Biology subjects

Zhang, F.

Publications and source records attributed to Zhang, F..

At least 19 recordsLinked to original sources

bioRxiv2026

CellMethMarker provides an integrated database of human cell- and tissue-type-specific DNA methylation markers

Cell- and tissue-type-specific DNA methylation markers provide epigenetic signatures of cellular identity and are widely used to infer cellular origin from bulk and single-cell methylomes. Existing resources mainly emphasize disease-associated biomarkers, methylome atlases, genomic annotations or RNA-based cell markers, leaving an unmet need for an integrated region-level resource dedicated to such markers. Here we present CellMethMarker (http://gu-zhanglab.hfcas.ac.cn/CellMethMarker), a curated database integrating 347,514 de novo markers derived from uniformly processed whole-genome and reduced-representation bisulfite sequencing datasets, 2,203,507 literature-derived marker records and harmonized metadata for 2,810 samples. CellMethMarker enables cell- and tissue-centered marker retrieval, coordinate-based search, genome-browser visualization, interactive methylation analysis and data download. In an independent single-cell methylome atlas, these markers recover the expected cell-type-associated methylation patterns, and a platform-matched reference built from them lowers the mean deconvolution error from 0.365 to 0.144. CellMethMarker therefore links region-level DNA methylation variation to cellular identity and supports methylome-based cell-type annotation and deconvolution.

bioinformatics↗

bioRxiv2026

Widespread Presence of Catalytic RNAs in All Domains of Life

Very few ribozymes have been found in complex eukaryotes. Here, we developed a novel method, AbRiDGed, for highly accurate and sensitive ab initio self-cleaving ribozyme discovery. Our results indicate the existence of millions of self-cleaving ribozymes in humans and other species. We characterized ~150 novel classes of self-cleaving ribozymes, including one class which exhibits noncanonical biochemical properties and widespread presence in all domains of life and appears to play specific functions in complex organisms. These results revealed that the true complexity of the "ribozyme-ome" is several orders of magnitude higher than previously estimated. We demonstrate that newly identified self-cleaving ribozymes influence organismal fitness by regulating gene expression and enhancing transcriptome complexity in vivo. Moreover, these ribozymes hold promise as foundational tools for biotechnological applications, such as direct RNA detection technologies. Overall, catalytic RNAs are prevalent and diverse, and may represent a hidden regulatory layer in various organisms.

molecular biology↗

bioRxiv2026

OTTER: Orchestrated Transcriptomic, Tumor-xenograft (PDX/CDX), and Epigenomic Reporting Workflow

Summary: Tumor xenograft sequencing libraries inevitably contain infiltrating murine host reads, but conventional deconvolution tools fail on bisulfite sequencing (BS-seq) because chemical C-to-T deamination collapses sequence entropy and destroys graft recall (< 0.015%). We present OTTER, an automated, compiled multi-omics workflow framework that decouples execution into static domain operators, an immutable reference registry, and a native strand-aware edit-distance engine, restoring bisulfite deconvolution (52%-54% recall across a 0%-99% compositional gradient) while preserving complete decision parity on transcriptomics. Availability and implementation: OTTER is implemented in Go and Rust, runs on x86_64 Linux (including CentOS 7 with glibc 2.17), and is freely available open-source under the MIT License at https://github.com/otterlab-bio/otter; test and validation data are provided at https://huggingface.co/datasets/fallingstar10/otter-data.

bioinformatics↗

bioRxiv2026

Xenograft supervision and joint methylation-fragment-end encoding define a reusable molecular scoring layer for cell-free tumor DNA

Circulating tumor DNA is often scarce in plasma, and regional methylation summaries can obscure informative differences between individual molecules. Here we present ctDNA-BERT, a Transformer that scores individual bisulfite reads without predefined differentially methylated regions by jointly encoding CpG methylation state, nucleotide context and the fragment 3' end. Xenografts provide molecule-level tumor supervision. The resulting frozen scorer supported tumor-fraction estimation in synthetic and patient-derived xenograft mixtures and enriched tumor-concordant methylation and chromatin-accessibility signals in independent hepatocellular carcinoma and stage III pan-cancer plasma without cohort-specific retraining. Features derived from selected reads discriminated stage I cancers from healthy controls in a retrospective cohort of 1,477 specimens, achieving an out-of-fold area under the receiver operating characteristic curve of 0.884. By separating molecular scoring from sample-level inference, ctDNA-BERT provides a reusable read-level scoring layer for cell-free DNA methylation analysis.

