Biology subjects

Xu, K.

Publications and source records attributed to Xu, K..

At least 127 records · Page 7Linked to original sources

bioRxiv2024

Cancer Stemness Online: A resource for investigating cancer stemness and associations with immune response

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features, which are potential culprit in immunotherapy resistance. Although the state-of-art predictive computational methods have facilitated predicting the cancer stemness, currently there is no efficient resource that can meet various requirements of usage. Here, we presented the Cancer Stemness Online, an integrated resource for efficiently scoring cancer stemness potential at bulk and single-cell level. The resource integrates 8 robust predictive algorithms as well as 27 signature gene sets associated with cancer stemness for predicting the stemness scores. Downstream analyses were performed from five different aspects, including identifying the signature genes of cancer stemness, exploring the association with cancer hallmarks, cellular states, immune response and communication with immune cells, investigating the contributions for patient survival and the robustness analysis of cancer stemness among different methods. Moreover, the pre-calculated cancer stemness atlas for more than 40 cancer types can be accessed by users. Both the tables and diverse visualization for the analytical results are available for download. Together, Cancer Stemness Online is a powerful resource for scoring cancer stemness and going deeper and wider in the downstream functional interpretation, including immune response as well as cancer hallmark. Cancer Stemness Online is freely accessible at http://bio-bigdata.hrbmu.edu.cn/CancerStemnessOnline.

bioinformatics↗

bioRxiv2024

Impaired central pattern generators due to abnormal EPHA4 signaling leads to idiopathic scoliosis

Idiopathic scoliosis (IS) is the most common form of spinal deformity with unclear pathogenesis. In this study, we firstly reanalyzed the loci associated with IS, drawing upon previous studies. Subsequently, we mapped these loci to candidate genes using either location-based or function-based strategies. To further substantiate our findings, we verified the enrichment of variants within these candidate genes across several large IS cohorts encompassing Chinese, East Asian, and European populations. Consequently, we identified variants in the EPHA4 gene as compelling candidates for IS. To confirm their pathogenicity, we generated zebrafish mutants of epha4a. Remarkably, the zebrafish epha4a mutants exhibited pronounced scoliosis during later stages of development, effectively recapitulating the IS phenotype. We observed that the epha4a mutants displayed defects in left-right coordination during locomotion, which arose from disorganized neural activation in these mutants. Our subsequent experiments indicated that the disruption of the central pattern generator (CPG) network, characterized by abnormal axon guidance of spinal cord interneurons, contributed to the disorganization observed in the mutants. Moreover, when knocked down efnb3b, the ligand for Epha4a, we observed similar CPG defects and disrupted left-right locomotion. These findings strongly suggested that ephrin B3-Epha4 signaling is vital for the proper functioning of CPGs, and defects in this pathway could lead to scoliosis in zebrafish. Furthermore, we identified two cases of IS in NGEF, a downstream molecule in the EPHA4 pathway. Collectively, our data provide compelling evidence that neural patterning impairments and disruptions in CPGs may underlie the pathogenesis of IS.

genetics↗

bioRxiv2024

Gut microbes of the cecum versus the colon drive more severe lethality and multi-organ damage

An intestinal perforation or puncture leads to a high risk of sepsis-associated morbidity. A perforation initiates the transfer of the intestinal contents (ICs) to the peritoneal cavity, leading to abdominal infections and varying with different prognoses. However, the mechanisms associated with different perforations in the cecum and colon remain unknown. We sought to examine how different gut flora contribute to prognoses in different intestinal perforation sites. We compared the microbiome of the ICs in the cecum and colon in a fecal-induced peritonitis mouse model. The results showed that cecum ICs developed more severe sepsis than colon ICs, including a shorter median survival time, increased biochemical indicators, more pathological changes in multiple organs and overwhelmed systematic inflammation. Moreover, our results demonstrated that cecum ICs hold more bacterial burden in unit weight than colon ICs, and the microbial communities differed between the ICs from the cecum and colon. A more detailed comparison of the two microbiome groups showed that the abundance of potentially pathogenic bacteria increased in the cecum ICs. Our data suggest that the sepsis severity developed by perforation was associated with bacterial burden and increased abundance of potentially pathogenic bacteria in the cecum. Our findings first compared the differences in the lethality associated with the ICs of the cecum and colon, which pointed out that the site of perforation could help providers predict the severity of sepsis.

immunology↗

bioRxiv2024

Current research on epigenetic age and cellular senescence: a bibliometric and visual analysis

