Biology subjects

Xiao, X.

Publications and source records attributed to Xiao, X..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Cholesterol Metabolism in a Murine Model of Vertical Sleeve Gastrectomy

The most effective and durable treatment for obesity is metabolic bariatric surgery. Currently, vertical sleeve gastrectomy (VSG) has become the preferred surgical method to achieve sustained weight loss and reverse obesity-related disease, including dyslipidemias. Human studies indicate that 40% of VSG patients can discontinue statin treatment by 6 months, while alterations in plasma bile acids and synthesis have been observed after bariatric surgery. Although it is well known that cholesterol is the precursor for all bile acids, the link between changes in plasma cholesterol and bile acids remains unclear in the context of VSG. Here, we present a murine model of VSG that leads to a marked decrease in plasma cholesterol levels despite no difference in body weight compared to sham surgery. Our results demonstrate that gastric resection lowers systemic cholesterol burden by promoting the hepatobiliary elimination of cholesterol through the upregulation of bile acid synthesis. These observations imply that gastric resection impacts overall hepatic physiology and cholesterol homeostasis independent of weight loss.

physiology↗

bioRxiv2025

Integrative microbiome- and metatranscriptome-based analyses reveal diagnostic biomarkers for peri-implantitis

Peri-implantitis is a severe biofilm-associated infection of the tissues around dental implants that increases the risk of implant failure. The prognosis of peri-implantitis treatment is compromised by the resistance of well-organized and mature bacterial biofilms. Thus, early diagnosis of a pathogenic biofilm would enable treatment at a prognostically favourable stage. However, relatively little is known about how the microbial constitution of the biofilm changes during disease development. The aim of this cross-sectional study was therefore to identify peri-implant taxonomic and functional biomarkers that reliably indicate peri-implantitis using paired data from full length 16S rRNA gene amplicon sequencing (full-16S) and metatranscriptomics (RNAseq). Disease signatures were identified using 24 healthy and 24 peri-implantitis-associated biofilm samples from 32 patients. The taxonomic measurements were validated with 68 additional full-16S samples from another 40 patients. Both full-16S and RNAseq revealed significant differences between healthy and peri-implantitis samples, with respect to both the microbiome and functional profiles. A shift from aerotolerant Gram-positive bacteria to anaerobic Gram-negative bacteria was observed in peri-implantitis. Distinct metabolic pathways were expressed in healthy and peri-implantitis samples. Our results, based on paired taxonomic and functional profiles, provide for the first time important insights into the complex peri-implant biofilm ecology related to amino acid metabolism. Integrating taxonomic and functional information improved the predictive ability (AUC = 0.85) of the machine learning models and revealed diagnostic biomarkers with large effect sizes (Cohens d > 0.8). Primary biomarkers included health-associated Streptococcus, Rothia species and enzymes associated with peri-implantitis (urocanate hydratase, tripeptide aminopeptidase, NADH:ubiquinone reductase, phosphoenolpyruvate carboxykinase and polyribonucleotide nucleotidyltransferase - mostly expressed by Fusobacteriia and Bacteroidia). Thus, biofilm profiling at these two molecular levels reveals highly predictive disease biomarkers and provide the basis for developing early diagnostics and individualized therapy approaches for peri-implant diseases.

microbiology↗

bioRxiv2025

Treating Myotonic Dystrophy with artificial RNA endonucleases to specifically degrade toxic RNA expansions

Myotonic dystrophy type 1 (DM1), the most common autosomal dominant muscular disorder, is driven by expanded CUG repeats in the 3' UTR of DMPK gene, which sequester RNA-binding proteins and cause aberrant alternative splicing. Despite extensive study, no effective treatment exists, and current care remains limited to symptomatic management. Using a customized dual-color reporter carrying RNA repeats in different lengths, we engineered a new class of programmable artificial enzymes (artificial RNA cleavers, or ARCs) that preferentially degrade the pathogenic repeats both in cells and in a mouse model. We further examined the in vivo efficacy of ARCs by systematically delivering ARCs into the DM1 mouse model with muscle-specific adeno-associated viruses, and validated that ARCs can rescue the muscular pathology and movement ability. The treatment by ARCs showed sustained efficacy, low immunogenicity, and minimal off-target effects in animal model, which are key concerns in clinical translation of gene therapies. Collectively, these findings establish ARCs as a potent and precise RNA-targeting platform with strong clinical potential for the treatment of DM1 and other repeat expansion disorders.

