Biology subjects

Xie, Z.

Publications and source records attributed to Xie, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Variability and Landmark Quality in Anatomical Co-Registration in Neuronavigation for Brain Stimulation

Accurate neuronavigation depends not only on the technical performance of the tracking system but also on the reproducibility with which anatomical landmarks are physically localized. We quantified operator- and landmark-specific variability in repeated facial and cranial landmark localization in 19,630 measurements from 16 participants, four operators, and 13 landmarks under marked and unmarked conditions. Within-operator precision, inter-operator agreement, and marked-unmarked displacement were analyzed separately. Median within-operator precision was 1.60 mm and median inter-operator distance was 2.91 mm, with substantial differences across both operators and landmarks. The left medial canthus showed the greatest within-operator precision (1.43 mm), whereas the pronasale showed the closest inter-operator agreement (2.05 mm). Marking the positions before the procedure did not significantly improve within-operator precision overall but reduced inter-operator distances by 29.2%, which indicates convergence on a shared spatial reference rather than improved intrinsic repeatability. Between-operator differences also contained systematic directional components; signed offsets approached 1.5 mm. Exploratory analyses did not suggest general left--right difference in precision, although the approach side produced systematic spatial shifts for midline landmarks despite comparable repeatability. These findings demonstrate that anatomical landmark localization is both operator- and landmark-specific. Standardized landmark definitions, operator calibration, consistent pointer approach, and the use of multiple well-defined registration features should therefore be considered integral components of neuronavigation quality control.

neuroscience↗

bioRxiv2026

The vagus nerve drives cognitive function through the default mode network

The afferent vagus nerve transmits interoceptive signals to the brain but a systematic understanding of how vagal signals affect brain-wide neuronal dynamics remains elusive. Here, comprehensive spatiotemporal analyses across the entire mouse brain revealed that vagus nerve stimulation activated neuronal ensembles within the default mode network (DMN). In contrast, vagal-independent mechanisms predominantly affected the salience network. The vagal-DMN association was mediated by locus coeruleus neuronal subpopulations. Selective activation or silencing of vagal-responsive DMN neuronal ensembles replicated or abolished VNS-induced enhancement of cognitive behaviors and progressive problem-solving behaviors. These findings establish a unifying framework for how the vagus nerve drives cognitive functions by engaging the DMN.

neuroscience↗

bioRxiv2026

Sum-h2, enabling genetic discovery for deep learning-derived phenotypes through a fast evaluation framework and arena of performance

In the recent growing interest of AI research toward biology, genetic association studies of AI- derived phenotypes from high-content modalities such as images emerges as a powerful means for biological discovery. However, such AI-phenotyping methods still lacks a good optimization target and an efficient evaluation framework. The number of discovered loci was the major criterion for evaluating the quality of AI-derived phenotypes. However, the time and computational resources required for running the GWAS and subsequent loci-clumping are substantial, limiting the rapid development and iteration of deep learning representation algorithms. Here we present a 1000x faster and lightweight framework, sum-h2, than traditional GWAS framework for evaluating genetic discovery through total heritability. We revisit tr(P^(-1) G), previously proposed in the context of evolutionary studies, as a measure of multi-phenotype heritability. We showed that sum-h2, the sum of heritability over phenotypic Principal Components (PCs), is equivalent to the linear transformation-invariant tr(P^(-1) G), through both theoretical proof and simulation studies. Moreover, sum-h2 can be estimated rapidly with minimal information loss over a relatedness-enriched sample, while preserving the relative ranking of endophenotypes by GWAS loci counts. Based on selected UKB data and sum-h2, we set up a Genetic Discovery Arena, enabling rapid and fair comparisons for the development and optimization of deep learning-derived phenotyping methods.

