Application of non-HDL cholesterol for population-based cardiovascular risk stratification: results from the Multinational Cardiovascular Risk Consortium.

BACKGROUND: The relevance of blood lipid concentrations to long-term incidence of cardiovascular disease and the relevance of lipid-lowering therapy for cardiovascular disease outcomes is unclear. We investigated the cardiovascular disease risk associated with the full spectrum of bloodstream non-HDL cholesterol concentrations. We also created an easy-to-use tool to estimate the long-term probabilities for a cardiovascular disease event associated with non-HDL cholesterol and modelled its risk reduction by lipid-lowering treatment. METHODS: In this risk-evaluation and risk-modelling study, we used Multinational Cardiovascular Risk Consortium data from 19 countries across Europe, Australia, and North America. Individuals without prevalent cardiovascular disease at baseline and with robust available data on cardiovascular disease outcomes were included. The primary composite endpoint of atherosclerotic cardiovascular disease was defined as the occurrence of the coronary heart disease event or ischaemic stroke. Sex-specific multivariable analyses were computed using non-HDL cholesterol categories according to the European guideline thresholds, adjusted for age, sex, cohort, and classical modifiable cardiovascular risk factors. In a derivation and validation design, we created a tool to estimate the probabilities of a cardiovascular disease event by the age of 75 years, dependent on age, sex, and risk factors, and the associated modelled risk reduction, assuming a 50% reduction of non-HDL cholesterol. FINDINGS: Of the 524 444 individuals in the 44 cohorts in the Consortium database, we identified 398 846 individuals belonging to 38 cohorts (184 055 [48·7%] women; median age 51·0 years [IQR 40·7-59·7]). 199 415 individuals were included in the derivation cohort (91 786 [48·4%] women) and 199 431 (92 269 [49·1%] women) in the validation cohort. During a maximum follow-up of 43·6 years (median 13·5 years, IQR 7·0-20·1), 54 542 cardiovascular endpoints occurred. Incidence curve analyses showed progressively higher 30-year cardiovascular disease event-rates for increasing non-HDL cholesterol categories (from 7·7% for non-HDL cholesterol <2·6 mmol/L to 33·7% for ≥5·7 mmol/L in women and from 12·8% to 43·6% in men; p<0·0001). Multivariable adjusted Cox models with non-HDL cholesterol lower than 2·6 mmol/L as reference showed an increase in the association between non-HDL cholesterol concentration and cardiovascular disease for both sexes (from hazard ratio 1·1, 95% CI 1·0-1·3 for non-HDL cholesterol 2·6 to <3·7 mmol/L to 1·9, 1·6-2·2 for ≥5·7 mmol/L in women and from 1·1, 1·0-1·3 to 2·3, 2·0-2·5 in men). The derived tool allowed the estimation of cardiovascular disease event probabilities specific for non-HDL cholesterol with high comparability between the derivation and validation cohorts as reflected by smooth calibration curves analyses and a root mean square error lower than 1% for the estimated probabilities of cardiovascular disease. A 50% reduction of non-HDL cholesterol concentrations was associated with reduced risk of a cardiovascular disease event by the age of 75 years, and this risk reduction was greater the earlier cholesterol concentrations were reduced. INTERPRETATION: Non-HDL cholesterol concentrations in blood are strongly associated with long-term risk of atherosclerotic cardiovascular disease. We provide a simple tool for individual long-term risk assessment and the potential benefit of early lipid-lowering intervention. These data could be useful for physician-patient communication about primary prevention strategies. FUNDING: EU Framework Programme, UK Medical Research Council, and German Centre for Cardiovascular Research

