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Yang, J.

Publications and source records attributed to Yang, J..

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

Inability to improve performance with control shows limited access to inner states

Any repeatedly performed action is characterised by endogenous variability, affecting both speed and accuracy - for a large part presumably caused by fluctuations in underlying brain and body states. The current research questions were: 1) whether such states are accessible to us, and 2) whether we can act upon this information to reduce variability. For example, when playing a game of darts, there is an implicit assumption that people can wait to throw until they are in the right perceptual-attentional state. If this is true, taking away the ability to self-pace the game should worsen performance. We first tested precisely this assumption asking participants to play darts in a self-paced and a fixed-paced condition. There was no benefit of self-pacing, showing that participants were unable to use such control to improve their performance and reduce their variability. Next, we replicated these findings in two computer-based tasks, in which participants performed a rapid action-selection and a visual detection task in one self-paced and three forced-paced conditions. Over four different empirical tests, we show that the self-paced condition did not lead to improved performance or reduced variability, nor to reduced temporal dependencies in the reaction time series. Overall, it seems that, if people have any access to their fluctuating performance-relevant inner states, this access is limited and not relevant for upcoming performance.

neuroscience↗

Structural basis for the recognition of K48-linked Ub chain by proteasomal receptor Rpn13

The interaction between K48-linked ubiquitin (Ub) chain and Rpn13 is important for proteasomal degradation of ubiquitinated substrate proteins. Only the complex structure between the N-terminal domain of Rpn13 (Rpn13NTD) and Ub monomer has been characterized, and it remains unclear how Rpn13 specifically recognizes K48-linked Ub chain. Using single-molecule FRET, here we show that K48-linked diubiquitin (K48-diUb) fluctuates among three distinct conformational states, and a preexisting compact state is selectively enriched by Rpn13NTD. The same binding mode is observed for full-length Rpn13 and longer K48-linked Ub chain. Using solution NMR spectroscopy, we have solved the complex structure between Rpn13NTD and K48-diUb. In the structure, Rpn13NTD simultaneously interacts with proximal and distal Ub subunits of K48-diUb that remain associated in the complex, thus corroborating smFRET findings. The proximal Ub interacts with Rpn13NTD similarly as the Ub monomer in the known Rpn13NTD:Ub structure, while the distal Ub binds to a largely electrostatic surface of Rpn13NTD. Thus, a charge reversal mutation in Rpn13NTD can weaken the interaction between Rpn13 and K48-linked Ub chain, causing accumulation of ubiquitinated proteins. Moreover, blockage of the access of the distal Ub to Rpn13NTD with a proximity attached Ub monomer can also disrupt the interaction between Rpn13 and K48-diUb. Together, the bivalent interaction of K48-linked Ub chain with Rpn13 provides the structural basis for Rpn13 linkage selectivity, which opens a new window for modulating proteasomal function.

biophysics↗

Requirement for TRanslocon-Associated Protein (TRAP) α in insulin biogenesis

The mechanistic basis for the biogenesis of peptide hormones and growth factors is poorly understood. Here we show that the conserved endoplasmic reticulum (ER) membrane translocon-associated protein (TRAP) , also known as signal sequence receptor 1 (SSR1)1, plays a critical role in the biosynthesis of insulin. A genetic screen in the nematode Caenorhabditis elegans revealed trap-1, which encodes the C. elegans TRAP ortholog, as a modifier of DAF-2 insulin receptor (InsR) signaling. Genetic analysis indicates that TRAP-1 acts upstream of DAF-2/InsR to control C. elegans development. Endogenous C. elegans TRAP-1 and mammalian TRAP both localized to the ER. In pancreatic beta cells, TRAP deletion impaired preproinsulin translocation but did not affect the synthesis of 1-antitrypsin, indicating that TRAP selectively influences the translocation of a subset of secreted proteins. Surprisingly, loss of TRAP function also resulted in disruption of distal steps in insulin biogenesis including proinsulin processing and secretion. These results show that TRAP assists in the ER translocation of preproinsulin and unveil unanticipated additional consequences of TRAP loss-of-function on the intracellular trafficking and maturation of proinsulin. The association of common intronic single nucleotide variants in the human TRAP gene with susceptibility to Type 2 diabetes and pancreatic beta cell dysfunction2 suggests that impairment of preproinsulin translocation and proinsulin trafficking may contribute to the pathogenesis of Type 2 diabetes.

