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Different developmental histories of beta-cells generate functional and proliferative heterogeneity during islet growth.
The proliferative and functional heterogeneity among seemingly uniform cells is a universal phenomenon. Identifying the underlying factors requires single-cell analysis of function and proliferation. Here we show that the pancreatic beta-cells in zebrafish exhibit different growth-promoting and functional properties, which in part reflect differences in the time elapsed since birth of the cells. Calcium imaging shows that the beta-cells in the embryonic islet become functional during early zebrafish development. At later stages, younger beta-cells join the islet following differentiation from post-embryonic progenitors. Notably, the older and younger beta-cells occupy different regions within the islet, which generates topological asymmetries in glucose responsiveness and proliferation. Specifically, the older beta-cells exhibit robust glucose responsiveness, whereas younger beta-cells are more proliferative but less functional. As the islet approaches its mature state, heterogeneity diminishes and beta-cells synchronize function and proliferation. Our work illustrates a dynamic model of heterogeneity based on evolving proliferative and functional beta-cell states.Βeta-cells have recently been shown to be heterogeneous with regard to morphology and function. Here, the authors show that β-cells in zebrafish switch from proliferative to functional states with increasing time since β-cell birth, leading to functional and proliferative heterogeneity.
Asterias: a parallelized web-based suite for the analysis of expression and aCGH data.
The analysis of expression and CGH arrays plays a central role in the study of complex diseases, especially cancer, including finding markers for early diagnosis and prognosis, choosing an optimal therapy, or increasing our understanding of cancer development and metastasis. Asterias (http://www.asterias.info) is an integrated collection of freely-accessible web tools for the analysis of gene expression and aCGH data. Most of the tools use parallel computing (via MPI) and run on a server with 60 CPUs for computation; compared to a desktop or server-based but not parallelized application, parallelization provides speed ups of factors up to 50. Most of our applications allow the user to obtain additional information for user-selected genes (chromosomal location, PubMed ids, Gene Ontology terms, etc.) by using clickable links in tables and/or figures. Our tools include: normalization of expression and aCGH data (DNMAD); converting between different types of gene/clone and protein identifiers (IDconverter/IDClight); filtering and imputation (preP); finding differentially expressed genes related to patient class and survival data (Pomelo II); searching for models of class prediction (Tnasas); using random forests to search for minimal models for class prediction or for large subsets of genes with predictive capacity (GeneSrF); searching for molecular signatures and predictive genes with survival data (SignS); detecting regions of genomic DNA gain or loss (ADaCGH). The capability to send results between different applications, access to additional functional information, and parallelized computation make our suite unique and exploit features only available to web-based applications.
Network inference in matrix-variate Gaussian models with non-independent noise
Inferring a graphical model or network from observational data from a large number of variables is a well studied problem in machine learning and computational statistics. In this paper we consider a version of this problem that is relevant to the analysis of multiple phenotypes collected in genetic studies. In such datasets we expect correlations between phenotypes and between individuals. We model observations as a sum of two matrix normal variates such that the joint covariance function is a sum of Kronecker products. This model, which generalizes the Graphical Lasso, assumes observations are correlated due to known genetic relationships and corrupted with non-independent noise. We have developed a computationally efficient EM algorithm to fit this model. On simulated datasets we illustrate substantially improved performance in network reconstruction by allowing for a general noise distribution.
Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells.
Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14+ monocytes, CD16+ neutrophils, and naive CD4+ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.
eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data.
Epigenome-wide association studies (EWAS) provide an alternative approach for studying human disease through consideration of non-genetic variants such as altered DNA methylation. To advance the complex interpretation of EWAS, we developed eFORGE (http://eforge.cs.ucl.ac.uk/), a new standalone and web-based tool for the analysis and interpretation of EWAS data. eFORGE determines the cell type-specific regulatory component of a set of EWAS-identified differentially methylated positions. This is achieved by detecting enrichment of overlap with DNase I hypersensitive sites across 454 samples (tissues, primary cell types, and cell lines) from the ENCODE, Roadmap Epigenomics, and BLUEPRINT projects. Application of eFORGE to 20 publicly available EWAS datasets identified disease-relevant cell types for several common diseases, a stem cell-like signature in cancer, and demonstrated the ability to detect cell-composition effects for EWAS performed on heterogeneous tissues. Our approach bridges the gap between large-scale epigenomics data and EWAS-derived target selection to yield insight into disease etiology.
Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.
Large-scale whole-genome sequence data sets offer novel opportunities to identify genetic variation underlying human traits. Here we apply genotype imputation based on whole-genome sequence data from the UK10K and 1000 Genomes Project into 35,981 study participants of European ancestry, followed by association analysis with 20 quantitative cardiometabolic and hematological traits. We describe 17 new associations, including 6 rare (minor allele frequency (MAF) < 1%) or low-frequency (1% < MAF < 5%) variants with platelet count (PLT), red blood cell indices (MCH and MCV) and HDL cholesterol. Applying fine-mapping analysis to 233 known and new loci associated with the 20 traits, we resolve the associations of 59 loci to credible sets of 20 or fewer variants and describe trait enrichments within regions of predicted regulatory function. These findings improve understanding of the allelic architecture of risk factors for cardiometabolic and hematological diseases and provide additional functional insights with the identification of potentially novel biological targets.
The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease.
Many common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal.
A rare variant in APOC3 is associated with plasma triglyceride and VLDL levels in Europeans.
The analysis of rich catalogues of genetic variation from population-based sequencing provides an opportunity to screen for functional effects. Here we report a rare variant in APOC3 (rs138326449-A, minor allele frequency ~0.25% (UK)) associated with plasma triglyceride (TG) levels (-1.43 s.d. (s.e.=0.27 per minor allele (P-value=8.0 × 10(-8))) discovered in 3,202 individuals with low read-depth, whole-genome sequence. We replicate this in 12,831 participants from five additional samples of Northern and Southern European origin (-1.0 s.d. (s.e.=0.173), P-value=7.32 × 10(-9)). This is consistent with an effect between 0.5 and 1.5 mmol l(-1) dependent on population. We show that a single predicted splice donor variant is responsible for association signals and is independent of known common variants. Analyses suggest an independent relationship between rs138326449 and high-density lipoprotein (HDL) levels. This represents one of the first examples of a rare, large effect variant identified from whole-genome sequencing at a population scale.
MultiMeta: an R package for meta-analyzing multi-phenotype genome-wide association studies.
UNLABELLED: As new methods for multivariate analysis of genome wide association studies become available, it is important to be able to combine results from different cohorts in a meta-analysis. The R package MultiMeta provides an implementation of the inverse-variance-based method for meta-analysis, generalized to an n-dimensional setting. AVAILABILITY AND IMPLEMENTATION: The R package MultiMeta can be downloaded from CRAN. CONTACT: dragana.vuckovic@burlo.trieste.it; vi1@sanger.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
The UK10K project identifies rare variants in health and disease.
The contribution of rare and low-frequency variants to human traits is largely unexplored. Here we describe insights from sequencing whole genomes (low read depth, 7×) or exomes (high read depth, 80×) of nearly 10,000 individuals from population-based and disease collections. In extensively phenotyped cohorts we characterize over 24 million novel sequence variants, generate a highly accurate imputation reference panel and identify novel alleles associated with levels of triglycerides (APOB), adiponectin (ADIPOQ) and low-density lipoprotein cholesterol (LDLR and RGAG1) from single-marker and rare variant aggregation tests. We describe population structure and functional annotation of rare and low-frequency variants, use the data to estimate the benefits of sequencing for association studies, and summarize lessons from disease-specific collections. Finally, we make available an extensive resource, including individual-level genetic and phenotypic data and web-based tools to facilitate the exploration of association results.
Significant impact of miRNA-target gene networks on genetics of human complex traits.
The impact of microRNA (miRNA) on the genetics of human complex traits, especially in the context of miRNA-target gene networks, has not been fully assessed. Here, we developed a novel analytical method, MIGWAS, to comprehensively evaluate enrichment of genome-wide association study (GWAS) signals in miRNA-target gene networks. We applied the method to the GWAS results of the 18 human complex traits from >1.75 million subjects, and identified significant enrichment in rheumatoid arthritis (RA), kidney function, and adult height (P < 0.05/18 = 0.0028, most significant enrichment in RA with P = 1.7 × 10(-4)). Interestingly, these results were consistent with current literature-based knowledge of the traits on miRNA obtained through the NCBI PubMed database search (adjusted P = 0.024). Our method provided a list of miRNA and target gene pairs with excess genetic association signals, part of which included drug target genes. We identified a miRNA (miR-4728-5p) that downregulates PADI2, a novel RA risk gene considered as a promising therapeutic target (rs761426, adjusted P = 2.3 × 10(-9)). Our study indicated the significant impact of miRNA-target gene networks on the genetics of human complex traits, and provided resources which should contribute to drug discovery and nucleic acid medicine.