bioinformatics↗

bioRxiv2026

LigRNA: High-accuracy prediction of RNA-ligand binding sites via dynamic chemical complementarity and language model encoding

RNA-binding small molecules represent a validated therapeutic modality, yet pinpointing their exact RNA binding sites remains critical for mechanistic understanding and drug optimization. Experimental methods, while precise, are costly and low-throughput, necessitating robust computational alternatives. Approaches based on static feature concatenation may overlook input-dependent relationships between RNA residues and ligand functional groups. Here we present LigRNA, a graph neural network framework that predicts RNA-ligand binding sites through joint cross-modal encoding of both molecules. LigRNA integrates structure-guided language model embeddings with physicochemical features-solvent-accessible surface area and secondary structure types, to construct comprehensive RNA representations. Ligands are encoded as attributed molecular graphs via graph attention networks, preserving atom-level chemical topology rather than compressing molecules into fixed fingerprints. A cross-attention mechanism then enables dynamic, ligand-dependent modulation of RNA nucleotide attention, capturing context-specific interaction patterns. Benchmarked on TE9 and TE18 datasets, LigRNA achieves F1 scores of 0.73 and 0.58, improving on GATRsite by 7.4% and 18.4%, respectively. Ablation studies identify the RNA residual encoder as the most consistent contributor across the two benchmarks. The source code is freely available at https://github.com/WillowWang0216/LigRNA.

bioinformatics↗

bioRxiv2026

AMPK-dependent brain-wide norepinephrine release promotes recovery from metabolic coma in mice

Coma is a pathological brain state characterized by loss of consciousness. How the brain recovers from coma remains largely unknown. Using mouse models of metabolic coma, we identified an endogenous neuromodulatory pathway that actively promotes coma recovery. In mice subjected to hypoglycemic coma, hypoglycemia triggered AMPK-dependent excitation of nucleus tractus solitarius (NTS) neurons, which activated locus coeruleus (LC) norepinephrine neurons through a glutamatergic NTS-LC circuit. LC activation drives widespread norepinephrine release across cortical and subcortical regions, thereby promoting post-coma arousal. Notably, coma-associated brain-wide norepinephrine release was unconventional in both scale and duration, with a magnitude approximately 10-fold higher than physiological norepinephrine release during sleep-wake cycles. This neuromodulatory process also operated during hypercapnic coma. Vagus nerve stimulation accelerated coma recovery by engaging this endogenous anti-coma mechanism. Our findings uncover an AMPK-dependent brainstem circuit that actively restores arousal from metabolic coma, revealing a neural defense strategy against profound metabolic stress.

neuroscience↗

bioRxiv2026

FMR1 Perturbations Disrupt Sensory Corticostriatal Circuits and Mitochondrial Energetics in Fragile X Syndrome

