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 stratificatio

Diagnostic potential of the plasma lipidome in infectious disease: application to acute SARS-CoV-2 infection

Improved methods are required for investigating the systemic metabolic effects of SARS-CoV-2 infection and patient stratification for precision treatment. We aimed to develop an effective method using lipid profiles for discriminating between SARS-CoV-2 infection, healthy controls, and non-SARS-CoV-2 respiratory infections. Targeted liquid chromatography–mass spectrometry lipid profiling was performed on discovery (20 SARS-CoV-2-positive; 37 healthy controls; 22 COVID-19 symptoms but SARS-CoV-2negative) and validation (312 SARS-CoV-2-positive; 100 healthy controls) cohorts. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and Kruskal–Wallis tests were applied to establish discriminant lipids, significance, and effect size, followed by logistic regression to evaluate classification performance. OPLS-DA reported separation of SARS-CoV-2 infection from healthy controls in the discovery cohort, with an area under the curve (AUC) of 1.000. A refined panel of discriminant features consisted of six lipids from different subclasses (PE, PC, LPC, HCER, CER, and DCER). Logistic regression in the discovery cohort returned a training ROC AUC of 1.000 (sensitivity = 1.000, specificity = 1.000) and a test ROC AUC of 1.000. The validation cohort produced a training ROC AUC of 0.977 (sensitivity = 0.855, specificity = 0.948) and a test ROC AUC of 0.978 (sensitivity = 0.948, specificity = 0.922). The lipid panel was also able to differentiate SARS-CoV-2-positive individuals from SARS-CoV-2-negative individuals with COVID-19-like symptoms (specificity = 0.818). Lipid profiling and multivariate modelling revealed a signature offering mechanistic insights into SARS-CoV-2, with strong predictive power, and the potential to facilitate effective diagnosis and clinical management

An NMR-based model to investigate the metabolic phenoreversion of COVID-19 patients throughout a longitudinal study.

After SARS-CoV-2 infection, the molecular phenoreversion of the immunological response and its associated metabolic dysregulation are required for a full recovery of the patient. This process is patient-dependent due to the manifold possibilities induced by virus severity, its phylogenic evolution and the vaccination status of the population. We have here investigated the natural history of COVID-19 disease at the molecular level, characterizing the metabolic and immunological phenoreversion over time in large cohorts of hospitalized severe patients (n = 886) and non-hospitalized recovered patients that self-reported having passed the disease (n = 513). Non-hospitalized recovered patients do not show any metabolic fingerprint associated with the disease or immune alterations. Acute patients are characterized by the metabolic and lipidomic dysregulation that accompanies the exacerbated immunological response, resulting in a slow recovery time with a maximum probability of around 62 days. As a manifestation of the heterogeneity in the metabolic phenoreversion, age and severity become factors that modulate their normalization time which, in turn, correlates with changes in the atherogenesis-associated chemokine MCP-1. Our results are consistent with a model where the slow metabolic normalization in acute patients results in enhanced atherosclerotic risk, in line with the recent observation of an elevated number of cardiovascular episodes found in post-COVID-19 cohorts

Sensitive detection of SARS-CoV-2 seroconversion by flow cytometry reveals the presence of nucleoprotein-reactive antibodies in unexposed individuals

