Biology subjects

Xu, X.

Publications and source records attributed to Xu, X..

At least 379 records · Page 21Linked to original sources

bioRxiv2023

Distinct eLPB ChAT projections for methamphetamine anxiety and relapse

Choline acetyltransferase-positive neurons in the external lateral parabrachial nucleus (eLPBChAT) send projections to PKC{delta}-positive (PKC{delta}+) neurons in lateral portion of central nucleus of amygdala (lCeAPKC{delta}) and oval portion of bed nucleus of the stria terminalis (ovBNSTPKC{delta}), forming eLPBChAT-lCeAPKC{delta} and eLPBChAT-ovBNSTPKC{delta} pathways. At least in part, the eLPBChAT neurons positively innervate lCeAPKC{delta} and ovBNSTPKC{delta} through regulating synaptic elements of presynaptic acetylcholine (Ach) release and postsynaptic nicotinic acetylcholine receptors (nAChRs). Methamphetamine (METH) withdrawal anxiety and METH-primed reinstatement of conditioned place preference (CPP) recruit eLPBChAT-lCeAPKC{delta} pathway and eLPBChAT-ovBNSTPKC{delta} pathway in male METH-exposed mice, respectively.

neuroscience↗

bioRxiv2023

VRP Assembler: haplotype-resolved de novo assembly of diploid and polyploid genomes using quantum computing

Precision medicines emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the VRP assembler: a novel approach that transforms this task into a vehicle routing problem, a combinatorial optimization problem solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Googles OR-Tools, achieving a haplotype-resolved assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to hifiasm with the phasing accuracy first approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.

bioinformatics↗

bioRxiv2023

Cnot3 is required for male germ cell development and spermatogonial stem cell maintenance

The foundation of spermatogenesis and lifelong fertility is provided by spermatogonial stem cells (SSCs). SSCs divide asymmetrically to either replenish their numbers (self-renewal) or produce undifferentiated progenitors that proliferate before committing to differentiation. However, regulatory mechanisms governing SSC maintenance are poorly understood. Here, we show that the CCR4-NOT mRNA deadenylase complex subunit CNOT3 plays a critical role in maintaining spermatogonial populations in mice. Cnot3 is highly expressed in undifferentiated spermatogonia, and its deletion in spermatogonia resulted in germ cell loss and infertility. Single cell analyses revealed that Cnot3 deletion led to the de-repression of transcripts encoding factors involved in spermatogonial differentiation, including those in the glutathione redox pathway that are critical for SSC maintenance. Together, our study reveals that CNOT3 - likely via the CCR4-NOT complex - actively degrades transcripts encoding differentiation factors to sustain the spermatogonial pool and ensure the progression of spermatogenesis, highlighting the importance of CCR4-NOT-mediated post-transcriptional gene regulation during male germ cell development. HighlightsO_LICnot3 is predominantly expressed in undifferentiated spermatogonia, and its expression decreases in later stages. C_LIO_LIConditional deletion of Cnot3 leads to total germ cell loss and male infertility. C_LIO_LICNOT3 represses transcripts encoding factors involved in spermatogonial differentiation. C_LIO_LICNOT3 protects spermatogonial stem cells maintenance via GSH/redox pathway. C_LI Graphical AbstractCnot3 promotes SSC self-renewal by balancing ROS production via the regulation of GSH production O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/562256v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@3afef9org.highwire.dtl.DTLVardef@139cb19org.highwire.dtl.DTLVardef@1450c47org.highwire.dtl.DTLVardef@293300_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2023

Climate warming suppresses soil abundant fungal taxa and reduces soil carbon efflux in a semi-arid grassland

