| 1. |
- Anstee, Quentin M., et al.
(author)
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Genome-wide association study of non-alcoholic fatty liver and steatohepatitis in a histologically-characterised cohort
- 2020
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In: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 73:3, s. 505-515
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Journal article (peer-reviewed)abstract
- BACKGROUND AND AIMS: Genetic factors associated with non-alcoholic fatty liver disease (NAFLD) remain incompletely understood. To date, most GWAS studies have adopted radiologically assessed hepatic triglyceride content as reference phenotype and so cannot address steatohepatitis or fibrosis. We describe a genome-wide association study (GWAS) encompassing the full spectrum of histologically characterized NAFLD.METHODS: The GWAS involved 1483 European NAFLD cases and 17781 genetically-matched population controls. A replication cohort of 559 NAFLD cases and 945 controls was genotyped to confirm signals showing genome-wide or close to genome-wide significance.RESULTS: Case-control analysis identified signals showing p-values ≤ 5 x 10-8 at four locations (chromosome (chr) 2 GCKR/C2ORF16; chr4 HSD17B13; chr19 TM6SF2; chr22 PNPLA3) together with two other signals with p<1 x10-7 (chr1 near LEPR and chr8 near IDO2/TC1). Case-only analysis of quantitative traits steatosis, disease activity score, NAS and fibrosis showed that the PNPLA3 signal (rs738409) was genome-wide significantly associated with steatosis, fibrosis and NAS score and identified a new signal (PYGO1 rs62021874) with close to genome-wide significance for steatosis (p=8.2 x 10-8). Subgroup case-control analysis for NASH confirmed the PNPLA3 signal. The chr1 LEPR SNP also showed genome-wide significance for this phenotype. Considering the subgroup with advanced fibrosis (≥F3), the signals on chromosomes 2, 19 and 22 remained genome-wide significant. With the exception of GCKR/C2ORF16, the genome-wide significant signals replicated.CONCLUSIONS: This study confirms PNPLA3 as a risk factor for the full histological spectrum of NAFLD at genome-wide significance levels, with important contributions from TM6SF2 and HSD17B13. PYGO1 is a novel steatosis modifier, suggesting relevance of Wnt signalling pathways in NAFLD pathogenesis.
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| 2. |
- Govaere, Olivier, et al.
(author)
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A proteo-transcriptomic map of non-alcoholic fatty liver disease signatures
- 2023
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In: Nature Metabolism. - : NATURE PORTFOLIO. - 2522-5812. ; 5:4, s. 572-578
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Journal article (peer-reviewed)abstract
- Govaere et al. integrate circulating protein data from more than 300 patients with non-alcoholic fatty liver disease (NAFLD) with transcriptomics and develop a non-invasive diagnostics tool to identify patients with at-risk NAFLD based on body mass index, type 2 diabetes status and four circulating proteins. Non-alcoholic fatty liver disease (NAFLD) is a common, progressive liver disease strongly associated with the metabolic syndrome. It is unclear how progression of NAFLD towards cirrhosis translates into systematic changes in circulating proteins. Here, we provide a detailed proteo-transcriptomic map of steatohepatitis and fibrosis during progressive NAFLD. In this multicentre proteomic study, we characterize 4,730 circulating proteins in 306 patients with histologically characterized NAFLD and integrate this with transcriptomic analysis in paired liver tissue. We identify circulating proteomic signatures for active steatohepatitis and advanced fibrosis, and correlate these with hepatic transcriptomics to develop a proteo-transcriptomic signature of 31 markers. Deconvolution of this signature by single-cell RNA sequencing reveals the hepatic cell types likely to contribute to proteomic changes with disease progression. As an exemplar of use as a non-invasive diagnostic, logistic regression establishes a composite model comprising four proteins (ADAMTSL2, AKR1B10, CFHR4 and TREM2), body mass index and type 2 diabetes mellitus status, to identify at-risk steatohepatitis.
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| 3. |
- Govaere, Olivier, et al.
