Biology subjects

Jiang, R.

Publications and source records attributed to Jiang, R..

107 records · Page 6Linked to original sources

bioRxiv2019

Single-cell immune repertoire tracing identifies rituximab refractory B cells that emerge during relapse

Rituximab, a B cell-depleting therapy, is indicated for treating a growing number of autoantibody-mediated autoimmune disorders. However, relapses can occur after treatment and autoantibody-producing B cell subsets may be found during relapses. It is not understood if these autoantibody-producing B cell subsets emerge from the failed depletion of pre-existing B cells or are re-generated de novo. To further define the mechanisms that cause post-rituximab relapse, we studied patients with autoantibody-mediated muscle-specific kinase (MuSK) myasthenia gravis (MG) who relapsed after treatment. We carried out single-cell transcriptional and B cell receptor (BCR) profiling on longitudinal B cell samples. We identified clones present prior to therapy that continued to persist during relapse. Persistent B cell clones included both antibody-secreting cells and memory B cells characterized by gene expression signatures associated with B cell survival. A subset of persistent antibody-secreting cells and memory B cells were specific for the MuSK autoantigen. These results demonstrate that rituximab is not fully effective at eliminating autoantibody-producing B cells and provide a mechanistic understanding of post-rituximab relapse in MuSK MG.

immunology↗

bioRxiv2019

Incorporating gene expression in genome-wide prediction of chromatin accessibility via deep learning

Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcription factors and regulatory elements to achieve interpretable annotations of chromatin accessibility across diverse cellular contexts. Towards this problem, we propose DeepCAGE, a deep learning framework that integrates sequence information and binding status of transcription factors, for the accurate prediction of chromatin accessible regions at a genome-wide scale in a variety of cell types. DeepCAGE takes advantage of a densely connected deep convolutional neural network architecture to automatically learn sequence signatures of known chromatin accessible regions, and then incorporates such features with expression levels and binding activities of human core transcription factors to predict novel chromatin accessible regions. In a series of systematic comparisons with existing methods, DeepCAGE exhibits superior performance in not only the classification but also the regression of chromatin accessibility signals. In detailed analysis of transcription factor activities, DeepCAGE successfully extracts novel binding motifs and measures the contribution of a transcription factor to the regulation with respect to a specific locus in a certain cell type. When applied to whole-genome sequencing data analysis, our method successfully prioritizes putative deleterious variants underlying a human complex trait, and thus provides insights into the understanding of disease-associated genetic variants. DeepCAGE can be downloaded from https://github.com/kimmo1019/DeepCAGE.

bioinformatics↗

bioRxiv2019

EnClaSC: A novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes

BackgroundIn recent years, the rapid development of single-cell RNA-sequencing (scRNA-seq) techniques enables the quantitative characterization of cell types at a single-cell resolution. With the explosive growth of the number of cells profiled in individual scRNA-seq experiments, there is a demand for novel computational methods for classifying newly-generated scRNA-seq data onto annotated labels. Although several methods have recently been proposed for the cell-type classification of single-cell transcriptomic data, such limitations as inadequate accuracy, inferior robustness, and low stability greatly limit their wide applications.\n\nResultsWe propose a novel ensemble approach, named EnClaSC, for accurate and robust cell-type classification of single-cell transcriptomic data. Through comprehensive validation experiments, we demonstrate that EnClaSC can not only be applied to the self-projection within a specific dataset and the cell-type classification across different datasets, but also scale up well to various data dimensionality and different data sparsity. We further illustrate the ability of EnClaSC to effectively make cross-species classification, which may shed light on the studies in correlation of different species. EnClaSC is freely available at https://github.com/xy-chen16/EnClaSC.\n\nConclusionsEnClaSC enables highly accurate and robust cell-type classification of single-cell transcriptomic data via an ensemble learning method. We expect to see wide applications of our method to not only transcriptome studies, but also the classification of more general data.

bioinformatics↗

bioRxiv2019

Selective Activation of TASK-3-containing K+ Channels Reveals Their Therapeutic Potentials in Analgesia