synthetic biology↗

bioRxiv2025

Diet-Seg: Dynamic Hardness-Aware Learning for Enhanced Brain Tumor Segmentation

Accurate brain tumor segmentation in magnetic resonance imaging (MRI) remains a critical challenge due to complex tumor heterogeneity, fuzzy boundaries, and significant inter-patient variability. In this study, we propose Diet-Seg (Difficulty-Informed Edge-enhanced Tiny Segmentation), a novel segmentation framework that integrates entropy-based pixel-wise hardness estimation into the training process via a dynamic learning rate modulation strategy. Specifically, we employ a pretrained 3D U-Net information model to quantify voxel-level prediction uncertainty, which is then used to guide the optimization of the main segmentation model. Diet-Seg is further enhanced by an RWKV-based U-Net backbone to capture global spatial dependencies and an EdgeNet module to preserve tumor boundaries through edge-aware fusion. Extensive experiments on the BraTS2018-2021 datasets demonstrate that Diet-Seg consistently outperforms state-of-the-art baselines across all tumor subregions. Notably, Diet-Seg achieves superior generalization when trained on one dataset and validated across multiple years. Moreover, the hardness maps offer interpretable insights into segmentation difficulty, potentially enabling human-AI collaboration in clinical practice. These results highlight the promise of entropy-aware training as a general strategy for robust and efficient medical image segmentation. The work is implemented in the open-source project available on GitHub (https://github.com/ManuelTurner/Diet-Seg)

bioinformatics↗

bioRxiv2025

Cross-species analysis of experimental PH at single cell resolution reveals prominent contributions Ednrb+ EC and Dhcr24+ macrophage populations

BackgroundAnimal models are used widely to study pulmonary hypertension (PH). The cell populations that respond to disease-inducing stimuli in these models and their relationship to human disease remain incompletely defined. Materials and methodThis study analyzed the relationship between several rodent models of PH and human disease at single-cell resolution. scRNA-seq was performed on lungs from mice exposed to hypoxia or Sugen/hypoxia, rats exposed to monocrotaline, and controls. A cross-species single-cell dataset was integrated with human lung cell atlas (HLCA) and single-cell dataset from idiopathic pulmonary arterial hypertension (IPAH) to identify overlapping cell subsets between experimental and human disease and species. ResultsHigh levels of overlap were found between species and models of PH, HLCA, and IPAH datasets. Cell subsets perturbed in rat and mouse PH were similar to those found in human disease, with macrophages and endothelial cells being most affected. A novel Dhcr24high macrophage subset harboring both tissue-remodeling and pro- inflammatory features was consistently increased across models. Several functionally diverse endothelial subtypes were found, including novel Ednrb+ and Nox2+ subpopulations, reflecting enhanced apoptosis, dysregulated angiogenesis and proliferation, and reactive oxygen species-mediated stress. These macrophage and endothelial subtypes expressed numerous PH drug target genes, and exhibited potential disease-specific intercellular interactions involving Angptl4, Cxcl12, and Sema3 signaling. Disease-associated changes in these populations were confirmed by immunofluorescence in lung tissues from animals and patients. ConclusionsWe established a comprehensive cross-species single-cell atlas of mainstream rodent PH models, highlighting several novel macrophage and endothelial subtypes and signaling motifs potentially contributing to human disease.

bioinformatics↗

bioRxiv2025

PTRAMP, CSS and Ripr form a conserved complex required for merozoite invasion of Plasmodium species into erythrocytes