The aim of this study is to summarize and visualize the current state of research on epigenetic age and cellular senescence over the last two decades using bibliometric methods and to explore the current research priorities and future development directions. Relevant literature on the topic Epigenetic age and cellular senescence data published between January 2000 and January 2024 were searched in the Web of Science core database and three bibliometric mapping tools (Biblimetrix R Package, VOS Viewer and CiteSpace) were used to provide an overview of the Literature to give and analyze keywords, the co-occurrence of reference citations, authors and institutions, to analyze the current status of epigenetic aging research and hot changes in recent years. A total of 2,193 relevant studies were included, and the co-occurrence network identified seven clusters of associations between epigenetic age and cellular senescence, including effects on human tissues, genetic clock analyses, the relationship of age-related diseases, precise genetic changes and accelerated cellular senescence, localized cellular transformations, and genetic immune regulation. The analysis shows that research in this area has shifted from disease-level studies to mechanism-based studies, with cancer being the main disease and the main pathogenic mechanism being related to DNA methylation. The research direction in recent years has focused on the mechanism study of epigenetic age and cellular senescence as well as target therapy. The bibliometric and visualization analyzes provide a comprehensive understanding of the research progress and the role between epigenetic age and human cellular senescence and enrich the review literature in this field.

cell biology↗

bioRxiv2024

Mesenchymal stem cell-derived extracellular vesicles attenuate symptoms in dextran sodium sulfate-induced ulcerative colitis mouse model

Inflammatory bowel disease (IBD) refers to a group of multifactorial and chronic inflammation affecting intestinal tract. Based on the pathogenic mechanisms, IBD mainly comprised of two categories: ulcerative colitis (UC) and Crohns disease (CD). In this study, we established a dextran sulfate sodium (DSS)-induced colitis model to mimic UC. Mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) as therapeutic vectors have shown regenerating effect of damaged tissues. Here, intravenous administration of MSC-EVs ameliorated IBD symptoms including gaining weight, reducing disease activity index and restoring colon length. In addition, the protective effect of MSC-EVs were demonstrated by repairing colon mucosa and reducing the infiltration of macrophages in submucosa. Collectively, MSC-EV shows a therapeutic potential for IBD.

physiology↗

bioRxiv2024

Human Motor Cortex Encodes Complex Handwriting Through a Sequence of Primitive Neural States

How the human motor cortex (MC) orchestrates sophisticated fine movements such as handwriting remains a puzzle1-3. Here, we investigate this question through Utah array recordings from human MC hand knob, during imagined handwriting of Chinese characters (306 characters tested, 6.3 {+/-} 2.0 strokes per character). We find MC programs the writing of complicated characters by sequencing a small set of primitive states: The directional tuning of motor neurons remains stable within each primitive state but strongly varies across states. Furthermore, the occurrence of a primitive state is encoded by a separate set of neurons not directly involved in movement control. By automatically identifying the primitive states and corresponding neuronal tuning properties, we can reconstruct a recognizable writing trajectory for each character (84% improvement in reconstruction accuracy compared with baseline). Our findings unveil that skilled, sophisticated movements are decomposed into a sequence of primitive movements that are programmed through state-specific neural configurations, and this hierarchical control mechanism sheds new light on the design of high-performance brain-computer interfaces.

neuroscience↗

bioRxiv2024

A hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data

BackgroundMethylation Quantitative Trait Loci (meQTLs) are chromosomal regions that harbor genetic variants affecting DNA methylation levels. The identification of meQTLs can be accomplished through quantifying the effects of single nucleotide polymorphisms (SNPs) on DNA methylation levels, and these inferred meQTLs can shed light on the complex interplay between the genome and methylome. However, most meQTL studies to date utilize bulk methylation datasets composed of different cell types that may have distinct methylation patterns in each cell type. Current technological challenges hinder the comprehensive collection of large-scale, cell-type-specific (CTS) methylation data, which limits our understanding of CTS methylation regulation. To address this challenge, we propose a hierarchical Bayesian interaction model (HBI) to infer CTS meQTLs from bulk methylation data. ResultsOur HBI method integrates bulk methylations data from a large number of samples and CTS methylation data from a small number of samples to estimate CTS meQTLs. Through simulations, we show that HBI improves the estimation (accuracy and power) of CTS genetic effects on DNA methylation. To systematically characterize genome-wide SNP-methylation level associations in multiple cell types, we apply HBI to bulk methylation data measured in peripheral blood mononuclear cells (PBMC) from a cohort of 431 individuals together with flow-sorted cell-derived methylation sequencing (MC-seq) data measured in isolated white blood cells (CD4+ T-cells, CD8+ T-cells, CD16+ monocytes) for 47 individuals. We demonstrate that HBI can identify CTS meQTLs and improve the functional annotation of SNPs. ConclusionsHBI can incorporate strong and robust signals from MC-seq data to improve the estimation of CTS meQTLs. Applying HBI to link the methylome and genome data helps to identify biologically relevant cell types for complex traits.

