Biology subjects

Xiao, Y.

Publications and source records attributed to Xiao, Y..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

OmicGlaze: Spatial Multi-Omic Mapping of Traumatic Brain Injury

Traumatic brain injury (TBI) is a major cause of mortality and long-term disability worldwide, giving rise to complex neurological complications that impact millions of individuals each year. Cellular stress and neuronal injury vary dramatically across cortical layers, vascular niches, and between the ipsilateral (injured) or contralateral (uninjured) hemispheres. There is a critical need for quantitative measures that capture the spatial distribution of injury-induced cellular changes, as well as the gene regulatory elements that drive them. Here, we developed OmicGlaze, an experimental and computational workflow for systematically profiling the spatial transcriptome and epigenome of mouse brains following mild traumatic brain injury. We established a spatial scoring system, and identified region-specific biological processes post injury, including changes in neuronal activities, cellular stress, immune response, and gliosis. Spatial assay for transposase-accessible chromatin with sequencing (Spatial ATAC-seq) generated the first epigenetic map of traumatic brain injury near single-cell resolution. Notably, we identified the Activator Protein-1 family transcription factor Atf3 as a key gene regulator of injury-induced cellular stress. Together, these spatial multi-omics analyses revealed gene regulatory network in TBI and provided a broadly applicable framework for dissecting cellular and molecular mechanisms underlying complex neurological disorders.

systems biology↗

bioRxiv2026

KuafuPrimer: Machine learning empowers the design of 16S amplicon sequencing primers toward minimal bias for bacterial communities

Amplicon sequencing protocol targeting the 16S rRNA gene is a widely used and cost-effective method for exploring bacterial communities. However, its performance is often limited by primer bias arising from the arbitrary use of universal primers across diverse microbial communities and habitats. We propose KuafuPrimer to design the optimal 16S rRNA gene primers toward minimal bias for targeted bacterial communities, using few-shot machine learning to guide the primer design procedure based on a small number of samples. Simulations on 809 samples across 26 representative environments and habitats showed that KuafuPrimer-designed primers outperformed the universal primers in taxonomic accuracy, achieving an averaged 16.31% relative reduction in primer bias, with reductions up to 46.08% in plant samples. Notably, KuafuPrimer detected 29 rare and key taxa undetectable by the universal primers. Validation with 317 longitudinal gut microbiota samples demonstrated that KuafuPrimer-designed primers consistently outperformed the universal primers across temporal, individual, and cohort levels, with relative bias reductions of 5.03%, 3.53%, and 3.10%, respectively. Finally, in real PCR experiments on human gut samples from Clostridioides difficile-infected and healthy groups showed that polymerase chain reaction products using KuafuPrimer-designed primers correlated better with metagenomic data compared to the universal primers. More importantly, KuafuPrimer successfully detected Clostridioides difficile, the key pathogen missed by the universal primers, highlighting its potential for improving clinical diagnostics. In summary, KuafuPrimer provides a machine learning-based primer design strategy for targeted bacterial communities, with demonstrated utility in large-scale microbiome initiatives, longitudinal surveys and clinical diagnostics.

bioinformatics↗

bioRxiv2026

EEG-SSFormer: Towards a Robust Mamba-Based Architecture for Dementia Detection from Resting State Electroencephalography

Resting-state electroencephalography (rs-EEG) offers a cost effective and portable alternative to conventional neuroimaging for dementia screening, yet the lengthy, multichannel nature of rs-EEG makes learning robust representations challenging. Convolutional and Transformer based architectures dominate current deep learning based approaches, but often struggle with long-range dependencies and may not properly preserve channel-dependent features. In this work, we propose EEG-ChiMamba, a state space model based architecture designed for the classification of mild cognitive impairment (MCI) and dementia from normal controls using raw channel-independent rs-EEG signals. Our method decouples channel-wise representation learning from modeling cross-channel interactions and leverages Mamba layers for effective long-sequence modeling. We evaluate our method on the Chung-Ang University EEG dataset (CAUEEG) with 1,155 subjects, the largest public rs-EEG dataset for challenging MCI and dementia differential diagnosis. We achieve a 3-class accuracy of 57.65% using a strict subject-wise split, and relate task-specific features learned by our model as revealed by feature occlusion-based explainability techniques to clinical literature, highlighting that state space models can facilitate interpretable and scalable clinical rs-EEG screening tools for cognitive degeneration. The code for the study is publicly available at: https://github.com/HealthX-Lab/EEG-ChiMamba

neuroscience↗

bioRxiv2026

Therapeutic targeting inflammation linking periodontitis and atherosclerotic comorbidities using cell-free DNA-capturing nanomaterials

