Biology subjects

Xiao, W.

Publications and source records attributed to Xiao, W..

89 records · Page 5Linked to original sources

bioRxiv2020

A Comprehensive Multi-Center Cross-platform Benchmarking Study of Single-cell RNA Sequencing Using Reference Samples

Single-cell RNA sequencing (scRNA-seq) has become a very powerful technology for biomedical research and is becoming much more affordable as methods continue to evolve, but it is unknown how reproducible different platforms are using different bioinformatics pipelines, particularly the recently developed scRNA-seq batch correction algorithms. We carried out a comprehensive multi-center cross-platform comparison on different scRNA-seq platforms using standard reference samples. We compared six pre-processing pipelines, seven bioinformatics normalization procedures, and seven batch effect correction methods including CCA, MNN, Scanorama, BBKNN, Harmony, limma and ComBat to evaluate the performance and reproducibility of 20 scRNA-seq data sets derived from four different platforms and centers. We benchmarked scRNA-seq performance across different platforms and testing sites using global gene expression profiles as well as some cell-type specific marker genes. We showed that there were large batch effects; and the reproducibility of scRNA-seq across platforms was dictated both by the expression level of genes selected and the batch correction methods used. We found that CCA, MNN, and BBKNN all corrected the batch variations fairly well for the scRNA-seq data derived from biologically similar samples across platforms/sites. However, for the scRNA-seq data derived from or consisting of biologically distinct samples, limma and ComBat failed to correct batch effects, whereas CCA over-corrected the batch effect and misclassified the cell types and samples. In contrast, MNN, Harmony and BBKNN separated biologically different samples/cell types into correspondingly distinct dimensional subspaces; however, consistent with this algorithms logic, MNN required that the samples evaluated each contain a shared portion of highly similar cells. In summary, we found a great cross-platform consistency in separating two distinct samples when an appropriate batch correction method was used. We hope this large cross-platform/site scRNA-seq data set will provide a valuable resource, and that our findings will offer useful advice for the single-cell sequencing community.

genomics↗

bioRxiv2020

Reconstruction of the Hypothalamo-Neurohypophysial System and Functional Dissection of Magnocellular Oxytocin Neurons in the Brain

The hypothalamo-neurohypophysial system (HNS), comprising hypothalamic magnocellular neuroendocrine cells (MNCs) and the neurohypophysis, plays a pivotal role in regulating reproduction and fluid homeostasis by releasing oxytocin and vasopressin into the bloodstream. However, it remains incompletely understood on its structure and whether it contributes to the central actions of oxytocin and vasopressin. Using viral tracing and whole brain imaging, we reconstructed the three-dimensional architecture of the HNS and uncovered that subsets of MNCs collaterally project to multiple extrahypothalamic regions. Moreover, selective activation of magnocellular oxytocin neurons promoted peripheral oxytocin release and facilitated central oxytocin-mediated social interactions. Further, MNCs-released oxytocin in the caudate putamen enhanced locomotion to orchestrate social investigation. Our work reveals the previously unrecognized complexity of the HNS and provides structural and functional evidence for MNCs in coordinating both peripheral and central oxytocin-mediated actions, which will shed light on the mechanistic understanding of oxytocin-related psychiatric diseases.

neuroscience↗

bioRxiv2020

DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning

MotivationAccurately predicting essential genes using computational methods can greatly reduce the effort in finding them via wet experiments at both time and resource scales, and further accelerate the process of drug discovery. Several computational methods have been proposed for predicting essential genes in model organisms by integrating multiple biological data sources either via centrality measures or machine learning based methods. However, the methods aiming to predict human essential genes are still limited and the performance still need improve. In addition, most of the machine learning based essential gene prediction methods are lack of skills to handle the imbalanced learning issue inherent in the essential gene prediction problem, which might be one factor affecting their performance. ResultsWe proposed a deep learning based method, DeepHE, to predict human essential genes by integrating features derived from sequence data and protein-protein interaction (PPI) network. A deep learning based network embedding method was utilized to automatically learn features from PPI network. In addition, 89 sequence features were derived from DNA sequence and protein sequence for each gene. These two types of features were integrated to train a multilayer neural network. A cost-sensitive technique was used to address the imbalanced learning problem when training the deep neural network. The experimental results for predicting human essential genes showed that our proposed method, DeepHE, can accurately predict human gene essentiality with an average AUC higher than 94%, the area under precision-recall curve (AP) higher than 90%, and the accuracy higher than 90%. We also compared DeepHE with several widely used traditional machine learning models (SVM, Naive Bayes, Random Forest, Adaboost). The experimental results showed that DeepHE greatly outperformed the compared machine learning models. ConclusionsWe demonstrated that human essential genes can be accurately predicted by designing effective machine learning algorithm and integrating representative features captured from available biological data. The proposed deep learning framework is effective for such task. Availability and ImplementationThe python code will be freely available upon the acceptance of this manuscript at https://github.com/xzhang2016/DeepHE. Contactxue.zhang@tufts.edu