The paucity of selective agonists for TASK-3, a member of two-pore domain K+ (K2P) channels, has contributed to our limited understanding of its biological functions. By targeting a novel druggable transmembrane cavity using a structure-based drug design approach, we discovered a biguanide compound, CHET3, as a highly selective allosteric activator for TASK-3-containing K2P channels, including TASK-3 homomer and TASK-3/TASK-1 heteromer. CHET3 displayed unexpectedly potent analgesic effects in vivo in a variety of acute and chronic pain models in rodents that could be abolished by pharmacology or genetic ablation of TASK-3. We further found that TASK-3-containing channels anatomically define a unique subset population of small-sized, TRPM8, TRPV1 or tyrosine hydroxylase-positive nociceptive sensory neurons and functionally regulate their membrane excitability, supporting CHET3 analgesia in thermal hyperalgesia and mechanical allodynia under chronic pain. Overall, our proof-of-concept study reveals TASK-3-containing K2P channels as a novel druggable target for treating pain.\n\nOne Sentence SummaryIdentification of a novel drug target and its new hit compounds for developing new-generation non-opioid analgesics.

pharmacology and toxicology↗

bioRxiv2019

Slow Microtubule Binding Kinetics of Membrane-bound Kinesin Predicts High Motor Copy Numbers on Intracellular Cargo

Bidirectional vesicle transport along microtubules is necessary for cell viability and function, particularly in neurons. When multiple motors are attached to a vesicle, the distance a vesicle travels before dissociating is determined by the race between detachment of the bound motors and attachment of the unbound motors. Motor detachment rate constants (koff) can be measured via single-molecule experiments, but motor reattachment rate constants (kon) are generally unknown, as they involve diffusion through the bilayer, geometrical considerations of the motor tether length, and the intrinsic microtubule binding rate of the motor. To understand motor attachment dynamics during vesicle transport, we quantified the microtubule accumulation rate of fluorescently-labeled kinesin-1 motors in a 2D system where motors were linked to a supported lipid bilayer. From the first-order accumulation rate at varying motor densities, we extrapolated a koff that matched single-molecule measurements, and measured a two-dimensional kon for membrane-bound kinesin-1 motors binding to the microtubule. This kon is consistent with kinesin-1 being able to reach roughly 20 tubulin subunits when attaching to a microtubule. By incorporating cholesterol to reduce membrane diffusivity, we demonstrate that this kon is not limited by the motor diffusion rate, but instead is determined by the intrinsic motor binding rate. For intracellular vesicle trafficking, this two-dimensional kon predicts that long-range transport of 100 nm diameter vesicles requires 35 kinesin-1 motors, suggesting that teamwork between different motor classes and motor clustering may play significant roles in long-range vesicle transport.\n\nSignificance StatementLong-distance transport of membrane-coated vesicles involves coordination of multiple motors such that at least one motor is bound to the microtubule at all times. Microtubule attachment of a membrane-bound motor comprises two steps - diffusing through the lipid bilayer to a binding zone near the microtubule, followed by binding. Using a 2D supported lipid bilayer system, we show that membrane diffusion is not the limiting factor for motor attachment. This result suggests that in cells kinesin-1 binding kinetics are not altered by the membrane composition of vesicle cargos. The intrinsically slow binding properties of kinesin-1 suggest that divergent motor binding kinetics and motor clustering regulate long-range vesicle transport.

biophysics↗

bioRxiv2019

OPENANNO: annotating genomic regions with chromatin accessibility

Chromatin accessibility, as a powerful marker of active DNA regulatory elements, provides valuable information for understanding regulatory mechanisms. The revolution in high-throughput methods has accumulated massive chromatin accessibility profiles in public repositories. Nevertheless, utilization of these data is hampered by cumbersome collection, time-consuming processing, and manual chromatin accessibility (openness) annotation of genomic regions. To fill this gap, we developed OpenAnnotate (http://health.tsinghua.edu.cn/openannotate/) as the first web server for efficiently annotating openness of massive genomic regions across various biosample types, tissues, and biological systems. In addition to the annotation resource from 2729 comprehensive profiles of 614 biosample types of human and mouse, OpenAnnotate provides user-friendly functionalities, ultra-efficient calculation, real-time browsing, intuitive visualization, and elaborate application notebooks. We show its unique advantages compared to existing databases and toolkits by effectively revealing cell type-specificity, identifying regulatory elements and 3D chromatin contacts, deciphering gene functional relationships, inferring functions of transcription factors, and unprecedentedly promoting single-cell data analyses. We anticipate OpenAnnotate will provide a promising avenue for researchers to construct a more holistic perspective to understand regulatory mechanisms.