Invasion of erythrocytes by members of the Plasmodium genus is an essential step of the parasite lifecycle, orchestrated by numerous host-parasite interactions. In P. falciparum Rh5, with PfCyRPA, PfRipr, PfCSS, and PfPTRAMP, forms the essential PCRCR complex which binds basigin on the erythrocyte surface. Rh5 is restricted to P. falciparum and its close relatives; however, PTRAMP, CSS and Ripr orthologs are present across the Plasmodium genus. We investigated PTRAMP, CSS and Ripr orthologs from three species to elucidate common features of the complex. Like P. falciparum, PTRAMP and CSS form a disulfide-linked heterodimer in both P. vivax and P. knowlesi with all three species forming a complex (PCR) with Ripr by binding its C-terminal region. Cross-reactive antibodies targeting the PCR complex differentially inhibit merozoite invasion. Cryo-EM visualization of the P. knowlesi PCR complex confirmed predicted models and revealed a core invasion scaffold in Plasmodium spp. with implications for vaccines targeting multiple species of malaria-causing parasites.

microbiology↗

bioRxiv2025

Cytosine-to-uracil RNA editing is upregulated by pro-inflammatory stimulation of myeloid cells

Myeloid cells undergo large changes to their gene expression profile in response to inflammatory stimulation. This includes an increase in post-transcriptional modifications carried out by adenosine-to-inosine (A-to-I) and cytosine-to-uracil (C-to-U) RNA editing enzymes. However, the precise RNA editing targets altered by stimulation and the consequences of RNA editing on gene expression and the proteome have been understudied. We present a comprehensive RNA editing analysis of stimulated myeloid cells across three independent cohorts totalling 297 samples, including monocytes and IPS-derived microglia. We observed that C-to-U editing, while less abundant, has a higher effect size in response to stimulation than A-to-I, and has a greater potential to recode the proteome. We investigated the consequences of RNA editing on RNA stability and gene expression using in silico and in vitro reporter methods, and identified a recoding C-to-U site in ARSB that mimics a reported lysosomal storage disorder mutation.

genomics↗

bioRxiv2025

Single-cell-level condition-related signal estimation with batch effect removal through neural discrete representation learning

Advances in single-cell sequencing techniques and the growing volume of single-cell data have created unprecedented opportunities for uncovering the changes in gene expression patterns induced by perturbations or associated with diseases. However, batch effects and non-linearity in single-cell data make single-cell-level estimation challenging. To address these drawbacks, we developed NDreamer, an approach that combines neural discrete representation learning with counterfactual causal matching. NDreamer can be used to estimate the batch-effect-free and condition-related or perturbation-induced signal-preserved expression data from the raw expressions and then estimate single-cell-level perturbation-induced or condition-related signals. Benchmarked on datasets across platforms, organs, and species, NDreamer robustly outperformed previous single-cell-level perturbation effect estimation methods and batch effect denoising methods. Finally, we applied NDreamer to a large Alzheimers disease cohort and uncovered meaningful gene expression patterns between the dementia patients and health controls.

bioinformatics↗

bioRxiv2025

Validation of bronchial airway gene expression associated with bronchiectasis in nasal epithelium

ObjectivesExamination of bronchial epithelium-derived gene expression signature of bronchiectasis (BE) in nasal epithelium. MethodsWe studied 220 participants from the Detection of Early Lung Cancer Among Military Personnel study with bulk RNA-seq of nasal epithelium brushings. Gene set enrichment analysis (GSEA) was used to examine whether genes previously identified as increased or decreased in the bronchial epithelium of individuals with radiologic BE are significantly enriched among the genes most significantly altered in the nasal epithelium. GSEA and cell-type specific gene expression signatures were used to examine changes in the cellular composition of nasal epithelium associated with radiologic BE. ResultsGenes previously found to have significantly altered expression in bronchial epithelium in radiologic BE are concordantly enriched among the genes most increased or decreased in nasal epithelium in radiologic BE in this validation set (p = 3x10-7, increased; p = 6x10-8, decreased). GSEA performed using cell-type signatures demonstrates that in nasal epithelium, BE is associated with increased multiciliated and deuterosomal cell-related gene expression and decreased expression of basal cell- related gene expression, consistent with our previous findings in bronchial epithelium. ConclusionOur work validates our bronchial signature of BE in an independent dataset and demonstrates that similar gene expression changes are associated with radiologic BE in the bronchial and nasal epithelium. These findings support considering nasal epithelium brushing as a less invasive tool for screening and monitoring BE. SummaryThis study validates a previously reported bronchial signature of bronchiectasis (BE) in nasal epithelium. Changes in gene expression and cell-type composition associated with radiologic BE are similar across both bronchial and nasal epithelium.

