoB and ArcoC. ArcoAB sense the geometry of the cell to oscillate between the two cell poles, generating a time-averaged ArcoC gradient which is high at the cell poles but low at the midcell, where FtsZs can assemble into the Z ring to initiate cytokinesis. As a result, deletion of the Arco system caused aberrant FtsZ assembly throughout the cell, leading to irregular division and formation of minicells. Interestingly, the Arco system is globally distributed among archaea and co-occurs with FtsZ1, suggesting that it emerged in the last archaeal common ancestor (LACA). The oscillatory behavior and phenotypes of the archaeal Arco system is remarkably analogous to the bacterial Min system and yet share no protein homology, representing a remarkable example of convergent evolution. Overall, these results suggest that archaea, similar to bacteria and eukaryotes, independently evolved self-organized Turing reaction diffusion systems to sense geometry and spatially regulate cell division.

microbiology↗

bioRxiv2026

Systematic identification of cell division proteins in haloarchaea and the discovery of a membrane anchor for the Z ring

Most archaea rely on the tubulin-like protein FtsZ for division. In the last decade, several novel cell division proteins have been discovered in the model haloarchaeon Haloferax volcanii which contains two FtsZ proteins, FtsZ1 and FtsZ2. However, the composition of this FtsZ-based archaeal divisome is largely uncharacterized. Here, using in vivo crosslinking coupled with immunoprecipitation and mass spectrometry (CLIP-MS), we identified ten proteins that localize to the division site, including wide-spread proteins predicted to be involved in DNA binding and energy metabolism. Deletion analysis indicate that most of these proteins have a modest or minor impact on cell division, but HVO_0399, renamed as Cell Division Protein C (CdpC), is important for cell division and functions as a membrane anchor for FtsZ1. CdpC consists of three domains, an N-terminal domain (NTD) containing an amphipathic helix critical for membrane binding, a long intrinsically disordered linker, and a C-terminal domain (CTD) that binds FtsZ1 with high affinity and likely promotes its polymerization. Notably, both the NTD and CTD of CdpC harbor a zinc finger that is indispensable for its function. Phylogenetic analysis indicates that CdpC sequences evolved fast across haloarchaea, but exhibited high conservation in domain structure and critical residues found in this study. Overall, our study expands the repertoire of candidate division proteins and establishes CdpC as a membrane anchor for FtsZ1 in haloarchaea. These findings pave the way for in-depth studies of arcaheal cell division and broaden the function of zinc finger proteins in archaea.

microbiology↗

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

MedMisBench: Measuring Epistemic Resilience of LLMs Under Misleading Medical Context

Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients increasingly use them for health advice. We show this assumption is fragile: when misleading context is injected into questions that LLMs originally answer correctly, they abandon the correct answer. We call the ability to maintain correct judgment under adversarial context epistemic resilience, and introduce MedMisBench to measure it. MedMisBench contains 10,932 medical question items and 48,889 misleading context-option pairs spanning medical reasoning, agentic capability, and patient-journey evaluation. Across 11 model configurations, mean accuracy falls from 71.1% on original questions to 38.0% under focused misleading context, with 51.5% attack success. The most damaging injections are formal, rule-like fabrications: authority-framed falsehoods reach 69.5% attack success and exception-poisoning claims reach 64.1%. A 14-member clinical panel from 7 countries identified serious potential harm in 38.2% of reviewed cases. MedMisBench exposes a structural blind spot in LLM evaluation in medical settings: existing benchmarks measure what models know, but not whether they preserve correct medical judgment under misleading context.1

bioengineering↗

bioRxiv2026

SCIMETAR-seq tracks immunophenotype, demethylation, mutations, and transcriptomes in single cells undergoing HMA therapy