bioinformatics↗

bioRxiv2019

Mining the human tonsillar microbiota as autoimmune modulator

Palatine tonsils are important lymphoid organs featuring constant cross-talks between the commensal microorganisms and immune system, and have been implicated as critical autoimmunity origins for immune-related diseases, including rheumatoid arthritis (RA), a common autoimmune disorder. However, there was no evidence to show link between tonsillar microbiota and RA. Here, we identified a significant dysbiosis of RA tonsillar microbiota, with loss of Streptococcus salivarius and its functional molecules salivaricins (a type of antibacterial peptides). Consistent with the niche-preference, S. salivarius and salivaricins administrated intranasally or intraorally conferred prophylactic and therapeutic efficacies against experimental arthritis. Moreover, we demonstrated, for the first time, that S. salivarius and salivaricins exerted immunosuppressive capacities via inhibiting CD4+effector T cell subsets and autoantibody production in mice and human. These results uncover a communication between tonsillar microbiota and host autoimmunity, and identify the active components from tonsillar microbes in modulating immune homeostasis.\n\nOne sentence summaryTonsillar microbiota regulate host autoimmunity via antibacterial peptides

microbiology↗

bioRxiv2019

Systems-level analysis identifies key regulators driving epileptogenesis in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most prevalent and often devastating form of epilepsy. The molecular mechanism underlying the development of TLE remains largely unknown, which hinders the discovery of effective anti-epileptogenic drugs. In this study, we built a systems-level analytic framework which integrates gene meta-signatures, gene coexpression network and cellular regulatory network to unveil the evolution landscape of epileptogenic process and to identify key regulators that govern the transition between different epileptogenesis stages. The time-specific hippocampal transcriptomic profiles from five independent rodent TLE models were grouped into acute, latent and chronic stages of epileptogenesis, and were utilized for generating stage-specific gene expression signatures. 13 cell-type specific functional modules were identified from the epilepsy-context coexpression network, and five of them were significantly associated with the entire epileptogenic process. By inferring the differential protein activity of gene regulators in each stage, 265 key regulators underlying epileptogenesis were obtained. Among them, 122 regulators were demonstrated being associated with high seizure frequency and/or hippocampal sclerosis in human TLE patients. Importantly, we discovered four new gene regulators (ANXA5, FAM107A, SEPT2 and SPARC) whose upregulation may drive the process of epileptogenesis and further lead to chronic recurrent seizures or hippocampal sclerosis. Our findings provide a landscape of the gene network dynamics underlying epileptogenesis and uncovered candidate regulators that may serve as potential targets for future anti-epileptogenic therapy development.

bioinformatics↗

bioRxiv2019

Robust Cancer Mutation Detection with Deep Learning Models Derived from Tumor-Normal Sequencing Data

Accurate detection of somatic mutations is challenging but critical to the understanding of cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network based somatic mutation detection approach and demonstrated performance advantages on in silico data. In this study, we used the first comprehensive and well-characterized somatic reference samples from the SEQC-II consortium to investigate best practices for utilizing deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for these reference samples by the consortium, we identified strategies for building robust models on multiple datasets derived from samples representing real scenarios. The proposed strategies achieved high robustness across multiple sequencing technologies such as WGS, WES, AmpliSeq target sequencing for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages (ranging from 10x - 2000x). NeuSomatic significantly outperformed conventional detection approaches in general, as well as in challenging situations such as low coverage, low mutation frequency, DNA damage, and difficult genomic regions.

genomics↗

bioRxiv2019

Towards best practice in cancer mutation detection with whole-genome and whole-exome sequencing

Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from errors introduced at each step of next generation sequencing (NGS). For NGS to successfully improve patient lives, discriminating between true mutations and artifacts is crucial.\n\nWe systematically interrogated somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy. Different types of samples with varying input amount and tumor purity were processed using multiple library construction protocols. Whole-genome and whole-exome sequencing were carried out at six sequencing centers followed by processing with nine bioinformatics pipelines to evaluate their reproducibility. We identified artifacts due to sample and library processing and evaluated the capabilities and limitations of bioinformatics tools for artifact detection and removal.\n\nBy examining the interaction and effect of various wet lab and computational parameters concomitantly, here we recommend actionable best practices for mutation detection in clinical applications using NGS technologies.