(author)
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Transcriptomic profiling across the nonalcoholic fatty liver disease spectrum reveals gene signatures for steatohepatitis and fibrosis
- 2020
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In: Science Translational Medicine. - Washington, DC, United States : American Association for the Advancement of Science (AAAS). - 1946-6234 .- 1946-6242. ; 12:572
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Journal article (peer-reviewed)abstract
- The mechanisms that drive nonalcoholic fatty liver disease (NAFLD) remain incompletely understood. This large multicenter study characterized the transcriptional changes that occur in liver tissue across the NAFLD spectrum as disease progresses to cirrhosis to identify potential circulating markers. We performed high-throughput RNA sequencing on a discovery cohort comprising histologically characterized NAFLD samples from 206 patients. Unsupervised clustering stratified NAFLD on the basis of disease activity and fibrosis stage with differences in age, aspartate aminotransferase (AST), type 2 diabetes mellitus, and carriage of PNPLA3 rs738409, a genetic variant associated with NAFLD. Relative to early disease, we consistently identified 25 differentially expressed genes as fibrosing steatohepatitis progressed through stages F2 to F4. This 25-gene signature was independently validated by logistic modeling in a separate replication cohort (n = 175), and an integrative analysis with publicly available single-cell RNA sequencing data elucidated the likely relative contribution of specific intrahepatic cell populations. Translating these findings to the protein level, SomaScan analysis in more than 300 NAFLD serum samples confirmed that circulating concentrations of proteins AKR1B10 and GDF15 were strongly associated with disease activity and fibrosis stage. Supporting the biological plausibility of these data, in vitro functional studies determined that endoplasmic reticulum stress up-regulated expression of AKR1B10, GDF15, and PDGFA, whereas GDF15 supplementation tempered the inflammatory response in macrophages upon lipid loading and lipopolysaccharide stimulation. This study provides insights into the pathophysiology of progressive fibrosing steatohepatitis, and proof of principle that transcriptomic changes represent potentially tractable and clinically relevant markers of disease progression.
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| 4. |
- Johnson, Katherine, et al.
(author)
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Increased serum miR-193a-5p during non-alcoholic fatty liver disease progression : Diagnostic and mechanistic relevance
- 2022
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In: JHEP Reports. - : Elsevier. - 2589-5559. ; 4:2
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Journal article (peer-reviewed)abstract
- Background & Aims: Serum microRNA (miRNA) levels are known to change in non-alcoholic fatty liver disease (NAFLD) and may serve as useful biomarkers. This study aimed to profile miRNAs comprehensively at all NAFLD stages.Methods: We profiled 2,083 serum miRNAs in a discovery cohort (183 cases with NAFLD representing the complete NAFLD spectrum and 10 population controls). miRNA libraries generated by HTG EdgeSeq were sequenced by Illumina NextSeq. Selected serum miRNAs were profiled in 372 additional cases with NAFLD and 15 population controls by quantitative reverse transcriptase PCR.Results: Levels of 275 miRNAs differed between cases and population controls. Fewer differences were seen within individual NAFLD stages, but miR-193a-5p consistently showed increased levels in all comparisons. Relative to NAFL/non-alcoholic steatohepatitis (NASH) with mild fibrosis (stage 0/1), 3 miRNAs (miR-193a-5p, miR-378d, and miR378d) were increased in cases with NASH and clinically significant fibrosis (stages 2-4), 7 (miR193a-5p, miR-378d, miR-378e, miR-320b, miR-320c, miR-320d, and miR-320e) increased in cases with NAFLD activity score (NAS) 5-8 compared with lower NAS, and 3 (miR-193a-5p, miR-378d, and miR-378e) increased but 1 (miR-19b-3p) decreased in steatosis, activity, and fibrosis (SAF) activity score 2-4 compared with lower SAF activity. The significant findings for miR-193a-5p were replicated in the additional cohort with NAFLD. Studies in Hep G2 cells showed that following palmitic acid treatment, miR-193a-5p expression decreased significantly. Gene targets for miR-193a-5p were investigated in liver RNAseq data for a case subgroup (n = 80); liver GPX8 levels correlated positively with serum miR-193a-5p.Conclusions: Serum miR-193a-5p levels correlate strongly with NAFLD activity grade and fibrosis stage. MiR-193a-5p may have a role in the hepatic response to oxidative stress and is a potential clinically tractable circulating biomarker for progressive NAFLD.Lay summary: MicroRNAs (miRNAs) are small pieces of nucleic acid that may turn expression of genes on or off. These molecules can be detected in the blood circulation, and their levels in blood may change in liver disease including non-alcoholic fatty liver disease (NAFLD). To see if we could detect specific miRNA associated with advanced stages of NAFLD, we carried out miRNA sequencing in a group of 183 patients with NAFLD of varying severity together with 10 population controls. We found that a number of miRNAs showed changes, mainly increases, in serum levels but that 1 particular miRNA miR-193a-5p consistently increased. We confirmed this increase in a second group of cases with NAFLD. Measuring this miRNA in a blood sample may be a useful way to determine whether a patient has advanced NAFLD without an invasive liver biopsy.