genetics↗

bioRxiv2024

Therapeutic targeting of KRAS mutation-driven tumorigenesis by extracellular vesicles loaded with small interfering RNA

RAS is a well-known oncogene contributing to significant proportion of cancer incidences, yet it remains as an undruggable target to majority of therapeutic modalities. Here, we explored the potential of extracellular vesicles (EVs) for therapeutic targeting of KRAS mutation-driven tumorigenesis. EVs loaded with small interfering RNA (siRNA) against KRAS successfully inhibited tumor xenograft growth when injected intratumorally in mice models. Intriguingly, when injected intravenously, EVs were still able to accumulate in tumors and deliver KRAS targeting siRNA payload in sufficient amount to show tumor growth inhibition. Therefore, EV-siRNA platform show great promises for therapeutic targeting of KRAS mutations and other undruggable targets.

cancer biology↗

bioRxiv2024

A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity

Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.

cancer biology↗

bioRxiv2023

chronODE: A framework to integrate time-series multi-omics data based on ordinary differential equations combined with machine learning

Most functional genomic studies are conducted in steady-state conditions, therefore providing a description of molecular processes at a particular moment of cell differentiation or organismal development. Longitudinal studies can offer a deeper understanding of the kinetics underlying epigenetic events and their contribution to defining cell-type-specific transcriptional programs. Here we develop chronODE, a mathematical framework based on ordinary differential equations that uniformly models the kinetics of temporal changes in gene expression and chromatin features. chronODE employs biologically interpretable parameters that capture tissue-specific kinetics of genes and regulatory elements. We further integrate this framework with a neural-network architecture that can link and predict changes across different data modalities by solving multivariate time-series regressions. Next, we apply this framework to investigate region-specific kinetics of epigenome rewiring in the developing mouse brain, and we demonstrate that changes in chromatin accessibility within regulatory elements can accurately predict changes in the expression of putative target genes over the same time period. Finally, by integrating single-cell ATAC-seq data generated during the same time course, we show that regulatory elements characterized by fast activation kinetics in bulk measurements are active in early-appearing cell types, such as radial glial and other neural progenitors, whereas elements characterized by slow activation kinetics are specific to more differentiated cell types that emerge at later stages of brain development.

bioinformatics↗

bioRxiv2023

Nuclear cytoophidia assembly represses transcriptional activity to control skeletal development and homeostasis

Compartmentation via filamentation is an evolutionarily conserved subcellular structure that fine-tunes the inherent activity of proteins. Cytoophidia represent a typical class of filamentous structures controlling enzymatic activities. Despite eukaryotic cells containing both cytoplasmic cytoophidia and nuclear cytoophidia, the physiological significance of nuclear cytoophidia is largely unknown. Here we show that nuclear filamentation inhibits the transcriptional activity of Impdh2 required for limb formation and bone resorption. Impdh2 deletion in mouse limb mesenchymal progenitors causes severe skeletal dysplasia by impairing endochondral ossification and chondrocyte differentiation. Additionally, Impdh2 deficiency in myeloid lineages leads to an increased bone mass via impeding osteoclast differentiation. Furthermore, Impdh2 regulates osteoclastic mitochondrial biogenesis and function. We propose that the nuclear compartmentalization of Impdh2 regulates the transcriptional activity during skeletal development and homeostasis.

cell biology↗

bioRxiv2023

Global analysis of the heparin-enriched plasma proteome captures matrisome-associated proteins in Alzheimer's disease

Matrisome-associated heparin binding proteins (HBPs) with roles in extracellular matrix assembly are strongly correlated to {beta}-amyloid (A{beta}) and tau pathology in Alzheimers disease (AD) brain and cerebrospinal fluid (CSF). However, it remains challenging to detect these proteins in plasma using standard mass spectrometry (MS)-based proteomic approaches. Here we utilized heparin affinity chromatography for the capture and enrichment of HBPs in plasma from healthy control and individuals with AD. This method was highly reproducible and effectively enriched well-known HBPs like APOE and thrombin, while also efficiently depleting high-abundance proteins such as albumin. To increase the depth of our analysis of the heparin-enriched plasma proteome and compare differences in disease we applied off-line fractionation and tandem mass tag mass spectrometry (TMT-MS) to compare the proteomic profiles between AD and control individuals across two datasets (n = 121 total samples). This led to the identification of 2865 proteins, spanning 10 orders of magnitude in protein abundance within the plasma. Notably, HBPs were some of the most increased proteins in AD plasma compared to controls. This included members of the matrisome-associated module in brain, SMOC1, SMOC2, SPON1, MDK, OLFML3, FRZB, GPNMB and the {varepsilon}4 isoform of APOE. Heparin-enriched plasma proteins also exhibited strong correlations to conventional AD biomarkers including CSF A{beta}, total tau (tTau), and phosphorylated tau (pTau) as well as plasma pTau supporting their role as potential surrogate markers of underlying brain pathology. Utilizing a consensus AD brain protein co-expression network, we assessed relationship between the plasma and brain proteomes and observed that specific plasma proteins exhibited consistent direction of change in both brain and plasma, whereas others displayed divergent changes, further highlighting the complex interplay between the two compartments. In summary, these findings provide support for the integration of a heparin enrichment method with MS-based proteomics for identifying a wide spectrum of plasma biomarkers that mirror pathological changes in the AD brain.