genomics↗

bioRxiv2019

The Catalytic Core of DEMETER Guides Active DNA Demethylation in Arabidopsis

The Arabidopsis DEMETER (DME) DNA glycosylase demethylates the maternal genome in the central cell prior to fertilization, and is essential for seed viability. DME preferentially targets small transposons that flank coding genes, influencing their expression and initiating plant gene imprinting. DME also targets intergenic and heterochromatic regions, and how it is recruited to these differing chromatin landscapes is unknown. The C-terminal DME catalytic core consists of three conserved regions required for catalysis in vitro. We show that the catalytic core of DME guides active demethylation at endogenous targets, rescuing the developmental and genomic hypermethylation phenotypes of DME mutants. However, without the N-terminus, heterochromatin demethylation is significantly impeded, and abundant CG-methylated genic sequences are ectopically demethylated. We used comparative analysis to reveal that the conserved DME N-terminal domains are only present in the flowering plants, whereas the domain architecture of DME-like proteins in non-vascular plants mainly resembles the catalytic core, suggesting that it might represent the ancestral form of the 5mC DNA glycosylase found in all plant lineages. We propose a bipartite model for DME protein action and suggest that the DME N-terminus was acquired late during land plant evolution to improve specificity and facilitate demethylation at heterochromatin targets.

plant biology↗

bioRxiv2019

Stag1 and Stag2 regulate cell fate decisions in hematopoiesis through non-redundant topological control

Transcriptional regulators, including the cohesin complex member STAG2, are recurrently mutated in cancer. The role of STAG2 in gene regulation, hematopoiesis, and tumor suppression remains unresolved. We show Stag2 deletion in hematopoietic stem/progenitor cells (HSPC) results in altered hematopoietic function, increased self-renewal, and impaired differentiation. ChIP-sequencing revealed that while Stag2 and Stag1 can bind the same loci, a component of Stag2 binding sites are unoccupied by Stag1 even in Stag2-deficient HSPCs. While concurrent loss of Stag2 and Stag1 abrogated hematopoiesis, Stag2 loss alone decreased chromatin accessibility and transcription of lineage-specification genes, including Ebf1 and Pax5, leading to blunted HSPC commitment to the B-cell lineage. Our data illustrate a role for Stag2 in transformation and transcriptional dysregulation distinct from its shared role with Stag1 in chromosomal segregation.\n\nOne Sentence SummaryStag1 rescues topologically associated domains in the absence of Stag2, but cannot restore chromatin architecture required for hematopoietic lineage commitment

cell biology↗

bioRxiv2019

Evolving super stimuli for real neurons using deep generative networks

Finding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories. A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli. Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection. This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.\n\nHighlightsO_LIA generative deep neural network interacted with a genetic algorithm to evolve stimuli that maximized the firing of neurons in alert macaque inferotemporal and primary visual cortex.\nC_LIO_LIThe evolved images activated neurons more strongly than did thousands of natural images.\nC_LIO_LIDistance in image space from the evolved images predicted responses of neurons to novel images.\nC_LI

neuroscience↗

bioRxiv2018

Gating TrkB switch by methylglyoxal enables GLO1 as a target for depression

The endogenous metabolite methylglyoxal (MGO) has recently emerged as a potential mediator of psychiatric disorders, such as anxiety and depression, but its precise mechanism of action remains poorly understood. Here, we find that MGO concentrations are decreased in the prefrontal cortex and hippocampus in rats subjected to chronic stress, and low-dose MGO treatment remarkedly enhances resilience to stress and alleviates depression-like symptoms. This effect is achieved by MGOs promotion on the synaptic plasticity in prefrontal cortex and hippocampus. Both in vitro and in vivo experiments show that MGO provokes the dimerization and autophosphorylation of TrkB and the subsequent activation of downstream Akt/CREB signaling, which leads to a rapid and sustained expression of brain-derived neurotrophic factor (BDNF). We further demonstrate that MGO directly binds to the extracellular domain of TrkB, but not its intracellular domain. In addition, we also identify a natural product luteolin and its derivative lutD as potent inhibitors of Glyoxalase 1 and validate their antidepressant effects in chronic stress rat models. The antidepressant role of endogenous MGO provides a new basis for the understanding and therapeutic intervention design for stress-associated mental disorders.