Mice lacking endoglin in macrophages show an impaired immune response

24 p.-9 fig.-1 tab. Ojeda Fernández, Luisa et al.Endoglin is an auxiliary receptor for members of the TGF-β superfamily and plays an important role in the homeostasis of the vessel wall. Mutations in endoglin gene (ENG) or in the closely related TGF-β receptor type I ACVRL1/ALK1 are responsible for a rare dominant vascular dysplasia, the Hereditary Hemorrhagic Telangiectasia (HHT), or Rendu-OslerWeber syndrome. Endoglin is also expressed in human macrophages, but its role in macrophage function remains unknown. In this work, we show that endoglin expression is triggered during the monocyte-macrophage differentiation process, both in vitro and during the in vivo differentiation of blood monocytes recruited to foci of inflammation in wild-type C57BL/6 mice. To analyze the role of endoglin in macrophages in vivo, an endoglin myeloid lineage specific knock-out mouse line (Engfl/flLysMCre) was generated. These mice show a predisposition to develop spontaneous infections by opportunistic bacteria. Engfl/flLysMCre mice also display increased survival following LPS-induced peritonitis, suggesting a delayed immune response. Phagocytic activity is impaired in peritoneal macrophages, altering one of the main functions of macrophages which contributes to the initiation of the immune response. We also observed altered expression of TGF-β1 target genes in endoglin deficient peritoneal macrophages. Overall, the altered immune activity of endoglin deficient macrophages could help to explain the higher rate of infectious diseases seen in HHT1 patients.This work was funded by: Ministerio de Economía y Competitividad of Spain (SAF2011-23475 to LMB; SAF2013-43421-R and SAF2010- 19222 to CB.Peer reviewe

USH2A is a Meissner’s corpuscle protein necessary for normal vibration sensing in mice and humans

Fingertip mechanoreceptors comprise sensory neuron endings together with specialized skin cells that form the end-organ. Exquisitely sensitive, vibration-sensing neurons are associated with Meissner’s corpuscles in the skin. In the present study, we found that USH2A, a transmembrane protein with a very large extracellular domain, was found in terminal Schwann cells within Meissner’s corpuscles. Pathogenic USH2A mutations cause Usher’s syndrome, associated with hearing loss and visual impairment. We show that patients with biallelic pathogenic USH2A mutations also have clear and specific impairments in vibrotactile touch perception, as do mutant mice lacking USH2A. Forepaw rapidly adapting mechanoreceptors innervating Meissner’s corpuscles, recorded from Ush2a−/− mice, showed large reductions in vibration sensitivity. However, the USH2A protein was not found in sensory neurons. Thus, loss of USH2A in corpuscular end-organs reduced mechanoreceptor sensitivity as well as vibration perception. Thus, a tether-like protein is required to facilitate detection of small-amplitude vibrations essential for the perception of fine-grained tactile surfaces.The present study was funded by grants from the Deutsche Forschungsgemeinshaft (grant nos. SFB665-B6 to G.R.L., SFB1315 to J.F.A.P. and SFB1158-A01 to S.G.L.) and grants from the European Research Council (grant nos. 789128 to G.R.L. and ERC-2015-CoG-682422 to J.F.A.P.). Additional funding was from the Institute of Health Carlos III (Spanish Ministry of Science and Innovation, grant no. FIS PI16/00539 to J.M.).Peer reviewe

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

<p>Abstract</p> <p>Background</p> <p>Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.</p> <p>To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network.</p> <p>Results</p> <p>We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (<it>Simple Expression Ranking</it>). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the <it>Heat Kernel Diffusion Ranking </it>leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%.</p> <p>Conclusion</p> <p>In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype.</p

Integrative methylome-transcriptome analysis unravels cancer cell vulnerabilities in infant MLL-rearranged B cell acute lymphoblastic leukemia