genetics↗

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↗

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↗

Efficient estimation and applications of cross-validated genetic predictions

Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRS). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work we present a novel reference-free method to produce PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial over-fitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues, and can produce informative in-sample PRS over any existing cohort without over-fitting. We then demonstrate several novel applications of reference-free PRS including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.

genetics↗

TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

The transcriptome-wide association studies (TWAS) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWAS facilitates gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, we propose an improved Bayesian method that assumes a data-driven nonparametric prior to impute gene expression. Our method is general and flexible and includes both the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan. In real applications, our nonparametric Bayesian method fitted transcriptomic imputation models for 2X number of genes with 1.7X average regression R2 over PrediXcan, thus improving the power of follow-up TWAS. Hence, the nonparametric Bayesian model is preferred for modeling the complex genetic architecture of transcriptomes and is expected to enhance transcriptome-integrated genetic association studies. We implement our Bayesian approach in a convenient software tool \"TIGAR\" (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWAS using individual-level or summary-level GWAS data.

genetics↗

Genetic analyses of medication-use and implications for precision medicine

It is common that one medication is prescribed for several indications, and conversely that several medications are prescribed for the same indication, suggesting a complex biological network for disease risk and its relationship with pharmacological function. Genome-wide association studies (GWASs) of medication-use may contribute to understanding of disease etiology, generation of new leads relevant for drug discovery and quantify prospects for precision medicine. We conducted GWAS to profile self-reported medication-use from 23 categories in approximately 320,000 individuals from the UK Biobank. A total of 505 independent genetic loci that met stringent criteria for statistical significance were identified. We investigated the implications of these GWAS findings in relation to biological mechanism, drug target identification and genetic risk stratification of disease. Amongst the medication-associated genes were 16 known therapeutic-effect target genes for medications from 9 categories.

genetics↗

Testing Structural Models of Psychopathology at the Genomic Level

Genome-wide association studies (GWAS) have revealed hundreds of genetic loci associated with the vulnerability to major psychiatric disorders, and post-GWAS analyses have shown substantial genetic correlations among these disorders. This evidence supports the existence of a higher-order structure of psychopathology at both the genetic and phenotypic levels. Despite recent efforts by collaborative consortia such as the Hierarchical Taxonomy of Psychopathology (HiTOP), this structure remains unclear. In this study, we tested multiple alternative structural models of psychopathology at the genomic level, using the genetic correlations among fourteen psychiatric disorders and related psychological traits estimated from GWAS summary statistics. The best-fitting model included four correlated higher-order factors - externalizing, internalizing, thought problems, and neurodevelopmental disorders - which showed distinct patterns of genetic correlations with external validity variables and accounted for substantial genetic variance in their constituent disorders. A bifactor model including a general factor of psychopathology as well as the four specific factors fit worse than the above model. Several model modifications were tested to explore the placement of some disorders - such as bipolar disorder, obsessive-compulsive disorder, and eating disorders - within the broader psychopathology structure. The best-fitting model indicated that eating disorders and obsessive-compulsive disorder, on the one hand, and bipolar disorder and schizophrenia, on the other, load together on the same thought problems factor. These findings provide support for several of the HiTOP higher-order dimensions and suggest a similar structure of psychopathology at the genomic and phenotypic levels.

genomics↗

Non-CG methylation and multiple epigenetic layers associate child abuse with immune and small GTPase dysregulation

