Interplay between Genome, Metabolome and Microbiome in Colorectal Cancer

Background Colorectal cancer (CRC), a major health concern, is developed depending on environmental, genetic and microbial factors. The microbiome and metabolome have been analyzed to study their role in CRC. However, the interplay of host genetics with those layers in CRC remains unclear. Methods 120 individuals were sequenced and association analyses were carried out for adenoma and CRC risk, and for selected components of the microbiome and metabolome. The epistasis between genes located in cholesterol pathways was analyzed; modifiable risk factors were studied using Mendelian randomization; and the three omic layers were used to integrate their data and to build risk prediction models. Results We detected genetic variants that were associated to components of metabolome or microbiome and adenoma or CRC risk (e.g., in LINC01605, PROKR2 and CCSER1 genes). In addition, we found interactions between genes of cholesterol metabolism, and HDL cholesterol levels affected adenoma (p = 0.0448) and CRC (p = 0.0148) risk. The combination of the three omic layers to build risk prediction models reached high AUC values (>0.91). Conclusions The use of the three omic layers allowed for the finding of biological mechanisms related to the development of adenoma and CRC, and each layer provided complementary information to build risk prediction models

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

Arachidyl Amido Cholanoic Acid Improves Liver Glucose and Lipid Homeostasis in Nonalcoholic Steatohepatitis Via AMPK and mTOR Regulation

BACKGROUND Arachidyl amido cholanoic acid (Aramchol) is a potent downregulator of hepatic stearoyl-CoA desaturase 1 (SCD1) protein expression that reduces liver triglycerides and fibrosis in animal models of steatohepatitis. In a phase IIb clinical trial in patients with nonalcoholic steatohepatitis (NASH), 52 wk of treatment with Aramchol reduced blood levels of glycated hemoglobin A1c, an indicator of glycemic control. AIM To assess lipid and glucose metabolism in mouse hepatocytes and in a NASH mouse model [induced with a 0.1% methionine and choline deficient diet (0.1MCD)] after treatment with Aramchol. METHODS Isolated primary mouse hepatocytes were incubated with 20 mu mol/L Aramchol or vehicle for 48 h. Subsequently, analyses were performed including Western blot, proteomics by mass spectrometry, and fluxomic analysis with(13)C-uniformly labeled glucose. For thein vivopart of the study, male C57BL/6J mice were randomly fed a control or 0.1MCD for 4 wk and received 1 or 5 mg/kg/d Aramchol or vehicle by intragastric gavage for the last 2 wk. Liver metabolomics were assessed using ultra-high-performance liquid chromatography-time of flight-MS for the determination of glucose metabolism-related metabolites. RESULTS Combination of proteomics and Western blot analyses showed increased AMPK activity while the activity of nutrient sensor mTORC1 was decreased by Aramchol in hepatocytes. This translated into changes in the content of their downstream targets including proteins involved in fatty acid (FA) synthesis and oxidation [P-ACC alpha/beta(S79), SCD1, CPT1A/B, HADHA, and HADHB], oxidative phosphorylation (NDUFA9, NDUFB11, NDUFS1, NDUFV1, ETFDH, and UQCRC2), tricarboxylic acid (TCA) cycle (MDH2, SUCLA2, and SUCLG2), and ribosome (P-p70S6K[T389] and P-S6[S235/S236]). Flux experiments with(13)C-uniformely labeled glucose showed that TCA cycle cataplerosis was reduced by Aramchol in hepatocytes, as indicated by the increase in the number of rounds that malate remained in the TCA cycle. Finally, liver metabolomic analysis showed that glucose homeostasis was improved by Aramchol in 0.1MCD fed mice in a dose-dependent manner, showing normalization of glucose, G6P, F6P, UDP-glucose, and Rbl5P/Xyl5P. CONCLUSION Aramchol exerts its effect on glucose and lipid metabolism in NASH through activation of AMPK and inhibition of mTORC1, which in turn activate FA beta-oxidation and oxidative phosphorylation.Supported by the National Institutes of Health Grant, No. R01CA172086; Plan Nacional of I+D, No. SAF2017-88041-R; Ministerio de Economia y Competitividad de Espana, No. SAF2017-87301-R; Asociacion Espanola contra el Cancer, No. AECC17/302; Ayudas Fundacion BBVA a equipos de Investigacion Cientifica 2018; Fondo Europeo de Desarrollo Regional, Ministerio de Economia y Competitividad de Espana, No. PGC2018-099857-BI00; Basque Government Grants, No. IT1264-19; Ministerio de Economia y Competitividad de Espana for the Severo Ochoa Excellence Accreditation, No. SEV2016-0644. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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 accuracy.This research was funded by: Instituto de Salud Carlos III (ISCIII) co-financed by Fondo Europeo de Desarrollo Regional (FEDER) Una manera de hacer Europa, grant numbers: PI16/01126 (M.A.A.), PI19/00819 (M.J.M. and J.J.G.M.), PI15/01132, PI18/01075 and Miguel Servet Program CON14/00129 (J.M.B.); Fundación Científica de la Asociación Española Contra el Cáncer (AECC Scientific Foundation), grant name: Rare Cancers 2017 (J.M.U., M.L.M., J.M.B., M.J.M., R.I.R.M., M.G.F.-B., C.B., M.A.A.); Gobierno de Navarra Salud, grant number 58/17 (J.M.U., M.A.A.); La Caixa Foundation, grant name: HEPACARE (C.B., M.A.A.); AMMF The Cholangiocarcinoma Charity, UK, grant number: 2018/117 (F.J.C. and M.A.A.); PSC Partners US, PSC Supports UK, grant number 06119JB (J.M.B.); Horizon 2020 (H2020) ESCALON project, grant number H2020-SC1-BHC-2018–2020 (J.M.B.); BIOEF (Basque Foundation for Innovation and Health Research: EiTB Maratoia, grant numbers BIO15/CA/016/BD (J.M.B.) and BIO15/CA/011 (M.A.A.). Department of Health of the Basque Country, grant number 2017111010 (J.M.B.). La Caixa Foundation, grant number: LCF/PR/HP17/52190004 (M.L.M.), Mineco-Feder, grant number SAF2017-87301-R (M.L.M.), Fundación BBVA grant name: Ayudas a Equipos de Investigación Científica Umbrella 2018 (M.L.M.). MCIU, grant number: Severo Ochoa Excellence Accreditation SEV-2016-0644 (M.L.M.). Part of the equipment used in this work was co-funded by the Generalitat Valenciana and European Regional Development Fund (FEDER) funds (PO FEDER of Comunitat Valenciana 2014–2020). Gobierno de Navarra fellowship to L.C. (Leticia Colyn); AECC post-doctoral fellowship to M.A.; Ramón y Cajal Program contracts RYC-2014-15242 and RYC2018-024475-1 to F.J.C. and M.G.F.-B., respectively. The generous support from: Fundación Eugenio Rodríguez Pascual, Fundación Echébano, Fundación Mario Losantos, Fundación M Torres and Mr. Eduardo Avila are acknowledged. The CNB-CSIC Proteomics Unit belongs to ProteoRed, PRB3-ISCIII, supported by grant PT17/0019/0001 (F.J.C.). Comunidad de Madrid Grant B2017/BMD-3817 (F.J.C.).Peer reviewe