The abundance, diversity and activity of soil microorganisms critically control the fate of recent plant carbon (C) inputs as well as protecting soil organic C, regulating C-climate feedbacks. However, the effects of climate change drivers such as warming and precipitation change on soil microbial communities and C dynamics remain poorly understood. Utilizing a long-term field warming and precipitation manipulation in a semi-arid grassland on the Loess Plateau and a complementary incubation experiment, here we show that warming and rainfall reduction differentially affect the abundance and composition of bacteria and fungi, and soil C efflux. Warming significantly reduced the abundance of fungi but not bacteria, increasing the relative dominance of bacteria in the soil microbial community. In particular, warming shifted the community composition of abundant fungi in favor of oligotrophic Capnodiales and Hypocreales over potential saprotroph Archaeorhizomycetales. In contrast, precipitation reduction increased soil microbial biomass, but did not significantly affect either the abundance or diversity of bacteria. Furthermore, soil abundant, not rare, fungal community composition was significantly correlated with soil CO2 efflux. Our findings suggest that alterations in the fungal community composition, in response to changes in soil C and moisture, dominate the microbial responses to climate change and thus control soil C dynamics in semi-arid grasslands. Impact statementSemi-arid grasslands play a critical role in global carbon (C) cycle and potential feedbacks to climate change. Understanding the responses of soil microorganisms to warming and rainfall change is key to evaluating and predicting semi-arid grassland soil C dynamics under future climate change scenarios. Our study showed that warming induced a shift in the abundant fungal community, favoring oligotrophic fungi (i.e., Capnodiales and Hypocreales) over the potential saprotrophic Archaeorhizomycetales, and reduced C efflux. These findings advance our understanding of soil microbial and C responses to climate change drivers and may help predict and possibly manage soil C sequestration in semi-arid grasslands.

ecology↗

bioRxiv2023

BatchEval Pipeline: Batch Effect Evaluation Workflow for Multiple Datasets Joint Analysis

As genomic sequencing technology continues to advance, it becomes increasingly important to perform joint analyses of multiple datasets of transcriptomics. However, batch effect presents challenges for dataset integration, such as sequencing data measured on different platforms, and datasets collected at different times. Here, we report the development of BatchEval Pipeline, a batch effect workflow used to evaluate batch effect on dataset integration. The BatchEval Pipeline generates a comprehensive report, which consists of a series of HTML pages for assessment findings, including a main page, a raw dataset evaluation page, and several built-in methods evaluation pages. The main page exhibits basic information of the integrated datasets, a comprehensive score of batch effect, and the most recommended method for removing batch effect from the current datasets. The remaining pages exhibit evaluation details for the raw dataset, and evaluation results from the built-in batch effect removal methods after removing batch effect. This comprehensive report enables researchers to accurately identify and remove batch effects, resulting in more reliable and meaningful biological insights from integrated datasets. In summary, the BatchEval Pipeline represents a significant advancement in batch effect evaluation, and is a valuable tool to improve the accuracy and reliability of the experimental results. Availability & ImplementationThe source code of the BatchEval Pipeline is available at https://github.com/STOmics/BatchEval.

bioinformatics↗

bioRxiv2023

eNODAL: an experimentally guided nutriomics data clustering method to unravel complex drug-diet interactions

Unraveling the complex interplay between nutrients and drugs via their effects on omics features could revolutionize our fundamental understanding of nutritional physiology, personalized nutrition and ultimately human health-span. Experimental studies in nutrition are starting to use large-scale omics experiments to pick apart the effects of such interacting factors. However, the high dimensionality of the omics features, coupled with complex fully-factorial experimental designs together pose a challenge to the analysis. Current strategies for analyzing such types of data are based on between-feature correlations. However, these techniques risk overlooking important signals that arise from the experimental design and produce clusters that are hard to interpret. We present a novel approach for analyzing high-dimensional outcomes in nutriomics experiments, termed experiment-guided NutriOmics DatA cLustering (eNODAL). This three-step hybrid framework takes advantage of both ANOVA-type analyses and unsupervised learning methods to extract maximum information from experimental nutriomics studies. First, eNODAL categorizes the omics features into interpretable groups based on the significance of response to the different experimental variables using an ANOVA-like test. Such groups may include the main effects of a nutritional intervention, and drug exposure, or their interaction. Second, consensus clustering is performed within each interpretable group to further identify subclusters of features with similar response profiles to these experimental factors. Third, eNODAL annotates these subclusters based on their experimental responses and biological pathways enriched within the subcluster. We validate eNODAL using data from a mouse experiment to test for the interaction effects of macronutrient intake and drugs that target aging mechanisms in mice.

bioinformatics↗

bioRxiv2023

Rolling 2D Bioelectronic film into 1D: a Suturable Long-term Implantable Soft Microfiber