There is an ongoing need of developing sensitive and specific methods for the determination of SARS-CoV-2 seroconversion. For this purpose, we have developed a multiplexed flow cytometric bead array (C19BA) that allows the identification of IgG and IgM antibodies against three immunogenic proteins simultaneously: the spike receptor-binding domain (RBD), the spike protein subunit 1 (S1) and the nucleoprotein (N). Using different cohorts of samples collected before and after the pandemic, we show that this assay is more sensitive than ELISAs performed in our laboratory. The combination of three viral antigens allows for the interrogation of full seroconversion. Importantly, we have detected N-reactive antibodies in COVID-19-negative individuals. Here we present an immunoassay that can be easily implemented and has superior potential to detect low antibody titers compared to current gold standard serology methods.Acknowledgements: We thank Petros Tyrakis and Iván Martínez-Forero for critical reading and editing of the manuscript. Support was provided by the Severo Ochoa Excellence Accreditation from MCIU (SEV-2016-0644) and the SPRI I+D COVID-19 fund (Gobierno Vasco). Personal fellowships: A.A.-V. (La Caixa Inphinit LCF/BQ/DR20/11790022), A.B. (AECC Bizkaia), A.G.d.R (Bikaintek), A.P. (Ramón y Cajal), B.J.-L. (Gob. Vasco), and E.P.-F. (Juan de la Cierva-Formación). M.L.M.-C. acknowledges RTC2019-007125-1, DTS20/00138, SAF2017-87301-R, and BBVA UMBRELLA project. M.L.-H. acknowledges the ISCIII for grant COV20-0170 and the Government of Cantabria for grant 2020UIC22-PUB-0019. O.M., J.-M.M., and N.G.A.A. acknowledge the Agencia Estatal de Investigación (Spain) for grants CTQ2015-68756-R, RTI2018-101269-BI00, and RTI2018-095700-B-I00, respectively. A.P. has received grant funding from the European Research Council (ERC), grant agreement number 804236 (Horizon 2020), and the FERO Foundation

Diagnostic potential of the plasma lipidome in infectious disease: application to acute SARS-CoV-2 infection

Improved methods are required for investigating the systemic metabolic effects of SARS-CoV-2 infection and patient stratification for precision treatment. We aimed to develop an effective method using lipid profiles for discriminating between SARS-CoV-2 infection, healthy controls, and non-SARS-CoV-2 respiratory infections. Targeted liquid chromatography–mass spectrometry lipid profiling was performed on discovery (20 SARS-CoV-2-positive; 37 healthy controls; 22 COVID-19 symptoms but SARS-CoV-2negative) and validation (312 SARS-CoV-2-positive; 100 healthy controls) cohorts. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and Kruskal–Wallis tests were applied to establish discriminant lipids, significance, and effect size, followed by logistic regression to evaluate classification performance. OPLS-DA reported separation of SARS-CoV-2 infection from healthy controls in the discovery cohort, with an area under the curve (AUC) of 1.000. A refined panel of discriminant features consisted of six lipids from different subclasses (PE, PC, LPC, HCER, CER, and DCER). Logistic regression in the discovery cohort returned a training ROC AUC of 1.000 (sensitivity = 1.000, specificity = 1.000) and a test ROC AUC of 1.000. The validation cohort produced a training ROC AUC of 0.977 (sensitivity = 0.855, specificity = 0.948) and a test ROC AUC of 0.978 (sensitivity = 0.948, specificity = 0.922). The lipid panel was also able to differentiate SARS-CoV-2-positive individuals from SARS-CoV-2-negative individuals with COVID-19-like symptoms (specificity = 0.818). Lipid profiling and multivariate modelling revealed a signature offering mechanistic insights into SARS-CoV-2, with strong predictive power, and the potential to facilitate effective diagnosis and clinical management

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

Multi-Omics Integration Highlights the Role of Ubiquitination in CCl4-Induced Liver Fibrosis