bioinformatics↗

bioRxiv2026

Transcription factors read a second regulatory code in chromatin

Transcription factors (TFs) decode gene regulatory information written in DNA, yet how this vocabulary is interpreted within chromatin remains largely unexplored. Here, using an upgraded NCAP-SELEX platform, we systematically map the nucleosomal DNA recognition landscapes of 269 human TFs. We uncover a widespread, chromatin-dependent mode of sequence recognition: many TFs recognize motifs on nucleosomal DNA that are distinct from their canonical naked-DNA binding sites, revealing that nucleosome architecture encodes a second gene regulatory code in chromatin. Cryo-electron microscopy structures of TF-nucleosome complexes demonstrate that this second code is read through a combination of nucleosome-induced DNA deformation and direct protein-histone contacts. Functional analyses show that these chromatin-encoded motifs actively promote chromatin accessibility and drive cell-type-specific cis-regulatory activity in vivo. Strikingly, a single TF can deploy its canonical and nucleosome-derived motif repertoires to partition and govern entirely distinct physiological programs. Together, our findings establish that TFs interpret two complementary layers of genomic information: the primary DNA sequence and a second code written into the nucleosome architecture. This chromatin-encoded layer of regulatory information fundamentally expands our understanding of how TF specificity is achieved and how gene regulatory networks are wired in multicellular organisms.

systems biology↗

bioRxiv2026

Adaptive introgression in Tibetans spans focal and distributed architectures

The Denisovan-derived EPAS1 haplotype is a paradigmatic example of adaptive introgression, but whether archaic ancestry contributed beyond such large-effect loci remains unclear. We analyzed genomes from 1,039 Tibetans, Han Chinese, archaic hominins and ancient Tibetan Plateau individuals. Denisovan-derived adaptive variation was highly localized around EPAS1, whereas Neanderthal-derived variation was distributed across the genome; temporal and elevational patterns reinforced this contrast. Despite individual-level heterogeneity, Neanderthal-introgressed gene repertoires repeatedly converged on immune, metabolic and signal-transduction pathways. A Tibetan-enriched Neanderthal PPARGC1A haplotype was associated with lower fasting glucose and showed concordant support from an independent East Asian type 2 diabetes GWAS. These findings reveal that adaptive introgression can contribute to complex human adaptation through functionally convergent combinations of archaic variants, rather than only through shared large-effect haplotypes.

evolutionary biology↗

bioRxiv2026

Phagocytic activity of perivascular cell promotes CNS injury pathology

Injury, stroke, and neurological diseases cause persistent accumulation of cellular debris that deteriorates lesion microenvironment and impedes central nervous system (CNS) repair. Debris clearance in the injured CNS has long been attributed primarily to microglia and infiltrating macrophages. Here, we identify perivascular cells as previously unrecognized phagocytes that expand after injury and exhibit robust phagocytic activity. Perivascular cell phagocytosis is conserved across multiple mouse models of CNS injury and human stroke lesions. These cells exhibit key hallmarks of phagocytosis, including LC3-associated phagocytosis for efficient lysosomal degradation. Mechanistically, phosphatidylserine serves as the eat-me signal and Axl mediates myelin debris uptake. Myelin phagocytosis drives perivascular cell proliferation, fibrosis and lesion progression. Genetic deletion of Axl in perivascular cells or pharmacological inhibition with the FDA-approved Axl inhibitor Gilteritinib reduces pathology and improves functional recovery after spinal cord injury. Together, these findings establish Axl-dependent perivascular cell phagocytosis as a therapeutic target for CNS repair.

neuroscience↗

bioRxiv2026

Comprehensive Mapping of SGCA Variant Effects Reveals Domain-Specific Constraints Relevant to Sarcoglycanopathies

Pathogenic variants in SGCA, encoding -sarcoglycan, cause an autosomal recessive limb-girdle muscular dystrophy, LGMDR3/2D, yet clinical interpretation of SGCA variants remains challenging due to the high prevalence of rare missense variants. -sarcoglycan is an essential component of the sarcoglycan complex at the muscle cell membrane, and pathogenic variants frequently impair its membrane localization. Here, we systematically assess the effects of all possible single-nucleotide variants across the SGCA coding sequence using a saturation mutagenesis-based experimental assay that quantifies -sarcoglycan surface expression. We generate a comprehensive functional atlas that distinguishes tolerated and damaging variants, aligning with independent genetic and clinical evidence, and reveals domain-specific properties of the cytoplasmic region, in which C-terminal truncating variants retain membrane localization, suggesting possible pathogenic mechanisms beyond impaired trafficking. This work provides a scalable functional framework to support genetic diagnosis and variant interpretation in sarcoglycanopathies. Graphical AbstractSchematic overview of saturation mutagenesis-based functional mapping of SGCA. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/740214v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@19be841org.highwire.dtl.DTLVardef@ad2e49org.highwire.dtl.DTLVardef@1b9cb61org.highwire.dtl.DTLVardef@d77317_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