Early-life adversity (ELA) is a major predictor of psychopathology, and is thought to increase lifetime risk by epigenetically regulating the genome. Here, focusing on the lateral amygdala, a major brain site for emotional homeostasis, we described molecular cross-talk among multiple epigenetic mechanisms, including 6 histone marks, DNA methylation and the transcriptome, in subjects with a history of ELA and controls. We first uncovered, in the healthy brain, previously unknown interactions among epigenetic layers, in particular related to non-CG methylation in the CAC context. We then showed that ELA associates with methylomic changes that are as frequent in the CAC as in the canonical CG context, while these two forms of plasticity occur in sharply distinct genomic regions, features, and chromatin states. Combining these multiple data indicated that immune-related and small GTPase signaling pathways are most consistently impaired in the amygdala of ELA individuals. Overall, this work provides new insights into epigenetic brain regulation as a function of early-life experience.

neuroscience↗

Id proteins promote a cancer stem cell phenotype in triple negative breast cancer via Robo1-dependent c-Myc activation

Breast cancers display phenotypic and functional heterogeneity and several lines of evidence support the existence of cancer stem cells (CSCs) in certain breast cancers, a minor population of cells capable of tumor initiation and metastatic dissemination. Identifying factors that regulate the CSC phenotype is therefore important for developing strategies to treat metastatic disease. The Inhibitor of Differentiation Protein 1 (Id1) and its closely related family member Inhibitor of Differentiation 3 (Id3) (collectively termed Id) are expressed by a diversity of stem cells and are required for metastatic dissemination in experimental models of breast cancer. In this study, we show that ID1 is expressed in rare neoplastic cells within ER-negative breast cancers. To address the function of Id1 expressing cells within tumors, we developed two independent murine models of Triple Negative Breast Cancer (TNBC) in which a genetic reporter permitted the prospective isolation of Id1+ cells. Id1+ cells are enriched for self-renewal in tumorsphere assays in vitro and for tumor initiation in vivo. Conversely, depletion of Id1 and Id3 in the 4T1 murine model of TNBC demonstrates that Id1/3 are required for cell proliferation and self-renewal in vitro, as well as primary tumor growth and metastatic colonization of the lung in vivo. Using combined bioinformatic analysis, we have defined a novel mechanism of Id protein function via negative regulation of the Roundabout Axon Guidance Receptor Homolog 1 (Robo1) leading to activation of a Myc transcriptional programme.

cancer biology↗

The impact of sex on alternative splicing

Over 95% of human genes undergo alternative splicing (AS) in a developmental, tissue-specific, or signal transduction-dependent manner. Here, we present a large-scale survey of sex-biased differential alternative splicing (DAS) across 7027 samples of 39 tissues from 532 individuals (351 males and 181 females) from the Genotype-Tissue Expression project. We detected a total of 1278 statistically significant DAS events affecting 888 different genes and 4417 significant differential gene expression (DGE) events in 3221 genes. Only 267 (29.3%) of the differentially spliced genes were also differentially expressed. Genes that displayed sex-biased DGE or DAS across multiple tissues were enriched in functions related to signaling including histone demethylation. The probability of a gene showing significant differential AS varies by chromosome and is highest for the X chromosome, with differentially spliced X chromosomal genes additionally being more likely to escape X chromosomal inactivation. A small but significant association was found between sex-biased AS and transcripts that undergo physiological nonsense-mediated decay (NMD). We show a significant overlap of differential splicing and genes that display estrogen-induced alternative splicing, that are involved in estrogen response pathway. Further, we show overlap of the involved exons with estrogen-receptor bindings sites. Our results provide a comprehensive survey of sex-biased AS and its characteristics across a large collection of human tissues.

genomics↗

Geometry alone influences stem cell differentiation in a precision 3D printed stem cell niche