ATRX dysfunction induces replication defects in primary mouse cells.
The chromatin remodeling protein ATRX, which targets tandem repetitive DNA, has been shown to be required for expression of the alpha globin genes, for proliferation of a variety of cellular progenitors, for chromosome congression and for the maintenance of telomeres. Mutations in ATRX have recently been identified in tumours which maintain their telomeres by a telomerase independent pathway involving homologous recombination thought to be triggered by DNA damage. It is as yet unknown whether there is a central underlying mechanism associated with ATRX dysfunction which can explain the numerous cellular phenomena observed. There is, however, growing evidence for its role in the replication of various repetitive DNA templates which are thought to have a propensity to form secondary structures. Using a mouse knockout model we demonstrate that ATRX plays a direct role in facilitating DNA replication. Ablation of ATRX alone, although leading to a DNA damage response at telomeres, is not sufficient to trigger the alternative lengthening of telomere pathway in mouse embryonic stem cells.
The chromatin remodeller ATRX: a repeat offender in human disease.
The regulation of chromatin structure is of paramount importance for a variety of fundamental nuclear processes, including gene expression, DNA repair, replication, and recombination. The ATP-dependent chromatin-remodelling factor ATRX (α thalassaemia/mental retardation X-linked) has emerged as a key player in each of these processes. Exciting recent developments suggest that ATRX plays a variety of key roles at tandem repeat sequences within the genome, including the deposition of a histone variant, prevention of replication fork stalling, and the suppression of a homologous recombination-based pathway of telomere maintenance. Here, we provide a mechanistic overview of the role of ATRX in each of these processes, and propose how they may be connected to give rise to seemingly disparate human diseases.
Nucleosome remodeling and transcriptional repression are distinct functions of Isw1 in Saccharomyces cerevisiae.
The SANT domain is a nucleosome recognition module found in transcriptional regulatory proteins, including chromatin-modifying enzymes. It shows high functional degeneracy between species, varying in sequence and copy number. Here, we investigate functions in vivo associated with two SANT motifs, SANT and SLIDE, in the Saccharomyces cerevisiae Isw1 chromatin-remodeling ATPase. We show that differences in the primary structures of the SANT and SLIDE domains in yeast and Drosophila melanogaster reflect their different functions. In yeast, the SLIDE domain is required for histone interactions, while this is a function of the SANT domain in flies. In yeast, both motifs are required for optimal association with chromatin and for formation of the Isw1b complex (Isw1, Ioc2, and Ioc4). Moreover, nucleosome remodeling at the MET16 locus is defective in strains lacking the SANT or SLIDE domain. In contrast, the SANT domain is dispensable for the interaction between Isw1 and Ioc3 in the Isw1a complex. We show that, although defective in nucleosome remodeling, Isw1 lacking the SANT domain is able to repress transcription initiation at the MET16 promoter. Thus, chromatin remodeling and transcriptional repression are distinct activities of Isw1.
14-3-3 interaction with histone H3 involves a dual modification pattern of phosphoacetylation.
Histone modifications occur in precise patterns and are proposed to signal the recruitment of effector molecules that profoundly impact chromatin structure, gene regulation, and cell cycle events. The linked modifications serine 10 phosphorylation and lysine 14 acetylation on histone H3 (H3S10phK14ac), modifications conserved from Saccharomyces cerevisiae to humans, are crucial for transcriptional activation of many genes. However, the mechanism of H3S10phK14ac involvement in these processes is unclear. To shed light on the role of this dual modification, we utilized H3 peptide affinity assays to identify H3S10phK14ac-interacting proteins. We found that the interaction of the known phospho-binding 14-3-3 proteins with H3 is dependent on the presence of both of these marks, not just phosphorylation alone. This is true of mammalian 14-3-3 proteins as well as the yeast homologues Bmh1 and Bmh2. The importance of acetylation in this interaction is also seen in vivo, where K14 acetylation is required for optimal Bmh1 recruitment to the GAL1 promoter during transcriptional activation.