Global molecular landscape of early MASLD progression in human obesity
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Singapore Lipidomics Incubator, Life Sciences Institute, National University of Singapore, Singapore
- Cardiovascular & Metabolic Disease Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Anatomy and Physiology, School of Biomedical Sciences, Faculty of Medicine Dentistry and Health Sciences, University of Melbourne, Australia
- Department of Hepatology, The First Hospital of Hunan University of Chinese Medicine, China
- Department of Clinical Sciences Malmö and Lund University Diabetes Centre (LUDC), Lund University, Clinical Research Centre (CRC), Sweden
- Department of Physiology and Pharmacology and Centre of Molecular Medicine, Karolinska Institutet, Sweden
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Germany
- SCIEX, R&D, Singapore
- Department of Surgery, School of Translational Medicine, Monash University, Australia
- Australia and Bariatric Unit, Department of General Surgery, The Alfred Hospital, Australia
Figures
Figure 1
Study overview.
(A) The overall study design. (B) Alluvial diagram of patient composition of groupings across liver histological features. (C) Relationship between clinical test results and stages of liver histological features. Node size was determined by –log10(p-value) in ANOVA tests. Color indicates the significance degree [–log10(p-value)] and the direction of change from early to late stages. (D) Number of metabolites analyzed in the liver and plasma. (E, F) Principal component analysis of liver metabolome, plasma metabolome, and liver transcriptome. (G) Disease spectra covered in this cohort and comparison to two published datasets. All source data are available in Supplementary file 2.
Figure 2 with 2 supplements
Hepatic and circulating metabolome in obese individuals with metabolic dysfunction-associated steatotic liver disease (MASLD).
(A) Number of metabolites in each class that were significantly associated with histological features in the liver and plasma (q-value <0.05). Metabolite classes with at least five metabolites with significance are shown in the plot. (B) Associations between steatosis grades and lipid species in each lipid class. Dots were colored by double bond numbers (left) and carbon numbers (right) of lipid species, respectively. (C) Partial correlation network of the plasma metabolome and clinical covariates. Node size reflects the degree of connectivity, with larger nodes indicating connections to a greater number of metabolite nodes. (D) Heatmaps for pairwise analysis between plasma lipid classes and clinical variables. Linear regression analysis was performed for numerical variables, while logistic regression was conducted for binary variables. Significance values refer to –log10(p-value)*sign(coefficients) from regression models. (E) Comparisons of liver metabolome and plasma metabolome regarding their associations with liver steatosis and fibrosis. All source data are available in Supplementary files 3 and 4.
Figure 2—figure supplement 1
Metabolomic analysis in this metabolic dysfunction-associated steatotic liver disease (MASLD) cohort.
(A) Plasma urea and creatinine from untargeted metabolomics correlated with clinical assays. (B) Liver and plasma metabolites associated with the disease progression in each matrix (q < 0.05 or q < 0.1). Compound classes with at least five hits associated with any histological feature were shown in the plot. (C) Associations between fatty acid composition of triglycerides (TAGs) and histological outcomes. Significance values refer to –log10(p-value)*sign(coefficient) from linear regression models.
Figure 2—figure supplement 2
Blood lipoprotein cholesterol levels in patients with different steatosis and fibrosis grades.
HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; NonHDL-Chol, non-high-density lipoprotein cholesterol; TotalChol, total blood lipoprotein cholesterol.
Figure 3 with 2 supplements
Integrative view of key metabolic pathways implicated in liver metabolism in obese individuals with metabolic dysfunction-associated steatotic liver disease (MASLD).