Liver fibrosis is the excessive accumulation of extracellular matrix proteins that occurs in chronic liver disease. Ubiquitination is a post-translational modification that is crucial for a plethora of physiological processes. Even though the ubiquitin system has been implicated in several human diseases, the role of ubiquitination in liver fibrosis remains poorly understood. Here, multi-omics approaches were used to address this. Untargeted metabolomics showed that carbon tetrachloride (CCl4)-induced liver fibrosis promotes changes in the hepatic metabolome, specifically in glycerophospholipids and sphingolipids. Gene ontology analysis of public deposited gene array-based data and validation in our mouse model showed that the biological process “protein polyubiquitination” is enriched after CCl4-induced liver fibrosis. Finally, by using transgenic mice expressing biotinylated ubiquitin (bioUb mice), the ubiquitinated proteome was isolated and characterized by mass spectrometry in order to unravel the hepatic ubiquitinated proteome fingerprint in CCl4-induced liver fibrosis. Under these conditions, ubiquitination appears to be involved in the regulation of cell death and survival, cell function, lipid metabolism, and DNA repair. Finally, ubiquitination of proliferating cell nuclear antigen (PCNA) is induced during CCl4-induced liver fibrosis and associated with the DNA damage response (DDR). Overall, hepatic ubiquitome profiling can highlight new therapeutic targets for the clinical management of liver fibrosis.This work was supported by grants from Gobierno Vasco-Departamento de Salud 2013111114 (to M.L.M.-C.), ELKARTEK 2016, Departamento de Industria del Gobierno Vasco (to M.L.M.-C.), Ministerio de Ciencia, Innovación y Universidades MICINN: SAF2017-87301-R, SAF2017-88041-R, RTI2018-096759-A-100 and SAF2016-76898-P integrado en el Plan Estatal de Investigación Cientifica y Técnica y Innovación, cofinanciado con Fondos FEDER (to M.L.M.-C., J.M.M., T.C.D. and U.M. respectively); AECC Bizkaia (M.S.-M.); Asociación Española contra el Cáncer (T.C.D.), Fundación Científica de la Asociación Española Contra el Cancer (AECC Scientific Foundation) Rare Tumor Calls 2017 (to M.L.M., J.M.B., M.A.A., J.J.G.M.), La Caixa Foundation Program (to M.L.M.), 2018 BBVA Foundation Grants for Scientific Research Teams (to M.L.M.-C.). This research was also funded by the CIBERehd (EHD15PI05/2016) and “Fondo de Investigaciones Sanitarias, Instituto de Salud Carlos III”, Spain (PI16/00598 and PI19/00819, co-funded by European Regional Development Fund/European Social Fund, “Investing in your future”); Spanish Ministry of Economy, Industry and Competitiveness (SAF2016-75197-R); “Junta de Castilla y Leon” (SA063P17); AECC Scientific Foundation (2017/2020), Spain; “Centro Internacional sobre el Envejecimiento” (OLD-HEPAMARKER, 0348_CIE_6_E), Spain; University of Salamanca Foundation, Spain (PC-TCUE18-20_051), and Fundació Marato TV3 (Ref. 201916-31), Spain (to J.J.G.M.). The UPV/EHU Lab and the Proteomics Platform are members of Proteored, PRB3 and is supported by grant PT17/0019, of the PE I + D + i 2013-2016, funded by ISCIII and ERDF. Ciberehd_ISCIII_MINECO is funded by the Instituto de Salud Carlos III. We thank MINECO for the Severo Ochoa Excellence Accreditation to CIC bioGUNE (SEV-2016-0644)

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)

Genomic and functional regulation of TRIB1 contributes to prostate cancer pathogenesis

Prostate cancer is the most frequent malignancy in European men and the second worldwide. One of the major oncogenic events in this disease includes amplification of the transcription factor cMYC. Amplification of this oncogene in chromosome 8q24 occurs concomitantly with the copy number increase in a subset of neighboring genes and regulatory elements, but their contribution to disease pathogenesis is poorly understood. Here we show that TRIB1 is among the most robustly upregulated coding genes within the 8q24 amplicon in prostate cancer. Moreover, we demonstrate that TRIB1 amplification and overexpression are frequent in this tumor type. Importantly, we find that, parallel to its amplification, TRIB1 transcription is controlled by cMYC. Mouse modeling and functional analysis revealed that aberrant TRIB1 expression is causal to prostate cancer pathogenesis. In sum, we provide unprecedented evidence for the regulation and function of TRIB1 in prostate cancer