5-azacytidine improves haematopoiesis and delays leukaemic progression in myelodysplastic neoplasms, but responses vary and are complicated by clonal mosaicism and heterogenous demethylation. Using a novel single-cell pipeline ("SCIMETAR-seq"), we found 5-azacytidine induced clonally distinct differentiation responses, with nuclear DNA demethylation occurring primarily in cycling progenitors. Despite the absence of significant nuclear DNA demethylation, quiescent stem cells underwent transcriptional remodelling in vivo, accompanied by 5-azacytidine-induced C*G-to-G*C mutations in mitochondria.

cancer biology↗

bioRxiv2026

Single cell multiomics reveal clonal and functional dynamics of MDS stem/progenitor cells during hypomethylating therapy

Progressive somatic mutations in hematopoietic stem cells (HSCs) drive the development of myelodysplastic neoplasms (MDS). Hypomethylating agents such as azacitidine (AZA) can improve blood counts and reduce blasts, although responses are rarely durable. Determinants of AZA response are complex and incompletely understood, although accumulating evidence suggests that epigenetic rewiring of mutated HSCs underlies improved hematopoietic output. Using single cell multiomics on longitudinal bone marrow samples, we show that AZA responsiveness involves expansion of cells with transcriptomic profiles shared with hematopoietic stem and progenitor cells (HSPCs) from healthy donors. These regenerating cells are depleted of copy number variations and of TP53 mutations. We also identify patient-restricted cell populations, some of which recede through transcriptional restoration or AZA cytotoxicity, and others which expand, regardless of initial clinical response, and dominate at progression. Individual patients carried multiple patient-restricted populations which had unique surface immunophenotypes and were genetically distinct. Strikingly, sorted cells from in vivo progression clones that were AZA-refractive in patients regained AZA-sensitivity when cultured in vitro, suggesting that lack of AZA response at the cellular level can be modulated by cell-extrinsic factors in vivo. Overall, we find that AZA response involves partial hematopoietic regeneration via functional differentiation of mutated, but not cytogenetically abnormal HSPCs, and that persistence of AZA-refractive sub-populations contributes to eventual disease progression.

cancer biology↗

bioRxiv2026

Molecular Glue-Induced Homodimerization Drives Targeted CRBN Autodegradation

Molecular glue degraders (MGDs) offer a sophisticated, proximity-based approach to protein modulation. In this study, we introduce LJY-3-60, a novel proximity-inducing agent that unexpectedly triggers the potent and selective autodegradation of CRBN. Evidence from CRISPR-Cas9 screening and IP-MS reveals that this degradation process is strictly governed by the intrinsic CRL4CRBN machinery, independent of any extrinsic E3 recruitment. Through a combination of cellular and biophysical characterizations, we demonstrate that LJY-3-60 acts as a molecular bridge to template CRBN homodimerization. This mechanism is unequivocally elucidated by the atomic-resolution co-crystal structure of the CRBNMidi-LJY-3-60 complex. The structure explicitly delineates the homodimerization interface, revealing how the ligand reorganizes the protein surface to stabilize a non-canonical architecture that drives trans-autoubiquitination and subsequent proteasomal degradation. Furthermore, LJY-3-60 serves as a highly effective, controllable off-switch to mitigate PROTAC-induced toxicity. Ultimately, this work delivers a robust chemical tool for modulating CRBN stability. By demonstrating how a small molecule can functionally mimic an endogenous E3 substrates degron to catalyse targeted autodegradation, this study establishes a rational structural framework for designing the next generation of self-destructive modulators in targeted protein degradation (TPD) therapeutics.

biochemistry↗

bioRxiv2026

Membrane Curvature Activates Src kinase and Promotes Metastatic Cancer Cell Survival

Src family kinases (SFKs) play key roles in cancer metastasis. While SFKs are classically regulated by cell adhesions and transmembrane receptors, how they become activated following tumor cell detachment remains unclear. Here, we report curvature-induced kinase activation (CIKA), a distinct mechanism through which plasma membrane curvature directly promotes Src activation. Mechanistically, membrane curvature promotes the oligomerization of TOCA-family curvature-sensing proteins, inducing local biomolecular condensation. These condensates recruit Src, stabilize its open conformation, and exclude the negative regulator Csk, converting curved membrane domains into discrete kinase activation hubs. Disruption of CIKA using TOCA mutants inhibits curvature-induced Src activation and selectively impairs the viability of detached but not adherent cells. Functionally, curvature-induced Src activation promotes anchorage-independent survival, and its disruption suppresses metastatic colonization in xenograft mouse models. These findings reveal membrane curvature as a biophysical activator of Src and suggest CIKA inhibition as a potential therapeutic strategy to target metastatic cancer cells.