(A) Heatmap of log2 fold changes from pairwise analysis of lipid metabolism-related genes. Association with steatosis (S) and fibrosis (F) is indicated by black dots (q-value <0.05, top). Results were cross-referenced with two published cohorts (VA cohort, GSE130970; EU cohort, GSE135251, bottom). Asterisks indicate genes with a q-value <0.05 and a consistent change in direction within our cohort. (B) KEGG metabolic pathways with at least four genes or metabolites significantly associated with steatosis [S] or fibrosis [F]. (C) Integrative map of gene and metabolite alterations associated with steatosis (left half of each box/circle) and fibrosis (right half of each box/circle). (D) Metabolite alterations corresponding to the advancement of steatosis grades (x-axis). All source data are available in Supplementary files 4 and 5.
Figure 3—figure supplement 1
Integrative map of liver metabolism in obese metabolic dysfunction-associated steatotic liver disease (MASLD) patients.
(A) An integrative map of transcriptomics and metabolomics of glycerolipid and glycerophospholipid metabolism in early MASLD. (B) Gene expression changes in fatty acid β-oxidation, mitochondrial respiratory chain, and lipid droplet (LD) metabolism associated with steatosis and fibrosis. There was a notable downregulation of genes involved in the electron transport chain of the inner mitochondrial membrane, specifically in complex I (NDUF family), complex II (SDH family), complex III (UQCR), complex IV (COX), and complex V (ATP5 family) as fibrosis progressed. Genes involved in respiratory electron transport were colored by the significance [–log10(p-value)*sign(coefficient)] in relation to fibrosis grades. Inset: gene expression changes in the electron transport chain are depicted.
Figure 3—figure supplement 2
Ratios of metabolites in one-carbon metabolism in individuals with different steatosis grades.
SAM, S-adenosylmethionine; SAH, S-adenosylhomocysteine.
Figure 4 with 1 supplement
Dysregulated mitochondrial function and autophagy during the progression of steatosis and fibrosis.
Gene expression patterns related to mitochondrial metabolism (A), mitochondria-related biological processes (A), autophagy activation (B), and lysosomal biogenesis (B) with statistical association to steatosis and fibrosis. S, steatosis; F, fibrosis. All source data are available in Supplementary files 6 and 7.
Figure 4—figure supplement 1
Autophagy regulation in the liver.
(A) Hepatic free cholesterol, CE 18:1, and CE 18:2 levels in this cohort in relation to steatosis grade (x-axis). (B) Heatmap of autophagy genes in relation to histological features, indicating active lipophagy in the liver of obese individuals. Asterisks in the main heatmap denote the significance (p < 0.05) from comparisons of each subgroup. Association with steatosis (S), NAS (N), and fibrosis (F) was indicated by black dots (q < 0.05, left). Results were cross-referenced with two published cohorts (VA cohort, GSE130970; EU cohort, GSE135251) (*q < 0.05 and with consistent direction of changes as in our cohort, bottom).
Figure 5 with 2 supplements
Steatosis and fibrosis as independent processes.
(A) Network of genes, metabolites, and histological features (fibrosis and steatosis) using partial correlation analysis as described in the methods. (B) Venn diagrams depicting the number of genes significantly associated with steatosis and fibrosis (q-value <0.05) in the liver transcriptome. (C) Gene ontology (GO) enrichment of gene sets specific to steatosis or fibrosis. All source data are available in Supplementary files 8–10.
Figure 5—figure supplement 1
Functional enrichment of gene sets associated with steatosis and fibrosis in the liver transcriptome.
Node size is depicted by circles and p-value by color.
Figure 5—figure supplement 2
Statistical power and subgroup analysis of associations between gene expressions and steatosis or fibrosis grades.
Statistical power in the original linear regression analysis of steatosis and fibrosis was plotted against p-values (A). Differential features associated with steatosis (q < 0.05) showed power >0.8, whereas those associated with fibrosis showed power >0.7. Subgroup analyses of participants with and without diabetes were conducted to evaluate associations between gene expression and steatosis (B) and fibrosis (C). The x-axis shows coefficients from the original analysis including all participants. Corresponding coefficients from the analyses of non-diabetic (left panel) and diabetic (middle-left panel) individuals are displayed alongside the statistical power for each subgroup analysis (middle-right and right panels). Red dots represent genes that were significant in the original analysis (q < 0.05). Subgroup analyses suggest that the results in the non-diabetic subgroup (n = 71) were highly consistent with findings from the original analysis (n = 94, adjusted for diabetes), indicating that the originally reported gene signatures, after correction for diabetic status, remain valid in non-diabetic individuals.