Long-term implantable bioelectronics can efficiently evaluate the function of the nervous system. 1D fiber-shaped devices require small surgical incisions significantly benefiting long-term biocompatibility. We report a strategy of rolling to reduce the dimension of bioelectronic devices from 2D film to 1D microfiber. The soft and stretchable multifunctional microfiber accommodates more than 60-channel longitudinally-distributed electrodes for bio-electric and -mechanical monitoring. It can be sutured into muscle with tiny incision for 8-month bio-electrical monitoring in rats. Importantly, after 13-month implantation, the fibroblast encapsulation around the fiber is negligible. Moreover, it can wander inside a brain under magnetic field. This strategy not only opens up the study of multi-channel and -function soft and stretchable fiber-shaped sensors but offers a platform for minimally-invasive implantable bioelectronics. One-Sentence SummaryThe rolling enables stretchable electrode array along one single microfiber and multimodal long-term in-vivo monitoring.

neuroscience↗

bioRxiv2023

Adaptive Functions of Structural Variants in Human Brain Development

Quantifying the structural variants (SVs) in nonhuman primates could provide a niche to clarify the genetic backgrounds underlying human-specific traits, but such resource is largely lacking. Here, we report an accurate SV atlas in a population of 562 rhesus macaques, verified by two public SV benchmarks, an inhouse benchmark of eight macaque genomes with long-read sequencing and another inhouse benchmark of one macaque genome with whole-genome assembly. This accurate, quantitative SV map indicates stronger purifying selection on inversions, one type of poorly-clarified SVs to date, especially for those located on regulatory regions, suggesting a strategy for prioritizing inversions with the most important functions. Based on the distribution and the evolutionary features of these inversions in macaque population, we then identified 75 human-specific inversions, clarified their functional effects and prioritized them. Notably, the top-ranked inversions have substantially shaped the human transcriptome, through their dual-effects of reconfiguring the ancestral genomic architecture and introducing regional mutation hotspots at the inverted regions. As a proof-of-concept, we linked APCDD1, located on one of these inversions with the highest rank score and downregulated in human brains, to neuronal maturation. The accumulation of human-specific mutations on its promoter region, accelerated by the formation of the inversion, contributed to the decreased expression in humans. Notably, the overexpression of APCDD1 could accelerate the neuronal maturation, while its depletion in mice delays the neuronal maturation. This study thus highlights the contribution of SVs, especially the inversions, to the distinct features in human brain development.

evolutionary biology↗

bioRxiv2023

Divide-and-conquer quantum algorithm for hybrid de novo genome assembly of short and long reads

Computational biology holds immense promise as a domain that can leverage quantum advantages due to its involvement in a wide range of challenging computational tasks. Researchers have recently explored the applications of quantum computing in genome assembly implementation. However, the issue of repetitive sequences remains unresolved. In this paper, we propose a hybrid assembly quantum algorithm using high-accuracy short reads and error-prone long reads to deal with sequencing errors and repetitive sequences. The proposed algorithm builds upon the variational quantum eigensolver and utilizes divide-and-conquer strategies to approximate the ground state of larger Hamiltonian while conserving quantum resources. Using simulations of 10-qubit quantum computers, we address problems as large as 140 qubits, yielding optimal assembly results. The convergence speed is significantly improved via the problem-inspired ansatz based on the known information about the assembly problem. Besides, entanglement within quantum circuits is qualitatively verified to notably accelerate the assembly path optimization.

bioinformatics↗

bioRxiv2023

Massively parallel in vivo Perturb-seq reveals cell type-specific transcriptional networks in cortical development

Systematic analysis of gene function across diverse cell types in vivo is hindered by two challenges: obtaining sufficient cells from live tissues and accurately identifying each cells perturbation in high-throughput single-cell assays. Leveraging AAVs versatile cell type tropism and high labeling capacity, we expanded the resolution and scale of in vivo CRISPR screens: allowing phenotypic analysis at single-cell resolution across a multitude of cell types in the embryonic brain, adult brain, and peripheral nervous system. We undertook extensive tests of 86 AAV serotypes, combined with a transposon system, to substantially amplify labeling and accelerate in vivo gene delivery from weeks to days. Using this platform, we performed an in utero genetic screen as proof-of-principle and identified pleiotropic regulatory networks of Foxg1 in cortical development, including Layer 6 corticothalamic neurons where it tightly controls distinct networks essential for cell fate specification. Notably, our platform can label >6% of cerebral cells, surpassing the current state-of-the-art efficacy at <0.1% (mediated by lentivirus), and achieve analysis of over 30,000 cells in one experiment, thus enabling massively parallel in vivo Perturb-seq. Compatible with various perturbation techniques (CRISPRa/i) and phenotypic measurements (single-cell or spatial multi-omics), our platform presents a flexible, modular approach to interrogate gene function across diverse cell types in vivo, connecting gene variants to their causal functions.