B cell acute lymphoblastic leukemia (B-ALL) is the most common childhood cancer. As predicted by its prenatal origin, infant B-ALL (iB-ALL) shows an exceptionally silent DNA mutational landscape, suggesting that alternative epigenetic mechanisms may substantially contribute to its leukemogenesis. Here, we have integrated genome-wide DNA methylome and transcriptome data from 69 patients with de novo MLL-rearranged leukemia (MLLr) and non-MLLr iB-ALL leukemia uniformly treated according to the Interfant-99/06 protocol. iB-ALL methylome signatures display a plethora of common and specific alterations associated with chromatin states related to enhancer and transcriptional control in normal hematopoietic cells. DNA methylation, gene expression, and gene coexpression network analyses segregated MLLr away from non-MLLr iB-ALL and identified a coordinated and enriched expression of the AP-1 complex members FOS and JUN and RUNX factors in MLLr iB-ALL, consistent with the significant enrichment of hypomethylated CpGs in these genes. Integrative methylome-transcriptome analysis identified consistent cancer cell vulnerabilities, revealed a robust iB-ALL–specific gene expression–correlating dmCpG signature, and confirmed an epigenetic control of AP-1 and RUNX members in reshaping the molecular network of MLLr iB-ALL. Finally, pharmacological inhibition or functional ablation of AP-1 dramatically impaired MLLr-leukemic growth in vitro and in vivo using MLLr-iB-ALL patient–derived xenografts, providing rationale for new therapeutic avenues in MLLr-iB-ALL.We thank CERCA/Generalitat de Catalunya (SGR180) and Fundació Josep Carreras-Obra Social la Caixa for their institutional support. Financial support for this work was obtained from the European Research Council (CoG-2014-646903 and PoC-2018-811220 to PM), the Spanish Ministry of Economy and Competitiveness (SAF-2019-108160-R and SAF2016-76758-R to PM and IV, respectively), the Spanish Association against cancer (AECC-CI-2015 and PROYE18061FERN to CB and MFF), the Fundación Uno entre Cienmil (to PM), the Health Institute Carlos III (ISCIII/FEDER, PI17/01028, PI15/00892, PI18/01527 to CB and AFF/MFF, respectively). We also acknowledge the Plan de Ciencia, Tecnología e Innovación from the Asturias Government cofunding 2018–2022/FEDER (IDI/2018/146to MFF). MFF also acknowledges funding from Fundación General CSIC (0348_CIE_6_E). PM also acknowledges financial support from Fundación Leo Messi. JRT and MV are supported by Juan de la Cierva fellowships by the Spanish Ministry of Science and Innovation (FJCI-2015-26965, IJC2018-36825-I, IJCI-2017-3317) and IUOPA-ISPA-FINBA (The IUOPA is supported by the Obra Social Cajastur-Liberbank, Spain). RTR is supported by a fellowship from the AECC scientific foundation. RFP and PSO are supported by the Severo Ochoa program (BP17-114 and BP17-165, respectively).Peer reviewe

Expert range maps of global mammal distributions harmonised to three taxonomic authorities