Figure 6 with 5 supplements
Liver fibrosis signatures and potential therapeutic targets of fibrosis initiation.
(A) Top 60 fibrosis markers in the liver. A total of 213 genes were identified, from which the top 30 upregulated and top 30 downregulated genes are visualized. The middle plot (blue bar on top) shows the comparison of individuals with fibrosis and without pathology (baseline – ‘no MASLD’). For the right plot (red bar), individuals with fibrosis were compared to those with steatosis but no fibrosis (baseline – ‘steatosis but no fibrosis’). Results were cross-referenced with two published cohorts (VA cohort, GSE130970; EU cohort, GSE135251, right). Asterisks indicate genes with a q-value <0.05 and a consistent change in direction within our cohort. The average gene expressions of liver cell types were obtained from GSE115469 at the log2CPM level (left). Dots in the single-cell map indicate zero expression in the corresponding cell types. (B) Enrichment of pathways in the transition from simple steatosis to the onset of fibrosis. (C) Protein-protein interaction network of GTPases and their regulators. Nodes are colored based on gene type, with borders indicating the direction of gene regulation. Node size corresponds to the significance of the genes in relation to fibrosis grades within this cohort. GEFs: guanine nucleotide exchange factors. GAPs: GTPase-activating proteins. (D) Comparison of expression level changes in GTPase-related genes between this human cohort and an independent 3D spheroid MASH system. Log2 fold change for the human cohort was calculated by comparing patients with grade 2 or 3 fibrosis to those without fibrosis or steatosis. Genes with the same direction of change are colored in red, while others are colored in purple. (E) Expression of GTPase-related genes in patient-derived 3D liver spheroids: control spheroids, spheroids from patients with MASH, and Elafibranor-treated MASH spheroids (n = 4). Sixty-eight genes with a p-value <0.05 from the ANOVA test were plotted in the diagram, with blue lines highlighting 24 genes that exhibited increased expression in the MASH group compared to the control group. All source data are available in Supplementary files 11–13.
Figure 6—figure supplement 1
Liver fibrosis pathways and gene signatures.
(A) Pathway analysis of the enrichment of pathways for the 213 fibrosis gene signatures. (B) TGF-β and SMAD-related gene expression associated with fibrosis grades (x-axis) is depicted. Significance is indicated by black dots; •p < 0.05, ••p < 0.01, •••p < 0.001.
Figure 6—figure supplement 2
GTPases and their regulation emerge as a potential target for liver fibrosis.
(A) Heatmap of GTPase-related genes displays a better correlation with fibrosis grades. Asterisks in the main heatmap denote the significance (p < 0.05) from comparisons of each subgroup. (B) Enrichment map of selected genes constituting the core of the protein–protein interaction network of GTPase-related genes. Nodes are colored according to BH (Benjamini–Hochberg) adjusted p-value, and node sizes are proportional to the number of genes in the term. (C) Correlation matrix of gene expression levels for TGF-β and GTPase-related genes. The correlation plot was created of Pearson’s r values for GTPase-related genes and TGF-β genes (TGFB1 and TGFB3). The left annotation bar indicates the significance of associations between gene expression and fibrosis grades.
Figure 6—figure supplement 3
GTPase inhibition and Elafibranor treatment in HSC-derived LX2 cells.
