Biology subjects

Wu, Y.

Publications and source records attributed to Wu, Y..

At least 1,027 records · Page 57Linked to original sources

bioRxiv2019

FGF/MAPK signaling pathway regulates the genome-wide occupancy of Polycomb Repressive Complex 2 in murine embryonic stem cells

The recruitment of Polycomb repressive complex 2 (PRC2) to gene promoters is critical for its function in repressing gene expression in murine embryonic stem cells (mESCs). However, previous studies have demonstrated although the expression of early lineage-specific genes is largely repressed, the genome-wide PRC2 occupancy is unexpectedly reduced in naive mESCs. In this study, we provide evidence to show the FGF/ERK signaling determines the global PRC2 occupancy through regulating the expression of PRC2-recruting factor JARID2 in naive mESCs. At the transcriptional level, the de-repression of bivalent genes is predominantly determined by the presence of cell signaling-associated transcription factors but not the status of PRC2 occupancy at gene promoters. Hence, this study not only reveals a key molecular mechanism by which the FGF/ERK signaling in regulating the PRC2 occupancy in mESCs, but also elucidates a fundamental question regarding the functional roles of transcription factors and Polycomb-mediated epigenetic mechanisms in transcriptional regulation.

genetics↗

bioRxiv2019

Drosophila female-specific brain neuron elicits persistent position- and direction-selective male-like social behaviors

Latent neural circuitry in the female brain encoding male-like mating behaviors has been revealed in both mice and flies. In Drosophila, a key component of this circuitry consists of the doublesex-expressing pC1 neurons, which were deemed to exist in both sexes and function based on the amount of cells being activated. Here, we identify pC1-alpha, a female-specific subtype of pC1, as responsible for inducing persistent male-like social behaviors in females. We demonstrate that activation of a single pC1-alpha neuron is sufficient for such induction in a position- and direction-selective manner, and activity of pC1-alpha neurons as a whole is indispensable for maintaining normal sexual receptivity. These dual functions of pC1-alpha may require different neurotransmission, with acetylcholine specifically required for the former but not the latter. Our findings suggest that pC1-alpha may be the female counterpart of male P1 due to their shared similarities in morphology, lineage, and social promoting function.

neuroscience↗

bioRxiv2019

A Polybasic Domain in aPKC Mediates Par-6-Dependent Control of Plasma Membrane Targeting and Kinase Activity

Mechanisms coupling the atypical PKC (aPKC) kinase activity to its subcellular localization are essential for cell polarization. Unlike other members of the PKC family, aPKC has no well-defined plasma membrane (PM) or calcium binding domains, leading to the assumption that its subcellular localization relies exclusively on protein-protein interactions. Here we show that in both Drosophila and mammalian cells the pseudosubstrate region (PSr) of aPKC acts as a polybasic domain capable of targeting aPKC to the PM via electrostatic binding to PM PI4P and PI(4,5)P2. However, physical interaction between aPKC and Par-6 is required for the PM-targeting of aPKC, likely by allosterically exposing the PSr to bind PM. Binding of Par-6 also inhibits aPKC kinase activity and such inhibition can be relieved through Par-6 interaction with apical polarity protein Crumbs. Our data suggest a potential mechanism in which allosteric regulation of polybasic PSr by Par-6 couples the control of both aPKC subcellular localization and spatial activation of its kinase activity. eTOC SummaryDong et al. discover that the pseudo-substrate region (PSr) in aPKC is a polybasic domain capable of electrostatically targeting aPKC to plasma membrane. Allosteric regulation of PSr by Par-6 couples the control of both aPKC subcellular localization and spatial activation of kinase activity.

