Biology subjects

Fu, X.

Publications and source records attributed to Fu, X..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

Unfolded Von Willebrand Factor Binds Protein S and Reduces Anticoagulant Activity

Protein S (PS), the critical plasma cofactor for the anticoagulants tissue factor (TF) pathway inhibitor (TFPI) and activated protein C (APC), circulates in two functionally distinct pools: free (anticoagulant) or bound to complement component 4b-binding protein (C4BP) (anti-inflammatory). Acquired free PS deficiency is detected in several viral infections, but its cause is unclear. Here, we identified a shear-dependent interaction between PS and von Willebrand Factor (VWF) by mass spectrometry. Consistently, plasma PS and VWF comigrated in both native and agarose gel electrophoresis. The PS/VWF interaction was blocked by TFPI but not APC, suggesting an interaction with the C-terminal sex hormone binding globulin (SHBG) region of PS. Microfluidic systems, mimicking arterial laminar flow or disrupted turbulent flow, demonstrated that PS stably binds VWF as VWF unfolds under turbulent flow. PS/VWF complexes also localized to platelet thrombi under laminar arterial flow. In thrombin generation-based assays, shearing plasma decreased PS activity, an effect not seen in the absence of VWF. Finally, free PS deficiency in COVID-19 patients, measured using an antibody that binds near the C4BP binding site in SHBG, correlated with changes in VWF, but not C4BP, and with thrombin generation. Our data suggest that PS binds to a shear-exposed site on VWF, thus sequestering free PS and decreasing its anticoagulant activity, which would account for the increased thrombin generation potential. As many viral infections present with free PS deficiency, elevated circulating VWF, and increased vascular shear, we propose that the PS/VWF interaction reported here is a likely contributor to virus-associated thrombotic risk. Key PointsO_LIVon Willebrand Factor (VWF) binds Protein S (PS) in a shear-dependent manner, reducing the free PS pool and its anticoagulant activity. C_LIO_LIThe PS/VWF complex forms under turbulent flow conditions, is stable in whole blood, and localizes to growing platelet thrombi. C_LI

biochemistry↗

bioRxiv2024

Individualized functional connectivity markers associated with motor and mood symptoms of Parkinson's disease

Parkinsons disease (PD) is a complex neurological disorder characterized by many motor and non-motor symptoms. While most studies focus on the motor symptoms of the disease, it is important to identify markers that underlie different facets of the disease. In this case-control study, we sought to discover reliable, individualized functional connectivity markers associated with both motor and mood symptoms of PD. Using functional MRI, we extensively sampled 166 patients with PD (64 women, 102 men; mean age=61.8 years, SD=7.81) and 51 healthy control participants (32 women, 19 men; mean age=55.68 years, SD=7.62). We found that a model consisting of 44 functional connections predicted both motor (UPDRS-III: Pearson r=0.21, FDR-adjusted p=0.006) and mood symptoms (HAMD: Pearson r=0.23, FDR-adjusted p=0.006; HAMA: Pearson r=0.21, FDR-adjusted p=0.006). Two sets of connections contributed differentially to these predictions. Between-network connections, mainly connecting the sensorimotor and visual large-scale functional networks, substantially contributed to the prediction of motor measures, while within-network connections in the insula and sensorimotor network contributed more so to mood prediction. The middle to posterior insula region played a particularly important role in predicting depression and anxiety scores. We successfully replicated and generalized our findings in two independent PD datasets. Taken together, our findings indicate that sensorimotor and visual network markers are indicative of PD brain pathology, and that distinct subsets of markers are associated with motor and mood symptoms of PD.

neuroscience↗

bioRxiv2024

Simultaneous Identification of Changepoints and Model Parameters in Switching Dynamical Systems

Many complex natural systems undergo shifts in dynamics at particular points in time. Examples include phase transitions in gene expression during the cell cycle, introduced species affecting predator-prey interactions, and disease outbreaks responding to intervention measures. Such changepoints partition timeseries into different dynamical regimes characterized by distinct parameter sets, and inference on both the changepoints and regime-specific dynamical parameters is of primary interest. Conventional approaches to analyzing switching dynamical systems first estimate changepoints, and then estimate dynamical parameters assuming the changepoints are fixed and known. Such two-stage approaches are ad-hoc, can introduce biases in the analysis, and do not fully account for uncertainty. Here, we introduce a rigorous, simulation-based inference framework that simultaneously estimates changepoints and model parameters from noisy data while admitting full uncertainty. We use simulation studies of oscillatory predator-prey dynamics and stochastic gene expression to demonstrate that our method yields accurate estimates of changepoints and model parameters together with appropriate uncertainty bounds. We then apply our approach to a real-world case study of COVID-19 intervention effects, and show that our inferred changepoints aligned closely with the actual dates of intervention implementation. Taken together, these results suggest that our framework will have broad utility in diverse scientific domains.