neuroscience↗

bioRxiv2023

TRAFICA: Improving Transcription Factor Binding Affinity Prediction using Large Language Model on ATAC-seq Data

AO_SCPLOWBSTRACTC_SCPLOWIn silico transcription factor and DNA (TF-DNA) binding affinity prediction plays a vital role in examining TF binding preferences and understanding gene regulation. The existing tools employ TF-DNA binding profiles from in vitro high-throughput technologies to predict TF-DNA binding affinity. However, TFs tend to bind to sequences in open chromatin regions in vivo, such TF binding preference is seldomly considered by these existing tools. In this study, we developed TRAFICA, an open chromatin language model to predict TF-DNA binding affinity by integrating the characteristics of sequences from open chromatin regions in ATAC-seq experiments and in vitro TF-DNA binding profiles from high-throughput technologies. We applied self-supervised learning to pre-train TRAFICA on over 13 million nucleotide sequences from the peaks in ATAC-seq experiments to learn the TF binding preference in vivo. TRAFICA was further fine-tuned using the TF-DNA binding profiles from PBM and HT-SELEX technologies to learn the association between TFs and their target DNA sequences. We observed that TRAFICA significantly outperformed both machine learning-based and deep learning-based tools in predicting in vitro and in vivo TF-DNA binding affinity. These findings indicate that considering the characteristics of sequences from open chromatin regions could significantly improve TF-DNA binding affinity prediction, particularly when limited TF-DNA binding profiles from high-throughput technologies are available for specific TFs.

bioinformatics↗

bioRxiv2023

The mtDNA-derived lncMtDloop promotes mitochondrial homeostasis maintenance and implications in AD

Maintaining mitochondrial homeostasis stands as a critical factor for cell survival and the health of organisms, as evidenced by the links between mitochondrial dysfunction and a spectrum of diseases, including Alzheimers disease (AD). Here we report that lncMtDloop, a lncRNA originating from the mtDNA D-loop, upholds mitochondrial homeostasis. LncMtDloop demonstrates an affinity for mitochondrial transcription factor A (TFAM), thereby facilitating TFAMs recruitment to mtDNA promoters and enhancing gene transcription. We further observed decreased lncMtDloop expression in the brains of human AD patients and 3xTg mice. Through the introduction of allotropic lncMtDloop with the 3UTR of MRPS12, a significant improvement in mitochondrial homeostasis and a concurrent amelioration of AD-like pathology were found, which exerts a positive influence on synaptic plasticity and behavioral deficits observed in AD mice. Our study provides mechanistic insights into lncMtDloop as a regulator of mitochondrial homeostasis, shedding light on a perspective regarding its contribution to Alzheimers pathogenesis.

molecular biology↗

bioRxiv2023

LAVASET: Latent Variable Stochastic Ensemble of Trees. A novel ensemble method for correlated datasets.

MotivationRandom Forests (RFs) can deal with a large number of variables, achieve reasonable prediction scores, and yield highly interpretable feature importance values. As such, RFs are appropriate models for feature selection and further dimension reduction (DR). However, RFs are often not appropriate for correlated datasets due to their mode of selecting individual features for splitting. Addressing correlation relationships in high dimensional datasets is imperative for reducing the number of variables that are assigned high importance, hence making the DR most efficient. Here, we propose the LAtent VAriable Stochastic Ensemble of Trees (LAVASET) method that derives latent variables based on the distance characteristics of each feature and aims to incorporate the correlation factor in the splitting step. ResultsWithout compromising on performance in the majority of examples, LAVASET outperforms RF by accurately determining feature importance across all correlated variables and ensuring proper distribution of importance values. LAVASET yields mostly non-inferior prediction accuracies to traditional RFs when tested in simulated and real 1D datasets, as well as more complex and high-dimensional 3D datatypes. Unlike traditional RFs, LAVASET is unaffected by single important noisy features (false positives), as it considers the local neighbourhood. LAVASET, therefore, highlights neighbourhoods of features, reflecting real signals that collectively impact the models predictive ability. LAVASET is freely available as a standalone package from https://github.com/melkasapi/LAVASET.

