Biology subjects

Xiao, F.

Publications and source records attributed to Xiao, F..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Restriction of individual branched-chain amino acids has distinct effects on the development and progression of Alzheimers disease in 3xTg mice

Dietary protein is a critical regulator of metabolic health and aging in diverse species. Recent discoveries have determined that many benefits of a low protein diet are the result of reduced consumption of the three branched-chain amino acids (BCAAs), leucine, isoleucine, and valine. Intriguingly, each BCAA has distinct physiological and molecular effects, with restriction of isoleucine alone being sufficient to improve metabolic health and extend the lifespan of mice. While restriction of protein or all three BCAAs improves cognition in mouse models of Alzheimers disease (AD), the impact of restricting each individual BCAA on the progression and development of AD is unknown. Here, we investigate the effect of restricting each individual BCAA on metabolic health, AD pathology, molecular signaling, and cognition in the 3xTg mouse model. We find that restriction of isoleucine and valine, but not leucine, promotes metabolic health. Restriction of each BCAA had distinct effects on AD pathology and molecular signaling, with transcriptomic analysis of the brain revealing both distinct and shared, and highly sex-specific, molecular impacts of restricting each BCAA. Restricting any of the three BCAAs improved short-term memory in males, with isoleucine restriction having the strongest effect, while restricting valine had the greatest cognitive benefits in females. We identify a set of significantly altered pathways strongly associated with reduced AD pathology and improved cognitive performance in males. Our findings suggest that restricting any of the BCAAs, particularly isoleucine or valine, may form the basis of a novel sex-specific approach to prevent or delay the progression of AD.

physiology↗

bioRxiv2025

Ketogenesis is dispensable for the metabolic adaptations to caloric restriction

Caloric restriction (CR) extends the health and lifespan of diverse species. When fed once daily, CR-treated mice rapidly consume their food and endure a prolonged fast between meals. As fasting is associated with a rise in circulating ketone bodies, we investigated the role of ketogenesis in CR using mice with whole-body ablation of Hmgcs2, the rate-limiting enzyme producing the main ketone body {beta}-hydroxybutyrate ({beta}HB). Here, we report that Hmgcs2 is largely dispensable for many metabolic benefits of CR, including CR-driven changes in adiposity, glycemic control, liver autophagy, and energy balance. Although we observed sex-specific effects of Hmgcs2 on insulin sensitivity, fuel selection, and adipocyte gene expression, the overall physiological response to CR remained robust in mice lacking Hmgcs2. To gain insight into why the deletion of Hmgcs2 does not disrupt CR, we measured fasting {beta}HB levels as mice initiated a CR diet. Surprisingly, as mice adapt to CR, they no longer engage high levels of ketogenesis during the daily fast. Our work suggests that the metabolic benefits of long-term CR are not mediated by ketogenesis.

physiology↗

bioRxiv2025

An in vivo rat lumbar spine instability model induced by intervertebral disc injury

Intervertebral disc (IVD) degeneration is a potential contributor to low-back pain. While experimental IVD injury models have demonstrated IVD structural changes, the early mechanical consequences remain unclear. This study aimed to establish a rat model of lumbar spine instability via IVD injury and assess back musculature adaptations to IVD injury. Thirty-one adult male Wistar rats were assigned to three groups: IVD knife stab lesion (knife), IVD needle puncture (needle), and sham surgery control (control). In the knife and needle groups, L4/L5 IVDs were injured at 14 weeks of age. One-two weeks post-intervention, lumbar multifidus (MF) and medial longissimus (ML) muscles were excised, L4-L5 spinal segments were harvested for mechanical testing, and IVDs were collected for histology. The needle group exhibited lower peak stiffness, peak moment, and hysteresis than controls in flexion, with no difference in lateral bending. IVD height and area did not differ between groups, but the needle group had a smaller nucleus relative to annulus area compared to controls. Morphological changes were observed in both injury groups. The needle group showed higher normalized ML mass, while normalized MF mass was unchanged. In conclusion, a rat model of lumbar spine instability was successfully established via IVD needle injury.