Periodontitis-associated systemic inflammation makes it a great challenge to explore therapeutic options applicable to periodontitis and atherosclerotic comorbidities. Here, we identify the crucial role of cell-free DNA (cfDNA) that underlies these comorbidities. Hypothesizing cfDNA as a therapeutic target, we engineer polyamidoamine dendrimer-functionalized nanomaterials to modulate such local-systemic inflammatory crosstalk. Periodontium-originated DNA can be systemically captured by cationic nanomaterials, and capturing cfDNA, whether locally or systemically, alleviates both periodontitis and atherosclerosis prior to severe atherosclerotic development in vivo. The transcriptomic and single-cell RNA sequencing analyses together reveal that cfDNA-capturing nanomaterials regulate inflammatory foam cell transformation in macrophages by modulating the expression of lipid-related foamy markers Spp1 and Fabp4. This study provides a proof of concept for cfDNA-driven periodontitis-atherosclerosis crosstalk, and offers a cfDNA-capturing nanoplatform for therapeutic intervention targeting periodontitis and atherosclerotic comorbidities in a holistic fashion.

immunology↗

bioRxiv2026

The ChIP-FRiP pipeline quantifies co-binding and reveals how antibody background contributes to cohesin ChIP-seq patterns

Loop extruding cohesin complexes are positioned by CTCF barriers to generate locus-specific 3D genome folding patterns. Quantifying cohesin accumulation at CTCF sites is crucial for deriving insight into loop extrusion and its consequences. Here we confronted expectations from loop extrusion simulations with experimental data by developing a pipeline, ChIP-FRiP, and analysis framework to reliably quantify cohesin positioning. We used ChIP-FRiP to uniformly re-process 140 cohesin ChIP-seq datasets from 13 publicly available studies. This analysis revealed that non-specific antibody binding can skew measurements of cohesin positioning. To mitigate this bias, we developed a biochemical model of background ChIP-seq signal and a strategy to estimate and correct this background using spike-in ChIP-seq data and relative protein abundance before and after depletion. Our results establish a framework for comparative analysis, demonstrating that accurate background correction is requisite for interpreting the roles of cohesin cofactors in cohesin positioning.

genomics↗

bioRxiv2026

Multi-agent reasoning enables predictive design of living materials

Artificial intelligence is increasingly used to accelerate scientific discovery, but most successful frameworks operate within well-defined molecular, protein or materials spaces. Living materials present a more formidable computational problem because functions emerge from context dependent coupling among cells, matrices, fabrication processes and evaluation conditions. Here we introduce LiveMat, a multi-agent reasoning framework that transforms unstructured literature into a computable design space for living materials. LiveMat standardizes 34,215 living material records, integrating 16,769 microorganism and 17,446 polymer entries into a knowledge graph linking living components, abiotic matrices, functional outputs, evaluation contexts and performance metrics. Benchmarking across five large language models shows that living material reasoning is limited mainly by cross-domain feature integration rather than coarse classification. LiveMat overcomes this limitation through constraint decomposition, provenance-aware extraction, consistency checking and expert-anchored ranking. In a prospective wound-healing task, it prioritizes a four-component design with state-of-the-art in vivo performance, establishing a scalable infrastructure for interpretable, evidence-grounded living material discovery.

biochemistry↗

bioRxiv2026

Neuronal synapses are stabilised by activity-dependent vesicular release from oligodendrocyte precursor cells

