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Neurocomic
Nonfiction graphic novel explaining the physiology of the brain and describing theoretical and experimental developments that led to our present understanding.
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Rabbit genome analysis reveals a polygenic basis for phenotypic change during domestication
The genetic changes underlying the initial steps of animal domestication are still poorly understood. We generated a high-quality reference genome for the rabbit and compared it to resequencing data from populations of wild and domestic rabbits. We identified more than 100 selective sweeps specific to domestic rabbits but only a relatively small number of fixed (or nearly fixed) single-nucleotide polymorphisms (SNPs) for derived alleles. SNPs with marked allele frequency differences between wild and domestic rabbits were enriched for conserved noncoding sites. Enrichment analyses suggest that genes affecting brain and neuronal development have often been targeted during domestication. We propose that because of a truly complex genetic background, tame behavior in rabbits and other domestic animals evolved by shifts in allele frequencies at many loci, rather than by critical changes at only a few domestication loci.
Journal Article
Roscovitine Worsens Mycobacterium abscessus Infection by Reducing DUOX2-mediated Neutrophil Response
Persistent neutrophilic inflammation associated with chronic pulmonary infection causes progressive lung injury and, eventually, death in individuals with cystic fibrosis (CF), a genetic disease caused by biallelic mutations in the CF transmembrane conductance regulator (CFTR) gene. Therefore, we examined whether roscovitine, a cyclin-dependent kinase inhibitor that (in other conditions) reduces inflammation while promoting host defense, might provide a beneficial effect in the context of CF. Herein, using CFTR-depleted zebrafish larvae as an innovative vertebrate model of CF immunopathophysiology, combined with murine and human approaches, we sought to determine the effects of roscovitine on innate immune responses to tissue injury and pathogens in the CF condition. We show that roscovitine exerts antiinflammatory and proresolution effects in neutrophilic inflammation induced by infection or tail amputation in zebrafish. Roscovitine reduces overactive epithelial reactive oxygen species (ROS)-mediated neutrophil trafficking by reducing DUOX2/NADPH-oxidase activity and accelerates inflammation resolution by inducing neutrophil apoptosis and reverse migration. It is important to note that, although roscovitine efficiently enhances intracellular bacterial killing of Mycobacterium abscessus in human CF macrophages ex vivo, we found that treatment with roscovitine results in worse infection in mouse and zebrafish models. By interfering with DUOX2/ NADPH oxidase-dependent ROS production, roscovitine reduces the number of neutrophils at infection sites and, consequently, compromises granuloma formation and maintenance, favoring extracellular multiplication of M. abscessus and more severe infection. Our findings bring important new understanding of the immune-targeted action of roscovitine and have significant therapeutic implications for safely targeting inflammation in CF. Copyright
Journal Article
Lifelong Reduction of LDL-Cholesterol Related to a Common Variant in the LDL-Receptor Gene Decreases the Risk of Coronary Artery Disease—A Mendelian Randomisation Study
Rare mutations of the low-density lipoprotein receptor gene (LDLR) cause familial hypercholesterolemia, which increases the risk for coronary artery disease (CAD). Less is known about the implications of common genetic variation in the LDLR gene regarding the variability of cholesterol levels and risk of CAD.
Imputed genotype data at the LDLR locus on 1 644 individuals of a population-based sample were explored for association with LDL-C level. Replication of association with LDL-C level was sought for the most significant single nucleotide polymorphism (SNP) within the LDLR gene in three European samples comprising 6 642 adults and 533 children. Association of this SNP with CAD was examined in six case-control studies involving more than 15 000 individuals.
Each copy of the minor T allele of SNP rs2228671 within LDLR (frequency 11%) was related to a decrease of LDL-C levels by 0.19 mmol/L (95% confidence interval (CI) [0.13-0.24] mmol/L, p = 1.5x10(-10)). This association with LDL-C was uniformly found in children, men, and women of all samples studied. In parallel, the T allele of rs2228671 was associated with a significantly lower risk of CAD (Odds Ratio per copy of the T allele: 0.82, 95% CI [0.76-0.89], p = 2.1x10(-7)). Adjustment for LDL-C levels by logistic regression or Mendelian Randomisation models abolished the significant association between rs2228671 with CAD completely, indicating a functional link between the genetic variant at the LDLR gene locus, change in LDL-C and risk of CAD.
A common variant at the LDLR gene locus affects LDL-C levels and, thereby, the risk for CAD.
Journal Article
Modulation of innate immune signalling by lipid-mediated MAVS transmembrane domain oligomerization
RIG-I-like receptors detect viral RNA in infected cells and promote oligomerization of the outer mitochondrial membrane protein MAVS to induce innate immunity to viral infection through type I interferon production. Mitochondrial reactive oxygen species (mROS) have been shown to enhance anti-viral MAVS signalling, but the mechanisms have remained obscure. Using a biochemical oligomerization-reporter fused to the transmembrane domain of MAVS, we found that mROS inducers promoted lipid-dependent MAVS transmembrane domain oligomerization in the plane of the outer mitochondrial membrane. These events were mirrored by Sendai virus infection, which similarly induced lipid peroxidation and promoted lipid-dependent MAVS transmembrane domain oligomerization. Our observations point to a role for mROS-induced changes in lipid bilayer properties in modulating antiviral innate signalling by favouring the oligomerization of MAVS transmembrane domain in the outer-mitochondrial membrane.