ecology↗

bioRxiv2024

Nephrectomy and high-salt diet inducing pulmonary hypertension and kidney damage by increasing Ang II concentration in rats

BackgroundPulmonary hypertension (PH) is a common complication in patients with chronic kidney disease (CKD), affecting prognosis. However, the pathogenesis is not clear, and the lack of a stable animal model is a significant factor. MethodsIn this study, a rat model of chronic kidney disease with pulmonary hypertension (CKD-PH) was developed through 5/6 nephrectomy combined with a high-salt diet. The models hemodynamics and pathological changes in multiple organs were dynamically assessed. Lung tissues and serum were collected from the model rats to measure the expression of ACE2, the expression levels of vascular active components related to the renin-angiotensin-aldosterone system (RAAS), and changes in the serum metabolic profile of the model. ResultsAfter 14 weeks post-surgery, the CKD-PH rat model exhibited significant changes in hemodynamic parameters indicative of pulmonary arterial hypertension, along with alterations such as right ventricular hypertrophy. However, no evidence of pulmonary vascular remodeling was observed. An imbalance in the renin-angiotensin-aldosterone system was identified in the CKD-PH rat models. Downregulation of ACE2 expression was observed in pulmonary tissues. The serum metabolic profile of the CKD-PH rat models showed distinct differences compared to the sham surgery group. ConclusionsThe development of pulmonary arterial hypertension in CKD-PH rats may be primarily attributed to the disruption of the renin-angiotensin-aldosterone system (RAAS), coupled with a decrease in ACE2 expression in pulmonary vascular endothelial tissues and metabolic disturbances.

pathology↗

bioRxiv2024

The Mechanism Insight into Bacterial Degradation of Pentachlorobiphenyl

Bacterial degradation mechanism for high chlorinated pentachlorobiphenyl (PentaCB) with worse biodegradability has not been fully elucidated, which could limit the full remediation of PCBs-combined pollution. In this research, using enzymatic screening method, a new PentaCB-degrading bacterium M. paraoxydans that has not been reported was obtained. The characteristic of its intracellular enzymes, proteome and metabolome variation during PentaCB degradation were investigated systematically. The results showed that PentaCB (PCB101, 1 mg/L) degradation rate could arrive 23.9% within 4 h till complete degradation within 12 h. The intracellular enzyme compound was optimally active at pH 6.0. The 12 up-regulated characterized proteins involved ABC transporter substrate-binding protein, translocase protein TatA and signal peptidase I (SPase I) indicated that functional proteins for PentaCB degradation were present both in the cytoplasm and outer surface of cytoplasmic membrane. There were also 5 differential metabolites strongly associated with above proteins in which the up-regulated 1, 2, 4-benzenetriol was enriched into the degradation pathways of benzoate, chlorocyclohexane, chlorobenzene and aminobenzoate. Bacterial degradation of PentaCB necessitates transmembrane transport, energy consumption, protein export, biofilm formation and quorum sensing. These findings hold significant theory and application value for PCBs biodegradation.

microbiology↗

bioRxiv2024

ArborSim: Articulated, branching, OpenSim routing for constructing models of multi-jointed appendages with complex muscle-tendon architecture

Computational models of musculoskeletal systems are essential tools for understanding how muscles, tendons, bones, and actuation signals generate motion. In particular, the OpenSim family of models has facilitated a wide range of studies on diverse human motions, clinical studies of gait, and even non-human locomotion. However, biological structures with many joints, such as fingers, necks, tails, and spines, have been a longstanding challenge to the OpenSim modeling community, especially because these structures comprise numerous bones and are frequently actuated by extrinsic muscles that span multiple joints--often more than three--and act through a complex network of branching tendons. Existing model building software, typically optimized for limb structures, makes it difficult to build OpenSim models that accurately reflect these intricacies. Here, we introduce ArborSim, customized software that efficiently creates musculoskeletal models of highly jointed structures and can build branched muscle-tendon architectures. We used ArborSim to construct toy models of articulated structures to determine which morphological features make a structure most sensitive to branching. By comparing the joint kinematics of models constructed with branched and parallel muscle-tendon units, we found that the number of tendon branches and the number of joints between branches are most sensitive to branching modeling method--notably, the differences between these models showed no predictable pattern with increased complexity. As the proportion of muscle increased, the kinematic differences between branched and parallel models units also increased. Our findings suggest that stress and strain interactions between distal tendon branches and proximal tendon and muscle greatly affect the overall kinematics of a musculoskeletal system. By incorporating complex muscle-tendon branching into OpenSim models using ArborSim, we can gain deeper insight into the interactions between the axial and appendicular skeleton, model the evolution and function of diverse animal tails, and understand the mechanics of more complex motions and tasks.