Fragile X syndrome (FXS) features sensory, social, and cognitive dysfunction, but how fragile X messenger ribonucleoprotein 1 (FMR1) deficiency disrupts sensory corticostriatal circuitry and whether distinct FMR1 perturbations converge on shared cellular mechanisms remain incompletely defined. We integrated diffusion magnetic resonance imaging (MRI) in individuals with FXS and Fmr1-knockout (KO) mice with projection-defined calcium recording, bidirectional optogenetic manipulation, primary somatosensory cortex (S1)-targeted expression of three patient-associated FMR1 missense variants, regional transcriptomics, and optical manipulation of mitochondrial energetic state. Individuals with FXS exhibited disrupted sensory corticostriatal structural connectivity, with mouse analyses further localizing abnormalities to S1-striatal pathways. Fmr1 deficiency impaired recruitment of the S1-dorsomedial striatum (DMS) pathway during social interaction, and projection-specific manipulation demonstrated that S1-DMS dysfunction contributes to social and cognitive deficits. Three patient-associated FMR1 variants, I234T, E329G, and R586Q, converged on social behavioral deficits, which were improved by S1-DMS activation. Transcriptomic analyses revealed convergent oxidative phosphorylation (OXPHOS)-related mitochondrial remodeling in S1 and DMS across Fmr1 deficiency and variant models. Systemic treatment with the mitochondria-targeted antioxidant MitoQ attenuated novel-location-recognition, social-interaction, and social-novelty deficits in Fmr1-KO mice. Moreover, mt-EcGAPR-based Mitochondrial OptoEnergetic Activation (Mito-OptoEA) enhanced mitochondrial energetic state in S1 and improved novel-location recognition and sociability in both Fmr1-KO and I234T models. Together, these findings define a sensory corticostriatal circuit-metabolic framework for FXS in which S1-DMS circuit dysfunction and convergent mitochondrial energetic abnormalities contribute to FMR1-related social and cognitive phenotypes.

neuroscience↗

bioRxiv2026

Information regimes in population-graph neuroimaging: cohort context and implementation-dependent evaluation sensitivity

Population-graph models aggregate a query subject with an unlabeled cohort, so their reported performance can depend on the evaluation regime rather than on predictive information alone. Evaluation context has two separable axes: the visibility of the cohort during training, and the context available at inference. We study the training-time axis under a protocol fixed before analysis, on an ABIDE-I cohort of 871 subjects (20 acquisition sites). Two population-graph implementations are compared: a learned/adaptive graph (EV-GCN) and a static weighted graph (Parisot-GCN). Within each implementation the cohort, connectivity features, outer splits and training and selection rules are held fixed, and only the information available to the graph varies. The primary estimand is the implementation-by-training-context interaction: the difference between the P0-to-P1 AUC changes of the two implementations ({Delta}{Delta}AUC). Across three preselected outer splits (666, 1024, 2024) the direction is consistent and positive, with observed interaction range 0.06285-0.13090; uncertainty is assessed separately within each split by paired subject and site-cluster bootstrap resampling and is reported per split. Splits are replication units: they are not pooled and no cross-split average is reported, so the result is not a claim of invariance to arbitrary splits. These findings tie reported population-graph performance to the evaluation information regime and motivate explicit reporting of cohort visibility. The contribution is methodological: a two-axis taxonomy of information regimes, preselected cross-split evidence that the sensitivity of reported performance to cohort context depends on the implementation, and a reusable reporting checklist.

neuroscience↗

bioRxiv2026

Autonomic and neurotrophic signaling in alcohol-associated liver disease

Alcohol-associated liver disease (ALD) is a leading cause of liver-related morbidity and mortality, yet its pathogenesis remains incompletely understood. While neural regulation is critical for hepatic homeostasis, how autonomic and neurotrophic signaling is regulated in the liver during ALD progression remains largely unexplored. Here, we performed an integrative analysis of bulk and single-cell transcriptomic datasets of human livers across the spectrum of ALD. We identified stage-dependent remodeling and coordinated expression patterns of autonomic and neurotrophic genes. Autonomic signaling exhibited a consistent decline, with reduced expression of sympathetic (DBH, TH) and the parasympathetic (ChAT) markers. In contrast, pan-neuronal and neuroplasticity-associated genes (UCHL1, TUBB3, STMN2) were upregulated, indicating active neural remodeling. In parallel, factors such as MANF, GDNF, and NRG2, with the receptor NTRK1, were upregulated and positively correlated with clinical indicators of disease severity, inflammation and fibrosis. In contrast, trophic factors including NTF3 and CDNF, and receptors such as GFRA1 and GFRA3, were downregulated and inversely associated with these parameters. Single-cell analyses revealed that neurotrophic factors are predominantly expressed by hepatic stellate cells, supporting their role as resident neuroglia-like cells in the liver. In another independent cohort, we observed a coordinated downregulation of NTF3 and sympathetic nerve markers in alcohol-associated hepatitis (AH), suggesting impaired NTF3-mediated neurotrophic support may contribute to sympathetic dysfunction in AH. Collectively, these findings uncover previously unrecognized neuro-regulatory dimensions in ALD and suggest that dysregulated autonomic and neurotrophic signaling may contribute to disease progression. Conflict of interestThe authors have declared that no conflict of interest exists.