bioinformatics↗

bioRxiv2025

Unraveling and overcoming the ammonia toxicity in methanotrophs for sustainable biomanufacturing and methane removal

Replacement of nitrate with ammonium at large scale cultivation of methanotrophs can improve the economic feasibility of these bacteria in methane-based biomanufacturing and methane removal. However, ammonia toxicity and N2O emission impede this option. The mechanism of ammonia oxidation in methanotrophs remains elusive, limiting the effort to detoxify ammonia via genetic engineering. Using an industrially promising methanotroph as a model, we identified a porin PorA that facilitated ammonium uptake. Inactivation of PorA remarkably relieved ammonia toxicity and reduced N2O production. Meanwhile, we demonstrated that haoA, cytL and hcp contributed to ammonia detoxification and cytL was involved in the conversion of NH2OH to N2O. A mutant strain with increased ammonium-utilizing ability and decreased N2O emission was constructed. High growth rate and cell biomass were achieved in fed-batch fermentation with this strain using ammonium. These results deepen our understanding of ammonia oxidation in methanotrophs and promote their applications in biomanufacturing and methane removal. IMPORTANCEM. buryatense 5GB1C is becoming a methanotrophic model for both fundamental research and practical applications. Here, the genetic determinants of ammonia toxicity were identified and a porin gene that facilitated ammonium uptake was described for the first time, providing new understanding regarding the mechanism of nitrification in methanotrophs. Moreover, this study dramatically reduced ammonia toxicity and N2O production in this strain and enabled the use of ammonium as nitrogen source in fermentation, improving the sustainability of this strain in practical applications.

microbiology↗

bioRxiv2024

Microbial succession, community assembly and adaptation over five years in a newly discovered deep-sea cold seep

Deep-sea cold seeps, characterized by the release of hydrocarbon-rich fluids such as methane from the seafloor, create unique ecosystems with evolving biological communities. Microorganisms serve as pioneers, initially colonizing early-stage cold seeps and providing essential matter and energy for subsequent colonizers. However, microbial succession, community assembly, and adaptation in these environments remain largely unexplored due to challenges associated with locating early-stage cold seeps. Here, we collected sediment samples from a newly discovered early-stage cold seep site, QDN-W07, in the South China Sea at varying depths over five years. We also sampled two previously reported cold seep sites, QDN-S18 and HM-ROV01, for comparative analysis. Amplicon sequencing revealed a high relative abundance of ANME-3 (up to 99%) and Methylomonadaceae (up to 83%) in surface sediments, and indicated changes in microbial community structures over time. Dispersal limitation dominated community assembly, while the contribution of homogeneous selection decreased over time, accompanied by a declined in the intensity of microbial association. Metagenomic analysis of core microbiome, comprising 172 metagenomic-assembled genomes at QDN-W07, revealed tolerance to low temperatures and oxygen. Core microbiome genomes were enriched with genes related to aerobic methane oxidation, urea metabolism, and the oxidation of sulfur, ammonia and carbon monoxide. These results indicate that core microorganisms obtain energy from the oxidation of inorganic compounds, allowing them to thrive in cold, methane-rich and unstable conditions. Overall, this study enhances our understanding of deep biosphere life, biogeochemical cycling, and the ecological management of cold seep environments.

microbiology↗

bioRxiv2024

Dietary control of peripheral adipose storage capacity through membrane lipid remodelling

Complex genetic and dietary cues contribute to the development of obesity, but how these are integrated on a molecular level is incompletely understood. Here, we show that PPAR{gamma} supports hypertrophic expansion of adipose tissue via transcriptional control of LPCAT3, a membrane-bound O-acyltransferase that enriches diet-derived omega-6 (n-6) polyunsaturated fatty acids (PUFAs) in the phospholipidome. In high-fat diet-fed mice, lowering membrane n-6 PUFA levels by adipocyte-specific Lpcat3 knockout (Lpcat3AKO) or by dietary lipid manipulation leads to dysfunctional triglyceride (TG) storage, ectopic fat deposition and insulin resistance. Aberrant lipolysis of stored TGs in Lpcat3AKO adipose tissues instigates a non-canonical adaptive response that engages a futile lipid cycle to increase energy expenditure and limit further body weight gain. Mechanistically, we find that adipocyte LPCAT3 activity promotes TG storage by selectively enriching n-6 arachidonoyl-phosphatidylethanolamine at the ER-lipid droplet interface, which in turn favours the budding of large droplets that exhibit greater resistance to ATGL-dependent hydrolysis. Thus, our study highlights the PPAR{gamma}-LPCAT3 pathway as a molecular link between dietary n-6 PUFA intake, adipose expandability and systemic energy balance.