(A) Workflow of the LX2 experiment in panels A–F (n = 8–10). (B) GTPase inhibitors NSC23766 (Rac1 inhibitor) and ML141 (Cdc42 inhibitor) significantly reduced pro-collagen secretion from HSC-like LX-2 cells and (C) decreased gene expression of COL1A1 and COL1A2 under basal conditions. (D) TGF-β administration increased pro-collagen secretion by 32% and (E) doubled collagen gene expression in LX2 cells. (F) NSC23766-mediated GTPase inhibition impairs pro-collagen secretion from LX-2 cells after TGF-β treatment. Pro-collagen secretion was determined by ELISA and gene expression levels were assessed by quantitative PCR (qPCR). Asterisks (*) denote p-value <0.05 for statistical significance from one-way ANOVA with Holm–Sidak multiple comparisons (B, C), unpaired t-test (D, E), Kruskal–Wallis Test with Dunn's multiple comparisons (F). (G) Gene expression of selected GTPases in LX-2 cells with and without Elafibranor (n = 3).
Figure 6—figure supplement 4
GTPase-related genes in external systems.
(A) Human fibrosis signatures from the present study were partially reflected in a mouse study (GSE176042), where RNA sequencing was performed on hepatic stellate cells (HSCs) isolated from both acute and chronic mouse fibrosis models. GTPase-related genes, especially those involved in the initiation of fibrosis, were also upregulated in HSCs isolated from both acute and chronic liver fibrosis mouse models at different time points. Row annotations ‘Linear’ and ‘Transition’ indicate whether gene expression is linearly associated with fibrosis grades (Linear = 1) or shows a significant difference between patients with fibrosis and steatosis versus simple steatosis (Transition = 1). Asterisk denotes genes with adjusted p-value <0.05. (B) GTPase-related genes induced by TGF-β were elevated in hepatocytes and HSCs at different stages of a human liver organoid system (GSE207889). Significance was calculated by –log10(p-value)*sign(fold change). AH, adult hepatocyte‐like; cAH, cycling adult hepatocyte‐like; CHOL, cholangiocyte‐like; DC, ductal cell‐like; FH1, fetal hepatocyte 1‐like; FIB, fibroblast‐like; HB1, hepatoblast 1‐like; HB2, hepatoblast 2‐like; HSC, hepatic stellate cell‐like; SMC, smooth muscle cell‐like. (C) Schematic illustration of the hypothesized role of GTPase signaling in regulating liver fibrogenesis. TGF-β production is initiated in non-parenchymal liver cells, leading to the overexpression of GTPase regulators in both hepatic stellate cells and hepatocytes. This activation of GTPase signaling amplifies downstream fibrogenic effects, creating a feed-forward loop that intensifies fibrosis progression. Further investigation is needed to prove by which mechanisms GTPases regulate collagen expression and therefore fibrosis.
Figure 6—figure supplement 5
Expression of GTPase-related genes in spheroid co-culture, hepatocyte monoculture, and LX-2 cells.
RNA-sequencing-based expression of GTPase-related genes in spheroid co-culture, hepatocyte monoculture upon control (Ctrl), TGF-β1, or TGFβ – inhibitor treatment (A, B). Expression of GTPase-related genes in control (Ctrl) and TGF-β1-treated (24 hr) LX2 cells measured by quantitative PCR (qPCR) (C). Asterisks (*) denote p-value <0.05, (**) p < 0.005, (***) p < 0.0001 for statistical significance from unpaired t-test, numerical values above the bars indicate p-value. Bars were removed where data did not show strong directionality and statistical reliability, as indicated by (^) symbol. Symbol (@) indicates one or more expression values were not detected in the original dataset. Please note that ACTA2 expression in LX-2 cells is time dependent (unpublished results) and therefore we also included fibronectin (FN1) as a control for TGF-β1 treatment.