bioinformatics↗

bioRxiv2026

CryoMV: Structure-Prior-Guided Modeling and Real-Particle Validation of Continuous Conformational Transitions in Cryo-EM

Continuous protein conformations are essential for understanding fundamental biological processes and supporting drug discovery. Although cryo-EM can resolve individual states at high resolution, recovering continuous heterogeneity from 2D particle images remains challenging. High noise, motion blur, and limited structural priors make it difficult to accurately generate and validate high-resolution continuous conformations using raw particle data. Here, we introduce cryoMV, a framework that integrates structure-prior-guided modeling with real-particle validation for continuous conformational transitions. CryoMV uses reference density maps to establish structural anchors and motion priors, models candidate transition paths between selected conformations, and transfers the learned representation to raw 2D cryo-EM particle images. Each candidate conformation is subsequently evaluated using the estimated particle poses and contrast transfer functions. Supported conformations are reconstructed through raw particle back-projection and assessed using canonical half-maps and Fourier shell correlation. On EMPIAR-10516 and EMPIAR-10345, cryoMV achieves excellent performance in terms of robustness, verifiability, and reconstruction resolution. By incorporating structure-prior modeling and evidence from the raw particles, cryoMV offers an explicit mechanism for assessing whether generated conformations are supported by experimental data and provides a practical approach to reducing model-induced artifacts in continuous cryo-EM heterogeneity analysis.

bioinformatics↗

bioRxiv2026

Marine sulfated glycans inhibit the binding of Borrelia burgdorferi adhesins to heparin/GAGs

Recognition of host glycosaminoglycans (GAGs) is critical for the adhesion and colonization of Borrelia burgdorferi, but the structural requirements for these interactions remain incompletely understood. Using surface plasmon resonance (SPR), we characterized the heparin-binding properties of three Borrelia adhesins, i.e. Bgp, DbpA-A9, and DbpB-B31 and evaluated inhibitors comprising heparin oligosaccharides, selectively desulfated heparins, mammalian GAGs, marine sulfated glycans, pentosan polysulfate (PPS), and mucopolysaccharide polysulfate (MPS). All three adhesins bound directly to heparin, with equilibrium dissociation constants (KD) of 2.18 nM for Bgp and 36.4 nM for DbpB-B31. Competition assays identified N-sulfation and 6-O-sulfation as major determinants of binding, whereas 2-O-sulfation had a lower effect. Bgp showed chain-length dependence over dp4-dp20, whereas DbpB-B31 preferentially recognized longer oligosaccharides, particularly those of dp12 or greater. Chondroitin sulfate E also exhibited strong inhibitory activity, indicating that recognition was not restricted to heparin/heparan sulfate backbones. Among the tested sulfated glycans, fucosylated chondroitin sulfate (HfFucCS) from Holothuria floridana was the most potent inhibitor of Bgp, with an IC50 of approximately 4 ng/mL, whereas PPS was the strongest inhibitor of DbpB-B31, followed by MPS. These findings reveal protein-specific sulfated-glycan recognition and identify promising candidates for disrupting B. burgdorferi-host GAG interactions.

biochemistry↗

bioRxiv2026

Tracing high transductive cohort AUC to same-site supervision in a site-aware population GNN for multisite fMRI