cell biology↗

bioRxiv2025

Ferritin Nanoparticle-Based Indirect ELISA for Immunodominant Region Screening and B-cell Epitope Validation of African Swine Fever Virus

African swine fever virus (ASFV) poses a severe threat to the global pig industry, creating an urgent need for effective vaccines and reliable diagnostic tools. While numerous ASFV B-cell epitopes have been reported based on murine-derived monoclonal antibodies (mAbs), such epitopes may not fully represent the antibody targets recognized in naturally infected pigs. Therefore, we need an approach to screen immunodominant regions by evaluating the reactivity of pre-defined epitopes with ASFV-infected pigs sera. In this study, we developed an indirect enzyme-linked immunosorbent assay (iELISA) based on recombinant epitope-ferritin nanoparticles (EFNs), as a platform for immunodominant region screening and epitope validation. This assay, hereafter referred to as EFNs-iELISA, shows an approximately twofold improvement in sensitivity compared with synthetic peptide-based ELISA for linear B-cell epitopes, and improved discrimination over GST-His-peptide and BSA-conjugated peptide formats. Using EFNs-iELISA, we evaluated 22 known murine-derived B-cell epitopes and 17 predicted epitopes from ASFV proteins CD2v, P30, P54, and P72 with ASFV-positive and negative porcine sera. According to the calculated cut-off threshold, tested epitopes were classified into different groups with positive or negative reactivity. Our analysis confirmed that among 22 known murine-derived ASFV B-cell epitopes, 16 (72%) also showed positive reactions with porcine-derived sera. Whats more, we also screened 7 new porcine-specific immunoreactive regions on these proteins. The EFNs-iELISA method provides a practical tool for screening immunodominant regions of ASFV, with potential applications in vaccine and diagnostic research.

immunology↗

bioRxiv2025

Immunotherapy with Pro-regenerative Macrophages from Embryonic Stem Cells Ameliorate Osteoarthritis via TNFAIP3-Mediated Chondroprotection

Immune cell-based therapies for osteoarthritis (OA) remains underexplored. Macrophages coordinate tissue repair, yet their functional heterogeneity and therapeutic applicability, particularly for designed stem cell-derived populations with tailored reparative properties are not fully delineated. Inspired by embryonic skeletal development as a paradigm of scarless regeneration, we identified a pro-regenerative macrophage population through a single-cell RNA atlas of developing skeletal tissues. We subsequently established a mechanically dynamic induction approach to efficiently generate pro-regenerative macrophages from human embryonic stem cells (hESCs), mimicking this developmental phenotype. In vitro, these macrophages mitigate cartilage degradation in OA explants and suppress pro-inflammatory, catabolic signaling in chondrocytes. Injection into murine OA joints robustly attenuates disease progression, reduces extracellular matrix breakdown, and resolves synovial inflammation, representing the first demonstration of immune cell-based therapy for OA. Mechanistically, these macrophages orchestrate chondroprotection not through broad immunosuppression, but by activating a TNFAIP3-dependent signaling hub that restrains inflammatory and catabolic responses in chondrocytes. This TNFAIP3 activation precisely reversed the OA-associated gene signature, suppressing catabolic mediators while enhancing reparative factors, thereby restoring the anabolic-catabolic balance. Our findings uncover a developmental blueprint for engineering pro-regenerative macrophages from stem cells and establish a pioneering targeted immune cell therapy that engages an intrinsic chondroprotective program, offering a transformative and translationally relevant strategy for OA treatment.