bioRxiv2026

PFM: perturbed flow matching for structure-based drug design

Generating 3D molecules that bind to specific protein targets via generative models has shown great promise in structure-based drug design. Recently, diffusion-based methods have achieved promising results, but their reliance on high sampling steps poses risks of slowing the drug discovery process due to increased time and computational costs. In this work, we propose a novel method named Perturbed Flow Matching (PFM), which significantly reduces sampling steps by leveraging a Flow Matching framework. PFM introduces a unique perturbed conditional probability path design that incorporates pocket binding site information and atom type-coordinate coupled information to enhance molecular generation performance. Experiments on CrossDocked2020 dataset demonstrate that PFM generates molecules with competitive 3D structures and state-of-the-art (SOTA) binding affinities towards the protein targets, achieving an Avg. of -7.12. Additionally, PFM accelerates the generation of valid molecules by a factor of 21.3, while demonstrating potential for further improvement. The code is available at https://github.com/kurisu92725/PFM.

bioinformatics↗

bioRxiv2026

Astrocyte-to-microglia purinergic signaling mediates synaptic shielding and promotes neuronal activity

How glial cells sense and regulate neuronal hypoactivity remains poorly understood. Using in vivo imaging in anesthetized, sleeping, and behaving mice, we identified a glial purinergic signaling mechanism that converts neuronal hypoactivity into local circuit regulation. Cortical hypoactivity increased spatially confined ATP release through astrocytic pannexin-1 hemichannels, which were structurally enriched near perisomatic parvalbumin-positive boutons. Local ATP release directed microglial process movement and stabilized bulbous endings through microglial P2Y12 signaling, accompanied by localized Ca{superscript 2} activity in microglial processes. Disruption of astrocytic pannexin-1 or microglial P2Y12 impaired bulbous-ending stabilization and abolished rebound increases in neuronal activity during emergence from anesthesia. Similar ATP release and bulbous-ending formation were observed during chemogenetic neuronal silencing or natural sleep in freely behaving mice. These findings identify astrocyte-to-microglia purinergic signaling as a mechanism that converts neuronal hypoactivity into synapse-selective microglial regulation of circuit activity.

neuroscience↗

bioRxiv2026

Cholinergic interneuron control of GABAergic circuits targeting spiny projection neurons is disrupted in parkinsonian models

Parkinsons disease (PD) is known to alter the intrinsic properties of striatal cholinergic interneurons (ChIs). However, how PD shapes ChI control of intrastriatal GABAergic circuits regulating principal spiny projection neurons (SPNs) is unknown. To fill this gap, striatal circuits in healthy and parkinsonian mice were interrogated. In ex vivo brain slices from healthy mice, optogenetic stimulation of ChIs evoked GABAA receptor currents in both indirect and direct pathway SPNs that were attributable to nicotinic acetylcholine receptor (nAChR)-mediated activation of GABAergic interneurons (GIs). Simulations suggested that this circuit exerts a state-dependent control of SPN dendritic integration that was modulated by concomitant muscarinic receptor signaling. Surprisingly, in mouse models of prodromal and parkinsonian states, the ability of ChIs to engage this intrastriatal circuitry was disrupted because interneurons down-regulated nicotinic AChRs. Taken together, these studies suggest that impaired ChI control of GABAergic interneurons contributes to behavioral deficits in both prodromal and clinical PD states.

neuroscience↗

bioRxiv2026

Functional orthogonality of WRN inhibitor resistance enables alternating therapy and mutation-tolerant inhibitor design in MSI-H cancers

