Autoacetylation of the Ralstonia solanacearum Effector PopP2 Targets a Lysine Residue Essential for RRS1-R-Mediated Immunity in Arabidopsis

Type III effector proteins from bacterial pathogens manipulate components of host immunity to suppress defence responses and promote pathogen development. In plants, host proteins targeted by some effectors called avirulence proteins are surveyed by plant disease resistance proteins referred to as “guards”. The Ralstonia solanacearum effector protein PopP2 triggers immunity in Arabidopsis following its perception by the RRS1-R resistance protein. Here, we show that PopP2 interacts with RRS1-R in the nucleus of living plant cells. PopP2 belongs to the YopJ-like family of cysteine proteases, which share a conserved catalytic triad that includes a highly conserved cysteine residue. The catalytic cysteine mutant PopP2-C321A is impaired in its avirulence activity although it is still able to interact with RRS1-R. In addition, PopP2 prevents proteasomal degradation of RRS1-R, independent of the presence of an integral PopP2 catalytic core. A liquid chromatography/tandem mass spectrometry analysis showed that PopP2 displays acetyl-transferase activity leading to its autoacetylation on a particular lysine residue, which is well conserved among all members of the YopJ family. These data suggest that this lysine residue may correspond to a key binding site for acetyl-coenzyme A required for protein activity. Indeed, mutation of this lysine in PopP2 abolishes RRS1-R-mediated immunity. In agreement with the guard hypothesis, our results favour the idea that activation of the plant immune response by RRS1-R depends not only on the physical interaction between the two proteins but also on its perception of PopP2 enzymatic activity

Systematic Evaluation of Pleiotropy Identifies 6 Further Loci Associated With Coronary Artery Disease

Background: Genome-wide association studies have so far identified 56 loci associated with risk of coronary artery disease (CAD). Many CAD loci show pleiotropy; that is, they are also associated with other diseases or traits. Objectives: This study sought to systematically test if genetic variants identified for non-CAD diseases/traits also associate with CAD and to undertake a comprehensive analysis of the extent of pleiotropy of all CAD loci. Methods: In discovery analyses involving 42,335 CAD cases and 78,240 control subjects we tested the association of 29,383 common (minor allele frequency >5%) single nucleotide polymorphisms available on the exome array, which included a substantial proportion of known or suspected single nucleotide polymorphisms associated with common diseases or traits as of 2011. Suggestive association signals were replicated in an additional 30,533 cases and 42,530 control subjects. To evaluate pleiotropy, we tested CAD loci for association with cardiovascular risk factors (lipid traits, blood pressure phenotypes, body mass index, diabetes, and smoking behavior), as well as with other diseases/traits through interrogation of currently available genome-wide association study catalogs. Results: We identified 6 new loci associated with CAD at genome-wide significance: on 2q37 (KCNJ13-GIGYF2), 6p21 (C2), 11p15 (MRVI1-CTR9), 12q13 (LRP1), 12q24 (SCARB1), and 16q13 (CETP). Risk allele frequencies ranged from 0.15 to 0.86, and odds ratio per copy of the risk allele ranged from 1.04 to 1.09. Of 62 new and known CAD loci, 24 (38.7%) showed statistical association with a traditional cardiovascular risk factor, with some showing multiple associations, and 29 (47%) showed associations at p < 1 × 10−4 with a range of other diseases/traits. Conclusions: We identified 6 loci associated with CAD at genome-wide significance. Several CAD loci show substantial pleiotropy, which may help us understand the mechanisms by which these loci affect CAD risk

Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease. </p

A Low-Frequency Inactivating Akt2 Variant Enriched in the Finnish Population is Associated With Fasting Insulin Levels and Type 2 Diabetes Risk