Population-graph models can use cohort-level context, complicating interpretation of strong multisite neuroimaging performance. We investigated which information pathways accounted for high transductive cohort discrimination in a site-aware heterogeneous population graph neural network for autism classification. Using a frozen ABIDE-I cohort (871 participants, 20 sites) and evaluation protocol, we applied controlled graph, feature, supervision, and architecture interventions to a clean-room re-implementation across C-PAC and NIAK preprocessing pipelines. Cohort out-of-fold AUC was approximately 0.94. A site-only graph retained similarly high discrimination (0.948 versus 0.941 for the full model), with no statistically significant difference detected. Removing same-site supervision under a site-wise intervention reduced AUC to 0.480, whereas a supervision-budget control retaining same-site supervision achieved 0.921. Among the alternative heads evaluated, the high cohort AUC was observed only with the sex-heterogeneous dual-channel head; a canonical topology-only Parisot-GCN did not reproduce it. A demographic classifier using site, sex, and their interaction achieved a best pooled AUC of 0.51. Under leave-one-site-out evaluation, AUC was 0.522 for C-PAC and 0.532 for NIAK, with confidence intervals including 0.5 and no clear evidence of unseen-site discrimination; an imaging-only reference achieved 0.653 and 0.588. Because leave-one-site-out evaluation jointly changes supervision availability, target-site topology, and training-domain composition, the decrease cannot be attributed to a single factor. These findings distinguish cohort-visible transductive performance from unseen-site generalization and motivate explicit site-only, no-graph, and site-held-out controls.

neuroscience↗

bioRxiv2026

Test-Retest Reproducibility of Single- and Cross-Population White Matter Atlases in Diffusion MRI Tractography

Diffusion MRI tractography enables noninvasive mapping of white matter fiber tracts. Atlas-based white matter parcellation supports automated tract identification by assigning individual streamlines to atlas-defined clusters and anatomical tract labels. Because clustering-based white matter atlases are constructed from cohort-specific tractography data, they capture common white matter organization represented in the atlas-construction population. Although major white matter anatomy is shared across populations, subtle population-related anatomical variability may influence atlas representation and test-retest correspondence. Therefore, the reproducibility and cross-population generalizability of tractography-based white matter atlases are important considerations for quantitative neuroimaging studies. In this study, we evaluated whether incorporating cross-population anatomical variability during atlas construction improves test-retest reproducibility. To do so, we compared a single-population ORG atlas constructed from a Western cohort with the cross-population East-West White Matter Atlas constructed from both Eastern and Western cohorts. Test-retest diffusion MRI scans from the Human Connectome Project Young Adult (HCP-YA) dataset and the Connectivity-based Brain Imaging Research Database (C-BIRD) were analyzed as independent Western and Eastern test-retest cohorts, respectively. Whole-brain tractography was reconstructed for each dMRI scan and parcellated using both atlases. Reproducibility was assessed using tract detection rate at both cluster level and anatomical tract level, weighted Dice coefficient, and the relative difference of mean fractional anisotropy (FA). Both atlases showed stable tract detection across test-retest scans in both cohorts. Compared with the ORG atlas, the East-West White Matter Atlas achieved higher overall spatial overlap and lower test-retest variability in mean FA, although atlas performance varied across individual tracts. These findings suggest that integrating cross-population information during atlas construction improves the reproducibility and generalizability of white matter atlas mapping across independent populations and imaging protocols.

neuroscience↗

bioRxiv2026

Accurate and scalable decontamination of imaging-based spatial transcriptomics via optimal transport

Imaging-based spatial transcriptomics enables molecule-resolved profiling of gene expression and tissue organization in situ. However, segmentation errors, transcript spillover and three-dimensional cell overlap can introduce misassigned transcripts into cell-level expression profiles, compromising biological interpretation and obscuring genuine signals. Existing methods either remove suspect expression at the cost of signal loss or lack a biologically grounded criterion for transcript assignment. Here we present CellDot, an optimal-transport framework that determines the fate of each transcript by retaining it in its host cell, reassigning it to a plausible neighboring cell or removing it as background. By integrating reference-guided expression compatibility with spatial information and data-adaptive constraints, CellDot enables accurate and traceable molecule-level correction while preserving biologically meaningful variation. In evaluations across multiple human tumor datasets, CellDot exhibited superior performance compared to existing decontamination methods, successfully restoring spatial expression patterns that matched independent cross-platform measurements. Moreover, it significantly enhanced the recovery of cellular states, intercellular communication, and spatial niche programs. Our experiments using real data demonstrated CellDot's scalability and established it as the only method applicable to a whole-transcriptome Atera dataset, underscoring its distinct advantages in the field of spatial transcriptomics.