bioengineering↗

bioRxiv2025

Tracking White Matter Changes After Stereotaxic Radiosurgery inMiniature Pigs Using structural MRI, DTI, and FDG-PET

Stereotaxic radiosurgery (SRS) non-invasively and precisely ablates brain tumors or glioma located in the location where is surgically inaccessible with the aid of three- dimensional coordination system. This technique can also treat functional or psychiatric disorders, yet its dosimetry and curative mechanism remain to be elucidated. In this study, a miniature pig model was utilized to verify the effect of stereotaxic radiosurgery on a white matter tract with various doses delivered by CyberKnife. As porcine brain bears high resemblance to that of human in size and structure, the potential irradiation- induced change in structures and metabolism was monitored by conventional tools in clinical, including anatomical magnetic resonance image (MRI), diffusion tensor image (DTI) and 18F-Fluoro-D-Glucose positron emission tomography (FDG-PET). The right internal capsule was selected as the surgical target, and a one-year longitudinal study was conducted with whole brain images obtained once per three months. The results indicate that a dose equal to or higher than 60 Gy led to a late-onset radionecrosis which took a period close to 180 d to develop edema and breakage in the blood-brain barrier. In the meanwhile, DTI indices and differential tractography further illustrate a dose- and distance-dependent white matter injury along the tract of internal capsule. In contrast, doses of 40 Gy and below did not result in any discernible harm to the brain structure, but a sustained local inhibition in brain metabolism was observed in some pigs. The modulatory effect of low dose radiation awaits a comprehensive assessment throughout the whole brain in combination with behavioral or cognitive tasks built on pigs. This study showing the dose- and time-dependent changes will improve the understanding of the SRS dosimetry on the white matter and help investigators to decide an optimal window for brain imaging or behavioral assessment to take place on patients.

neuroscience↗

bioRxiv2025

TransBrain: A computational framework for translating brain-wide phenotypes between humans and mice

Despite remarkable advances in whole-brain imaging technologies, the lack of quantitative approaches to bridge rodent preclinical and human studies remains a critical challenge. Here we present TransBrain, a computational framework enabling bidirectional translation of brain-wide phenotypes between humans and mice. TransBrain improves human-mouse homology mapping accuracy through: (1) a novel detached region-specific deep neural network trained on integrated multi-modal human transcriptomics to improve cortical correspondence (89.5% improvement over the original transcriptome), which revealed two evolutionarily conserved gradients explaining >50% of cortical organizational variance, and (2) random walk-based graph representation learning to construct a unified cross-species latent space incorporating anatomical hierarchies and structural connectivity. We demonstrated TransBrains utility through three cross-species applications: quantitative assessment of resting-state brain organizational features, inferring human cognitive functions from mouse optogenetic circuits, and translating molecular insights from mouse models to individual-level mechanisms in autism. TransBrain enables quantitative cross-species comparison and mechanistic investigation of both normal and pathological brain functions.

neuroscience↗

bioRxiv2025

Decoding Mechanisms of PTEN Missense Mutations in Cancer and Autism Spectrum Disorder using Interpretable Machine Learning Approaches

Missense mutations in oncogenic proteins that are concurrently associated with neurodevelopmental disorders have garnered significant attention. Phosphatase and tensin homolog (PTEN) serves as a paradigmatic model for mapping its mutational landscape and identifying genotypic predictors of distinct phenotypic outcomes, including cancer and autism spectrum disorder (ASD). Despite extensive research into the genotype-phenotype correlations of PTEN mutations, the mechanisms underlying the dual association of specific PTEN mutations with both cancer and ASD (PTEN-cancer/ASD mutations) remain elusive. This study introduces an integrative approach that combines machine learning (ML) with structural dynamics to elucidate the molecular effects of PTEN-cancer/ASD mutations. Analysis of biophysical and network biology-based signatures reveals a complex energetic and functional landscape. Subsequently, an ML model and corresponding integrated score were developed to classify and predict PTEN-cancer/ASD mutations, underscoring the significance of protein dynamics in predicting cellular phenotypes. Further molecular dynamics simulations demonstrated that PTEN-cancer/ASD mutations induce dynamic alterations characterized by open conformational changes restricted to the P loop and coupled with inter-domain allosteric regulation. This research aims to enhance the genotypic and phenotypic understanding of PTEN-cancer/ASD mutations through an interpretable ML model integrated with structural dynamics analysis. By identifying shared mechanisms between cancer and ASD, the findings pave the way for the development of novel therapeutic strategies.