cell biology↗

bioRxiv2019

The signaling mechanism of Hcy-induced atrial fibrosis mediated by TRPC3

BackgroundHigh plasma levels of homocysteine (Hcy) are regarded as a risk factor for atrial fibrillation (AF), which is closely associated with the pathological consequence of atrial fibrosis and can lead to heart failure with a high mortality rate; Currently, there is no effective therapy for preventing atrial fibrosis, owing to a lack in fundamental understanding of the underlying mechanism. Here, we show that atrial fibrosis is mediated by the relationship between canonical transient receptor potential 3 (TRPC3) channels and sirtuin type 1 (SIRT1) under the stimulation of Hcy.\n\nMethodsThe left atrial appendage was obtained from patients with either sinus rhythm (SR) or AF, who underwent cardiothoracic surgery, and used to evaluate the relationship between the concentration of Hcy and a potential mechanism of cardiac fibrosis mediated by TRPC3 and SIRT1. We next performed transverse aortic constriction (TAC) in mouse to investigate the relationship. The mechanisms underlying atrial fibrosis involving TRPC3 and SIRT1 proteins were explored by co-immunoprecipitation (co-IP), bio-layer interferometry (BLI) and lentivirus transfection experiments. Quantitative polymerase chain reaction (qPCR) and western blotting (WB) were performed to analyse gene and protein expression, respectively.\n\nResultsThe majority of AF patients displayed atrial fibrosis, as demonstrated by Masson staining and immunohistochemistry. In the mouse model of TAC, more severe fibrosis was detected in the high-Hcy diet (HH) group, compared to NH mice; and the duration of induced AF was longer in the HH groups than in the normal diet (NH) group. Moreover, the HH group exhibited higher expression levels of TRPC3 and related fibrosis proteins, such as TGF-{beta} and Col-I, than the NH group, despite also showing a higher level of SIRT1 was observed. The activator of SIRT1 (Resveratrol, Res) attenuated the enhancement of TRPC3 and decrease in SIRT1 observed in the HH group. Further cell culture experiments confirmed that Hcy could promote the proliferation and differentiation of fibroblasts, the up-regulation of TRPC3, and the decrease in the protein level of SIRT1. Ultimately, the results of Co-IP and BLI indicated a direct interaction between TRPC3-C terminal domain (569-863) and SIRT1 proteins, in which the two proteins are antagonistic and in combination regulate the pathogenesis of atrial fibrosis.\n\nConclusionsThe higher level of atrial fibrosis were observed in the HH mouse group, compared with the NH mice group, Such results suggest that AF patients may be more susceptible to atrial fibrosis and possess a high probability of progressing to hyperhomocysteinemia. Moreover, our findings are consistent with the hypothesis that TRPC3 channel up-regulation leads to abnormal accumulation of collagen, with the down-regulation of SIRT1 as an aetiological factor of high Hcy, which in turn predisposes to atrial fibrosis and strongly enhances the possibility of AF.

molecular biology↗

bioRxiv2019

Ultra-compliant carbon nanotube stretchable direct bladder interface

The bladder, stomach, intestines, heart, and lungs all move dynamically to achieve their purpose. A long-term implantable device that can attach onto an organ, sense its movement, and deliver current to modify the organ function would be useful in many therapeutic applications. The bladder, for example, is a smooth muscle organ that can suffer from incomplete contractions that result in urinary retention with patients requiring using catheterization. Those affected may benefit from a combination of strain sensor and electrical stimulator to better control bladder emptying. We describe the materials and design of such a device made from thin layer carbon nanotube (CNT) and Ecoflex 00-50 and demonstrate its function with in vivo feline bladders. During bench-top characterization, the resistive and capacitive sensors exhibited reliable output throughout 5,000 stretching cycles under physiology condition. In vivo measurement with piezoresistive device showed a high correlation between sensor resistance and volume. Stimulation driven from Pt-PDMS composite electrodes successfully induced bladder contraction. We present method for reliable connection and packaging of medical grade wire to the CNT device. This work is an important step toward the translation of low-durometer elastomers, stretchable CNT percolation and Pt-PDMS composite, which are ideal for large strain bioelectric applications to sense or modulate dynamic organ states.

bioengineering↗

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

Landscape and dynamics of the transcriptional regulatory network during natural killer cell differentiation

Natural killer (NK) cells are essential in controlling cancer and infection. However, little is known about the dynamics of the transcriptional regulatory machinery during NK cell differentiation. In this study, we applied assay of transposase accessible chromatin with sequencing (ATAC-seq) technique in a self-developed in vitro NK cell differentiation system. Analysis of ATAC-seq data illustrated two distinct transcription factor (TF) clusters that dynamically regulate NK cell differentiation. Moreover, two TFs from the second cluster, FOSL2 and EGR2, were identified as novel essential TFs that control NK cell maturation and function. Knocking down either of these two TFs significantly impacted NK cell transformation. Finally, we constructed a genome-wide transcriptional regulatory network that provides an understanding of the regulatory dynamics during NK cell differentiation.

bioinformatics↗

bioRxiv2019

Pair-wise Comparison Analysis for Multiple Pool-sep: an efficient method identified anthocyanin biosynthesis genes in rice pericarp