neuroscience↗

bioRxiv2023

ULK1-dependent phosphorylation of PKM2 antagonizes O-GlcNAcylation and inhibits the Warburg effect in breast cancer

Pyruvate kinase M2 (PKM2) is a central metabolic enzyme driving the Warburg effect in tumor growth. Previous investigations have demonstrated that PKM2 is subject to O-linked {beta}-N-acetylglucosamine (O-GlcNAc) modification, which is a nutrient-sensitive post-translational modification. Here we found that unc-51 like autophagy activating kinase 1 (ULK1), a glucose-sensitive kinase, interacts with PKM2 and phosphorylates PKM2 at Ser333. Ser333 phosphorylation antagonizes PKM2 O-GlcNAcylation, promotes its tetramer formation and enzymatic activity, and decreases its nuclear localization. By downregulating glucose consumption and lactate production, PKM2 pS333 attenuates the Warburg effect. Through mouse xenograft assays, we demonstrate that the phospho-deficient PKM2-S333A mutant promotes tumor growth in vivo. In conclusion, we identified a ULK1-PKM2-c-Myc axis in inhibiting breast cancer, and a glucose-sensitive phosphorylation of PKM2 in modulating the Warburg effect.

biochemistry↗

bioRxiv2023

Functional connectome through the human life span

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimers disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

neuroscience↗

bioRxiv2023

Automated Bioinformatics Analysis via AutoBA

With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the bioinformatics analysis continues to grow. In response to this need, we introduce Automated Bioinformatics Analysis (AutoBA), an autonomous AI agent designed explicitly for fully automated multi-omic analyses based on large language models. AutoBA simplifies the analytical process by requiring minimal user input while delivering detailed step-by-step plans for various bioinformatics tasks. Through rigorous validation by expert bioinformaticians, AutoBAs robustness and adaptability are affirmed across a diverse range of omics analysis cases, including whole genome/exome sequencing (WGS/WES), chromatin immunoprecipitation assays with sequencing (ChIP-seq), RNA sequencing (RNA-seq), single-cell RNA-seq, spatial transcriptomics and so on. AutoBAs unique capacity to self-design analysis processes based on input data variations further underscores its versatility. Compared with online bioinformatic services, AutoBA offers multiple LLM backends, with options for both online and local usage, prioritizing data security and user privacy. Moreover, different from the predefined pipeline, AutoBA has adaptability in sync with emerging bioinformatics tools. Overall, AutoBA represents an advanced and convenient tool, offering robustness and adaptability for conventional multi-omic analyses.

bioinformatics↗

bioRxiv2023

Inferring cell trajectories of spatial transcriptomics via optimal transport analysis

The integration of cell transcriptomics and spatial coordinates to organize differentiation trajectories remains a challenge. Here we introduce spaTrack, a trajectory inference method using optimal transport to incorporate both transcriptomics and distance of spatial transcriptomics sequencing data into transition costs. spaTrack could construct fine spatial trajectories reflecting the true differentiation topology, as well as trace cell dynamics across multiple samples with temporal intervals. To capture the dynamic drivers, spaTrack models the cell fate as a function of expression profile along temporal intervals driven by transcription factors. Applying spaTrack, we successfully disentangle spatiotemporal trajectories of axolotl telencephalon regeneration and mouse midbrain development. Furthermore, we uncover diverse malignant lineages expanding in a primary tumor. One of the lineages with upregulated extracellular matrix organization implants to the metastatic site and subsequently colonizes to a secondary tumor. Overall, spaTrack greatly facilitates trajectory inference from spatial transcriptomics, providing insights in cell differentiation of broad areas.

bioinformatics↗

bioRxiv2023

Epigenetic Reshaping through Damage: Promoting Cell Fate Transition by BrdU and IdU Incorporation