physiology↗

bioRxiv2024

Accurate Cell Abundance Quantification using Multi-positive and Unlabeled Self-learning

Quantifying the abundance of different cell types in pathological samples can help to uncover the correlations between cell composition and pathological conditions, offering deeper insights into the roles of different cell types in complex diseases. Conventional methods for cell abundance estimation often employ unsupervised clustering or supervised learning to identify cell types and estimate their proportions. However, these methods face challenges in accurately quantifying cell abundances, as clustering results could be unreliable and supervised methods may misclassify cell types not presented in the training data. We introduce CleverXMBD1 (denoted as Clever) for quantifying cell abundance from complex samples using a multi-class classifier trained with a confidence-based multi-positive and unlabeled loss function. Our evaluations show that Clever consistently and substantially outperforms existing methods in quantifying cell type abundance across multiple single cell datasets derived from different modalities, including CyTOF and image mass cytometry.

bioinformatics↗

bioRxiv2024

Shade Inhibits Cambial Activity in Populus Stems by the SPL16/SPL23-Mediated Cytokinin Pathway

Trees in natural forests or plantations often encounter neighbor proximity signal that negatively impacts wood production. However, the molecular basis underlying shade-regulation of vascular cambial activity during stem radial growth remains unknown in woody species. Here, we revealed that high stand density and simulated shade (low R/FR ratio) suppress the division and differentiation of cambial cells in poplar stems. A genome-wide screen for Populus SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) genes identified that SPL16 and SPL23 are preferentially expressed in the phloem and cambium, being downregulated by simulated shade. Knocking out SPL16/23 impaired cambial activity, whereas phloem-specific overexpression of SPL16 stimulated cambial proliferation and mitigated the shade-inhibition of cambial activity. Additionally, shade decreased bioactive cytokinin (CK) levels by suppressing the expression of CK biosynthesis genes IPT5a, IPT5b and LOG1b in poplar stems. Molecular and genetic studies reveled that SPL16/23 directly activate IPT5s/LOG1b expression to promote CK biosynthesis and cambial activity. Moreover, elevated miR156 expression in shade-treated stems regulated SPL16/23 at the post-transcriptional level, mediating shades effects on cambial activity. Collectively, our findings unravel that the miR156-SPL16/23-IPT5/LOG1-cytokinin pathway operates in the shade-mediated inhibition of cambial activity, providing potential targets for the genetic improvement of shade-tolerant trees.

plant biology↗

bioRxiv2024

Cholesterol binding to VCAM-1 promotes vascular inflammation

Hypercholesterolemia has long been implicated in endothelial cell (EC) dysfunction, but the mechanisms by which excess cholesterol causes vascular pathology are incompletely understood. Here we used a cholesterol-mimetic probe to map cholesterol-protein interactions in primary human ECs and discovered that cholesterol binds to and stabilizes the adhesion molecule VCAM-1. We show that accessible plasma membrane (PM) cholesterol in ECs is acutely responsive to inflammatory stimuli and that the nonvesicular cholesterol transporter Aster-A regulates VCAM-1 stability in activated ECs by controlling the size of this pool. Deletion of Aster-A in ECs increases VCAM-1 protein, promotes immune cell recruitment to vessels, and impairs pulmonary immune homeostasis. Conversely, depleting cholesterol from the endothelium in vivo dampens VCAM-1 induction in response to inflammatory stimuli. These findings identify cholesterol binding to VCAM-1 as a key step during EC activation and provide a biochemical explanation for the ability of excess membrane cholesterol to promote immune cell recruitment to the endothelium.