Tables
Table 1
Patient characteristics.
| Patients(N) | No MASLD(N = 33) | MASLD(N = 76) | p-value | ||
|---|---|---|---|---|---|
| Patient information | |||||
| Age (years), median ± MAD | 109 | 36 (14.8) | 41.5 (9.6) | 0.942 | |
| Sex (male), n (%) | 108 | 4 (12.1) | 22 (29.3) | 0.092 | |
| Diabetes, n (%) | 109 | 2 (6.1) | 27 (35.5) | 0.003 | |
| Hypertension, n (%) | 93 | 6 (18.2) | 16 (26.7) | 0.505 | |
| BMI (kg/m2), median ± MAD | 108 | 42.6 (6.6) | 44.5 (9.1) | 0.045 | |
| Clinical chemistry parameters | |||||
| ALT (U/l), median ± MAD | 104 | 24 (11.9) | 42 (25.2) | 0.507 | |
| AST (U/l), median ± MAD | 104 | 24 (7.4) | 30 (13.3) | 0.39 | |
| ALT/AST, median ± MAD | 104 | 1 (0.3) | 1.33 (0.4) | 0.005 | |
| GGT (U/l), median ± MAD | 104 | 23 (10.4) | 28 (13.3) | 0.041 | |
| Total cholesterol (mmol/l), median ± MAD | 104 | 4 (0.9) | 4.15 (1.0) | 0.072 | |
| HDL (mmol/l), median ± MAD | 102 | 1.04 (0.2) | 0.94 (0.2) | 0.053 | |
| LDL (mmol/l), median ± MAD | 101 | 2.2 (0.7) | 2.6 (0.7) | 0.081 | |
| Non-HDL cholesterol (mmol/l), median ± MAD | 108 | 2.76 (0.9) | 3.27 (1.0) | 0.003 | |
| Blood triglyceride (mmol/l), median ± MAD | 103 | 1.2 (0.6) | 1.5 (0.6) | 0.013 | |
| Insulin (mU/l), median ± MAD | 100 | 7.95 (5.6) | 10.35 (7.1) | 0.043 | |
| FBG (mmol/l), median ± MAD | 102 | 4.9 (0.7) | 5.2 (1.0) | 0.114 | |
| C-peptide (nmol/l), median ± MAD | 101 | 0.8 (0.4) | 1.12 (0.4) | 0.002 | |
| HOMA2 – IR, median ± MAD | 92 | 1.13 (0.7) | 1.43 (0.9) | 0.034 | |
| Liver histology | |||||
| Steatosis (S0/S1/S2/S3) | 109 | 33/0/0/0 | 0/41/27/8 | <0.001 | |
| Inflammation (I0/I1/I2/I3) | 109 | 30/3/0/0 | 21/46/7/2 | <0.001 | |
| Ballooning (B0/B1/B2) | 109 | 33/0/0 | 65/10/1 | 0.07 | |
| NAS (N0/N1/N2/N3/ N4/N5/N6/N7) | 109 | 30/3/0/0/ 0/0/0/0 | 0/15/29/13/ 14/2/2/1 | <0.001 | |
| Fibrosis (F0/F1/F2/F3) | 109 | 27/6/0/0 | 23/27/20/6 | <0.001 | |
-
Additional patient data is available in Supplementary file 1.
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MASLD: metabolic dysfunction-associated steatotic liver disease; MAD: median absolute deviation; BMI: body mass index; ALT: alanine transaminase; AST: aspartate aminotransferase; GGT: gamma-glutamyl transpeptidase; HOMA2 – IR: homeostasis model assessment 2 of insulin resistance; HDL: high-density lipoprotein; LDL: low-density lipoprotein; FBG: fasting blood glucose; NAS: nonalcoholic fatty liver disease activity score.