bioinformatics↗

bioRxiv2019

Parallel Group ICA + ICA: Joint Estimation of Linked Functional Network Variability and Structural Covariation with Application to Schizophrenia

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called \"parallel group ICA+ICA\" that incorporates temporal fMRI information from GICA into a parallel ICA framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g. structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate inter-modality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.

bioinformatics↗

bioRxiv2019

VPAC: Variational projection for accurate clustering of single-cell transcriptomic data

BackgroundSingle-cell RNA-sequencing (scRNA-seq) technologies have advanced rapidly in recent years and enabled the quantitative characterization at a microscopic resolution. With the exponential growth of the number of cells profiled in individual scRNA-seq experiments, the demand for identifying putative cell types from the data has become a great challenge that appeals for novel computational methods. Although a variety of algorithms have recently been proposed for single-cell clustering, such limitations as low accuracy, inferior robustness, and inadequate stability greatly impede the scope of applications of these methods.\n\nResultsWe propose a novel model-based algorithm, named VPAC, for accurate clustering of single-cell transcriptomic data through variational projection, which assumes that single-cell samples follow a Gaussian mixture distribution in a latent space. Through comprehensive validation experiments, we demonstrate that VPAC can not only be applied to datasets of discrete counts and normalized continuous data, but also scale up well to various data dimensionality, different dataset size and different data sparsity. We further illustrate the ability of VPAC to detect genes with strong unique signatures of a specific cell type, which may shed light on the studies in system biology. We have released a user-friendly python package of VPAC in Github (https://github.com/ShengquanChen/VPAC). Users can directly import our VPAC class and conduct clustering without tedious installation of dependency packages.\n\nConclusionsVPAC enables highly accurate clustering of single-cell transcriptomic data via a statistical model. We expect to see wide applications of our method to not only transcriptome studies for fully understanding the cell identity and functionality, but also the clustering of more general data.

bioinformatics↗

bioRxiv2018

Hyocholic acid species and the risk of type 2 diabetes

Hyocholic acid (HCA) and its derivatives are found in only trace amounts in human blood, but constitute approximately 76 % of the bile acid (BA) pool in the pig, a species known for its exceptional resistance to type 2 diabetes mellitus (T2DM). Here we show that HCA species play a crucial role in maintaining glucose homeostasis and preventing T2DM. We found that in two cohort studies (n=1,213), both obesity and diabetes were associated with lower serum concentrations of HCA species. Serum HCA levels in apparently healthy individuals (n=132) were found to be strong predictors for metabolic health 10 years later. Oral administration of HCA increased serum fasting GLP-1, to a greater extent than metformin, in healthy and diabetic mouse models. HCA upregulated GLP-1 secretion in intestinal enteroendocrine cells via simultaneously activating G-protein-coupled BA receptor, TGR5, and inhibiting farnesoid X receptor, a unique mechanism that is not found in other BA species.

systems biology↗

bioRxiv2018

Astroglial dysfunctions drive aberrant synaptogenesis in developing brain with lengthy general anesthesia

Lengthy use of general anesthetics (GAs) causes cognitive deficits in developing brain, which has raised significant clinical concerns such that FDA is warning on the use of GAs in children younger than 3 years. However, the molecular and cellular mechanisms for GAs-induced neurotoxicity remain largely unknown. Here we report that sevoflurane, a commonly used GA in pediatrics, causes compromised astrocyte morphogenesis, spatiotemporally correlated to the synaptic overgrowth with reduced synaptic function in developing cortex in a regional-, exposure-length- and age-specific manner. Sevoflurane disrupts astrocyte Ca2+ homeostasis both acutely and chronically, which leads to the down regulation of Ezrin, an actin-binding membrane protein, which we found is critically involved in astrocyte morphogenesis in vivo. Importantly, in normal developing brain, the genetic intervention of astrocyte morphogenesis is sufficient to produce the aberrant synaptic structure and function virtually identical to the ones induced by lengthy sevoflurane exposure. Our data uncover that astrocytes are unexpectedly central targets for GAs to exert toxic effects, and that astrocyte morphological integrity is crucial for synaptogenesis in the developing brain.