The complex traits are derived from multiple genes and exhibit a large variety of phenotypes. High-throughput sequencing technologies have become the new strategies for mapping the important traits of crops. However, these methods have their own disadvantages and limitations. Here we introduced Pair-wise Comparison Analysis for Multiple Pool-seq (PCAMP) for mapping the candidate genomic regions involved in anthocyanin biosynthesis in rice pericarp. In this protocol, the second filial generation (F2) populations obtained by crossing two parents with different target traits were divided into n (n>=3) subpopulations according to their phenotypes. Thirty phenotypically identical individuals were selected from each subpopulation and DNA samples were extracted to form a pool for sequencing. Finally, we compared the SNP-index between every two Pool-seqs to map the candidate genomic regions. We applied PCAMP to analyse F2 populations and successfully identified five known genes and five new candidate genomic regions for anthocyanin biosynthesis in rice pericarp. These results demonstrate that PCAMP is an efficient new method for dissecting the complex traits of crops.

molecular biology↗

bioRxiv2019

DeepSV: Accurate calling of genomic deletions from high throughput sequencing data using deep convolutional neural network

BackgroundCalling genetic variations from sequence reads is an important problem in genomics. There are many existing methods for calling various types of variations. Recently, Google developed a method for calling single nucleotide polymorphisms (SNPs) based on deep learning. Their method visualizes sequence reads in the forms of images. These images are then used to train a deep neural network model, which is used to call SNPs. This raises a research question: can deep learning be used to call more complex genetic variations such as structural variations (SVs) from sequence data? ResultsIn this paper, we extend this high-level approach to the problem of calling structural variations. We present DeepSV, an approach based on deep learning for calling long deletions from sequence reads. DeepSV is based on a novel method of visualizing sequence reads. The visualization is designed to capture multiple sources of information in the sequence data that are relevant to long deletions. DeepSV also implements techniques for working with noisy training data. DeepSV trains a model from the visualized sequence reads and calls deletions based on this model. We demonstrate that DeepSV outperforms existing methods in terms of accuracy and efficiency of deletion calling on the data from the 1000 Genomes Project. ConclutionsOur work shows that deep learning can potentially lead to effective calling of different types of genetic variations that are complex than SNPs. Availability and implementationDeepSVs source code and sample result as part of this project are readily available from GitHub at https://github.com/CSuperlei/DeepSV/.

bioinformatics↗

bioRxiv2019

Rapid and Active Stabilization of Visual Cortical Firing Rates Across Light-Dark Transitions

The dynamics of neuronal firing during natural vision are poorly understood. Surprisingly, mean firing rates of neurons in primary visual cortex (V1) of freely behaving rodents are similar during prolonged periods of light and darkness, but it is unknown whether this reflects a slow adaptation to changes in natural visual input, or insensitivity to rapid changes in visual drive. Here we use chronic electrophysiology in freely behaving rats of either sex to follow individual V1 neurons across many dark-light (D-L) and light-dark (L-D) transitions. We show that, even on rapid timescales (1s to 10 min), neuronal activity was only weakly modulated by transitions that coincided with the expected 12h/12h light-dark cycle. In contrast, a larger subset of V1 neurons consistently responded to unexpected L-D and D-L transitions, and disruption of the regular L-D cycle with 60 hours of complete darkness induced a robust increase in V1 firing upon re-introduction of visual input. Thus, V1 neurons fire at similar rates in the presence or absence of natural stimuli, and significant changes in activity arise only transiently in response to unexpected changes in the visual environment. Further, although mean rates were similar in L and D, pairwise correlations were significantly stronger during natural vision, suggesting that information about natural scenes in V1 is more readily extractable from correlations than from individual firing rates. Together, our findings show that V1 firing rates are rapidly and actively stabilized during expected changes in visual input, and are remarkably stable at both short and long timescales.

neuroscience↗

bioRxiv2019

Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features

ObjectiveTo investigate whether or not functional connectivity (FC) could be used as a potential biomarker for classification of primary insomnia (PI) at the individual level by using multivariate pattern analysis (MVPA). MethodsThirty-eight drug-naive patients with PI and 44 healthy controls (HC) underwent resting-state functional MR imaging. Three commonly used FC metrics were calculated for each participant. We used the MVPA framework using linear support vector machine (SVM) with the three types of metrics as features separately. Subsequently, an unbiased N-fold cross-validation strategy was used to generate a classification system and was then used to evaluate its classification performances. Finally, FC metrics with significant high classification performance were compared between the two groups and were correlated with clinical characteristics, i.e., Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS). ResultsThe best classifier could reach up to an accuracy of 81.5%, with sensitivity of 84.9%, specificity of 79.1% and area under the receiver operating characteristic curve (AUC) of 83.0% (all P < 0.001). Right fronto-insular cortex, left precuneus and left middle frontal gyrus showed high classification weights. In addition, right fronto-insular cortex and left middle frontal gyrus were the overlapping regions between MVPA and group comparison. Correlation analysis showed that functional connectivity strength (FCS) in left middle frontal gyrus and head of right caudate nucleus were correlated with PSQI and SDS respectively. ConclusionThe current study suggests abnormal FCS might serve as a potential neuromarkers for PI. Key PointsFCS in fronto-insular cortex and middle frontal gyrus may be a neuroimaging biomarker for insomnia. FCS can be used to distinguish between patients with primary insomnia from healthy controls with high classification accuracy (81.5%; P < 0.001). FCS in left middle frontal gyrus and head of right caudate nucleus were correlated with PSQI and SDS respectively. Abbreviations