bioinformatics↗

bioRxiv2025

Tissue-specific effects of dietary protein on cellular senescence are mediated by branched-chain amino acids

Dietary protein is a key regulator of healthy aging in both mice and humans. In mice, reducing dietary levels of the branched-chain amino acids (BCAAs) recapitulates many of the benefits of a low protein diet; BCAA-restricted diets extend lifespan, reduce frailty, and improve metabolic health, while BCAA supplementation shortens lifespan, promotes obesity, and impairs glycemic control. Recently, high protein diets have been shown to promote cellular senescence, a hallmark of aging implicated in many age-related diseases, in the liver of mice. Here, we test the hypothesis that the effects of high protein diets on metabolic health and on cell senescence are mediated by BCAAs. We find that reducing dietary levels of BCAAs protects male and female mice from the negative metabolic consequences of both normal and high protein diets. Further, we identify tissue-specific effects of BCAAs on cellular senescence, with restriction of all three BCAAs - but not individual BCAAs - protecting from hepatic cellular senescence while potentiating cell senescence in white adipose tissue. We find that the effects of BCAAs on hepatic cellular senescence are cell-autonomous, with lower levels of BCAAs protecting cultured cells from antimycin-A induced senescence. Our results demonstrate a direct effect of a specific dietary component on a hallmark of aging and suggest that cellular senescence may be highly susceptible to dietary interventions.

physiology↗

bioRxiv2025

protPheMut: An Interpretable Machine Learning Tool for Classification of Cancer and Neurodevelopmental Disorders in Human Missense Variants

MotivationRecent advances in human genomics have revealed that missense mutations in a single protein can lead to distinctly different phenotypes. In particular, some mutations in oncoproteins like Ras, MEK, PI3K, PTEN, and SHP2 are linked various cancers and Neurodevelopmental Disorders (NDDs). While numerous tools exist for predicting the pathogenicity of missense mutations, linking these variants to certain phenotypes remains a major challenge, particularly in the context of personalized medicine. ResultsTo fill this gap, we developed protPheMut (Protein Phenotypic Mutations Analyzer), leveraging multiple interpretable machine learning methods and integrate diverse biophysics and network dynamics-based signatures, for the prediction of mutations of the same protein can promote cancer, or NDDs. We illustrate the utility of protPheMut in phenotypes (cancer/NDDs) prediction by the mutation analysis of two protein cases, that are PI3K and PTEN. Compared to seven other predictive tools, protPheMut demonstrated exceptional accuracy in forecasting phenotypic effects, achieving an AUROC of 0.8501 for PI3K mutations related to cancer and Cowden syndrome. For multi-phenotypes prediction of PTEN mutations related to cancer, PHTS, and HCPS, protPheMut achieved an AUC of 0.9349 through micro-averaging. Using SHAP model explanations, we gained insights into the mechanisms driving phenotype formation. A userfriendly website deployment is also provided. AvailabilitySource code and data are available at https://github.com/Spencer-JRWang/protPheMut. We also provide a user-friendly website at http://netprotlab.com/protPheMut. Supplementary informationSupplementary data are available at Bioinformatics online. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/631365v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@df55e3org.highwire.dtl.DTLVardef@7fe3f9org.highwire.dtl.DTLVardef@501cccorg.highwire.dtl.DTLVardef@192c98e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

HapCNV: A Comprehensive Framework for CNV Detection in Low-input DNA Sequencing Data