bioinformatics↗

bioRxiv2023

Structural and modelling insights into the dynamic association between the transcription factor and DNA

The DNA recognition mechanisms by transcription factor (TF) was a significant scientific issue in the gene transcription and regulation. Multiple research technology including the experimental and modelling method has been introduced into the study of this aspect. In this article bioinformatic, protein modelling and dynamic simulation method was employed to display the overview of the dynamic binding between TF and DNA. Physical properties of positional change and freedom of atoms in addition with the volume exchange and the interaction analysis revealed the flexible binding sites of this element. The association of TF increased its stability with dynamic conformational change. The different levels of resistance to the sequential fluctuations of the residues and the nucleotides in the binding site stabilize the overall structure of the complex and initiated the open of the double helix that indicated the molecular mechanisms of the recognition and regulation of the elements.

bioinformatics↗

bioRxiv2023

New insights into tissue-specific responses and interactive characteristics of crop-microbe "One Health" system to soil chromium and ofloxacin pollution

This study firstly investigated the tissue-specific responses and interactive characteristics of the crop-microbe system to co-pollution with ofloxacin (OFL) and chromium (Cr) in soil. The results emphasized the hormesis effect induced by low-dose OFL (1 mg L-1) on ginger plants subjected to soil Cr stress. However, high-dose OFL (100 mg L-1) and Cr co-stressed plants exhibited reduced growth, root activity, antioxidant enzyme activities and photosynthesis-fluorescence performances, while the reactive oxygen species (ROS) reflected by O2{middle dot}- and H2O2 significantly increased up to 43.34% and 78.63%, respectively, compared to other treatments. In addition, high-throughput sequencing indicated that OFL influenced rhizosphere microbial diversity, composition, and evolution, favoring Proteobacteria proliferation under co-pollution. Meanwhile, root exudate patterns shifted, with humic-like exudates, potentially interacting with pollutants and microbes. Notably, enrichments of antibiotic resistance gene (qnrS) in edible rhizome and potential pathogenic bacteria in ginger rhizosphere were observed under OFL and Cr co-pollution, raising environmental and food chain concerns. Through structural equation modeling, we quantitatively established correlations within soil-crop-microbe factors, emphasizing their interconnected nature. Overall, this study sheds light on the complex responses of the crop-microbe system to OFL and Cr co-pollution, highlighting the importance of understanding pollutant interactions for enhancing plant resilience and mitigating environmental risks. HighlightsO_LIRevealed a hormesis from low-dose OFL on Cr-polluted ginger, enhancing resilience. C_LIO_LIRhizosphere microbial composition shift under co-pollution, favoring Proteobacteria. C_LIO_LIqnrS and potential pathogens increased under co-pollution, posing food chain risks. C_LIO_LIStructural equation modeling quantified correlations in soil- crop-microbe factors. C_LIO_LIClarified crop-microbe "One Health" interactions role under Cr & OFL co-pollution. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/561380v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@bd3d20org.highwire.dtl.DTLVardef@1f8024eorg.highwire.dtl.DTLVardef@15f6154org.highwire.dtl.DTLVardef@ef0aeb_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2023

A Modular Design for Synthetic Membraneless Organelles Enables Compositional and Functional Control

Living cells organize a wide variety of processes through compartmentalization into membraneless organelles, known as biomolecular condensates. Given their ubiquitous presence across a wide spectrum of different organisms and cell types, biomolecular condensates are increasingly considered to offer great potential for biotechnological applications. However, native condensates remain difficult to harness for engineering applications, both due to their intertwined mechanisms of assembly and compositional control, and potential disruptions to native cellular processes. Here, we demonstrate a modular framework for the formation of synthetic condensates designed to decouple cluster formation and protein recruitment. Synthetic condensates are built through constitutive oligomerization of intrinsically-disordered regions (IDRs), which drive the formation of condensates whose composition can be independently defined through fused interaction domains. The composition of the proteins driven to partition into the condensate can be quantitatively described using a binding equilibrium model, demonstrating predictive control of how component expression levels and interaction affinity determine the degree of protein recruitment. Finally, the engineered system is utilized to regulate protein interactions and metabolic flux by harnessing the systems compositional tunability.

synthetic biology↗