Werner syndrome helicase (WRN) is a synthetic-lethal vulnerability in microsatellite instability-high (MSI-H)/mismatch repair-deficient (dMMR) cancers1-5, and non-covalent and covalent WRN inhibitors are now entering clinical development6-9. A central unresolved question is whether resistance to one WRN inhibitor class inevitably compromises the target, or instead creates actionable vulnerabilities to an alternate modality. Here we generated ten stable acquired-resistance models across three MSI-H cell lines using the non-covalent inhibitor HRO761 and the covalent inhibitor VVD-214. Whole-exome sequencing, biochemical reconstitution and isogenic knock-in models identified recurrent on-target WRN missense mutations as dominant resistance drivers, but with sharply modality-specific spectra: HRO761 resistance clustered at G729/F730/I852, whereas VVD-214 resistance concentrated at E846. These mutations impaired inhibitor engagement and abolished the canonical WRN inhibitor (WRNi)-induced pharmacodynamic cascade, including WRN reduction, DNA damage response (DDR) activation and G2/M arrest. Crucially, most resistance mutations retained biochemical, cellular and in vivo sensitivity to the alternate inhibitor modality, revealing a functional orthogonality that enabled a 7-day cyclic alternating regimen to delay tumour regrowth in xenografts. We further identified F730L as an engineered cross-resistant bottleneck model and used structure-guided, artificial intelligence (AI)-enabled optimization to generate GBA-007, a proof-of-concept mutation-tolerant WRN inhibitor candidate with promising activity against F730L in vitro and in vivo. Thus, clinically relevant WRN inhibitor classes impose distinct on-target resistance trajectories that can be exploited through schedule design, while cross-resistant bottlenecks can be addressed by rapid mutation-aware inhibitor engineering.

Cancer Biology↗

bioRxiv2026

Spatial Gene Set Enrichment Analysis with Applications to Spatially Resolved Transcriptomic Data

Spatially resolved transcriptomics enables the systematic characterization of spatial gene expression variation across tissue sections. Spatially variable genes within the same biological pathway often exhibit similar spatial expression patterns, reflecting shared biological functions and tissue organization. However, existing gene set enrichment analysis methods typically ignore this spatial dependence, which may reduce power to detect spatially organized pathways and limit the interpretability of pathway-level findings. To address this limitation, we propose spaGSE, a Bayesian hierarchical model for spatial pathway enrichment analysis that integrates genelevel summary statistics from spatial expression analysis with predefined gene set annotations. spaGSE models latent spatially variable gene signals through a Gaussian mixture framework and links spatial variation to gene set membership using logistic regression. To support robust and interpretable inference, we impose a spike-and-slab prior on the enrichment coefficient. Through simulation studies and analyses of four public SRT datasets, we show that spaGSE is scalable and achieves higher power while maintaining false positive rate control compared with existing approaches. In real-data applications, spaGSE identifies biologically relevant pathways with coordinated spatial organization across cancer and developmental tissues, demonstrating the value of incorporating spatial information into pathway-level inference for spatial transcriptomics.

bioinformatics↗

bioRxiv2026

PRISMA: A tensor-based framework for deconstructing the genetic architecture of complex diseases, with application to diabetic retinopathy

Complex-disease GWAS compress heterogeneous, tissue-dependent genetic effects into global locus-level statistics, masking local regulatory routes through which polygenic risk contributes to disease architecture. Here we introduce PRISMA (Polygenic Risk Integration via Summary-statistics Multi-tissue Array-decomposition), a summary-statistics framework for quantifying tissue-resolved genetic heterogeneity hidden within aggregate GWAS signals. PRISMA integrates GWAS and multi-tissue cis-eQTL data using graph Laplacian-regularized block-wise factorization, preserving local linkage disequilibrium topology during decomposition. Applied to diabetic retinopathy, PRISMA deconvolved aggregated polygenic risk into three tissue-biased genetic axes corresponding to vascular-metabolic, systemic immune-inflammatory, and retina-specific neurodegenerative trajectories. The framework prioritized 549 axis-associated targets, including 403 below the conventional genome-wide significance threshold, and showed higher tissue-regulatory resolution than PCA, NMF, and K-means. A height GWAS negative-control analysis supported trait-dependent regulatory reprioritization within a shared eQTL-mappable framework. Independent single-cell transcriptomic analyses supported axis-specific enrichment across fibrovascular, immune, and retinal compartments. Exploratory vitreous humor proteomic and metabolomic profiling further nominated candidate molecular correlates of downstream convergence. PRISMA reframes complex-disease GWAS from aggregate locus discovery toward quantitative, LD-aware mapping of tissue-resolved genetic trajectories.