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Cell line (Homo sapiens) | LX-2 Human Hepatic Stellate Cell Line | Merck | Cat. #: SCC064 RRID:CVCL_5792 | See Appendix |
| Cell line (Homo sapiens) | Patient-derived 3D MASH model | This paper | PMID:39605182 | |
| Biological sample (Homo sapiens) | Human plasma from obese individuals with MASLD | This paper | See Materials and methods | |
| Biological sample (Homo sapiens) | Human liver biopsies from obese individuals with MASLD | This paper | See Materials and methods | |
| Sequence-based reagent | COL1A1_F | This paper | PCR primers | GAACGCGTGTCATCCCTTGT |
| Sequence-based reagent | COL1A1_R | This paper | PCR primers | GAACGAGGTAGTCTTTCAGCAACA |
| Sequence-based reagent | COL1A2_F | This paper | PCR primers | GTGGTTACTACTGGATTGAC |
| Sequence-based reagent | COL1A2_R | This paper | PCR primers | CTGCCAGCATTGATAGTTTC |
| Sequence-based reagent | 18S_F | This paper | PCR primers | AACTTTCGATGGTAGTCGCCG |
| Sequence-based reagent | 18S_R | This paper | PCR primers | CCTTGGATGTGGTAGCCGTTT |
| Sequence-based reagent | PPARA_F | This paper | PCR primers | TCGGCGAGGATAGTTCTGGAAG |
| Sequence-based reagent | PPARA_R | This paper | PCR primers | GACCACAGGATAAGTCACCGAG |
| Sequence-based reagent | PPARG_F | This paper | PCR primers | AGCCTGCGAAAGCCTTTTGGTG |
| Sequence-based reagent | PPARG_R | This paper | PCR primers | GGCTTCACATTCAGCAAACCTGG |
| Sequence-based reagent | PPARD_F | This paper | PCR primers | GGCTTCCACTACGGTGTTCATG |
| Sequence-based reagent | PPARD_R | This paper | PCR primers | CTGGCACTTGTTGCGGTTCTTC |
| Sequence-based reagent | TGFB1_F | This paper | PCR primers | TACCTGAACCCGTGTTGCTCTC |
| Sequence-based reagent | TGFB1_R | This paper | PCR primers | GTTGCTGAGGTATCGCCAGGAA |
| Sequence-based reagent | TGFB2_F | This paper | PCR primers | GTCTGTGGATGACCTGGCTAAC |
| Sequence-based reagent | TGFB2_R | This paper | PCR primers | GACATCGGTCTGCTTGAAGGAC |
| Sequence-based reagent | FN1_F | This paper | PCR primers | ACAACACCGAGGTGACTGAGAC |
| Sequence-based reagent | FN1_R | This paper | PCR primers | GGACACAACGATGCTTCCTGAG |
| Sequence-based reagent | ACTA2_F | This paper | PCR primers | CTATGCCTCTGGACGCACAACT |
| Sequence-based reagent | ACTA2_R | This paper | PCR primers | CAGATCCAGACGCATGATGGCA |
| Sequence-based reagent | RAC1_F | This paper | PCR primers | CGGTGAATCTGGGCTTATGGGA |
| Sequence-based reagent | RAC1_R | This paper | PCR primers | GGAGGTTATATCCTTACCGTACG |
| Sequence-based reagent | RHOU_F | This paper | PCR primers | ACTGCCTTCGACAACTTCTCCG |
| Sequence-based reagent | RHOU_R | This paper | PCR primers | GAGCAGGAAGATGTCTGTGTTGG |
| Sequence-based reagent | VAV1_F | This paper | PCR primers | TCAGTGCGTGAACGAGGTCAAG |
| Sequence-based reagent | VAV1_R | This paper | PCR primers | CCATAGTGAGCCAGAGACTGGT |
| Sequence-based reagent | DOCK2_F | This paper | PCR primers | TGAAGCTGGACCACGAGGTAGA |
| Sequence-based reagent | DOCK2_R | This paper | PCR primers | GCCTTTGACCAGGTTCACGAAG |
| Sequence-based reagent | RAB32_F | This paper | PCR primers | TCATCAAGCGCTACGTCCACCA |
| Sequence-based reagent | RAB32_R | This paper | PCR primers | GGTCATGTTGCCAAATCGCTCC |
| Sequence-based reagent | RAB6A_F | This paper | PCR primers | CTCTTTCGACGTGTAGCAGCAG |
| Sequence-based reagent | RAB6A_R | This paper | PCR primers | CTGACGCAAAGAGAGCTGTCTC |
| Sequence-based reagent | ARL4A_F | This paper | PCR primers | CCTGTGCAATCATAGGAGATGGC |
| Sequence-based reagent | ARL4A_R | This paper | PCR primers | CAGAGAAAACCTACTCCACACAG |
| Sequence-based reagent | RAB27B_F | This paper | PCR primers | TGGCAACAAGGCAGACCTACCA |
