Biology subjects

Zeng, J.

Publications and source records attributed to Zeng, J..

At least 163 records · Page 9Linked to original sources

bioRxiv2019

Serotonin Neurons in the Dorsal and Medial Raphe Nuclei: from Single-Cell Transcriptomes to Whole-Brain Projections

Serotonin neurons of the dorsal and medial raphe nuclei (DR and MR) collectively innervate the entire forebrain and midbrain, modulating diverse physiology and behavior. To gain a fundamental understanding of their molecular heterogeneity, we used plate-based single-cell RNA-sequencing to generate a comprehensive dataset comprising eleven transcriptomically distinct serotonin neuron clusters. Systematic in situ hybridization mapped specific clusters to the principal DR, caudal DR, or MR. These transcriptomic clusters differentially express a rich repertoire of neuropeptides, receptors, ion channels, and transcription factors. We generated novel intersectional viral-genetic tools to access specific subpopulations. Whole-brain axonal projection mapping revealed that DR serotonin neurons co-expressing vesicular glutamate transporter-3 preferentially innervate the cortex, whereas those co-expressing thyrotropin-releasing hormone innervate subcortical regions in particular the hypothalamus. Reconstruction of 50 individual DR serotonin neurons revealed segregated axonal projection patterns at the single-cell level. Together, these results provide a molecular foundation of the heterogenous serotonin neuronal phenotypes.

neuroscience↗

bioRxiv2019

Does deliberation really need more effort than intuition? A test using event-related brain potential

Intuition and deliberation are two strategies for problem-solving and decision-making. It is commonly believed that deliberation requires more effort than intuition. However, to date, neural evidence approving or disapproving this point is scarce. To explore this issue, we asked participants to play two well-matched games requiring either deliberation or intuition. Using event-related brain potential (ERP) technique, we found: (1) Deliberation elicited more deflective ERP than intuition during -1000 ~ -500 ms before the response, which is consistent with the common belief. (2) More importantly, intuition evoked more deflective ERP than deliberation during 550 ~ 650 ms after the onset of the stimulus, which suggests that intuition may need more effort than deliberation at early stage, contradicting the common belief.

neuroscience↗

bioRxiv2019

Promoter-anchored chromatin interactions predicted from genetic analysis of epigenomic data

Promoter-anchored chromatin interactions (PAIs) play a pivotal role in transcriptional regulation. Current high-throughput technologies for detecting PAIs, such as promoter capture Hi-C, are not scalable to large cohorts. Here, we present an analytical approach that uses summary-level data from cohort-based DNA methylation (DNAm) quantitative trait locus (mQTL) studies to predict PAIs. Using mQTL data from human peripheral blood (n=1,980), we predicted 34,797 PAIs which showed strong overlap with the chromatin contacts identified by previous experimental assays. The promoter-interacting DNAm sites were enriched in enhancers or near expression QTLs. Genes whose promoters were involved in PAIs were more actively expressed, and gene pairs with promoter-promoter interactions were enriched for co-expression. Integration of the predicted PAIs with GWAS data highlighted interactions among 601 DNAm sites associated with 15 complex traits. This study demonstrates the use of mQTL data to predict PAIs and provides insights into the role of PAIs in complex trait variation.

genetics↗

bioRxiv2019

Improved polygenic prediction by Bayesian multiple regression on summary statistics

The capacity to accurately predict an individuals phenotype from their DNA sequence is one of the great promises of genomics and precision medicine. Recently, Bayesian methods for generating polygenic predictors have been successfully applied in human genomics but require the individual level data, which are often limited in their access due to privacy or logistical concerns, and are computationally very intensive. This has motivated methodological frameworks that utilise publicly available genome-wide association studies (GWAS) summary data, which now for some traits include results from greater than a million individuals. In this study, we extend the established summary statistics methodological framework to include a class of point-normal mixture prior Bayesian regression models, which have been shown to generate optimal genetic predictions and can perform heritability estimation, variant mapping and estimate the distribution of the genetic effects. In a wide range of simulations and cross-validation using 10 real quantitative traits and 1.1 million variants on 350,000 individuals from the UK Biobank (UKB), we establish that our summary based method, SBayesR, performs similarly to methods that use the individual level data and outperforms other state-of-the-art summary statistics methods in terms of prediction accuracy and heritability estimation at a fraction of the computational resources. We generate polygenic predictors for body mass index and height in two independent data sets and show that by exploiting summary statistics on 1.1 million variants from the largest GWAS meta-analysis (n {approx} 700, 000) that the SBayesR prediction R2 improved on average across traits by 6.8% relative to that estimated from an individual-level data BayesR analysis of data from the UKB (n {approx} 450, 000). Compared with commonly used state-of-the-art summary-based methods, SBayesR improved the prediction R2 by 4.1% relative to LDpred and by 28.7% relative to clumping and p-value thresholding. SBayesR gave comparable prediction accuracy to the recent RSS method, which has a similar model, but at a computational time that is two orders of magnitude smaller. The methodology is implemented in a very efficient and user-friendly software tool titled GCTB.