immunology↗

bioRxiv2025

A Population-scale Single-cell Spatial Transcriptomic Atlas of the Human Cortex

The genetic and spatial determinants of cell type diversity in human cerebral cortices remain poorly defined. Here, we present a population-level single-cell spatial transcriptomic atlas of human cortices from 71 donors across the lifespan. We identified 906 layer-specific genes showing conserved and divergent laminar expression patterns between humans and other species. Spatial analysis revealed neuronal vulnerability and glial activation during aging, together with a decline in the proportion of superficial SST neurons and their interactions with other cells. Disease-associated genes exhibited high cell-type and layer-specific expression, implicating the pathogenic role of spatially specific gene expression. Spatial cis-eQTL analysis identified regulatory variants linked to genes related to diseases like Tourette syndrome. Cross-species comparison demonstrated glial expansion in the human cortex, accompanied by enhanced neuron-glia communication via the neuregulin signaling. Together, we provide a comprehensive single-cell atlas of the human cortex that is essential for understanding aging, evolution, and disease pathogenesis.

neuroscience↗

bioRxiv2025

Continuous Serial Electron Diffraction for High Quality Protein Structures

Determining macromolecular structures is crucial for understanding biological mechanisms and advancing drug discovery. Three-dimensional electron diffraction (3D ED), also known as microcrystal electron diffraction (MicroED) using continuous sample rotation has emerged as a powful method for solving structures from sub-micrometre-sized crystals. However, the resolution of MicroED data from protein crystals is often limited by radiation damage. Serial electron diffraction (SerialED) overcomes this limitation by merging single-shot diffraction patterns from thousands of crystals, but its widespread use has been hindered by the complexity and scarcity of equipment required for single shot data acquisition. Here, we introduce continuous SerialED (c-SerialED) - a simple, robust and widely accessible protocol. This approach collects diffraction data quickly and efficiently from all crystals within a given area, without prior crystal identification. We show that only using a standard cryo-EM instrument equipped with a simple widely available CMOS detector, c-SerialED greatly reduces radiation damage while improving the data quality. We demonstrate that c-SerialED enables determination of lysozyme structures at atomic resolution (0.83 [A]) and improves the data resolution of Dype Type Peroxidase Aa (DTPAa) crystals from 2.5 [A] (MicroED) to 1.3 [A]. Remarkably, the resulting structures are virtually free of radiation damage. The improved data quality and resolution allow visualization of radiation sensitive chemical features and protein-ligand interactions to state-of-the-art accuracy. By providing a convenient, fast, and damage-minimizing workflow on existing cryo-EM setups, c-SerialED significantly enhances the applicability of electron diffraction in structural biology. We anticipate our protocol will enable a wide range of studies requiring high-quality diffraction data from radiation-sensitive macromolecular crystals.

molecular biology↗

bioRxiv2025

scSTAR2: a multiomics integration algorithm to reveal disease specific cellular signatures by bridging single-cell resolution features and clinical metadata

Single-cell techniques, pivotal in characterizing intricate cell and spatial structures within tissues, face challenges in contextualizing clinical phenotypes. Currently, most investigations of the clinical phenotype related cell/spatial heterogeneities relied on the phenotype information from single-cell data. However, a wealth of underutilized clinical metadata exists within conventional bulk sequencing data. Current methods for correlating clinical information with individual cells or spatial regions remain limited and lack robustness. Here we present scSTAR2, a novel algorithm that transcends existing limitations by reconstructively integrating multiomics data to identify cells associated with clinical phenotypes. Unlike existing methods, scSTAR2 reconstructs single-cell data guided by specific phenotypes, significantly reducing interference from irrelevant noise. By employing scSTAR2 to integrate scRNA-seq, scATAC-seq, and spatial transcriptomics and bulk RNA-seq, we not only confirmed a new heat-shock Treg subtype in tumors but also more sensitively identified TLS (tertiary lymphoid structure) areas than traditional methods. In conclusion, scSTAR2 has proven to significantly enhance single-cell data interpretation across diverse clinical scenarios.

bioinformatics↗