neuroscience↗

bioRxiv2018

SIP: An Interchangeable Pipeline for scRNA-seq Data Processing

Multiple steps of bioinformatics processing are needed to convert the raw scRNA-seq data to information that can be used in downstream analyses and in building cell atlases. Dozens of software packages have been developed and different labs tend to have different preferences on choices of the workflow. Such diversity can cause difficulties in future efforts of aggregating data from multiple labs, and also difficulties for new labs to start in this field. A few pipelines have been developed to help integrating multiple steps into a whole, but the fixed software architecture makes it hard for developers to add new features or exchange parts in the pipeline.\n\nWe presented SIP, a Single-cell Interchangeable Pipeline. It is a one-stop platform for the processing of scRNA-seq data from multiple platforms, and will also support for other types of data like scATAC-seq data. SIP utilizes container technology to solve the deployment dilemma when handling multiple packages and provides an easy-to-use interface for users to conduct the complicated multi-step process from raw data to final results with a single command. It also allows advanced users to assemble different versions of the pipeline by interchanging parts or adding new modules. SIP is available at https://github.com/XuegongLab/SIP under the GPL-3.0 license.

bioinformatics↗

bioRxiv2018

DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers

The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, though having successfully reported enhancers in typical cell lines, are still too costly and time consuming to perform systematic identification of enhancers specific to different cell lines under a variety of disease status. Existing computational methods, though capable of predicting regulatory elements purely relying on DNA sequences, lack the power of cell line-specific screening. Recent studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation, and thus may provide useful information in identifying regulatory elements. Motivated by the above understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. We demonstrate that our model not only consistently outperforms existing methods in the classification of enhancers against background sequences, but also accurately predicts enhancers across different cell lines. We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs. We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers.

bioinformatics↗

bioRxiv2018

DeepTACT: predicting high-resolution chromatin contacts via bootstrapping deep learning

High-resolution interactions among regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanism. Hi-C technique allows the genome-wide detection of chromatin contacts. However, unless extremely deep sequencing is performed on a very large number of input cells, current Hi-C experiments do not have high enough resolution to resolve contacts among regulatory elements. Here, we develop DeepTACT, a bootstrapping deep learning model, to integrate genome sequences and chromatin accessibility data for the prediction of chromatin contacts among regulatory elements. In tests based on promoter capture Hi-C data, DeepTACT is seen to offer improved resolution over existing methods. DeepTACT analysis also identifies a class of hub promoters, which are active across cell lines, enriched in housekeeping genes, functionally related to fundamental biological processes, and capable of reflecting cell similarity. Finally, the utility of high-resolution chromatin contact information in the study of human diseases is illustrated by the association of IFNA2 and IFNA1 to coronary artery disease via an integrative analysis of GWAS data and high-resolution contacts inferred by DeepTACT.

bioinformatics↗

bioRxiv2018

Integrating distal and proximal information to predict gene expression via a densely con-nected convolutional neural network

MotivationInteractions among such cis-regulatory elements as enhancers and promoters are main driving forces shaping context-specific chromatin structure and gene expression. Although there have been computational methods for predicting gene expression from genomic and epigenomic information, most of them overlook long-range enhancer-promoter interactions, due to the difficulty in precisely linking regulatory enhancers to target genes. Recently, a novel high-throughput experimental approach named HiChIP has been developed and generating comprehensive data on high-resolution interactions between promoters and distal enhancers. On the other hand, plenty of studies have suggested that deep learning achieves state-of-the-art performance in epigenomic signal prediction, and thus promoting the understanding of regulatory elements. In consideration of these two factors, we integrate proximal promoter sequences and HiChIP distal enhancer-promoter interactions to accurately model gene expression.\n\nResultsWe propose DeepExpression, a densely connected convolutional neural network to predict gene expression using both promoter sequences and enhancer-promoter interactions. We demonstrate that our model consistently outperforms baseline methods not only in the classification of binary gene expression status but also in the regression of continuous gene expression levels, in both cross-validation experiments and cross-cell lines predictions. We show that sequential promoter information is more informative than experimental enhancer information while enhancer-promoter interactions are most beneficial from those within {+/-}100 kbp around the TSS of a gene. We finally visualize motifs in both promoter and enhancer regions and show the match of identified sequence signatures and known motifs. We expect to see a wide spectrum of applications using HiChIP data in deciphering the mechanism of gene regulation.\n\nAvailabilityDeepExpression is freely available at https://github.com/wanwenzeng/DeepExpression.\n\nContactruijiang@tsinghua.edu.cn, ywang@amss.ac.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Pathogenic DDX3X mutations impair RNA metabolism and neurogenesis during fetal cortical development