Copy number variants (CNVs) are prevalent in both diploid and haploid genomes, with the latter containing a single copy of each gene. Studying CNVs in genomes from single or few cells is significantly advancing our knowledge in human disorders and disease susceptibility. Low-input including low-cell and single-cell sequencing data for haploid and diploid organisms generally displays shallow and highly non-uniform read counts resulting from the whole genome amplification steps that introduce amplification biases. In addition, haploid organisms typically possess relatively short genomes and require a higher degree of DNA amplification compared to diploid organisms. However, most CNV detection methods are specifically developed for diploid genomes without specific consideration of effects on haploid genomes. Challenges also reside in reference samples or normal controls which are used to provide baseline signals for defining copy number losses or gains. In traditional methods, references are usually pre-specified from cells that are assumed to be normal or disease-free. However, the use of pre-defined reference cells can bias results if common CNVs are present. Here, we present the development of a comprehensive statistical framework for data normalization and CNV detection in haploid single- or low-cell DNA sequencing data called HapCNV. The prominent advancement is the construction of a novel genomic location specific pseudo-reference that selects unbiased references using a preliminary cell clustering method. This approach effectively preserves common CNVs. Using simulations, we demonstrated that HapCNV outperformed existing methods by generating more accurate CNV detection, especially for short CNVs. Superior performance of HapCNV was also validated in detecting known CNVs in a real P. falciparum parasite dataset. In conclusion, HapCNV provides a novel and useful approach for CNV detection in haploid low-input sequencing datasets, with easy applicability to diploids.

bioinformatics↗

bioRxiv2024

Replication stress increases de novo CNVs across the malaria parasite genome

Changes in the copy number of large genomic regions, termed copy number variations (CNVs), contribute to important phenotypes. CNVs are readily identified using conventional approaches when present in a large fraction of the cell population. However, CNVs in only a few genomes are often overlooked but important; if beneficial, a de novo CNV that arises in a single genome can expand during selection to create a population of cells with novel characteristics. While single cell methods for studying de novo CNVs are increasing, we continue to lack information about CNV dynamics in rapidly evolving microbial populations. Here, we investigated de novo CNVs in the genome of the Plasmodium parasite that causes human malaria. The highly AT-rich P. falciparum genome readily accumulates CNVs that facilitate rapid adaptation. We employed low-input genomics and specialized computational tools to evaluate the impact of sub-lethal stress on the de novo CNV rate. We observed a significant increase in genome-wide de novo CNVs following treatment with an antimalarial compound that inhibits replication. De novo CNVs encompassed genes from various cellular pathways participating in human infection. This snapshot of CNV dynamics emphasizes the connection between replication stress, DNA repair, and CNV generation in this important microbial pathogen.

genomics↗

bioRxiv2024

Wnt7a is Required for Regeneration of Dystrophic Skeletal Muscle

Intramuscular injection of Wnt7a has been shown to accelerate and augment skeletal muscle regeneration and to ameliorate dystrophic progression in mdx muscle, a model for Duchenne muscular dystrophy (DMD). However, loss-of-function studies to investigate the requirement for Wnt7a in muscle regeneration has not been evaluated. Here, we assessed muscle regeneration and function in wild type (WT) and mdx mice where Wnt7a was specifically deleted in muscle using a conditional Wnt7a floxed allele and a Myf5-Cre driver. We found that both WT and mdx mice with deletion of Wnt7a in muscle, exhibited marked deficiencies in muscle regeneration at 21 d following cardiotoxin (CTX) induced injury. Unlike WT, deletion of Wnt7a in mdx resulted in a marked decrease in specific force generation prior to CTX injury. However, both WT and mdx muscle lacking Wnt7a displayed decreased specific force generation following CTX injection. Notably the regeneration deficit observed in mdx mice lacking Wnt7a in muscle was rescued by a single tail vein injection of an extracellular vesicle preparation containing Wnt7a (Wnt7a-EVs). Therefore, we conclude that the regenerative capacity of muscle in mdx mice is due to the upregulation of endogenous Wnt7a following injury, and that systemic delivery of Wnt7a-EVs represents a therapeutic strategy for treating DMD.