neuroscience↗

bioRxiv2018

Identification and characterization of cherry (Cerasus pseudocerasus G. Don) genes in response to parthenocarpy induced by GA3 through transcriptome analysis

Fruit set after successful pollination is a key process in the production of sweet cherries, but low fruit rate is the main problem for crop production in sweet cherries. Gibberellin treatment can directly induce parthenogenesis without pollination; therefore, gibberellin treatment is a very important method to improve the fruit setting rate of sweet cherries. Exogenous gibberellin can satisfy the hormone requirement during fruit growth and development. Some related studies have mainly focused on physiological aspects, such as breeding, branching, fertilization, etc., and the molecular mechanism is not clear. In this study, we analyzed the transcriptome of Meizao sweet cherry fruit treated with gibberellin during the anthesis and hard-core period to determine the genes associated with parthenocarpic fruit set. A total of 765 and 186 differentially expressed genes (DEGs) were found at anthesis and the hard-core stage after gibberellin 3 (GA3) treatment, respectively. The differentially expressed genes between the control and GA3 treatment showed that the GA3 response mainly included parthenocarpic fruit set and cell division. Exogenous gibberellin stimulated sweet cherry parthenocarpy and enlargement, which were verified by qRT-PCR results of related genes and the parthenocarpic fruit set and fruit size. Based on our research and previous studies on Arabidopsis thaliana, we identified the key genes associated with parthenocarpic fruit set and cell division. Briefly, we found patterns in the sweet cherry fruit setting-related DEGs, especially those associated with hormone balance, cytoskeleton formation and cell wall modification. Overall, the result provides a possible molecular mechanism regulating parthenocarpic fruit set, which is important for basic research and industrial development of sweet cherries.\n\nHighlightcherry genes in response to parthenocarpy and promote to fruit setting induced by GA3.

molecular biology↗

bioRxiv2017

Pharmacological manipulation of olfactory bulb granule cell excitability modulates beta oscillations: Testing a model

The mammalian olfactory bulb (OB) generates gamma (40 - 100 Hz) and beta (15 - 30 Hz) oscillations of the local field potential (LFP). Gamma oscillations arise at the peak of inhalation supported by dendrodendritic interactions between glutamatergic mitral cells (MCs) and GABAergic granule cells (GCs). Beta oscillations occur in response to odorants in learning or odor sensitization paradigms, but their generation mechanism and function are still poorly understood. When centrifugal inputs to the OB are blocked, beta oscillations disappear, but gamma oscillations persist. Centrifugal input targets primarily GABAergic interneurons in the GC layer (GCL) and regulates GC excitability, which suggests a causal link between beta oscillations and GC excitability. Previous modeling work from our laboratory predicted that convergence of excitatory/inhibitory inputs onto MCs and centrifugal inputs onto GCs can increase GC excitability sufficiently to drive beta oscillations primarily through voltage dependent calcium channel (VDCC) mediated GABA release, independently of NMDA channels. We test this model by examining the influence of NMDA and muscarinic acetylcholine receptors on GC excitability and beta oscillations. Intrabulbar scopolamine (muscarinic antagonist) infusion decreased or completely suppressed odor-evoked beta in response to a strong stimulus, but increased beta power in response to a weak stimulus, as predicted by our model. Piriform cortex (PC) beta power was unchanged. Oxotremorine (muscarinic agonist) tended to suppress all oscillations, probably from over-inhibition. APV, an NMDA receptor antagonist, suppressed gamma oscillations selectively (in OB and PC), lending support to the models prediction that beta oscillations can be supported by VDCC mediated currents.\n\nNew and NoteworthyO_LIOlfactory bulb beta oscillations rely on granule cell excitability.\nC_LIO_LIReducing granule cell excitability with scopolamine reduces high volatilityinduced beta power but increases low volatility-induced beta power.\nC_LIO_LIPiriform cortex beta oscillations maintain power when olfactory bulb beta power is low, and the system maintains beta band coherence.\nC_LI

neuroscience↗

bioRxiv2017

The roles of Conserved Domains in DEMETER-Mediated Active DNA Demethylation in planta