De novo germline mutations in the RNA helicase DDX3X account for 1-3% of unexplained intellectual disability (ID) cases in females, and are associated with autism, brain malformations, and epilepsy. Yet, the developmental and molecular mechanisms by which DDX3X mutations impair brain function are unknown. Here we use human and mouse genetics, and cell biological and biochemical approaches to elucidate mechanisms by which pathogenic DDX3X variants disrupt brain development. We report the largest clinical cohort to date with DDX3X mutations (n=78), demonstrating a striking correlation between recurrent dominant missense mutations, polymicrogyria, and the most severe clinical outcomes. We show that Ddx3x controls cortical development by regulating neuronal generation and migration. Severe DDX3X missense mutations profoundly disrupt RNA helicase activity and induce ectopic RNA-protein granules and aberrant translation in neural progenitors and neurons. Together, our study demonstrates novel mechanisms underlying DDX3X syndrome, and highlights roles for RNA-protein aggregates in the pathogenesis of neurodevelopmental disease.

genetics↗

bioRxiv2018

Long-term all-optical interrogation of cortical neurons in awake-behaving non-human primates

Whereas optogenetic techniques have proven successful in their ability to manipulate neuronal populations in species ranging from insects to rodents--with high spatial and temporal fidelity--significant obstacles remain in their application to non-human primates (NHPs). Robust optogenetics-activated behavior and long-term noninvasive monitoring of target neurons, have been challenging in NHPs. Here we present a method for all-optical interrogation (AOI), integrating optical stimulation and simultaneous two-photon imaging of neuronal populations in the primary visual cortex (V1) of awake rhesus macaques. A red-shifted channel-rhodopsin transgene (C1V1) and genetically-encoded calcium indicators (GCaMP5 or GCaMP6s) were delivered by AAVs, and subsequently expressed in V1 neuronal populations for months with high stability. We achieved optogenetic stimulation using both single-photon (1P) activation of neuronal populations and two-photon (2P) activation of single-cells, while simultaneously recording 2P calcium imaging in awake monkeys. Optogenetic manipulations of V1 neuronal populations produced reliable artificial visual percepts. Together, our advances show the feasibility of precise and stable all-optical interrogation of cortical neurons in awake NHPs, which may facilitate broad applications in high-level cognition and pre-clinical testing studies.

neuroscience↗

bioRxiv2018

H3K4 methylation dependent and independent chromatin regulation by JHD2 and SET1 in budding yeast

Set1 and Jhd2 regulate the methylation state of histone H3 lysine-4 (H3K4me) through their opposing methyltransferase and demethylase activities in the budding yeast Saccharomyces cerevisiae. H3K4me associates with actively transcribed genes and, like both SET1 and JHD2 themselves, is known to regulate gene expression diversely. It remains unclear, however, if Set1 and Jhd2 act solely through H3K4me. Relevantly, Set1 methylates lysine residues in the kinetochore protein Dam1 while genetic studies of the S. pombe SET1 ortholog suggest the existence of non-H3K4 Set1 targets relevant to gene regulation. We interrogated genetic interactions of JHD2 and SET1 with essential genes involved in varied aspects of the transcription cycle. Our findings implicate JHD2 in genetic inhibition of the histone chaperone complexes Spt16-Pob3 (FACT) and Spt6-Spn1. This targeted screen also revealed that JHD2 inhibits the Nrd1-Nab3-Sen1 (NNS) transcription termination complex. We find that while Jhd2s impact on these transcription regulatory complexes likely acts via H3K4me, Set1 governs the roles of FACT and NNS through opposing H3K4-dependent and -independent functions. We also identify diametrically opposing consequences for mutation of H3K4 to alanine or arginine, illuminating that caution must be taken in interpreting histone mutation studies. Unlike FACT and NNS, detailed genetic studies suggest an H3K4me-centric mode of Spt6-Spn1 regulation by JHD2 and SET1. Chromatin immunoprecipitation and transcript quantification experiments show that Jhd2 opposes the positioning of a Spt6-deposited nucleosome near the transcription start site of SER3, a Spt6-Spn1 regulated gene, leading to hyper-induction of SER3. In addition to confirming and extending an emerging role for Jhd2 in the control of nucleosome occupancy near transcription start sites, our findings suggest some of the chromatin regulatory functions of Set1 are independent of H3K4 methylation.

genetics↗