genetics↗

bioRxiv2024

With the leisure of time, kinetic proofreading does reliable ligand discrimination after all

In a recent PNAS publication, Kirby and Zilman studied kinetic proofreading for receptor signaling and argued that having more proofreading steps does not generally improve ligand discrimination when stochastic effects are taken into account. In their analysis, however, the authors made an implicit assumption about a fixed discrimination time, which is not always warranted. We show that when the discrimination time is allowed to vary in order to maintain a fixed level of signaling activity, the proofreading performance can, in fact, uniformly improve with the number of steps. We therefore call for a comprehensive study of the interplay between discrimination time, discrimination accuracy, and signaling activity, where the two seemingly contradictory results will be slices of the trade-off surface along different axes.

systems biology↗

bioRxiv2023

OSCAA: A Two-Dimensional Gaussian Mixture Model for Copy Number Variation Association Analysis

Copy number variants (CNVs) are prevalent in the human genome which provide profound effect on genomic organization and human diseases. Discovering disease associated CNVs is critical for understanding the pathogenesis of diseases and aiding their diagnosis and treatment. However, traditional methods for assessing the association between CNVs and disease risks adopt a two-stage strategy conducting quantitative CNV measurements first and then testing for association, which may lead to biased association estimation and low statistical power, serving as a major barrier in routine genome wide assessment of such variation. In this article, we developed OSCAA, a flexible algorithm to discover disease associated CNVs for both quantitative and qualitative traits. OSCAA employs a two-dimensional Gaussian mixture model that is built upon the principal components from copy number intensities, accounting for technical biases in CNV detection while simultaneously testing for their effect on outcome traits. In OSCAA, CNVs are identified and their associations with disease risk are evaluated simultaneously in a single step, taking into account the uncertainty of CNV identification in the statistical model. Our simulations demonstrated that OSCAA outperformed the existing one-stage method and traditional two-stage methods by yielding a more accurate estimate of the CNV-disease association, especially for short CNVs or CNVs with weak signal. In conclusion, OSCAA is a powerful and flexible approach for CNV association testing with high sensitivity and specificity, which can be easily applied to different traits and clinical risk predictions.

genomics↗

bioRxiv2023

Spatial pattern and differential expression analysis with spatial transcriptomic data

The emergence of spatial transcriptomic technologies has opened new avenues to investigate gene activities while preserving the spatial context of tissues. Utilizing data generated by such technologies, the identification of spatially variable (SV) genes is an essential step in investigating tissue landscapes and biological processes. Particularly in typical experimental designs such as case-control or longitudinal studies, identifying SV genes between groups is crucial for discovering significant biomarkers or developing targeted therapies for diseases. However, current methods available for analyzing spatial transcriptomic data are still in their infancy, and none of the existing methods are capable of identifying SV genes between groups. To overcome this challenge, we developed SPADE for spatial pattern and differential expression analysis to identify SV gene in spatial transcriptomic data. SPADE is based on a machine learning model of Gaussian process regression with a gene-specific Gaussian kernel, enabling the detection of SV genes both within and between groups. Through extensive simulations and real data analyses, we have demonstrated the superior performance of SPADE compared to existing methods in detecting SV genes within and between groups. The SPADE source code and documentation are publicly available at https://github.com/thecailab/SPADE.

bioinformatics↗

bioRxiv2023

Wnt binding to Coatomer proteins directs secretion on exosomes independently of palmitoylation