neuroscience↗

bioRxiv2019

LAmbDA: Label Ambiguous Domain Adaption Dataset Integration Reduces Batch Effects and Improves Subtype Detection

MotivationRapid advances in single cell RNA sequencing have produced more granular subtypes of cells in multiple tissues from different species. There exists a need to develop rigorous methods that can i) model multiple datasets with ambiguous labels across species and studies and ii) remove systematic biases across datasets and species. ResultsWe developed a species- and dataset-independent transfer learning framework (LAmbDA) to train models on multiple datasets and applied our framework on scRNA-seq experiments. These models mapped corresponding cell types between datasets with inconsistent labels while simultaneously reducing batch effects. We achieved high accuracy in labeling cellular subtypes (weighted accuracy pancreas: 91%, brain: 78%) using LAmbDA Random Forest. LAmbDA Feedforward 1 Layer Neural Network achieved higher weighted accuracy in labeling cellular subtypes than CaSTLe or MetaNeighbor in brain (48%, 32%, 20% respectively). Furthermore, LAmbDA Feedforward 1 Layer Neural Network was the only method to correctly predict ambiguous cellular subtype labels in both pancreas and brain compared to CaSTLe and MetaNeighbor. LAmbDA is model- and dataset-independent and generalizable to diverse data types representing an advance in biocomputing. Availability: github.com/tsteelejohnson91/LAmbDA Contact: kunhuang@iu.edu, jizhan@iu.edu

bioinformatics↗

bioRxiv2019

FabriEVEs: A dedicated platform for endogenous viral elements in fishes, amphibians, birds, reptiles and invertebrates

Endogenous viral elements (EVEs) are heritable viral deriving elements present in the genomes of other species. As DNA fossils left by ancient viruses, EVEs were used to infer the characteristics of extinct viruses. EVEs in mammals have been well classified by several databases, however, EVEs in non-mammalian organisms are poorly documented. Here, we report FabriEVEs (http://tfbsbank.co.uk/FabriEVEs), the first dedicated and comprehensive online platform for the collection, classification and annotation of EVEs in fishes, amphibians, birds, reptiles and invertebrates. In total, nearly 1.5 million EVEs from 82 species deriving from class I (dsDNA), II (ssDNA), III (dsRNA), IV (positive ssRNA), V (negative ssRNA), VI (ssRNA-RT) and VII (dsDNA-RT) viruses were recorded in FabriEVEs, accompanying with comprehensive annotation including the species name, location, genomic features, virus family and associated literature. Flexible and powerful query options were provided to pinpoint desired EVEs. Furthermore, FabriEVEs provides free access to all EVEs data in case users need to download them for further analysis. Taken together, our database provided systematic classification and annotation of EVEs in non-mammal species, which paves the way for comparative analysis of EVEs and throws light upon the co-evolution of EVEs and their hosts.

evolutionary biology↗

bioRxiv2019

Bacterial and Archaeal Communities in Recycling Effluents from a Bauxite Flotation Plant

Recycling effluent has become a bottleneck and an environmental risk associated with the regular production of bauxite via flotation and the sustainable development of the aluminum industry in China. To find a practical direction for biotreatment, the bacterial and archaeal communities in recycling effluents containing concentrate and tailings from bauxite flotation plants were investigated by a metagenomic sequencing method in association with the evaluated geochemical properties. The results showed that Paracoccus, Desulfomicrobium, Exiguobacterium, Tindallia, Ercella and Anoxynatronum were the primary bacterial genera and Methanothrix, Methanobacterium, Nitrososphaera and Methanosarcina were the dominant archaeal genera. Upon combining the microbial diversity and the geochemical properties of the two sample types, the microbial community containing Desulfomicrobium, Paracoccus, Tindallia, Methanobacterium, Methanothrix and Nitrososphaera was better adapted to the biodegradation of flotation collectors, and the microbial community consisting of Paracoccus, Exiguobacterium, Methanothrix and Methanobacterium was more efficient at hydrolyzed polyacrylamide (HPAM) biodegradation. In addition, a large proportion of unclassified OTUs has indicated that recycling effluent is a worthy resource for isolating new strains from the Firmicutes phylum.