physiology↗

bioRxiv2024

Genome-wide CRISPRi-seq identified ferredoxin-NADP reductase FprB as a synergistic target for gallium therapy in Pseudomonas aeruginosa

With the rise of antibiotic-resistant bacteria, non-antibiotic therapies like gallium are increasingly gaining attention. Gallium ions exhibit potent activity against multidrug-resistant bacteria and intravenous gallium nitrite is under phase 2 clinical trials to treat chronic Pseudomonas aeruginosa infections in cystic fibrosis patients. However, its clinical efficacy is constrained by the achievable peak concentration in human tissue. To address this limitation, we applied a genome-wide CRISPR interference approach (CRISPRi-seq), to identify potential synergistic targets with gallium. Through the systemic screening, we classified the essential genes by response time and growth reduction, pinpointing the most vulnerable therapeutic targets in this species. In addition, we identified a highly conserved gene fprB, encoding a ferredoxin-NADP+ reductase, the downregulation of which dramatically sensitized the cells to gallium. Using a null mutant, we confirmed the loss of fprB lowers the minimum inhibitory concentration of gallium from 320 {micro}M to 10 {micro}M and shifted galliums mode of action from bacteriostatic to bactericidal. Further investigation revealed that FprB plays a critical role in modulating oxidative stress induced by gallium, via control of the iron homeostasis and reactive oxygen species accumulation. Deleting fprB also enhanced galliums efficacy against biofilms formation and improved outcomes in murine lung infection model of P. aeruginosa, suggesting FprB as a promising drug target in combination with gallium. Overall, our data showed CRISPRi-seq as a powerful tool for systematic genetic analysis of P. aeruginosa, advancing identification of novel therapeutic targets.

microbiology↗

bioRxiv2024

Investigation of pair-wise single-cell interactions by statistically interpreting spatial cell state correlation learned by self-supervised graph inductive bias transformer

Cell-cell interactions (CCI), driven by distance-dependent signaling, are important for tissue development and organ function. While imaging-based spatial transcriptomics offers unprecedented opportunities to unravel CCI at single-cell resolution, current analyses face challenges such as limited ligand-receptor pairs measured, insufficient spatial encoding, and low interpretability. We present GITIII, a lightweight, interpretable, self-supervised graph transformer-based model that conceptualizes cells as words and their surrounding cellular neighborhood as context that shapes the meaning or state of the central cell. GITIII infers CCI by examining the correlation between a cells state and its niche, enabling us to understand how sender cells influence the gene expression of receiver cells, visualize spatial CCI patterns, perform CCI-informed cell clustering, and construct CCI networks. Applied to four spatial transcriptomics datasets across multiple species, organs, and platforms, GITIII effectively identified and statistically interpreted CCI patterns in the brain and tumor microenvironments.

bioinformatics↗

bioRxiv2024

Lipid- and protein-directed photosensitizer proximity labeling captures the cholesterol interactome

The physical properties of cellular membranes, including fluidity and function, are influenced by protein and lipid interactions. In situ labeling chemistries, most notably proximity-labeling interactomics are well suited to characterize these dynamic and often fleeting interactions. Established methods require distinct chemistries for proteins and lipids, which limits the scope of such studies. Here we establish a singlet-oxygen-based photocatalytic proximity labeling platform (POCA) that reports intracellular interactomes for both proteins and lipids with tight spatiotemporal resolution using cell-penetrant photosensitizer reagents. Using both physiologically relevant lipoprotein-complexed probe delivery and genetic manipulation of cellular cholesterol handling machinery, cholesterol-directed POCA captured established and unprecedented cholesterol binding proteins, including protein complexes sensitive to intracellular cholesterol levels and proteins uniquely captured by lipoprotein uptake. Protein-directed POCA accurately mapped known intracellular membrane complexes, defined sterol-dependent changes to the non-vesicular cholesterol transport protein interactome, and captured state-dependent changes in the interactome of the cholesterol transport protein Aster-B. More broadly, we find that POCA is a versatile interactomics platform that is straightforward to implement, using the readily available HaloTag system, and fulfills unmet needs in intracellular singlet oxygen-based proximity labeling proteomics. Thus, we expect widespread utility for POCA across a range of interactome applications, spanning imaging to proteomics.

biochemistry↗