Metabolic subtypes of patients with NAFLD exhibit distinctive cardiovascular risk profiles

Background and Aims We previously identified subsets of patients with NAFLD with different metabolic phenotypes. Here we align metabolomic signatures with cardiovascular disease (CVD) and genetic risk factors. Approach and Results We analyzed serum metabolome from 1154 individuals with biopsy-proven NAFLD, and from four mouse models of NAFLD with impaired VLDL-triglyceride (TG) secretion, and one with normal VLDL-TG secretion. We identified three metabolic subtypes: A (47%), B (27%), and C (26%). Subtype A phenocopied the metabolome of mice with impaired VLDL-TG secretion; subtype C phenocopied the metabolome of mice with normal VLDL-TG; and subtype B showed an intermediate signature. The percent of patients with NASH and fibrosis was comparable among subtypes, although subtypes B and C exhibited higher liver enzymes. Serum VLDL-TG levels and secretion rate were lower among subtype A compared with subtypes B and C. Subtype A VLDL-TG and VLDL-apolipoprotein B concentrations were independent of steatosis, whereas subtypes B and C showed an association with these parameters. Serum TG, cholesterol, VLDL, small dense LDL5,6, and remnant lipoprotein cholesterol were lower among subtype A compared with subtypes B and C. The 10-year high risk of CVD, measured with the Framingham risk score, and the frequency of patatin-like phospholipase domain-containing protein 3 NAFLD risk allele were lower in subtype A. Conclusions Metabolomic signatures identify three NAFLD subgroups, independent of histological disease severity. These signatures align with known CVD and genetic risk factors, with subtype A exhibiting a lower CVD risk profile. This may account for the variation in hepatic versus cardiovascular outcomes, offering clinically relevant risk stratification.National Institutes of Health (R01DK123763, R01DK119437, HL151328, P30DK52574, P30DK56341, and UL1TR002345); Ministerio de Economía y Competitividad de España (SAF2017-88041-R); Ministerio de Economía y Competitividad de España for the Severo Ochoa Excellence Accreditation (SEV-2016-0644); CIBERehd (Biomedical Research Center in Hepatic and Digestive Diseases) and Netherlands Organization for Applied Scientific Research Program (PMC13 and PMC15); Spanish Carlos III Health Institute (PI15/01132 and PI18/01075); Miguel Servet Program (CON14/00129 and CPII19/00008); Fondo Europeo de Desarrollo Regional, CIBERehd, Department of Industry of the Basque Country (Elkartek: KK-2020/00008); La Caixa Scientific Foundation (HR17-00601); Liver Investigation: Testing Marker Utility in Steatohepatitis consortium funded by the Innovative Medicines Initiative Program of the European Union (777377), which receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA; Newcastle NIHR Biomedical Research Center; Czech Ministry of Health (RVO-VFN64165/2020); Fondo Nacional De Ciencia y Tecnología de Chile (1191145); and the Comisión Nacional de Investigación, Ciencia y Tecnología (AFB170005, CARE Chile UC); Agencia Nacional de Investigación y Desarrollo (ANID ACE 210009); European Union's Horizon 2020 Research and Innovation Program (825510)