Activity-dependent regulation of synapses has long been attributed to interactions between neurons alone. Here, using in vivo imaging in the zebrafish visual system we show that one type of glia called the oligodendrocyte precursor cell (OPC) closely interacts with pre-synapses of retinal ganglion cell (RGC) axons, and exhibits activity-dependent Vamp2/3-mediated vesicular release. Critically, blocking OPC vesicle release using botulinum toxin, and silencing OPC activity integration through dampening calcium signalling, destabilises RGC pre-synapses. These observations suggest that OPCs provide a non-neuronal pathway for activity-dependent feedback to shape circuit wiring.

neuroscience↗

bioRxiv2026

Metabolic plasticity of sphingolipids governs cancer cell fitness in acidic tumor ecosystems

Cell state plasticity enables cancer cells to rapidly adapt to fluctuating microenvironments without requiring genetic alteration, shaping tumor evolution under stress. Extracellular acidosis is a persistent feature of solid tumors that impose strong selective pressure, yet how cancer cells maintain fitness under acute and chronic acidic conditions remains unclear. Here, we show that adaptation to acidosis is mediated by plastic rewiring of sphingolipid metabolism centered on ceramide turnover. Spatial multi-omics analysis of three-dimensional tumor models revealed enrichment of ceramides within acidic niches, consistent with a stress-induced phenotype. While acute acidosis promoted ceramide accumulation and reduced fitness, chronic exposure selected for cells capable of dynamically redistributing sphingolipid flux across multiple clearance pathways. Functional perturbation demonstrated that inhibition of individual pathways was insufficient to compromise survival, whereas simultaneous disruption of all ceramide clearance routes resulted in cell death, revealing a degenerate metabolic architecture. This network-level flexibility enables cancer cells to maintain fitness by switching between alternative metabolic states under acidic stress. Together, our findings identify sphingolipid metabolic plasticity as an adaptive strategy that supports tumor persistence in acidic ecosystems and suggest that targeting metabolic flexibility, rather than individual pathways, may provide a more effective therapeutic approach.

cancer biology↗

bioRxiv2026

Drought tolerance of Aedes aegypti mosquito eggs is influenced by adaptation to local climate conditions and associations with humans

Urbanization is intensifying human interactions with mosquitoes, exacerbating public health challenges. Densely populated areas provide ideal conditions for container-dwelling mosquitoes, with increased host availability and the presence of artificial breeding sites. These anthropophilic mosquitoes often exhibit distinct ecological adaptations compared to their rural counterparts. Since mosquito eggs are immobile and remain at the site of oviposition, they provide a valuable lens for assessing how urbanization, climate-driven shifts in temperature, and drought affect mosquito reproductive success. This study examined Ae. aegypti egg viability under varying temperature and dry conditions over five months, focusing on lineages with distinct ancestries from West African populations. Mosquitoes collected from urban habitats with a high human preference demonstrated higher egg survival under prolonged arid conditions. Analysis of climatic factors revealed that dry season temperature and precipitation during wet periods are significant predictors of egg drought tolerance. Modeling future climate scenarios based on input from our egg viability results suggests a projected shift and expansion in the seasonal survival window for Ae. aegypti by the end of the century. This study highlights the importance of understanding environmental constraints on the drought tolerance of mosquito eggs to predict and mitigate future mosquito outbreaks.