bioinformatics↗

bioRxiv2026

RNA-guided transcriptional repression by TIGR-Tas systems

Tandem interspaced guide RNA (TIGR)-TIGR-associated protein (Tas) systems are a widespread family of RNA-guided DNA-targeting proteins whose diversity has remained uncharacterized because their arrays, unlike CRISPR arrays, lack the sequence conservation required by existing annotation tools. We developed TIGRFinder, a motif-based pipeline that identified 6,685 TIGR arrays from genomic and metagenomic data. Phylogenetic and structural analysis of Tas proteins revealed clade-specific insertions in stem-loop binding Tas proteins that co-vary with features of their cognate tigRNAs. Cryo-electron microscopy structures of two stem-loop binding TasR ribonucleoprotein complexes demonstrate how these protein insertions directly accommodate extended tigRNA stems while maintaining DNA binding through catalytically inactive RuvC domains. We also show that these catalytically dead TasR proteins, along with the nuclease-lacking TasA, function as RNA-guided transcriptional repressors. These findings establish TIGR-Tas as a functionally diverse family of RNA-guided effectors, with comprehensive annotations available through TIGRSafari (https://tigr.bio) to support further exploration and engineering.

molecular biology↗

bioRxiv2026

CryoFlex characterizes structural motion between conformations directly from cryo-EM density maps

Heterogeneous cryo-EM reconstruction methods can resolve multiple conformational states, but understanding their functional implications often requires determining which regions move between states and quantifying their displacement magnitudes. Comparing atomic models can quantify motion, but flexible regions are often incompletely modeled. Direct map comparison is limited by variable map quality and ambiguous correspondence between displaced density features. Here, we present CryoFlex, a method that estimates the direction and magnitude of structural motion directly from reconstructed density maps of selected states. The resulting motion analysis supports functional interpretation and guides downstream structure processing. CryoFlex localized and quantified structural motion across diverse map pairs, including those with incomplete atomic-model coverage or weak local density. Against C; displacements, CryoFlex achieved an endpoint error of about 1 angstrom. In separate benchmarks, it outperformed registration baselines on clean and noisy inputs. Thus, CryoFlex complements heterogeneous reconstruction workflows with direct quantification of structural motion between reconstructed states.

bioinformatics↗

bioRxiv2026

BioIMA: a one-click desktop tool for standardized extraction of phenotypic traits from biological images

Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.

bioinformatics↗

bioRxiv2026

CryoForge: A Self-Correcting Agent for Cryo-EM Model Building That Learns When to Act and When to Stop

Automated atomic model building has accelerated cryo-EM structure determination, but different builders leave distinct residual error profiles requiring expert inspection. The post-building challenge is to decide which local interpretations are sufficiently supported by experimental evidence to be retained, corrected or rejected. Here we introduce CryoForge, an evidence-gated post-builder agent that separates repair proposal from repair acceptance. Rule and learning-based components identify candidate regions and prioritize legal actions, whereas an independent evidence gate evaluates each edit using map and half-map support, stereochemistry, connectivity and local structural context. Supported edits are retained; unsupported or conflicting modifications are rejected, rolled back, stopped or escalated for expert review. Across a resolution-stratified benchmark, 84.9% of 26,153 released trajectories yielded standard validated improvements and 3.7% yielded low-confidence partial improvements, with no quality-degrading edit retained in the final promoted models. Relative to rule-only control, learned prioritization reduced non-improving candidates and harmful actions while preserving global structural stability. External evaluations using an alternative initializer, same-team automated/manual-assisted challenge submissions and three recently released complex assemblies showed that CryoForge adapts to distinct residual error phenotypes and performs bounded, evidence-supported correction without uncontrolled remodeling. CryoForge provides a builder-independent, scalable and auditable correction layer between automated model generation and expert structural interpretation.

bioinformatics↗