genetics↗

bioRxiv2019

Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank

Genotype-by-environment interaction (GEI) is a fundamental component in understanding complex trait variation. However, it remains challenging to identify genetic variants with GEI effects in humans largely because of the small effect sizes and the difficulty of monitoring environmental fluctuations. Here, we demonstrate that GEI can be inferred from genetic variants associated with phenotypic variability in a large sample without the need of measuring environmental factors. We performed a genome-wide variance quantitative trait locus (vQTL) analysis of ~5.6 million variants on 348,501 unrelated individuals of European ancestry for 13 quantitative traits in the UK Biobank, and identified 75 significant vQTLs with P<2.0x10-9 for 9 traits, especially for those related to obesity. Direct GEI analysis with five environmental factors showed that the vQTLs were strongly enriched with GEI effects. Our results indicate pervasive GEI effects for obesity-related traits and demonstrate the detection of GEI without environmental data.

genetics↗

bioRxiv2018

ACME: Pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks

Prediction of peptide binding to MHC molecules plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Although numerous computational methods have been developed to this end, several challenges still remain in predicting peptide-MHC interactions. Many previous methods are allele-specific, training separate models for individual alleles and are thus unable to yield accurate predictions for those alleles with limited training data. Despite that there exist several pan-specific algorithms that train a common model for different alleles, they only adopt simple model structures that generally have limited performance in capturing the complex underlying patterns of peptide-MHC interactions. Here we present ACME (Attention-based Convolutional neural networks for MHC Epitope binding prediction), a new pan-specific algorithm to accurately predict the binding affinities between peptides and MHC class I molecules, even for those new alleles that are not seen in the training data. Extensive tests have demonstrated that ACME can significantly outperform other state-of-the-art prediction methods with an increase of the Pearson Correlation Coefficient by up to 23 percent. In addition, its ability to identify strong-binding peptides has been experimentally validated. Moreover, by integrating the convolutional neural network with attention mechanism, ACME is able to extract interpretable patterns that can provide useful and detailed insights into the binding preferences between peptides and their MHC partners. All these results have demonstrated that ACME can provide a powerful and practically useful tool for the studies of peptide-MHC class I interactions.

bioinformatics↗

bioRxiv2018

Rationalizing Translation Elongation by Reinforcement Learning

Translation elongation plays a crucial role in multiple aspects of protein biogenesis. In this study, we develop a novel deep reinforcement learning based framework, named RiboRL, to model the distributions of ribosomes on transcripts. In particular, RiboRL employs a policy network (PolicyNet) to perform a context-dependent feature selection to facilitate the prediction of ribosome density. Extensive tests demonstrate that RiboRL can outperform other state-of-the-art methods in predicting ribosome densities. We also show that the reinforcement learning based strategy can generate more informative features for the prediction task when compared to other commonly used attribution methods in deep learning. Moreover, the in-depth analyses and a case study also indicate the potential applications of the RiboRL framework in generating meaningful biological insights regarding translation elongation dynamics. These results have established RiboRL as a useful computational tool to facilitate the studies of the underlying mechanisms of translational regulation.