physiology↗

bioRxiv2026

Recognition in Confinement: The Dynamics of Homologous Gene Pairing

How can protein-independent, side-by-side alignment of homologous double-stranded DNA (ds-DNA) arise as an early step in genome repair and genetic exchange, and what determines their kinetics under confinement? Although recombination proteins are known to facilitate this process, experiments indicate that an initial side-by-side arrangement of homologs can occur even in their absence. Helical Coherence Theory (HCT) proposes that sequence-dependent distortions of the double helix lead to commensurate charge patterns between interacting dsDNAs, favouring homologous over non-homologous alignment. However, HCT has largely been developed for straight, rigid dsDNA rods, an approximation limited to roughly one persistence length ({ell}B {approx} 50 nm), leaving the question open as to how recognition manifests in longer, fluctuating chains. Moreover, the dynamics and timescales of homolog searching under confinement, and the internal dynamics of the paired state, such as transient formation of local unpaired regions ("bubbles") and end fraying, remain unexplored. Here we extend HCT to dsDNA chains of multiple persistence lengths under confinement using coarse-grained simulations with a custom HCT-based force field, in order to come closer to understanding how homologous genes pair in vivo. We simulate two homologous chains within spherical cavities of radii 0.3-1 times their free-space gyration radius under two limiting ionic conditions. We found that confinement warrants homologous pairing; it occurs on microsecond timescales and depends non-monotonically on confinement size, with an optimal confinement that accelerates pairing while avoiding tangled states and metastable trapping in an ion-dependent manner. To resolve pairing dynamics, we develop a kinetic theory and fit it to simulation trajectories, distinguishing rates of independent pairing modes underlying bubble formation and fraying. Together, these results support a physical mechanism for homologous pairing beyond the rigid-rod limit and suggest how confinement can promote both encounter and stabilisation within the HCT framework.

biophysics↗

bioRxiv2026

A robust low-dimensional manifold organizes neuronal responses to sustained input

Neuronal electrophysiological identity is expressed through responses to input, but how this identity is organized across distinct physiological input regimes remains unclear. Conventional descriptors such as firing rate, threshold, latency, or response amplitude capture local aspects of excitability, while different current-clamp protocols expose different dimensions of cellular response. Here, we asked whether protocol-specific response measurements reveal distinct physiological axes while preserving shared organization among genetically targeted cell groups. Using intracellular current-clamp recordings from mouse visual cortex, we constructed multivariate response geometries from long-pulse, ramp, and brief-pulse stimulation protocols, respectively probing sustained spiking output, dynamic recruitment, and fast transient response/gain. Each protocol revealed a structured and interpretable geometry: long-pulse responses organized cells along sustained-output and high-current response dimensions, ramp responses isolated recruitment-threshold structure, and brief-pulse responses captured fast transient response and gain-related variation. Driver-defined groups occupied ordered positions within these geometries, indicating that protocol-specific response axes were linked to cell-group organization. Across protocols, driver-defined group distance structures remained positively aligned after balanced resampling and comparison with driver-label shuffle null models, showing that protocol-specific geometries retained partially shared relational structure. These findings support a protocol-resolved view of cellular electrophysiological identity as a multidimensional response organization, in which distinct physiological probes reveal protocol-specific response modes embedded within shared cell-group structure.

neuroscience↗

bioRxiv2026

Coronavirus Nucleocapsid Proteins Hijack Host Protein Kinase A Catalytic Subunit α into Nucleus to Evade from STAT1 Signaling

The role of the cAMP/PKA pathway in antiviral innate immunity, including during coronavirus infections, remains unclear. We discovered coronavirus N proteins initiate cAMP-ADCY10-PKA cascade. Cytoplasmic PKA activates STAT1 independently of canonical JAK/TYR2 signaling. Coronavirus N proteins, via a conserved arginine (e.g., PEDV R58, SARS-CoV-2 R92), directly bind and sequester PKA C into the nucleus to evade STAT1 activation. Using PEDV as a model, mutant viruses with NmutNLS and NR58A were generated. Wildtype PEDV infection suppressed STAT1 activation in cells expressing PKA C/PKA CS339A deficient in STAT1 phosphorylation. However, in rXS0101mutNLS- or rXS0101R58A-infected cells, only PKA CS339A expression inhibited STAT1 activation. Downregulation of STAT1 activation is accompanied with increased viral replication. This study first elaborates that PKA C activates STAT1 in the cytoplasm of infected cells distinctly from canonical JAK/STAT1 signaling, while coronaviruses evade this antiviral response by sequestering PKA C into the nucleus via direct N protein interaction.

microbiology↗

bioRxiv2025

SuperFocus enables whole-slide cell-level spatial omics from spot-based measurements and histology across modalities