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| 5. |
- Lazarus, Jeffrey V, et al.
(author)
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European NAFLD Preparedness Index - Is Europe ready to meet the challenge of fatty liver disease?
- 2021
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In: JHEP Reports. - : Elsevier. - 2589-5559. ; 3:2
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Journal article (peer-reviewed)abstract
- Background & Aims: Non-alcoholic fatty liver disease (NAFLD), which is closely associated with obesity, metabolic syndrome, and diabetes, is a highly prevalent emerging condition that can be optimally managed through a multidisciplinary patientcentred approach. National preparedness to address NAFLD is essential to ensure that health systems can deliver effective care. We present a NAFLD Preparedness Index for Europe. Methods: In June 2019, data were extracted by expert groups from 29 countries to complete a 41-item questionnaire about NAFLD. Questions were classified into 4 categories: policies/civil society (9 questions), guidelines (16 questions), epidemiology (4 questions), and care management (12 questions). Based on the responses, national preparedness for each indicator was classified into low, middle, or high-levels. We then applied a multiple correspondence analysis to obtain a standardised preparedness score for each country ranging from 0 to 100. Results: The analysis estimated a summary factor that explained 71.3% of the variation in the dataset. No countries were found to have yet attained a high-level of preparedness. Currently, the UK (75.5) scored best, although falling within the midlevel preparedness band, followed by Spain (56.2), and Denmark (43.4), whereas Luxembourg and Ireland were the lowest scoring countries with a score of 4.9. Only Spain scored highly in the epidemiology indicator category, whereas the UK was the only country that scored highly for care management. Conclusions: The NAFLD Preparedness Index indicates substantial variation between countries readiness to address NAFLD. Notably, even those countries that score relatively highly exhibit deficiencies in key domains, suggesting that structural changes are needed to optimise NAFLD management and ensure effective public health approaches are in place. Lay summary: Non-alcoholic fatty liver disease (NAFLD), which is closely associated with obesity, metabolic syndrome, and diabetes, is a highly prevalent condition that can be optimally managed through a multidisciplinary patient-centred approach. National preparedness to address NAFLD is essential to allow for effective public health measures aimed at preventing disease while also ensuring that health systems can deliver effective care to affected populations. This study defined preparedness as having adequate policies and civil society engagement, guidelines, epidemiology, and care management. NAFLD preparedness was found to be deficient in all 29 countries studied, with great variation among the countries and the 4 categories studied. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL).
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| 6. |
- Lee, Jenny, et al.
(author)
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Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study
- 2023
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In: Hepatology. - : LIPPINCOTT WILLIAMS & WILKINS. - 0270-9139 .- 1527-3350. ; 78:1, s. 258-271
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Journal article (peer-reviewed)abstract
- Background and Aims: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F >= 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. Approach and Results: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS >= 4;53%), at-risk NASH (NASH with F >= 2;35%), significant (F >= 2;47%), and advanced fibrosis (F >= 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). Conclusions: Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis.
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| 7. |
- McGlinchey, Aidan J, 1984-, et al.
(author)
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Metabolic signatures across the full spectrum of non-alcoholic fatty liver disease
- 2022
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In: JHEP Reports. - : Elsevier. - 2589-5559. ; 4:5
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Journal article (peer-reviewed)abstract
- Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is a progressive liver disease with potentially severe complications including cirrhosis and hepatocellular carcinoma. Previously, we have identified circulating lipid signatures associating with liver fat content and non-alcoholic steatohepatitis (NASH). Here, we develop a metabolomic map across the NAFLD spectrum, defining interconnected metabolic signatures of steatosis (non-alcoholic fatty liver, NASH, and fibrosis).Methods: We performed mass spectrometry analysis of molecular lipids and polar metabolites in serum samples from the European NAFLD Registry patients (n = 627), representing the full spectrum of NAFLD. Using various univariate, multivariate, and machine learning statistical approaches, we interrogated metabolites across 3 clinical perspectives: steatosis, NASH, and fibrosis.Results: Following generation of the NAFLD metabolic network, we identify 15 metabolites unique to steatosis, 18 to NASH, and 15 to fibrosis, with 27 common to all. We identified that progression from F2 to F3 fibrosis coincides with a key pathophysiological transition point in disease natural history, with n = 73 metabolites altered.Conclusions: Analysis of circulating metabolites provides important insights into the metabolic changes during NAFLD progression, revealing metabolic signatures across the NAFLD spectrum and features that are specific to NAFL, NASH, and fibrosis. The F2-F3 transition marks a critical metabolic transition point in NAFLD pathogenesis, with the data pointing to the pathophysiological importance of metabolic stress and specifically oxidative stress.Clinical Trials registration: The study is registered at Clinicaltrials.gov (NCT04442334).Lay summary: Non-alcoholic fatty liver disease is characterised by the build-up of fat in the liver, which progresses to liver dysfunction, scarring, and irreversible liver failure, and is markedly increasing in its prevalence worldwide. Here, we measured lipids and other small molecules (metabolites) in the blood with the aim of providing a comprehensive molecular overview of fat build-up, liver fibrosis, and diagnosed severity. We identify a key metabolic 'watershed' in the progression of liver damage, separating severe disease from mild, and show that specific lipid and metabolite profiles can help distinguish and/or define these cases.