bioinformatics↗

bioRxiv2018

The effect of X-linked dosage compensation on complex trait variation

Quantitative genetics theory predicts that X-chromosome dosage compensation between sexes will have a detectable effect on the amount of genetic and therefore phenotypic trait variances at associated loci in males and females. Here, we systematically examine the role of dosage compensation in complex trait variation in humans in 20 complex traits in a sample of more than 450,000 individuals from the UK Biobank and in 1,600 gene expression traits from a sample of 2,000 individuals as well as across-tissue gene expression from the GTEx resource. We find, on average, twice as much genetic variation for complex traits due to X-linked loci in males compared to females, consistent with a negligible effect of predicted escape from X-inactivation on complex trait variation across traits and also detect biologically relevant X-linked heterogeneity between the sexes for a number of complex traits.

genetics↗

bioRxiv2018

A genetically-encoded fluorescent sensor enables rapid and specific detection of dopamine in flies, fish, and mice

Dopamine (DA) is a central monoamine neurotransmitter involved in many physiological and pathological processes. A longstanding yet largely unmet goal is to measure DA changes reliably and specifically with high spatiotemporal precision, particularly in animals executing complex behaviors. Here we report the development of novel genetically-encoded GPCR-Activation-Based-DA (GRABDA) sensors that enable these measurements. In response to extracellular DA rises, GRABDA sensors exhibit large fluorescence increases ({Delta}F/F0[~]90%) with sub-second kinetics, nanomolar to sub-micromolar affinities, and excellent molecular specificity. Importantly, GRABDA sensors can resolve a single-electrical-stimulus evoked DA release in mouse brain slices, and detect endogenous DA release in the intact brains of flies, fish, and mice. In freely-behaving mice, GRABDA sensors readily report optogenetically-elicited nigrostriatal DA release and depict dynamic mesoaccumbens DA changes during Pavlovian conditioning or during sexual behaviors. Thus, GRABDA sensors enable spatiotemporal precise measurements of DA dynamics in a variety of model organisms while exhibiting complex behaviors.

neuroscience↗

bioRxiv2018

Integrating Hi-C and FISH data for modeling 3D organizations of chromosomes

The new advances in various experimental techniques that provide complementary in-formation about the spatial conformations of chromosomes have inspired researchers to develop computational methods to fully exploit the merits of individual data sources and combine them to improve the modeling of chromosome structure. In this paper, we propose GEM-FISH, a first method for reconstructing the 3D models of chromosomes through systematically integrating both Hi-C and FISH data with the prior biophysical knowledge of a polymer model. Comprehensive tests on a set of chromosomes for which both Hi-C and FISH data were available have demonstrated that GEM-FISH can reconstruct the 3D models of chromosomes with more accurate spatial organizations of TADs and compartments than using only Hi-C data. In addition, GEM-FISH can accurately capture the spatial proximity of loop loci and the colocalization of loci from the same sub-compartments. Moreover, our reconstructed 3D models of chromosomes revealed novel patterns of spatial distributions of super-enhancers which can provide useful insights into understanding the functional roles of these super-enhancers in gene regulation. All these results demonstrated that, through integrating both Hi-C and FISH data into a unified framework, GEM-FISH can provide a better tool for modeling the 3D organizations of chromosomes than using the Hi-C data alone.

bioinformatics↗

bioRxiv2018

A genetically-encoded fluorescent acetylcholine indicator

Acetylcholine (ACh) regulates a diverse array of physiological processes throughout the body, yet cholinergic transmission in the majority of tissues/organs remains poorly understood due primarily to the limitations of available ACh-monitoring techniques. We developed a family of G-protein-coupled receptor activation-based ACh sensors (GACh) with sensitivity, specificity, signal-to-noise ratio, kinetics and photostability suitable for monitoring ACh signals in vitro and in vivo. GACh sensors were validated with transfection, viral and/or transgenic expression in a dozen types of neuronal and non-neuronal cells prepared from several animal species. In all preparations, GACh sensors selectively responded to exogenous and/or endogenous ACh with robust fluorescence signals that were captured by epifluorescent, confocal and/or two-photon microscopy. Moreover, analysis of endogenous ACh release revealed firing pattern-dependent release and restricted volume transmission, resolving two long-standing questions about central cholinergic transmission. Thus, GACh sensors provide a user-friendly, broadly applicable toolbox for monitoring cholinergic transmission underlying diverse biological processes.

neuroscience↗

bioRxiv2018

Low nutrient levels reduce the fitness cost of MexCD-OprJ efflux pump overexpression in ciprofloxacin-resistant Pseudomonas aeruginosa