Histopathology and molecular pathology are currently distinct diagnostic modalities for the most part, one revealing tissue morphology at cellular resolution and the other providing molecular measurements with limited or no spatial context. Projecting genome-scale molecular information onto histopathology images at single-cell resolution across whole tissue sections represents a long-sought goal for next-generation pathology. Here we present SuperFocus, a modality-agnostic computational platform that generates histopathology-integrated single-cell spatial multi-omics from spot-based spatial measurements acquired on the same or an adjacent section without requiring external reference data. SuperFocus combines constrained cascading imputation with feature-level and cell-level quality-control scores to reduce spurious predictions and quantify confidence. On a ground-truth spatial transcriptomics benchmark dataset, SuperFocus improves key accuracy metrics by 28-73% over existing methods. Across Patho-DBiT, spatial ATAC-RNA, spatial CITE-seq and Visium-MALDI-MSI (SMA) datasets, SuperFocus enables cell-resolved analyses of MALT lymphoma microenvironments, gene regulatory programs in human hippocampus, lipotoxic hepatocyte states in human MASH, and transcriptomic-metabolomic states linked to neurotransmission and neuroinflammation in Parkinsonian mouse brain. Overall, SuperFocus enables scalable whole-slide single-cell spatial multi-omics integrated with histopathology, bridging histology and genome-scale molecular profiling for next-generation molecular pathology.

bioinformatics↗

bioRxiv2025

Demonstrating and engineering the de novo synthesis of two aromatic chemicals estragole and anethole in Escherichia coli

Estragole and anethole are the major flavoring and aroma components of the essential oils extracted from basil (Ocimum basilicum L.), anise (Pimpinella anisum), star anise (Illicium verum), and fennel (Foeniculum vulgare). Besides, they also exhibit bioactivities, such as antioxidation, antimicrobial, immunomodulation, inti-inflammation, and anti-diabetes. These properties make them being faced with substantial applications in the industries of food, cosmetics, pharmaceutics, and agriculture. Commercial estragole and anethole are currently obtained from extraction from their original plants, which suffers from unstable quality and resources. Microbial synthesis is a promising alternative approach to sustainably obtain estragole and anethole, but has not been achieved. In this study, we modularly redesigned the synthetic pathways based on the Escherichia coli chassis. Firstly, a p-coumaryl alcohol over-accumulator (436.28 mg/L) was engineered to facilitate the validation of the undemonstrated downstream module. De novo biosynthesis of estragole and anethole was achieved by stepwise analysis and BioBricks assembly. The intracellular availability of S-adenosyl-L-methionine (SAM) was revealed to be the key limiting factor, and was enhanced by deregulating the synthesis. Finally, the productions of estragole and anethole were improved by 246.88% and 202.44% compared with the initial strains. This study represents the first synthesis of estragole and anethole in microbial chassis, and the demonstration of microbial synthesis of propenyl phenols. Besides, it provided an alternative sustainable approach to obtain flavoring and aroma chemicals.

synthetic biology↗

bioRxiv2025

Molecular Mechanisms Underlying the Regulation of VCAM-1 Expression by the Short-Chain Fatty Acid Butyrate

Over the past decade, cerebrovascular inflammation has been increasingly recognized as a contributor to the progression of neurodegenerative diseases, particularly Alzheimers disease (AD). One of the molecular hallmarks of cerebrovascular inflammation is the increased expression of vascular cell adhesion molecule (VCAM)-1 on blood-brain barrier (BBB) endothelial cells. Exposure to amyloid beta (A{beta}) peptides, one of the primary hallmarks of AD, and pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-) induces VCAM-1 expression on the BBB endothelium, which facilitates extravasation of leukocytes into the brain thereby promoting an inflammatory response. Therefore, it is crucial to explore therapeutic agents that can inhibit VCAM-1 expression induced by A{beta} and TNF-. Short-chain fatty acids, such as butyrate, produced by the gut microbiota as byproducts of dietary fiber metabolism, are recognized for their anti-inflammatory properties. In this study, we successfully tested the hypothesis that butyrate mitigates A{beta} and TNF--induced VCAM-1 expression in polarized human cerebral microvascular endothelial cell monolayers, a widely used BBB in vitro model. Our findings indicated that pre-treatment with butyrate significantly reduced A{beta}42 and TNF- mediated upregulation of VCAM-1. Furthermore, we have shown STAT3/GATA6 axis as a key mediator of anti-inflammatory effects of butyrate. These findings provide mechanistic insight into butyrates protective role and highlight its potential to mitigate A{beta} and TNF--induced cerebrovascular inflammation in AD.