Stem cells within epithelial tissues reside in anatomical structures known as crypts that are known to contribute to the mechanical and chemical milieu important for function. To date, epithelial stem cell therapies have largely ignored the niche and focussed solely on the cell population to be transplanted. Our aim was to recreate the precise geometry of the epithelial stem cell niche using two photon polymerisation and to determine the influence of this structure alone on stem cell phenotype. We were able to recreate crypt structures and following cell seeding, a zonation in cell phenotype along the z-axis emerged. This illustrates that geometry alone, without the use of exogenous signalling molecules, influences cell response. Understanding the role of geometry in the regulation of the stem cell niche will enable significant advances in our ability to influence stem cell behaviour to expedite cellular therapies to the clinic.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC=\"FIGDIR/small/489773v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (30K):\norg.highwire.dtl.DTLVardef@152d03eorg.highwire.dtl.DTLVardef@1e5b6ecorg.highwire.dtl.DTLVardef@4c6c02org.highwire.dtl.DTLVardef@14726a5_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

bioengineering↗

Production of very-high-amylose cassava by post-transcriptional silencing of branching enzyme genes

High amylose starch, a desired raw material in the starch industry, can be produced by plants deficient in the function of branching enzymes (BEs). Here we report the production of transgenic cassava plants with starches containing up to 50% amylose due to the constitutive expression of hair-pin dsRNAs targeting the BE1 or BE2 genes. A significant decrease in BE transcripts was confirmed in these transgenic plants by quantitative real-time RT-PCR. The absence of BE1 protein in the BE1-RNAi plant lines (BE1i) and a dramatically lower level of BE2 protein in the BE2-RNAi plant lines (BE2i) were further confirmed by Western blot assays. All transgenic plant lines were grown up in the field, but with reduced biomass production of the above-ground parts and storage roots compared to wild type (WT). Considerably high amylose content in the storage roots of BE2i plant lines was achieved, though not in BE1i plant lines. Storage starch granules of BE1i and BE2i plants had similar morphology as WT, however, the size of BE1i starch granules were bigger than that of WT. Comparisons of amylograms and thermograms of all three sources of storage starches revealed dramatic changes to the pasting properties and a higher melting temperature for BE2i starches. Glucan chain length distribution analysis showed a slight increase in chains of DP>36 in BE1i lines and a dramatic increase in glucan chains between DP 10-20 and DP>40 in BE2i lines, compared to that of WT starch. Furthermore, BE2i starches displayed a B-type X-ray diffraction pattern instead of the A-type pattern found in BE1i and WT starches. Therefore, cassava BE1 and BE2 function differently in storage root starch biosynthesis; silencing of cassava BE1 or BE2 caused various changes to starch physico-chemical properties and amylopectin structure. We also report that remarkably high amylose content in cassava starch has been first obtained in transgenic cassava by silencing of BE2 expression, thus showing a high potential for future industrial utilization.

bioengineering↗

DAZL is a master translational regulator of murine spermatogenesis

Expression of DAZ-like (DAZL) is a hallmark of vertebrate germ cells and essential for embryonic germ cell development and differentiation, yet gametogenic function of DAZL has not been fully characterized with most of its in vivo direct targets unknown. We showed that postnatal stage-specific deletion of Dazl in mouse germ cells did not affect female fertility, but caused complete male sterility with gradual loss of spermatogonial stem cells (SSCs), meiotic arrest and spermatid arrest respectively. Using the genome-wide HITS-CLIP and mass spectrometry approach, we found that DAZL bound to a large number of testicular mRNA transcripts (at least 3008) at 3' UnTranslated Region (3' UTR) and interacted with translation proteins including PABP. In the absence of DAZL, polysome-associated target transcripts, but not their total transcripts were significantly decreased, resulting in drastic reduction of an array of spermatogenic proteins and thus developmental arrest. Thus, DAZL is a master translational regulator essential for spermatogenesis.

