S-adenosylmethionine Levels Regulate the Schwann Cell DNA Methylome

SummaryAxonal myelination is essential for rapid saltatory impulse conduction in the nervous system, and malformation or destruction of myelin sheaths leads to motor and sensory disabilities. DNA methylation is an essential epigenetic modification during mammalian development, yet its role in myelination remains obscure. Here, using high-resolution methylome maps, we show that DNA methylation could play a key gene regulatory role in peripheral nerve myelination and that S-adenosylmethionine (SAMe), the principal methyl donor in cytosine methylation, regulates the methylome dynamics during this process. Our studies also point to a possible role of SAMe in establishing the aberrant DNA methylation patterns in a mouse model of diabetic neuropathy, implicating SAMe in the pathogenesis of this disease. These critical observations establish a link between SAMe and DNA methylation status in a defined biological system, providing a mechanism that could direct methylation changes during cellular differentiation and in diverse pathological situations

The L-Alpha-Lysophosphatidylinositol/G Protein-Coupled Receptor 55 System Induces the Development of Nonalcoholic Steatosis and Steatohepatitis

Background and Aims G protein-coupled receptor (GPR) 55 is a putative cannabinoid receptor, and l-alpha-lysophosphatidylinositol (LPI) is its only known endogenous ligand. Although GPR55 has been linked to energy homeostasis in different organs, its specific role in lipid metabolism in the liver and its contribution to the pathophysiology of nonalcoholic fatty liver disease (NAFLD) remains unknown. Approach and Results We measured (1) GPR55 expression in the liver of patients with NAFLD compared with individuals without obesity and without liver disease, as well as animal models with steatosis and nonalcoholic steatohepatitis (NASH), and (2) the effects of LPI and genetic disruption of GPR55 in mice, human hepatocytes, and human hepatic stellate cells. Notably, we found that circulating LPI and liver expression of GPR55 were up-regulated in patients with NASH. LPI induced adenosine monophosphate-activated protein kinase activation of acetyl-coenzyme A carboxylase (ACC) and increased lipid content in human hepatocytes and in the liver of treated mice by inducing de novo lipogenesis and decreasing beta-oxidation. The inhibition of GPR55 and ACC alpha blocked the effects of LPI, and the in vivo knockdown of GPR55 was sufficient to improve liver damage in mice fed a high-fat diet and in mice fed a methionine-choline-deficient diet. Finally, LPI promoted the initiation of hepatic stellate cell activation by stimulating GPR55 and activation of ACC. Conclusions The LPI/GPR55 system plays a role in the development of NAFLD and NASH by activating ACC.Supported by grants from the Fondo Europeo de Desarrollo Regional (FEDER)/Ministerio de Ciencia, Innovacion y Universidades (MCIU)/Agencia Estatal de Investigacion (AEI) (C.D.: BFU2017-87721; M.L.: RTI2018-101840-B-I00; R.N.: BFU2015-70664R; A.G.-R.: PI16/00823; C.G.-M.: PI17/00535), Xunta de Galicia (M.L.: 2015-CP079 and 2016-PG068; R.N.: 2015-CP080 and 2016-PG057), Fundacion Banco Bilbao Vizcaya Argentaria (BBVA; to R.N.), Fundacion Atresmedia (M.L. and R.N.), European Foundation for the Study of Diabetes (R. N.), and Fundacion Francisco Cobos (A.G.-R.). MCIU/AEI/FEDER, European Union, (RTI2018-095134-B-100 to P.A.) provided aid to support the research groups of Sistema Universitario Vasco (IT971-16 to P. A). MCIU provided SAF2017-87301-R and RTI2018-096759-1-100, which were integrated into the Plan Estatal de Investigacion Cientifica y Tecnica e Innovacion and were cofinanced with FEDER (to M.L.M.-C. and T.C. D. respectively), and La Caixa Foundation Program and 2018 Fundacion BBVA Grants for Scientific Research Teams (to M.L.M.-C.). The research leading to these results has also received funding from the European Community's H2020 Framework Programme under the following grant: European Research Council Synergy Grant 2019-WATCH-810331 to R.N. Centro de Investigacion Biomedica en Red (CIBER) de Fisiopatologia de la Obesidad y Nutricion and CIBER de Enfermedades Hepaticas y Digestivas are initiatives of the Instituto de Salud Carlos III (ISCIII) of Spain, which is supported by FEDER funds, Gilead Sciences International Research Scholars Program in Liver Disease (to MVR), PI16/01548 (to MM) and the Red de Trastornos Adictivos-RTA (RD16/0017/0023). This article was partially supported by grants from the Fondo Nacional de Desarrollo Cientifico y Tecnologico grants 1191145 (to M.A.), 1200227 to JPA and 1191183 (to F. B.) and by the Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT, AFB170005, CARE Chile UC, Basal Centre for Excellence in Science and Technology; to M.A.). We thank MINECO for the Severo Ochoa Excellence Accreditation provided to the Center for Cooperative Research in Biosciences (SEV-2016-0644)

Efeitos a curto e longo prazo de um grupo de desenvolvimento de habilidades sociais para universitários

Apesar da introdução na atuação comunitária de práticas baseadas em evidências, sabemos pouco sobre a eficácia das intervenções fora de contextos de pesquisa. A área dos treinamentos de habilidades sociais carece de estudos em settings de mundo real, com números suficientes de participantes e follow-up. Investigou-se, em 34 universitários, o efeito a curto e longo prazo de um grupo de treinamento de habilidades sociais, num contexto de mundo real. Escores de habilidades sociais (IHS) e ansiedade (IDATE) foram verificados antes, depois da intervenção e após um intervalo de follow up. Os escores em ambos os testes melhoraram do pré-teste para o pós-teste, e se mantiveram estáveis do pós-teste para o follow-up no intervalo de três meses a cinco anos

Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accurac

Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accurac