Aim: Comprehensive, global information on species' occurrences is an essential biodiversity variable and central to a range of applications in ecology, evolution, biogeography and conservation. Expert range maps often represent a species' only available distributional information and play an increasing role in conservation assessments and macroecology. We provide global range maps for the native ranges of all extant mammal species harmonised to the taxonomy of the Mammal Diversity Database (MDD) mobilised from two sources, the Handbook of the Mammals of the World (HMW) and the Illustrated Checklist of the Mammals of the World (CMW). Location: Global. Taxon: All extant mammal species. Methods: Range maps were digitally interpreted, georeferenced, error-checked and subsequently taxonomically aligned between the HMW (6253 species), the CMW (6431 species) and the MDD taxonomies (6362 species). Results: Range maps can be evaluated and visualised in an online map browser at Map of Life (mol.org) and accessed for individual or batch download for non-commercial use. Main conclusion: Expert maps of species' global distributions are limited in their spatial detail and temporal specificity, but form a useful basis for broad-scale characterizations and model-based integration with other data. We provide georeferenced range maps for the native ranges of all extant mammal species as shapefiles, with species-level metadata and source information packaged together in geodatabase format. Across the three taxonomic sources our maps entail, there are 1784 taxonomic name differences compared to the maps currently available on the IUCN Red List website. The expert maps provided here are harmonised to the MDD taxonomic authority and linked to a community of online tools that will enable transparent future updates and version control.Fil: Marsh, Charles J.. Yale University; Estados UnidosFil: Sica, Yanina. Yale University; Estados UnidosFil: Burguin, Connor. University of New Mexico; Estados UnidosFil: Dorman, Wendy A.. University of Yale; Estados UnidosFil: Anderson, Robert C.. University of Yale; Estados UnidosFil: del Toro Mijares, Isabel. University of Yale; Estados UnidosFil: Vigneron, Jessica G.. University of Yale; Estados UnidosFil: Barve, Vijay. University Of Florida. Florida Museum Of History; Estados UnidosFil: Dombrowik, Victoria L.. University of Yale; Estados UnidosFil: Duong, Michelle. University of Yale; Estados UnidosFil: Guralnick, Robert. University Of Florida. Florida Museum Of History; Estados UnidosFil: Hart, Julie A.. University of Yale; Estados UnidosFil: Maypole, J. Krish. University of Yale; Estados UnidosFil: McCall, Kira. University of Yale; Estados UnidosFil: Ranipeta, Ajay. University of Yale; Estados UnidosFil: Schuerkmann, Anna. University of Yale; Estados UnidosFil: Torselli, Michael A.. University of Yale; Estados UnidosFil: Lacher, Thomas. Texas A&M University; Estados UnidosFil: Wilson, Don E.. National Museum of Natural History; Estados UnidosFil: Abba, Agustin Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; ArgentinaFil: Aguirre, Luis F.. Universidad Mayor de San Simón; BoliviaFil: Arroyo Cabrales, Joaquín. Instituto Nacional de Antropología E Historia, Mexico; MéxicoFil: Astúa, Diego. Universidade Federal de Pernambuco; BrasilFil: Baker, Andrew M.. Queensland University of Technology; Australia. Queensland Museum; AustraliaFil: Braulik, Gill. University of St. Andrews; Reino UnidoFil: Braun, Janet K.. Oklahoma State University; Estados UnidosFil: Brito, Jorge. Instituto Nacional de Biodiversidad; EcuadorFil: Busher, Peter E.. Boston University; Estados UnidosFil: Burneo, Santiago F.. Pontificia Universidad Católica del Ecuador; EcuadorFil: Camacho, M. Alejandra. Pontificia Universidad Católica del Ecuador; EcuadorFil: de Almeida Chiquito, Elisandra. Universidade Federal do Espírito Santo; BrasilFil: Cook, Joseph A.. University of New Mexico; Estados UnidosFil: Cuéllar Soto, Erika. Sultan Qaboos University; OmánFil: Davenport, Tim R. B.. Wildlife Conservation Society; TanzaniaFil: Denys, Christiane. Muséum National d'Histoire Naturelle; FranciaFil: Dickman, Christopher R.. The University Of Sydney; AustraliaFil: Eldridge, Mark D. B.. Australian Museum; AustraliaFil: Fernandez Duque, Eduardo. University of Yale; Estados UnidosFil: Francis, Charles M.. Environment And Climate Change Canada; CanadáFil: Frankham, Greta. Australian Museum; AustraliaFil: Freitas, Thales. Universidade Federal do Rio Grande do Sul; BrasilFil: Friend, J. Anthony. Conservation And Attractions; AustraliaFil: Giannini, Norberto Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - Tucumán. Unidad Ejecutora Lillo; ArgentinaFil: Gursky-Doyen, Sharon. Texas A&M University; Estados UnidosFil: Hackländer, Klaus. Universitat Fur Bodenkultur Wien; AustriaFil: Hawkins, Melissa. National Museum of Natural History; Estados UnidosFil: Helgen, Kristofer M.. Australian Museum; AustraliaFil: Heritage, Steven. University of Duke; Estados UnidosFil: Hinckley, Arlo. Consejo Superior de Investigaciones Científicas. Estación Biológica de Doñana; EspañaFil: Holden, Mary. American Museum of Natural History; Estados UnidosFil: Holekamp, Kay E.. Michigan State University; Estados UnidosFil: Humle, Tatyana. University Of Kent; Reino UnidoFil: Ibáñez Ulargui, Carlos. Consejo Superior de Investigaciones Científicas. Estación Biológica de Doñana; EspañaFil: Jackson, Stephen M.. Australian Museum; AustraliaFil: Janecka, Mary. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Jenkins, Paula. Natural History Museum; Reino UnidoFil: Juste, Javier. Consejo Superior de Investigaciones Científicas. Estación Biológica de Doñana; EspañaFil: Leite, Yuri L. R.. Universidade Federal do Espírito Santo; BrasilFil: Novaes, Roberto Leonan M.. Universidade Federal do Rio de Janeiro; BrasilFil: Lim, Burton K.. Royal Ontario Museum; CanadáFil: Maisels, Fiona G.. Wildlife Conservation Society; Estados UnidosFil: Mares, Michael A.. Oklahoma State University; Estados UnidosFil: Marsh, Helene. James Cook University; AustraliaFil: Mattioli, Stefano. Università degli Studi di Siena; ItaliaFil: Morton, F. Blake. University of Hull; Reino UnidoFil: Ojeda, Agustina Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; ArgentinaFil: Ordóñez Garza, Nicté. Instituto Nacional de Biodiversidad; EcuadorFil: Pardiñas, Ulises Francisco J.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; ArgentinaFil: Pavan, Mariana. Universidade de Sao Paulo; BrasilFil: Riley, Erin P.. San Diego State University; Estados UnidosFil: Rubenstein, Daniel I.. University of Princeton; Estados UnidosFil: Ruelas, Dennisse. Museo de Historia Natural, Lima; PerúFil: Schai-Braun, Stéphanie. Universitat Fur Bodenkultur Wien; AustriaFil: Schank, Cody J.. University of Texas at Austin; Estados UnidosFil: Shenbrot, Georgy. Ben Gurion University of the Negev; IsraelFil: Solari, Sergio. Universidad de Antioquia; ColombiaFil: Superina, Mariella. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Tsang, Susan. American Museum of Natural History; Estados UnidosFil: Van Cakenberghe, Victor. Universiteit Antwerp; BélgicaFil: Veron, Geraldine. Université Pierre et Marie Curie; FranciaFil: Wallis, Janette. Kasokwa-kityedo Forest Project; UgandaFil: Whittaker, Danielle. Michigan State University; Estados UnidosFil: Wells, Rod. Flinders University.; AustraliaFil: Wittemyer, George. State University of Colorado - Fort Collins; Estados UnidosFil: Woinarski, John. Charles Darwin University; AustraliaFil: Upham, Nathan S.. University of Yale; Estados UnidosFil: Jetz, Walter. University of Yale; Estados Unido