microbiology↗

bioRxiv2019

Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture

The proportion of phenotypic variance attributable to the additive effects of a given set of genotyped SNPs (i.e. SNP-heritability) is a fundamental quantity in the study of complex traits. Recent works have shown that existing methods to estimate genome-wide SNP-heritability often yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and LD-dependent genetic architectures, it remains unclear which estimates of SNP-heritability reported in the literature are reliable. Here we show that genome-wide SNP-heritability can be accurately estimated from biobank-scale data irrespective of the underlying genetic architecture of the trait, without specifying a heritability model or partitioning SNPs by minor allele frequency and/or LD. We use theoretical justifications coupled with extensive simulations starting from real genotypes from the UK Biobank (N=337K) to show that, unlike existing methods, our closed-form estimator for SNP-heritability is highly accurate across a wide range of architectures. We provide estimates of SNP-heritability for 22 complex traits and diseases in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.

genomics↗

bioRxiv2019

Fast estimation of genetic correlation for Biobank-scale data

Genetic correlation is an important parameter in efforts to understand the relationships among complex traits. Current methods that analyze individual genotype data for estimating genetic correlation are challenging to scale to large datasets. Methods that analyze summary data, while being computationally efficient, tend to yield estimates of genetic correlation with reduced precision. We propose, SCORE, a randomized method of moments estimator of genetic correlation that is both scalable and accurate. SCORE obtains more precise estimates of genetic correlations relative to summary-statistic methods that can be applied at scale achieving a 50% reduction in standard error relative to LD-score regression (LDSC) and a 26% reduction relative to high-definition likelihood (HDL) (averaged over all simulations). The efficiency of SCORE enables computation of genetic correlations on the UK biobank dataset consisting of {approx} 300K individuals and {approx} 500K SNPs in a few hours (orders of magnitude faster than methods that analyze individual data such as GCTA). Across 780 pairs of traits in 291, 273 unrelated white British individuals in the UK Biobank, SCORE identifies significant genetic correlation between 200 additional pairs of traits over LDSC (beyond the 245 pairs identified by both).

bioinformatics↗

bioRxiv2019

Tandem Directional CTCF Sites Balance Protocadherin Promoter Usage

CTCF is a key insulator-binding protein and mammalian genomes contain numerous CTCF-binding sites (CBSs), many of which are organized in tandem arrays. Here we provide direct evidence that CBSs, if located between enhancers and promoters in the Pcdh and {beta}-globin clusters, function as an enhancer-blocking insulator by forming distinct directional chromatin loops, regardless whether enhancers contain CBS or not. Moreover, computational simulation and experimental capture revealed balanced promoter usage in cell populations and stochastic monoallelic expression in single cells by large arrays of tandem variable CBSs. Finally, gene expression levels are negatively correlated with CBS insulators located between enhancers and promoters on a genome-wide scale. Thus, single CBS insulators ensure proper enhancer insulation and promoter activation while tandem-arrayed CBS insulators determine balanced promoter usage. This finding has interesting implications on the role of topological insulators in 3D genome folding and developmental gene regulation.

molecular biology↗

bioRxiv2019

Scalable multi-component linear mixed models with application to SNP heritability estimation

Variance components analysis has emerged as a powerful tool in complex trait genetics, with applications ranging from heritability estimation to association mapping. While the application of these methods to large-scale genetic datasets can potentially reveal important insights into genetic architecture, existing methods for fitting variance components do not scale well to these datasets. Here, we present a new algorithm for variance components analysis that is accurate and highly efficient, capable of estimating one hundred variance components on a million individuals genotyped at a million SNPs in a few hours. We illustrate the utility of our method in estimating variation in a trait explained by genotyped SNPs (SNP heritability) as well in partitioning heritability across population and functional genomic annotations. Analyzing 22 diverse traits with genotypes from 300, 000 individuals across about 8 million common and low frequency SNPs (minor allele frequency > 0.1%), we observe that the allelic effect size increases with decreasing MAF (minor allele frequency) and LD (linkage disequilibrium) across the analyzed traits consistent with the action of negative selection. Partitioning heritability across 28 functional annotations, we observe enrichment of heritability in FANTOM5 enhancers in asthma, eczema, thyroid and autoimmune disorders.

bioinformatics↗