The long-term persistence of antibiotic resistance in the environment is a public health concern. Expression of an efflux pump, an important mechanism of resistance to antibiotics, is usually associated with a fitness cost in bacteria. In this study, we aimed to determine why antibiotic resistance conferred by overexpression of an efflux pump persists in environments such as drinking and source water in which antibiotic selective pressure may be very low or even absent. Competition experiments between wild-type Pseudomonas aeruginosa and ciprofloxacin-resistant mutants revealed that the fitness cost of ciprofloxacin resistance (strains cip_1, cip_2, and cip_3) significantly decreased (P < 0.05) under low-nutrient (0.5 mg/l total organic carbon (TOC)) relative to high-nutrient (500 mg/l TOC) conditions. Mechanisms underlying this fitness cost were analyzed. MexD gene expression in resistant bacteria (cip_3 strain) was significantly lower (P < 0.05) in low-nutrient conditions, with 10 mg/l TOC (8.01 {+/-} 0.82-fold), than in high-nutrient conditions, with 500 mg/l TOC (48.89 {+/-} 4.16-fold). Moreover, rpoS gene expression in resistant bacteria (1.36 {+/-} 0.13-fold) was significantly lower (P < 0.05) than that in the wild-type strain (2.78 {+/-} 0.29-fold) under low-nutrient conditions (10 mg/l TOC), suggesting a growth advantage. Furthermore, the difference in metabolic activity between the two competing strains was significantly smaller (P < 0.05) in low-nutrient conditions (5 and 0.5 mg/l TOC). These results suggest that nutrient levels are a key factor in determining the persistence and spread of antibiotic resistance conferred by efflux pumps in the natural environment with trace amounts or no antibiotics.\n\nImportanceThe widespread of antibiotic resistance has led to an increasing concern about the environmental and public health risks. Mechanisms associated with antibiotic resistance including efflux pumps often increase bacterial fitness cost. Our study showed that the fitness cost of ciprofloxacin resistance conferred by overexpression of MexCD-OprJ efflux pump significantly decreased under low-nutrient relative to high-nutrient conditions. The significance of our research is to reveal that nutrient levels are key factor in determining the persistence of antibiotic resistance conferred by efflux pumps under conditions with trace amounts or no antibiotics, which can be mediated by some mechanisms including MexD gene expression, SOURs differences, and rpoS gene regulation.

microbiology↗

bioRxiv2018

Novel susceptibility loci and genetic regulation mechanisms for type 2 diabetes

We conducted a meta-analysis of genome-wide association studies (GWAS) with [~]16 million genotyped/imputed genetic variants in 62,892 type 2 diabetes (T2D) cases and 596,424 controls of European ancestry. We identified 139 common and 4 rare (minor allele frequency < 0.01) variants associated with T2D, 42 of which (39 common and 3 rare variants) were independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2,765) and other T2D-relevant tissues (n = up to 385) with the GWAS results identified 33 putative functional genes for T2D, three of which were targeted by approved drugs. A further integration of DNA methylation (n = 1,980) and epigenomic annotations data highlighted three putative T2D genes (CAMK1D, TP53INP1 and ATP5G1) with plausible regulatory mechanisms whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. We further found evidence that the T2D-associated loci have been under purifying selection.

genetics↗

bioRxiv2018

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes associated with brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top associated cis-expression (cis-eQTLs or cis-mQTLs) between brain and blood for genes expressed (or CpG sites methylated) in both tissues, while accounting for errors in their estimated effects (rb). Using publicly available data (n = 72 to l,366), we find that the genetic effects of cis-eQTLs (PeQTL < 5x10-8) or mQTLs (PmQTL < 1x10-10) are highly correlated between independent brain and blood samples ([Formula] with SE = 0.015 for cis-eQTL and [Formula] with SE = 0.006 for cis-mQTLs). Using meta-analyzed brain eQTL/mQTL data (n = 526 to 1,194), we identify 61 genes and 167 DNA methylation (DNAm) sites associated with 4 brain-related traits and disorders. Most of these associations are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = l,980 to 14,115). We further find that cis-eQTLs with tissue-specific effects are approximately uniformly distributed across all the functional annotation categories, and that mean difference in gene expression level between brain and blood is almost independent of the difference in the corresponding cis-eQTL effect. Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL or cis-mQTL data with large sample sizes.