biophysics↗

bioRxiv2024

Deciphering the diversity and sequence of extracellular matrix and cellular spatial patterns in lung adenocarcinoma using topological data analysis

Extracellular matrix (ECM) organization influences cancer development and progression. It modulates the invasion of cancer cells and can hinder the access of immune cells to cancer cells. Effective quantification of ECM architecture and its relationship to the position of different cell types is, therefore, important when investigating the role of ECM in cancer development. Using topological data analysis (TDA), particularly persistent homology and Dowker persistent homology, we develop a novel analysis pipeline for quantifying ECM architecture, spatial patterns of cell positions, and the spatial relationships between distinct constituents of the tumour microenvironment. We apply the pipeline to 44 surgical specimens of lung adenocarcinoma from the lung TRACERx study stained with picrosirius red and haematoxylin. We show that persistent homology effectively encodes the architectural features of the tumour microenvironment. Inference using pseudo-time analysis and spatial mapping to centimetre scale tissues suggests a gradual and progressive route of change in ECM architecture, with two different end states. Dowker persistent homology enables the analysis of spatial relationship between any pair of constituents of the tumour microenvironment, such as ECM, cancer cells, and leukocytes. We use Dowker persistent homology to quantify the spatial segregation of cancer and immune cells over different length scales. A combined analysis of both topological and non-topological features of the tumour microenvironment indicates that progressive changes in the ECM are linked to increased immune exclusion and reduced oxidative metabolism.

cancer biology↗

bioRxiv2023

Benchmarking the translational potential of spatial gene expression prediction from histology

Spatial transcriptomics has enabled the quantification of gene expression at spatial coordinates, offering crucial insights into molecular underpinnings of diseases. In light of this, several methods predicting spatial gene expression from paired histology images have offered the opportunity of enhancing the utility of readily obtainable and cost-effective haematoxylin-and-eosin-stained histology images. To this end, we conducted a comprehensive benchmarking study encompassing six developed methods. These methods were reproduced and evaluated using HER2-positive breast tumour and human cutaneous squamous cell carcinoma datasets, followed by external validation using The Cancer Genome Atlas data. Our evaluation incorporates diverse metrics which capture the performance of predicted gene expression, model generalisability, translational potential, usability and computational efficiency of each method. Our findings demonstrate the capacity of methods to spatial gene expression from histology and highlight key areas that can be addressed to support the advancement of this emerging field.

bioinformatics↗

bioRxiv2023

Sc-TUSV-ext: Single-cell clonal lineage inference from single nucleotide variants (SNV), copy number alterations (CNA) and structural variants (SV)

Clonal lineage inference ("tumor phylogenetics") has become a crucial tool for making sense of somatic evolution processes that underlie cancer development and are increasingly recognized as part of normal tissue growth and aging. The inference of clonal lineage trees from single cell sequence data offers particular promise for revealing processes of somatic evolution in unprecedented detail. However, most such tools are based on fairly restrictive models of the types of mutation events observed in somatic evolution and of the processes by which they develop. The present work seeks to enhance the power and versatility of tools for single-cell lineage reconstruction by making more comprehensive use of the range of molecular variant types by which tumors evolve. We introduce Sc-TUSV-ext, an integer linear programming (ILP) based tumor phylogeny reconstruction method that, for the first time, integrates single nucleotide variants (SNV), copy number alterations (CNA) and structural variations (SV) into clonal lineage reconstruction from single-cell DNA sequencing data. We show on synthetic data that accounting for these variant types collectively leads to improved accuracy in clonal lineage reconstruction relative to prior methods that consider only subsets of the variant types. We further demonstrate the effectiveness on real data in resolving clonal evolution in the presence of multiple variant types, providing a path towards more comprehensive insight into how various forms of somatic mutability collectively shape tissue development.

cancer biology↗

bioRxiv2023

Robust and Adaptive Deep Model Ensemble Framework Fine-tuned by Structural Information for Drug-Target Interactions