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| 8. |
- McGlinchey, Aidan J, 1984-, et al.
(author)
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Metabolomics approaches to identify biomarkers of nonalcoholic fatty liver disease
- 2020
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In: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 73:Suppl. 1, s. S438-S438
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Journal article (other academic/artistic)abstract
- Background and Aims: Nonalcoholic fatty liver disease (NAFLD) is a progressive liver disease that is strongly associated with type 2 diabetes. Accurate, non-invasive diagnostic tests to deliniate the different stages: degree of steatosis, grade of nonalcoholic steatohepatitis (NASH) and stage fibrosis represent an unmet medical need. In our previous studies, we successfully identified specific serum molecular lipid signatures which associate with the amount of liver fat as well as with NASH. Here we report underlying associations between clinical data, lipidomic profiles, metabolic profiles and clinical outcomes, including downstream identification of potential biomarkers for various stages of the disease.Method: We leverage several statistical and machine-learning approaches to analyse clinical, lipidomic and metabolomic profiles of individuals from the European Horizon 2020 project: Elucidating Pathways of Steatohepatitis (EPoS). We interrogate data on patients representing the full spectrum of NAFLD/NASH derived from the EPoS European NAFLD Registry (n = 627). We condense the EPoS lipidomic data into lipid clusters and subsequently apply non-rejection-rate-pruned partial correlation network techniques to facilitate network analysis between the datasets of lipidomic, metabolomic and clinical data. For biomarker identification, random forest ensemble classification and neural network machine learning approaches were used to both search for valid disease biomarkers and to assess the relative improvement over clinical-data-only classification versus addition of our lipidomic and metabolomic datasets.Results: We found that steatosis grade was strongly associated with (1) an increase of triglycerides with low carbon number and double bond count as well as (2) a decrease of specific phospholipids, including lysophosphatidylcholines. In addition to the network topology as a result itself, we also present lipid clusters (LCs) of interest to the derived network of proposed interactions in our NAFLD data from the EPoS cohort, along with our proposed biomarkers for various disease outcomes, as put forward by our current machine learning analyses.Conclusion: Our findings suggest that dysregulation of lipid metabolism in progressive stages of NAFLD is reflected in circulation and may thus hold diagnostic value as well as offer new insights about the NAFLD pathogenesis. Using this cohort as a proof-of-concept, we demonstrate current progress in tuning the accuracy of neural network and random forest approaches with a view to predicting various subtypes of NAFLD patient using a minimal set of lipidomic and metabolic markers. A detailed network-based picture emerges between lipids, polar metabolites and clinical variables. Lipidomic/metabolomic markers may provide an alternative method of NAFLD patient classification and risk stratification to guide therapy.
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| 9. |
- McGlinchey, Aidan J, 1984-, et al.