genetics↗

bioRxiv2018

TFmapper: A tool for searching putative factors regulating gene expression using ChIP-seq data

BackgroundNext-generation sequencing coupled to chromatin immunoprecipitation (ChIP-seq), DNase I hypersensitivity (DNase-seq) and the transposase-accessible chromatin assay (ATAC-seq) has generated enormous amounts of data, markedly improved our understanding of the transcriptional and epigenetic control of gene expression. To take advantage of the availability of such datasets and provide clues on what factors, including transcription factors, epigenetic regulators and histone modifications, potentially regulates the expression of a gene of interest, a tool for simultaneous queries of multiple datasets using symbols or genomic coordinates as search terms is needed.\n\nResultsIn this study, we annotated the peaks of thousands of ChIP-seq datasets generated by ENCODE project, or ChIP-seq/DNase-seq/ATAC-seq datasets deposited in Gene Expression Omnibus and curated by CistromeDB; We built a MySQL database called TFmapper containing the annotations and associated metadata, allowing users without bioinformatics expertise to search across thousands of datasets to identify factors targeting a genomic region/gene of interest in a specified sample through a web interface. Users can also visualize multiple peaks in genome browsers and download the corresponding sequences.\n\nConclusionTFmapper will help users explore the vast amount of publicly available ChIP-seq/DNase-seq/ATAC-seq data, and perform integrative analyses to understand the regulation of a gene of interest. The web server is freely accessible at http://www.tfmapper.org/.

bioinformatics↗

bioRxiv2018

NeoDTI: Neural integration of neighborinformation from a heterogeneous network fordiscovering new drug-target interactions

MotivationAccurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks.\n\nResultsInspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks (CNNs) to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g., compound-protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning.\n\nAvailability and implementationThe source code and data used in NeoDTI are available at: https://github.com/FangpingWan/NeoDTI.\n\nContactzengjy321@tsinghua.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2018

DeepHINT: Understanding HIV-1 integration via deep learning with attention

MotivationHuman immunodeficiency virus type 1 (HIV-1) genome integration is closely related to clinical latency and viral rebound. In addition to human DNA sequences that directly interact with the integration machinery, the selection of HIV integration sites has also been shown to depend on the heterogeneous genomic context around a large region, which greatly hinders the prediction and mechanistic studies of HIV integration.\n\nResultsWe have developed an attention-based deep learning framework, named DeepHINT, to simultaneously provide accurate prediction of HIV integration sites and mechanistic explanations of the detected sites. Extensive tests on a high-density HIV integration site dataset showed that DeepHINT can outperform conventional modeling strategies by automatically learning the genomic context of HIV integration solely from primary DNA sequence information. Systematic analyses on diverse known factors of HIV integration further validated the biological relevance of the prediction result. More importantly, in-depth analyses of the attention values output by DeepHINT revealed intriguing mechanistic implications in the selection of HIV integration sites, including potential roles of several basic helix-loop-helix (bHLH) transcription factors and zinc-finger proteins. These results established DeepHINT as an effective and explainable deep learning framework for the prediction and mechanistic study of HIV integration.\n\nAvailabilityDeepHINT is available as an open-source software and can be downloaded from https://github.com/nonnerdling/DeepHINT\n\nContactlzhang20@mail.tsinghua.edu.cn and zengjy321@tsinghua.edu.cn

bioinformatics↗

bioRxiv2017

Three classes of response elements for human PRC2 and MLL1/2-trithorax complexes

Polycomb group (PcG) and trithorax group (TrxG) proteins are essential for maintaining epigenetic memory in both embryonic stem cells and differentiated cells. To date, how they are localized to hundreds of specific target genes within a vertebrate genome had remained elusive. Here, by focusing on short cis-acting DNA elements of single functions, we discovered, for the first time, to our knowledge, three classes of response elements in human genome: PcG response elements (PREs), MLL1/2-TrxG response elements (TREs) and PcG/TrxG response elements (P/TREs). We further demonstrated that, in contrast to their proposed roles in recruiting PcG proteins to PREs, YY1 and CpG islands are specifically enriched in TREs and P/TREs, but not in PREs. The three classes of response elements as unraveled in this study open new doors for a deeper understanding of PcG and TrxG mechanisms in vertebrates.

biochemistry↗