developmental biology↗

Miniaturized Permeability Assay for CRISPR-Mediated Genome-Wide Screens

The impermeability of the luminal endothelial cell monolayer is crucial for the normal performance of the vascular and lymphatic systems. A key to this function is the integrity of the monolayers intercellular junctions. The known repertoire of junction-regulating genes is incomplete. Current permeability assays are incompatible with high-throughput genome-wide screens that could identify these genes. To overcome these limitations, we designed a new permeability assay that consists of cell monolayers grown on [~]150 m microcarriers. Each microcarrier functions as a miniature individual assay of permeability (MAP). We demonstrate that false-positive results can be minimized, and that MAP sensitivity to thrombin-induced increase in monolayer permeability is similar to the sensitivity of the measurement of impedance. We validate the assay by showing that the expression of single guide RNAs (sgRNAs) that target genes encoding known thrombin signaling proteins blocks effectively thrombin-induced junction disassembly, and that MAPs carrying such cells can be separated effectively by a fluorescent probe from those that carry cells expressing non-targeting sgRNAs. These results indicate that MAPs are suitable for high-throughput experimentation and for genome-wide screens for genes that mediate the disruptive effect of thrombin on endothelial cell junctions.

cell biology↗

The role of glycaemic, lipid, blood pressure and obesity risk factors as mediators of the effect of height on Coronary Artery Disease and Type 2 Diabetes Mellitus: A Mendelian Randomisation Study.

We investigated by Mendelian Randomisation (MR) analyses the causal effect of adult height on coronary artery disease (CAD) (23,755 cases, 425,339 controls) and type 2 diabetes (T2D) (29,427 cases, 416,908 controls) in UK Biobank. We then examined whether such effects are mediated via known cardiometabolic risk factors including body mass index (BMI), glycaemic traits, lipid levels and blood pressure. We further cross-checked our findings by two-sample MR and Multivariate MR analyses with publicly available summary statistics data. One standard deviation (SD) higher genetically determined height (~6.5 cm) was causally associated with a 14% decrease in CAD risk (OR= 0.86, 95% CI= 0.790.94). This causal association remained significant after performing sensitivity analyses relaxing pleiotropy assumptions. The causal effect of height on CAD risk appeared to be independent of risk factors such as lipid levels, blood pressure and BMI; association was reduced by only 1-3% after adjustment for potential mediators. We observed no direct causal effect of height on T2D risk, once its effect on BMI was taken into account (OR= 0.99, 95% CI= 0.96-1.02), suggesting that BMI acts as a mediator.

genetics↗

Deep Learning on MRI Affirms the Prominence of the Hippocampal Formation in Alzheimer’s Disease Classification

Deep learning techniques on MRI scans have demonstrated great potential to improve the diagnosis of neurological diseases. Here, we investigate the application of 3D deep convolutional neural networks (CNNs) for classifying Alzheimers disease (AD) based on structural MRI data. In particular, we take on two challenges that are under-explored in the literature on deep learning for neuroimaging. First deep neural networks typically require large-scale data that is not always available in medical studies. Therefore, we explore the use of including longitudinal scans in classification studies, greatly increasing the amount of data for training and improving the generalization performance of our classifiers. Moreover, previous studies applying deep learning to classifying Alzheimers disease from neuroimaging have typically addressed classification based on whole brain volumes but stopped short of performing in-depth regional analyses to localize the most predictive areas. Additionally, we show a deep net trained to distinguish between AD and cognitively normal subjects can be applied to classify mild cognitive impairment patients, with classification scores aligning empirically with the likelihood of progression to AD. Our initial results demonstrate both that we can classify AD with an area under the receiver operator characteristic curve (AUROC) of .990 and that we can predict conversion to AD among patients in the MCI subgroup with an AURUC of 0.787. We then localize the predictive regions, by performing both saliency-based interpretation and rigorous slice and lobar level ablation studies. Interestingly, our regional analyses identified the hippocampal formation, including the entorhinal cortex, to be the most predictive region for our models. This finding adds evidence that the hippocampal formation is an anatomical seat of AD and a prominent feature in its diagnosis. Together, the results of this study further demonstrate the potential of deep learning to impact AD classification and to identify ADs structural neuroimaging signatures. The proposed classification and regional analyses methods constitute a general framework that can easily be applied to other disorders and imaging modalities.\n\nAcronyms

neuroscience↗