Genome-wide and gene-centric analyses of circulating myeloperoxidase levels in the charge and care consortia

Increased systemic levels of myeloperoxidase (MPO) are associated with the risk of coronary artery disease (CAD). To identify the genetic factors that are associated with circulating MPO levels, we carried out a genome-wide association study (GWAS) and a gene-centric analysis in subjects of European ancestry and African Americans (AAs). A locus on chromosome 1q31.1 containing the complement factor H (CFH) gene was strongly associated with serum MPO levels in 9305 subjects of European ancestry (lead SNP rs800292; P = 4.89 × 10−41) and in 1690 AA subjects (rs505102; P = 1.05 × 10−8). Gene-centric analyses in 8335 subjects of European ancestry additionally identified two rare MPO coding sequence variants that were associated with serum MPO levels (rs28730837, P = 5.21 × 10−12; rs35897051, P = 3.32 × 10−8). A GWAS for plasma MPO levels in 9260 European ancestry subjects identified a chromosome 17q22 region near MPO that was significantly associated (lead SNP rs6503905; P = 2.94 × 10−12), but the CFH locus did not exhibit evidence of association with plasma MPO levels. Functional analyses revealed that rs800292 was associated with levels of complement proteins in serum. Variants at chromosome 17q22 also had pleiotropic cis effects on gene expression. In a case–control analysis of ∼80 000 subjects from CARDIoGRAM, none of the identified single-nucleotide polymorphisms (SNPs) were associated with CAD. These results suggest that distinct genetic factors regulate serum and plasma MPO levels, which may have relevance for various acute and chronic inflammatory disorders. The clinical implications for CAD and a better understanding of the functional basis for the association of CFH and MPO variants with circulating MPO levels require further study