Thymidine analogs have long been recognized for their ability to randomly incorporate into DNA. However, their significance in the chemical induction of pluripotency (CIP) remains unclear. Here, we investigated the impact of BrdU/IdU incorporation on the transition of cell fate through DNA damage repair (DDR). Our findings reveal a substantial upregulation of reprogramming mediator gene H3K27ac and H3K9ac, as well as global DNA demethylation in response to DDR. This process creates a hypomethylated environment that promotes cell fate transition. We term this mechanism as Epigenetic Reshaping through Damage (ERD). Overall, our study sheds light on the dynamic interplay between thymidine analogs, DDR, and epigenetic modifications, providing valuable insights into the mechanisms underlying cell fate transition.

cell biology↗

bioRxiv2023

The role of neutrophil typing in homeostatic resilience protective model of inflammatory preconditioning

Sepsis is a systemic inflammatory response syndrome (SIRS) characterized by a dysregulated host response to infection that results in organ dysfunction. The response of neutrophils to early inflammation is critical, interferon regulatory factors 4 (IRF4) and interferon regulatory factors 5 (IRF5) are expressed in most cell types of the immune system, but the relationship between the role of IRF4 and IRF5 in preconditioning-protected septic peritonitis mouse model and neutrophils response remains unknown. In this study, we used an E.coli-induced septic peritonitis mouse model and zebrafish model to explore the relationship between neutrophil inflammation and IRF subtype changes in the process of septic peritonitis. Mechanistically, we found that the protein complex formed by polymorphonuclear neutrophils (PMN) and IRF5/MyD88 can secrete pro-inflammatory factors and directly kill invading bacteria. Author SummaryNumerous animal and clinical sepsis studies have shown that markedly impaired recruitment of neutrophils to sites of infection and failure to clear bacteria contribute to excessive inflammation and increased mortality in sepsis. In our previous work, we established an Escherichia coli (E.coli) lethal septic peritonitis model and a preconditioning-protected septic peritonitis mouse model. In the early stage of septic peritonitis, a large number of PMN infiltrate the peritoneal cavity, which produces an inflammatory response to invasive bacteria. In this study, the switching process between pro-inflammatory and anti-inflammatory attracted our attention. Our research shows that IRF5-IRF4 regulatory axis leads to a PMN phenotype switch in sepsis and the expression of IRF5 and IRF4 was related to PMN pro-inflammatory (N1 type) and anti-inflammatory (N2 type) phenotypes, respectively.

immunology↗

bioRxiv2023

SAW: An efficient and accurate data analysis workflow for Stereo-seq spatial transcriptomics

The basic analysis steps of spatial transcriptomics involve obtaining gene expression information from both space and cells. This process requires a set of tools to be completed, and existing tools face performance issues when dealing with large data sets. These issues include computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the process. To address these issues, a high-performance and accurate spatial transcriptomics data analysis workflow called Stereo-Seq Analysis Workflow (SAW) has been developed for the Stereo-Seq technology developed by BGI. This workflow includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation and clustering, and generate results files in a universal format for subsequent personalized analysis. The excutation time for the entire analysis process is [~]148 minutes on 1G reads 1*1 cm chip test data, 1.8 times faster than unoptimized workflow.

bioinformatics↗

bioRxiv2023

Deep Learning Enhanced Tandem Repeat Variation Identification via Multi-Modal Conversion of Nanopore Reads Alignment

Identification of tandem repeat (TR) variations plays a crucial role in advancing our understanding of genetic diseases, forensic analysis, evolutionary studies, and crop improvement, thereby contributing to various fields of research and practical applications. However, traditional TR identification methods are often limited to processing genomes obtained through sequence assembly and cannot directly start detection from sequencing reads. Furthermore, the inflexibility of detection mode and parameters hinders the accuracy and completeness of the identification, rendering the results unsatisfactory. These shortcomings result in existing TR variation identification methods being associated with high computational cost, limited detection sensitivity, precision and comprehensiveness. Here, we propose DeepTRs, a novel method for identifying TR variations, which enables direct TR variation identification from raw Nanopore sequencing reads and achieves high sensitivity, accuracy, and completeness results through the multi-modal conversion of Nanopore reads alignment and deep learning. Comprehensive evaluations demonstrate that DeepTRs outperform existing methods.

bioinformatics↗