genomics↗

bioRxiv2026

Replicability of unsupervised deep learning derived image phenotypes

Unsupervised deep-learning image phenotypes derived from brain MRI are propelling imaging genetics to link brain structure to genetic variation. However, their replicability across data sets has not been sufficiently evaluated, raising questions about whether they capture robust biological structure or reflect training-specific artifacts. Here, we assess the replicability of unsupervised deep-learning image phenotypes under variation in model initialization, data partitioning, and cohort, directly evaluating their stability across experimental conditions. We trained multiple models under (i) different training batch random seeds, (ii) cross-validation splits, and (iii) independent datasets (UKB and ADNI), across CNN and ViT architectures. We then derived representations from a separate UKB discovery cohort (N = 22,985) for both trained models and random initialized models without training. The representation stability was assessed using centered kernel alignment (CKA; mean ViT 0.74 vs random 0.27) and kernel canonical correlation analysis (KCCA; mean ViT 0.84 vs random 0.60), as well as genetic discovery stability using loci overlap ratio (mean ViT 0.45 vs random 0.08). We further applied weighted MAXVAR generalized CCA to 12 embeddings to extract a shared 30-dimensional subspace. Our result showed that UDIPs exhibit statistically significant stability (CKA, KCCA t test p < 0.001) across training perturbations and preserve biologically meaningful structure (loci overlap ratio t test p <0.001) across cohorts, supporting their use in imaging genetics.

bioinformatics↗

bioRxiv2026

Redesign selective protein binders using contrastive decoding

MotivationFixed-backbone sequence design methods such as ProteinMPNN operate on backbone coordinates alone and cannot represent target side-chains at the binding interface. Their decoding algorithm also lacks a mechanism to balance binding affinity and folding stability or to improve selectivity against structurally similar off-targets. These gaps limit the computational design of protein binders with high affinity and specificity. ResultsWe present RedNet, a multiscale graph neural network that encodes side-chain information of the binding target. We further develop a contrastive decoding algorithm, motivated by the thermodynamic decomposition of binding free energy, that addresses two objectives: (1) balancing binding affinity and folding stability, and (2) improving selectivity against structurally similar off-targets. RedNet reaches 43% native sequence recovery on heterodimers, compared with 37% for ProteinMPNN and 33% for ESM-IF. With contrastive decoding, it matches native-sequence co-folding success (68%) on high-confidence AlphaFold3 targets, exceeding ProteinMPNN (59%) and ESM-IF (61%). On a new benchmark of structurally similar on-/off-target pairs, RedNet with contrastive decoding reaches 64.8% energetic selectivity, ahead of PiFold (55.6%), ProteinMPNN (53.7%), and ESM-IF (53.7%). AvailabilitySource code and datasets are released at https://github.com/zw2x/rednet_public. Contactjinbo.xu@gmail.com

bioinformatics↗

bioRxiv2026

Motor abstraction training generalizes to the refinement of specific movement patterns