In the fields of new drug development and drug repositioning, drug-target interactions (DTI) play a pivotal role. Although deep learning models have already made significant contributions in this domain, the state-of-the-art models still exhibit shortcomings in predictive performance and issues of false-negative errors. Based on these observations, we constructed a streamlined yet effective base learner model. With our designed adaptive feature weight network, the model can capture key features within drugs (targets). Furthermore, by cross-partitioning the training data, multiple base learners are integrated into a powerful ensemble model named EADTN. The performance of the model is further enhanced as the number of base learners increases. Additionally, we employed a single-linkage clustering algorithm to cluster drugs and proteins and leveraged this clustering information to fine-tune the base learners, which elevates the value of EADTN in real-world applications like drug repositioning and targeted drug development. Our designed substructure importance ranking method also demonstrates the models exceptional capability to recognize key substructures. Benefiting from the models low generalization error capability, we successfully identified false-negative samples within the dataset, revealing new interaction relationships. Experimental results indicate that EADTN consistently outperforms existing state-of-the-art models across multiple datasets. More importantly, the ensemble learning and clustering fine-tuning approaches adopted by our model offer a fresh perspective for related fields.

bioinformatics↗

bioRxiv2023

The amino acid sequence determines protein abundance through its conformational stability and reduced synthesis cost.

Understanding what drives protein abundance is essential to biology, medicine, and biotechnology. Driven by evolutionary selection, the amino acid sequence is tailored to meet the required abundance of proteomes, underscoring the intricate relationship between sequence and functional demand. Yet, the specific role of amino acid sequences in determining proteome abundance remains elusive. Here, we demonstrate that the amino acid sequence predicts abundance by shaping a proteins conformational stability. We show that increasing the abundance provides metabolic cost benefits, underscoring the evolutionary advantage of maintaining a highly abundant and stable proteome. Specifically, using a deep learning model (BERT), we predict 56% of protein abundance variation in Saccharomyces cerevisiae solely based on amino acid sequence. The model reveals latent factors linking sequence features to protein stability. To probe these relationships, we introduce MGEM (Mutation Guided by an Embedded Manifold), a methodology for guiding protein abundance through sequence modifications. We find that mutations increasing abundance significantly alter protein polarity and hydrophobicity, underscoring a connection between protein stability and abundance. Through molecular dynamics simulations and in vivo experiments in yeast, we confirm that abundance-enhancing mutations result in longer-lasting and more stable protein expression. Importantly, these sequence changes also reduce metabolic costs of protein synthesis, elucidating the evolutionary advantage of cost-effective, high-abundance, stable proteomes. Our findings support the role of amino acid sequence as a pivotal determinant of protein abundance and stability, revealing an evolutionary optimization for metabolic efficiency.

evolutionary biology↗

bioRxiv2023

GET: a foundation model of transcription across human cell types

Transcriptional regulation, involving the complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate in unseen cell types and conditions. Here, we introduce GET, an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types. GET showcases remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovering universal and cell type specific transcription factor interaction networks. We evaluated its performance on prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors. Specifically, we show GET outperforms current models in predicting lentivirus-based massive parallel reporter assay readout with reduced input data. In fetal erythroblasts, we identify distal (>1Mbp) regulatory regions that were missed by previous models. In B cells, we identified a lymphocyte-specific transcription factor-transcription factor interaction that explains the functional significance of a leukemia-risk predisposing germline mutation. In sum, we provide a generalizable and accurate model for transcription together with catalogs of gene regulation and transcription factor interactions, all with cell type specificity.

bioinformatics↗

bioRxiv2023

Reward contingency modulates olfactory bulb output via pathway-dependent peri-somatic inhibition

Associating values to environmental cues is a critical aspect of learning from experiences, allowing animals to predict and maximise future rewards. Value-related signals in the brain were once considered a property of higher sensory regions, but its wide distribution across many brain regions is increasingly recognised. Here, we investigate how reward-related signals begin to be incorporated, mechanistically, at the earliest stage of olfactory processing, namely, in the olfactory bulb. In head-fixed mice performing Go/No-Go discrimination of closely related olfactory mixtures, rewarded odours evoke widespread inhibition in one class of output neurons, that is, in mitral cells but not tufted cells. The temporal characteristics of this reward-related inhibition suggest it is odour-driven, but it is also context-dependent since it is absent during pseudo-conditioning and pharmacological silencing of the piriform cortex. Further, the reward-related modulation is present in the somata but not in the apical dendritic tuft of mitral cells, suggesting an involvement of circuit component located deep in the olfactory bulb. Depth-resolved imaging from granule cell dendritic gemmules suggests that granule cells that target mitral cells receive a reward-related extrinsic drive. Our results support the notion that value-related modulation appears at the early stages of sensory processing and provide constraints on long-range and local circuit mechanisms.

neuroscience↗

bioRxiv2023

Cross-regulation between proteome reallocation and metabolic flux redistribution governs bacterial growth transition kinetics