molecular biology↗

bioRxiv2025

Unimeth: A unified transformer framework for accurate DNA methylation detection from nanopore reads

Nanopore sequencing enables direct detection of DNA modifications from native DNA. However, accurate methylation calling across species, sequence contexts, modification types and chemistries remains challenging. We present Unimeth, a transformer-based framework that jointly processes raw signals and basecalled sequences in read patches and predicts all target methylation sites within each patch. Unimeth uses a three-phase training strategy that combines signal pre-training, methylation fine-tuning and site-level calibration using methylation frequency information. We evaluated Unimeth for 5mC and 6mA detection using public and in-house datasets spanning 14 species, three nanopore chemistries and wild-type, mutant and enzyme-treated samples. Unimeth improved plant 5mC detection in non-CpG contexts, reduced false-positive calls in low-methylation samples and maintained high 5mCpG performance in mammalian datasets. For 6mA, Unimeth reduced background calls while preserving signals for Fiber-seq nucleosome and gene-level analyses. Unimeth provides a unified framework for nanopore-based methylation detection across methylation types and biological contexts.

bioinformatics↗

bioRxiv2025

PDLIM2 in Lung Adenocarcinoma Metastasis

Human and mouse studies have established the unique PDZ-LIM domain-containing protein PDLIM2 as a common tumor suppressor that is especially vital for suppressing the tumorigenesis and therapeutic resistance of lung cancer, the leading cause of cancer-related deaths among both men and women. However, the role of PDLIM2 in tumor metastasis, the predominant cause of cancer morbidity and mortality, is yet to be determined. Here, we report that PDLIM2 repression was positively associated with the metastasis of human lung adenocarcinoma, the major type of non-small cell lung cancer that accounts for more than 40% of all cases of human lung cancer. Interestingly, PDLIM2 repression was also correlated with oncogenic KRAS and/or TP53 mutations, two common drivers of human lung adenocarcinoma that often co-occur. In mice, in comparison to concurrently inducing mutant KRAS expression and TP53 deletion, additional co-ablation of PDLIM2 significantly increased the number and size of lung adenocarcinomas in the lung, and more importantly, the distant metastasis of lung tumor cells. The increased metastasis was accompanied by decreased anti-tumor immunity and increased pro-tumor inflammation. These data demonstrate the role of PDLIM2 in suppressing lung adenocarcinoma metastasis, thereby improving our understanding of this crucial tumor suppressor and lung cancer. They also provide a useful model for studying metastasis and testing new lung cancer treatments in vivo.

cancer biology↗

bioRxiv2025

Bioengineered bone marrows that can support stem cells and remodel to mimic human disease metabolism and chemotherapeutic effects

Blood cancer drug discovery is reliant on poorly predictive and costly animal models. Therefore, bioengineered, human cell containing, models are attractive to Pharma. Designing effective bone marrow (BM) models is complicated as they need to both regulate haematopoietic stem cell (HSC) phenotype and be able to undergo remodelling to mimic the blood cancer microenvironment. Here, we develop synthetic hybrid niches using poly(ethylacrylate) to organise laminin on hard, bone mimicking, surfaces and interface with soft, marrow mimicking, polyethylene glycol-fibronectin hydrogels. Optimisation of mesenchymal stromal cell (MSC) mechanobiology within the model offers both support for HSCs and remodelling in response to acute myeloid leukaemia derived cells. The remodelling has many parallels to in vivo and patient data including increased dependency on nestin+ve MSCs, enhanced cytoprotection and increased taurine metabolism. We also use the model to demonstrate, as has been seen in vivo, that targeting taurine enhances effects of chemotherapy.

bioengineering↗