The ability to abstract the invariant structure of an experience while discarding incidental details underlies generalization across virtually every domain of human cognition, from vision and language to concept learning. Yet whether the motor system generates such abstractions and whether they causally contribute to skill learning remain open questions. Here, we introduce a paradigm in which human participants learned to refine novel movement patterns by learning to precisely copy unfamiliar handwritten characters. To examine the role of motor abstractions in this form of motor learning, participants were trained on markedly rotated versions of the characters, which recruited substantially different lower-level movements while still maintaining the relevant abstract movement trajectory. Across eleven experiments, abstraction training drove robust skill improvements that were comparable to having repetitive practice on the canonical form of each character. Moreover, this learning was largely motoric in nature: it did not require a visual template, sustained trajectory feedback, or mental imagery, and was sensitive to the sequential structure of the movement trajectory. These findings establish a causal role for abstractions in motor learning, revealing that the motor system likely deploys abstractions in the earliest stages of skill acquisition.

neuroscience↗

bioRxiv2026

NeuroCDS: Integrating Local and Global Neural Network Representations via Structural Constrained Viterbi Decoding for Robust CDS Annotation

MotivationRobust annotation of Coding Sequences (CDS) is critical for downstream transcriptomics, yet heavily fragmented de novo RNA-Seq assemblies pose a severe challenge. Traditional computational tools rely on fixed, hand-crafted features that are prone to fail when canonical sequence signals are truncated. While recent deep learning models excel at automatically extracting complex representations, they predominantly treat these as isolated prediction tasks. Lacking a joint inference mechanism to enforce structural constraints, existing models occasionally output biologically invalid predictions. Therefore, a computational framework capable of fusing heterogeneous neural network representations for joint annotation is critically needed. ResultsWe present NeuroCDS, a reliable framework that bridges the effective representation capabilities of deep neural networks with the structural rigor of dynamic programming. NeuroCDS employs a dual-branch architecture: a Convolutional Neural Network (CNN) acts as a local sensor to extract Translation Initiation Sites (TIS), while a Temporal Convolutional Network (TCN) acts as a global sensor to evaluate continuous regional coding potential. The primary contribution of NeuroCDS lies in a structurally constrained Viterbi Decoding algorithm designed to fuse these heterogeneous signals. This joint inference mechanism strictly enforces biological grammars (e.g., reading frame preservation) to dynamically calculate the globally optimal transcript structure via a tripartite state space. Crucially, by introducing a dynamic length normalization mechanism, our formulation adaptively leverages global continuous representations to stably annotate both intact transcripts and highly truncated fragments. Comprehensive evaluations demonstrate that NeuroCDS achieves high F1-scores on full-length transcripts and maintains robust performance on complex Ribo-seq validated datasets, comparing favorably against traditional HMM-based and heuristic methodologies. AvailabilitySource code, pre-trained models, and datasets are freely available at https://github.com/hgcwei/NeuroCDS.

bioinformatics↗

bioRxiv2026

Effects of bimodal divided attention on cortical representations of linguistic context during continuous speech perception in noise

Speech perception often takes place in environments with competing sensory inputs, both within the auditory modality and across modalities; for example, following a conversation in a noisy cafe while simultaneously reading a menu. This study examined the extent to which dividing attention between auditory and visual modalities (bimodal divided attention) influences linguistic context processing across hierarchical levels during continuous speech perception in noise. Electroencephalographic (EEG) responses were recorded while participants listened to audiobook stories in multitalker babble as a secondary task, concurrently performing a demanding primary visual task that imposed either low or high cognitive load. Behaviorally, speech comprehension accuracy was significantly lower under high-load than low-load dual-task conditions. Multivariate temporal response function (mTRF) encoding models were used to predict EEG responses from information-theoretic measures (entropy and surprisal) indexing linguistic context at sublexical, word-form, and sentence levels. Significant neutral tracking was observed at the word-form and sentence levels, but not the sublexical level. Critically, neutral tracking of sentence-level linguistic representations was significantly reduced under high compared to low load, with effects emerging at latencies beyond 200 ms. In contrast, neutral tracking of word-form-level representations was unaffected by dual-task load. mTRF analyses further revealed that neutral tracking of acoustic features was not modulated by dual-task load. These findings indicate that bimodal divided attention selectively disrupts cortical representations of sentence-level linguistic context, while lower-level processing remains relatively preserved. Such impairments in higher-level linguistic processing may contribute to reduced speech comprehension during multitasking in noisy environments.

neuroscience↗