Journal Article
A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies
Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call 'trait prioritized sub-networks.' As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn's disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn's disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses.
Journal Article
Histopathology Reveals Correlative and Unique Phenotypes in a High-Throughput Mouse Phenotyping Screen
The Mouse Genetics Project (MGP) at the Wellcome Trust Sanger Institute aims to generate and phenotype over 800 genetically modified mouse lines over the next 5 years to gain a better understanding of mammalian gene function, and provide an invaluable resource to the scientific community for follow up studies. Phenotyping includes generation of a standardized biobank of paraffin embedded tissues for each mouse line, but histopathology is not routinely performed. In collaboration with the Pathology Core of the Centre for Modeling Human Disease (CMHD) we report the utility of histopathology in a high-throughput primary phenotyping screen. Histopathology was assessed in an unbiased selection of 50 mouse lines with (n=30) or without (n=20) clinical phenotypes detected by the standard MGP primary phenotyping screen. Our findings revealed that histopathology added correlating morphological data in 19 of 30 lines (63.3%) in which the primary screen detected a phenotype. In addition, 7 of the 50 lines (14%) presented significant histopathology findings that were not associated with or predicted by the standard primary screen. Three of these 7 lines had no clinical phenotype detected by the standard primary screen. Incidental and strain-associated background lesions were present in all mutant lines with good concordance to wild type controls. These findings demonstrate the complementary and unique contribution of histopathology to high throughput primary phenotyping of mutant mice.
Journal Article
Common Genetic Variation Near the Phospholamban Gene Is Associated with Cardiac Repolarisation: Meta-Analysis of Three Genome-Wide Association Studies
To identify loci affecting the electrocardiographic QT interval, a measure of cardiac repolarisation associated with risk of ventricular arrhythmias and sudden cardiac death, we conducted a meta-analysis of three genome-wide association studies (GWAS) including 3,558 subjects from the TwinsUK and BRIGHT cohorts in the UK and the DCCT/EDIC cohort from North America. Five loci were significantly associated with QT interval at P<1x10(-6). To validate these findings we performed an in silico comparison with data from two QT consortia: QTSCD (n = 15,842) and QTGEN (n = 13,685). Analysis confirmed the association between common variants near NOS1AP (P = 1.4x10(-83)) and the phospholamban (PLN) gene (P = 1.9x10(-29)). The most associated SNP near NOS1AP (rs12143842) explains 0.82% variance; the SNP near PLN (rs11153730) explains 0.74% variance of QT interval duration. We found no evidence for interaction between these two SNPs (P = 0.99). PLN is a key regulator of cardiac diastolic function and is involved in regulating intracellular calcium cycling, it has only recently been identified as a susceptibility locus for QT interval. These data offer further mechanistic insights into genetic influence on the QT interval which may predispose to life threatening arrhythmias and sudden cardiac death.
Journal Article
A Bayesian Method for Detecting and Characterizing Allelic Heterogeneity and Boosting Signals in Genome-Wide Association Studies
The standard paradigm for the analysis of genome-wide association studies involves carrying out association tests at both typed and imputed SNPs. These methods will not be optimal for detecting the signal of association at SNPs that are not currently known or in regions where allelic heterogeneity occurs. We propose a novel association test, complementary to the SNP-based approaches, that attempts to extract further signals of association by explicitly modeling and estimating both unknown SNPs and allelic heterogeneity at a locus. At each site we estimate the genealogy of the case-control sample by taking advantage of the HapMap haplotypes across the genome. Allelic heterogeneity is modeled by allowing more than one mutation on the branches of the genealogy. Our use of Bayesian methods allows us to assess directly the evidence for a causative SNP not well correlated with known SNPs and for allelic heterogeneity at each locus. Using simulated data and real data from the WTCCC project, we show that our method (i) produces a significant boost in signal and accurately identifies the form of the allelic heterogeneity in regions where it is known to exist, (ii) can suggest new signals that are not found by testing typed or imputed SNPs and (iii) can provide more accurate estimates of effect sizes in regions of association.
Journal Article
Hypothesis-Based Analysis of Gene-Gene Interactions and Risk of Myocardial Infarction
The genetic loci that have been found by genome-wide association studies to modulate risk of coronary heart disease explain only a fraction of its total variance, and gene-gene interactions have been proposed as a potential source of the remaining heritability. Given the potentially large testing burden, we sought to enrich our search space with real interactions by analyzing variants that may be more likely to interact on the basis of two distinct hypotheses: a biological hypothesis, under which MI risk is modulated by interactions between variants that are known to be relevant for its risk factors; and a statistical hypothesis, under which interacting variants individually show weak marginal association with MI. In a discovery sample of 2,967 cases of early-onset myocardial infarction (MI) and 3,075 controls from the MIGen study, we performed pair-wise SNP interaction testing using a logistic regression framework. Despite having reasonable power to detect interaction effects of plausible magnitudes, we observed no statistically significant evidence of interaction under these hypotheses, and no clear consistency between the top results in our discovery sample and those in a large validation sample of 1,766 cases of coronary heart disease and 2,938 controls from the Wellcome Trust Case-Control Consortium. Our results do not support the existence of strong interaction effects as a common risk factor for MI. Within the scope of the hypotheses we have explored, this study places a modest upper limit on the magnitude that epistatic risk effects are likely to have at the population level (odds ratio for MI risk 1.3-2.0, depending on allele frequency and interaction model).
Journal Article