Wnt proteins are secreted hydrophobic glycoproteins that act over long distances through poorly understood mechanisms. We discovered that Wnt7a is secreted on extracellular vesicles (EVs) following muscle injury. Structural analysis identified the motif responsible for Wnt7a secretion on EVs that we term the Exosome Binding Peptide (EBP). Addition of the EBP to an unrelated protein directed secretion on EVs. Disruption of palmitoylation, knockdown of WLS, or deletion of the N-terminal signal peptide did not affect Wnt7a secretion on purified EVs. Bio-ID analysis identified Coatomer proteins as candidates responsible for loading Wnt7a onto EVs. The crystal structure of EBP bound to the COPB2 coatomer subunit, the binding thermodynamics, and mutagenesis experiments, together demonstrate that a dilysine motif in the EBP mediates binding to COPB2. Other Wnts contain functionally analogous structural motifs. Mutation of the EBP results in a significant impairment in the ability of Wnt7a to stimulate regeneration, indicating that secretion of Wnt7a on exosomes is critical for normal regeneration in vivo. Our studies have defined the structural mechanism that mediates binding of Wnt7a to exosomes and elucidated the singularity of long-range Wnt signalling.

molecular biology↗

bioRxiv2023

Replication initiation in bacteria: precision control based on protein counting

Balanced biosynthesis is the hallmark of bacterial cell physiology, where the concentrations of stable proteins remain steady. However, this poses a conceptual challenge to modeling the cell-cycle and cell-size controls in bacteria, as prevailing concentration-based eukaryote models are not directly applicable. In this study, we revisit and significantly extend the initiator-titration model, proposed thirty years ago, and explain how bacteria precisely and robustly control replication initiation based on the mechanism of protein copy-number sensing. Using a mean-field approach, we first derive an analytical expression of the cell size at initiation based on three biological mechanistic control parameters for an extended initiator-titration model. We also study the stability of our model analytically and show that initiation can become unstable in multifork replication conditions. Using simulations, we further show that the presence of the conversion between active and inactive initiator protein forms significantly represses initiation instability. Importantly, the two-step Poisson process set by the initiator titration step results in significantly improved initiation synchrony with CV [~] 1/N scaling rather than the standard [Formula] scaling in the Poisson process, where N is the total number of initiators required for initiation. Our results answer two long-standing questions in replication initiation: (1) Why do bacteria produce almost two orders of magnitude more DnaA, the master initiator proteins, than required for initiation? (2) Why does DnaA exist in active (DnaA-ATP) and inactive (DnaA-ADP) forms if only the active form is competent for initiation? The mechanism presented in this work provides a satisfying general solution to how the cell can achieve precision control without sensing protein concentrations, with broad implications from evolution to the design of synthetic cells.

systems biology↗

bioRxiv2023

Considering Zeros in Single Cell Sequencing Data Correlation Analysis

Single-cell sequencing technology has enabled correlation analysis of genomic features at the cellular level. However, high levels of noise and sparsity in single-cell sequencing data make accurate assessment of correlations challenging. This study provides a toolkit, SCSC (https://github.com/thecailab/SCSC), for the estimation of correlation coefficients in single-cell sequencing data. It comprehensively assessed four strategies (classical, non-zero, dropout-weighted, imputation) and the impact of data features in various simulated scenarios. The study found that filtering zeros significantly improves estimation accuracy, and further improvement can be achieved by considering the drop-out probability. In addition, the study also identified data features including expression level, library size, and biological variations that affect correlation estimation.

bioinformatics↗

bioRxiv2023

A statistical learning method for simultaneous copy number estimation and subclone clustering with single cell sequencing data

The availability of single cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, since cells comprising a subpopulation are found to share genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified CNAs) in the procedure of CNA detection, hence diminishing the accuracy of subclone identification from a large complex cell population. In this study, we developed a CNA detection method based on a fused lasso model, referred to as FLCNA, which can simultaneously identify subclones in single cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA benchmarking to existing copy number estimation methods (SCOPE, HMMcopy) in combination with the existing and commonly used clustering methods. Interestingly, application of FLCNA to a real scDNA-seq dataset of breast cancer revealed remarkably different genomic variation patterns in neoadjuvant chemotherapy treated samples and pre-treated samples. We show that FLCNA is a practical and powerful method in subclone identification and CNA detection with scDNA-seq data.

genomics↗