DNA methylation plays critical roles in maintaining genome stability, genomic imprinting, transposon silencing, and development. In Arabidopsis genomic imprinting is established in the central cell by DEMETER (DME)-mediated active DNA demethylation, and is essential for seed viability. DME is a large polypeptide with multiple poorly characterized conserved domains. Here we show that the C-terminal enzymatic core of DME is sufficient to complement dme associated developmental defects. When targeted by a native DME promoter, nuclear-localized DME C-terminal region rescues dme seed abortion and pollen germination defects, and ameliorates CG hypermethylation phenotype in dme-2 endosperm. Furthermore, targeted expression of the DME N-terminal region in wild-type central cell induces dme-like seed abortion phenotype. Our results support a bipartite organization for DME protein, and suggest that the N-terminal region might have regulatory function such as assisting in DNA binding and enhancing the processivity of active DNA demethylation in heterochromatin targets.

plant biology↗

bioRxiv2017

Collection and Storage of HLA NGS Genotyping Data for the 17th International HLA and Immunogenetics Workshop

For over 50 years, the International HLA and Immunogenetics Workshops (IHIW) have advanced the fields of histocompatibility and immunogenetics (H&I) via community sharing of technology, experience and reagents, and the establishment of ongoing collaborative projects. In the fall of 2017, the 17th IHIW will focus on the application of next generation sequencing (NGS) technologies for clinical and research goals in the H&I fields. NGS technologies have the potential to allow dramatic insights and advances in these fields, but the scope and sheer quantity of data associated with NGS raise challenges for their analysis, collection, exchange and storage. The 17 th IHIW has adopted a centralized approach to these issues, and we have been developing the tools, services and systems to create an effective system for capturing and managing these NGS data. We have worked with NGS platform and software developers to define a set of distinct but equivalent NGS typing reports that record NGS data in a uniform fashion. The 17th IHIW database applies our standards, tools and services to collect, validate and store those structured, multi-platform data in an automated fashion. We are creating community resources to enable exploration of the vast store of curated sequence and allele-name data in the IPD-IMGT/HLA Database, with the goal of creating a long-term community resource that integrates these curated data with new NGS sequence and polymorphism data, for advanced analyses and applications.\n\nAbbreviations

bioinformatics↗

bioRxiv2017

The pomegranate (Punica granatum L.) genome provides insights into fruit quality and ovule developmental biology

Pomegranate (Punica granatum L.) with an uncertain taxonomic status has an ancient cultivation history, and has become an emerging fruit due to its attractive features such as the bright red appearance and the high abundance of medicinally valuable ellagitannin-based compounds in its peel and aril. However, the absence of genomic resources has restricted further elucidating genetics and evolution of these interesting traits. Here we report a 274-Mb high-quality draft pomegranate genome sequence, which covers approximately 81.5% of the estimated 336 Mb genome, consists of 2,177 scaffolds with an N50 size of 1.7 Mb, and contains 30,903 genes. Phylogenomic analysis supported that pomegranate belongs to the Lythraceae family rather than the monogeneric Punicaceae family, and comparative analyses showed that pomegranate and Eucalyptus grandis shares the paleotetraploidy event. Integrated genomic and transcriptomic analyses provided insights into the molecular mechanisms underlying the biosynthesis of ellagitannin-based compounds, the color formation in both peels and arils during pomegranate fruit development, and the unique ovule development processes that are characteristic of pomegranate. This genome sequence represents the first reference in Lythraceae, providing an important resource to expand our understanding of some unique biological processes and to facilitate both comparative biology studies and crop breeding.

genomics↗

bioRxiv2017

Sugar metabolism changes in response to the ultraviolet B irradiation of peach (Prunus persica L.)

The protected cultivation of peach (Prunuspersica L.) trees is more economical and efficient than traditional cultivation, resulting in increased farmers incomes, but the peach sugar contents are lower than in open planting. In the greenhouse, a high-sugar variety of peach Lumi 1 was irradiated with 1.44 KJ{middle dot}m-2.d-1 intensity ultraviolet B radiation. The soluble sugar contents in fruit, peel and leaf were quantified using liquid chromatography. Overall, sucrose and sorbitol increased before the second fruit-expansion period. To further understand the mechanisms regulating sucrose and sorbitol accumulation in peach fruit, expression profiles of genes involved in sugar metabolism and transport were measured. The activity and translocation protein contents of these enzymes were measured by enzyme-linked immunosorbent assay. The increased sucrose synthase activity and sucrose transporter level in the pericarp promoted the synthesis of sucrose and intake of sucrose into fruit. Sorbitol transport into fruit was promoted by the increased sorbitol transporter protein levels in leaves. In summary, greenhouse the sucrose and sorbitol contents were increased when supplemented with 1.44 kJ{middle dot}m-2{middle dot}d-1 ultraviolet B radiation before the second fruit-expansion period of peach.

plant biology↗