(author)
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The Metabolomics of Non-Alcoholic Fatty Liver Disease : Of Networks and Biomarkers
- 2021
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In: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 75:Suppl. 2, s. S579-S580
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Journal article (other academic/artistic)abstract
- Background and aims: Non-alcoholic fatty liver disease (NAFLD), the leading cause of chronic liver disease, affects 25%+ of people worldwide. Detailed understanding of the metabolomics of NAFLD, and non-invasive diagnostic techniques for the stages of NAFLD are unavailable. We identify specific serum molecular lipid signatures to these ends.First, we leverage lipidomic and polar metabolomic data (n = 643) subjects, to produce a clear, meaningful interaction map, linking lipids, metabolites, clinical factors and disease outcomes. We find non-spurious associations therein, as features of interest, and for downstream analysis.Third, NAFLD fibrosis biomarker identification was performed using machine learning, with our candidate lipids/metabolites to be forwarded to a successor project; the LITMUS project, towards clinically-applicable, non-invasive, sensitive and specific classification of NAFLD patients.Method: Serum lipids and polar metabolites were measured by mass spectrometry in the EPoS cohort of patients (n = 176 lipids and n = 36 polar metabolites), combined with clinical data from (n = 643 subjects), followed by model-based clustering, giving 10 lipid clusters (LCs).Correlations were calculated pairwise between (1) all LCs, (2) “input” clinical data (height, weight, BMI, blood platelet count) and (3) outcomes (fibrosis, steatosis, NAS score, etc.). Non-rejection rates (NRRs) were calculated for relationships, remove spurious associations (NRR > 0.4). We project the remaining associations as a network; a novel metabolomic overview NAFLD.ANOVA and Tukey’s Honest Significant Differences (Tukey HSDs) revealed detailed metabolic signatures across NAFLD, fibrosis and steatosis stages.Random forest machine learning was used to classify NAFLD patients: LOW (0-1 fibrosis grade) or HIGH (2–4 fibrosis grade), using individual lipids and metabolites, identifying putative biomarkers.Results: In linewith our previous findings, many lipids associate with steatosis and fibrosis in NAFLD. Our novel overview network revealsas sociations between specific LCs and clinical variables, such as TGs (LC3), and a subgroup of TGs of lowest and highest carbon numbers (LC9) along with PC (O)s (LC7) positively associating with NAFLD score and fibrosis. Conversely, LPCs (LC4), particularly sphingomyelins (SMs, LC6), negatively associated with these variables. Many other metabolites changing across NAFLD stages beg further discussion.Conclusion: In addition to generation of a novel metabolomic network of NAFLD, we demonstrate feasibility of lipidomic and metabolomic data to classify NAFLD patients’fibrosis grades (median AUC: 0.765), competitive with gold-standard clinical variables (age, BMI, sex, diabetes, liver AST/ALT, platelet count) (median AUC: 0.778). These biomarkers are being taken forward (LITMUS project) to develop clinical testing.
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| 10. |
- Sen, Partho, 1983-, et al.
(author)
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Genome-scale metabolic modeling of human hepatocytes reveals dysregulation of glycosphingolipid pathways in progressive non-alcoholic fatty liver disease
- 2021
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In: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 75:Suppl. 2, s. S256-S256
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Journal article (other academic/artistic)abstract
- Background and aims: Non-alcoholic fatty liver disease (NAFLD) is a spectrum of chronic liver diseases intertwined with the metabolic disorders. The prevalence of NAFLD is rapidly increasing worldwide, while the pathologyand the underlying mechanism driving NAFLD is not fully understood. In NAFLD, a series of metabolic changes takes place in the liver. However, the alteration of the metabolic pathways in the human liver along the progression of NAFLD,i.e., transition from non-alcoholic steatosis (NAFL) to steatohepatitis (NASH) through cirrhosis remains to be discovered. Here, we sought to examine the metabolic pathways of the human liver across the full histological spectrum of NAFLD.Method: We analyzed the whole liver tissue transcriptomic (RNA-Seq)1 and serum metabolomics data obtained from a large cohort of histologically characterized patients derived from the European NAFLD Registry (n = 206), and developed genome-scale metabolic models (GEMs) of human hepatocytes at different stages of NAFLD. The integrative approach employed in this study has enabled us to understand the regulation of the metabolic pathways of human liver in NAFL, and with progressive NASH-associated fibrosis (F0-F4).Results: Our study identified several metabolic signatures in the liver and blood of these patients, specifically highlighting the alteration of vitamins (A, E) and glycosphingolipids, and their link with complex glycosaminoglycans in advanced fibrosis. Furthermore, by applying genome-scale metabolic modeling, we were able to identify the metabolic differences among carriers of widely validated genetic variants associated with NAFLD/NASH disease severity in three genes (PNPLA3,TM6SF2andHSD17B13).Conclusion: The study provides insights into the underlying pathways of the progressive-fibrosing steatohepatitis. Of note, there is a marked dysregulation of the glycosphingolipid metabolism in the liver of the patients with advanced fibrosis.
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