Bacteria need to adjust their metabolism and protein synthesis simultaneously to adapt to changing nutrient conditions. Its still a grand challenge to predict how cells coordinate such adaptation due to the cross-regulation between the metabolic fluxes and the protein synthesis. Here we developed a dynamic Constrained Allocation Flux Balance Analysis method (dCAFBA), which integrates flux-controlled proteome allocation and protein limited flux balance analysis. This framework can predict the redistribution dynamics of metabolic fluxes without requiring detailed enzyme parameters. We reveal that during nutrient up-shifts, the calculated metabolic fluxes change in agreement with experimental measurements of enzyme protein dynamics. During nutrient down-shifts, we uncover a switch of metabolic bottleneck from carbon uptake proteins to metabolic enzymes, which disrupts the coordination between metabolic flux and their enzyme abundance. Our method provides a quantitative framework to investigate cellular metabolism under varying environments and reveals insights into bacterial adaptation strategies.

systems biology↗

bioRxiv2023

Shape-changing electrode array for minimally invasive large-scale intracranial brain activity mapping

Large-scale brain activity mapping is important for understanding the neural basis of behaviour. Electrocorticograms (ECoGs) have high spatiotemporal resolution, bandwidth, and signal quality. However, the invasiveness and surgical risks of electrode array implantation limit its application scope. We developed an ultrathin, flexible shape-changing electrode array (SCEA) for large-scale ECoG mapping with minimal invasiveness. SCEAs were inserted into cortical surfaces in compressed states through small openings in the skull or dura and fully expanded to cover large cortical areas. MRI and histological studies on rats proved the minimal invasiveness of the implantation process and the high chronic biocompatibility of the SCEAs. High-quality micro-ECoG activities mapped with SCEAs from rodent brains during seizures and canine brains during the emergence period revealed the spatiotemporal organization of different brain states with resolution and bandwidth that cannot be achieved using existing noninvasive techniques. The biocompatibility and ability to map large-scale physiological and pathological cortical activities with high spatiotemporal resolution, bandwidth, and signal quality in a minimally invasive manner offer SCEAs as a superior tool for applications ranging from fundamental brain research to brain-machine interfaces.

neuroscience↗

bioRxiv2023

Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data

Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, including cell-cell interactions, enabling great potential in biological discovery.

bioinformatics↗

bioRxiv2023

HDAC4-mediated deacetylation of GSDMD prevents pyroptosis by inhibiting GSDMD ubiquitination

Gasdermin D (GSDMD) functions as a key pyroptotic executor and induces cytokine secretion after cleavage by inflammatory caspases. However, less is known about the role of posttranslational modifications (PTMs) in GSDMD-mediated pyroptosis. Here, we report that GSDMD can be acetylated at Lysine 248 residue and the acetylation of GSDMD promotes pyroptosis. We identified histone deacetylase 4 (HDAC4) as the specific deacetylase that mediates GSDMD deacetylation and subsequent pyroptosis inhibition in vitro and in vivo. GSDMD deacetylation impairs the ubiquitination of GSDMD, for which pyroptosis is inhibited. Interestingly, the phosphorylation of HDAC4 is important for its ability of deacetylating GSDMD and suppressing GSDMD-mediated pyroptosis. The Protein phosphatase 1 (PP1) catalytic subunits (PP1 and PP1{gamma}) mediate the dephosphorylation of HDAC4, thereby abrogating its deacetylase activity on GSDMD. Therefore, our work unravels a complex regulatory mechanism involving HDAC4, PP1 and GSDMD, and provides novel insights into the crosstalk among acetylation, ubiquitination and phosphorylation.

cell biology↗

bioRxiv2023

High-resolution Dynamic Human Brain Neural Activity Recording Using 3T MRI

Despite extensive research over decades, a non-invasive technique capable of capturing neural activities in the human brain with high spatiotemporal resolution is still lacking. The recently proposed direct imaging of neuronal activity (DIANA) using ultrahigh-field magnetic resonance imaging (MRI) has shown promise, but the translation from anesthetized mice to awake humans poses a significant challenge. Here we present Time Resolved ImaGing of Global Electroneurophysiological Record (TRIGGER), a novel technique that enables the direct detection of neural activity in the awake human brain using 3T MRI. In 18 participants, visual responses were captured at 5-mm spatial resolution and 1.4-ms temporal resolution. Importantly, the delay in stimulus presentation reliably corresponded to the latency of neural responses on a millisecond scale. Furthermore, when stimuli were presented to one visual field, the responses in two hemispheres exhibited the expected time difference. This non-invasive mapping approach holds the potential to elucidate neural dynamics underlying human brain function and disorders.

neuroscience↗