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting insulin, a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in fasting plasma insulin (FI) levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-hour insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio=1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.Academy of Finland (129293, 128315, 129330, 131593, 139635, 139635, 121584, 126925, 124282, 129378, 258753); Action on Hearing Loss (G51); Ahokas Foundation; American Diabetes Association (#7-12-MN-02); Atlantic Canada Opportunities Agency; Augustinus foundation; Becket foundation; Benzon Foundation; Biomedical Research Council; British Heart Foundation (SP/04/002); Canada Foundation for Innovation; Commission of the European Communities, Directorate C-Public Health (2004310); Copenhagen County; Danish Centre for Evaluation and Health Technology Assessment; Danish Council for Independent Research; Danish Heart Foundation (07-10-R61-A1754-B838-22392F); Danish Medical Research Council; Danish Pharmaceutical Association; Emil Aaltonen Foundation; European Research Council Advanced Research Grant; European Union FP7 (EpiMigrant, 279143; FP7/2007-2013; 259749); Finland's Slottery Machine Association; Finnish Cultural Foundation; Finnish Diabetes Research Foundation; Finnish Foundation for Cardiovascular Research; Finnish Foundation of Cardiovascular Research; Finnish Medical Society; Finnish National Public Health Institute; Finska Läkaresällskapet; Folkhälsan Research Foundation; Foundation for Life and Health in Finland; German Center for Diabetes Research (DZD) ; German Federal Ministry of Education and Research; Health Care Centers in Vasa, Närpes and Korsholm; Health Insurance Foundation (2012B233) ; Helsinki University Central Hospital Research Foundation; Hospital districts of Pirkanmaa, Southern Ostrobothnia, North Ostrobothnia, Central Finland, and Northern Savo; Ib Henriksen foundation; Juho Vainio Foundation; Korea Centers for Disease Control and Prevention (4845–301); Korea National Institute of Health (2012-N73002-00); Li Ka Shing Foundation; Liv och Hälsa; Lundbeck Foundation; Marie-Curie Fellowship (PIEF-GA-2012-329156); Medical Research Council (G0601261, G0900747-91070, G0601966, G0700931); Ministry of Education in Finland; Ministry of Social Affairs and Health in Finland; MRC-PHE Centre for Environment and Health;Municipal Heath Care Center and Hospital in Jakobstad; Närpes Health Care Foundation; National Institute for Health Research (RP-PG-0407-10371); National Institutes of Health (U01 DK085526, U01 DK085501, U01 DK085524, U01 DK085545, U01 DK085584, U01 DK088389, RC2-DK088389, DK085545, DK098032, HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN, R01MH107666 and K12CA139160268201300050C, U01 DK062370, R01 DK066358, U01DK085501, R01HL102830, R01DK073541, PO1AG027734, R01AG046949, 1R01AG042188, P30AG038072, R01 MH101820, R01MH090937, P30DK020595, R01 DK078616, NIDDK K24 DK080140, 1RC2DK088389, T32GM007753); National Medical Research Council; National Research Foundation of Korea (NRF-2012R1A2A1A03006155); Nordic Center of Excellence in Disease Genetics; Novo Nordisk; Ollqvist Foundation; OrionFarmos Research Foundation; Paavo Nurmi Foundation; Perklén Foundation; Samfundet Folkhälsan; Signe and Ane Gyllenberg Foundation; Sigrid Juselius Foundation; Social Insurance Institution of Finland; South East Norway Health Authority (2011060); Swedish Cultural Foundation in Finland; Swedish Heart-Lung Foundation; Swedish Research Council; Swedish Research Council (Linné and Strategic Research Grant); The American Federation for Aging Research; The Einstein Glenn Center; The European Commission (HEALTH-F4-2007-201413); The Finnish Diabetes Association; The Folkhälsan Research Foundation; The Påhlssons Foundation; The provinces of Newfoundland and Labrador, Nova Scotia, and New Brunswick; The Sigrid Juselius Foundation; The Skåne Regional Health Authority; The Swedish Heart-Lung Foundation; Timber Merchant Vilhelm Bang’s Foundation; Turku University Foundation; Uppsala University; Wellcome Trust (064890, 083948, 085475, 086596, 090367, 090532, 092447, 095101/Z/10/Z, 200837/Z/16/Z, 095552, 098017, 098381, 098051, 084723, 072960/2/ 03/2, 086113/Z/08/Z, WT098017, WT064890, WT090532, WT098017, 098051, WT086596/Z/08/A and 086596/Z/08/Z). Detailed acknowledgment of funding sources is provided in the Additional Acknowledgements section of the Supplementary Materials

A saturated map of common genetic variants associated with human height

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes(1). Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel(2)) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants

A saturated map of common genetic variants associated with human height.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries

<it>In silico </it>toxicology models and databases as FDA Critical Path Initiative toolkits

<p>Abstract</p> <p><it>In silico </it>toxicology methods are practical, evidence-based and high throughput, with varying accuracy. <it>In silico </it>approaches are of keen interest, not only to scientists in the private sector and to academic researchers worldwide, but also to the public. They are being increasingly evaluated and applied by regulators. Although there are foreseeable beneficial aspects -- including maximising use of prior test data and the potential for minimising animal use for future toxicity testing -- the primary use of <it>in silico </it>toxicology methods in the pharmaceutical sciences are as decision support information. It is possible for <it>in silico </it>toxicology methods to complement and strengthen the evidence for certain regulatory review processes, and to enhance risk management by supporting a more informed decision regarding priority setting for additional toxicological testing in research and product development. There are also several challenges with these continually evolving methods which clearly must be considered. This mini-review describes <it>in silico </it>methods that have been researched as Critical Path Initiative toolkits for predicting toxicities early in drug development based on prior knowledge derived from preclinical and clinical data at the US Food and Drug Administration, Center for Drug Evaluation and Research.</p