| Sequence-based reagent | RAB27B_R | This paper | PCR primers | CTCCACATTCTGTCCAGTTGCTG |
| Sequence-based reagent | DIRAS2_F | This paper | PCR primers | CCATTACCAGCCGACAGTCCTT |
| Sequence-based reagent | DIRAS2_R | This paper | PCR primers | GGCTCTCATCACACTTGTTCCC |
| Sequence-based reagent | RPL27_F | This paper | PCR primers | ATCGCCAAGAGATCAAAGATAA |
| Sequence-based reagent | RPL27_R | This paper | PCR primers | TCTGAAGACATCCTTATTGACG |
| Commercial assay or kit | Human Pro-collagen 1A1 DuoSet ELISA | R&D Systems | Cat. #: DY6220-05 | |
| Commercial assay or kit | DuoSet ELISA Ancillary Reagent Kit 2 | R&D Systems | Cat. #: DY008B | |
| Chemical compound, drug | NSC23766 | MedChemExpress | Cat. #: HY-15723 | |
| Chemical compound, drug | ML141 | MedChemExpress | Cat. #: HY-12755 | |
| Chemical compound, drug | TGF-β1 | R&D Systems | Cat. #: 7754-BH | |
| Software, algorithm | R version 4.4.1 | The R Foundation | RRID:SCR_001905 | https://www.r-project.org/ |
| Software, algorithm | MetaboKit | Narayanaswamy et al., 2020 | https://github.com/MetaboKit/MetaboKit; Teo, 2025 | |
| Software, algorithm | ACCORD | Lee et al., 2025 | https://github.com/comp-stat/ACCORD/pkgs/container/accord; Kim, 2025 | |
| Software, algorithm | GraphPad Prism | GraphPad | RRID:SCR_002798 | |
| Software, algorithm | Metascape for Bioinformaticians | PMID:30944313 | RRID:SCR_016620 | https://metascape.org/gp/index.html#/menu/msbio |
| Software, algorithm | Cytoscape | PMID:14597658 | RRID:SCR_003032 | https://cytoscape.org/ |
Author response table 1
| Fibrosis grade 0 | Fibrosis grade 1 | Fibrosis grade 2 | Fibrosis grade 3 | |
|---|---|---|---|---|
| Diabetes | 8 | 9 | 9 | 3 |
| Non-diabetes | 42 | 24 | 11 | 3 |
Additional files
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Supplementary file 1
Additional patient characteristics.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp1-v1.docx
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Supplementary file 2
Top-enriched pathways of genes with nonzero loading scores on PC1 and PC2 in the sparse principal component analysis (PCA) of the liver transcriptome.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp2-v1.xlsx
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Supplementary file 3
Statistical analysis of metabolomics data for plasma samples.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp3-v1.xlsx
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Supplementary file 4
Statistical analysis of metabolomics data for liver samples.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp4-v1.xlsx
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Supplementary file 5
Statistical analysis of transcriptomics data for liver samples.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp5-v1.xlsx
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Supplementary file 6
Associations between mitochondrial function-related genes and liver histological grades.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp6-v1.xlsx
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Supplementary file 7
Associations between autophagy-related genes and liver histological grades.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp7-v1.xlsx
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Supplementary file 8
Over-representation analysis enrichment analysis of genes linearly associated with steatosis or fibrosis grades.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp8-v1.xlsx
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Supplementary file 9
Steatosis- and fibrosis-specific gene signatures.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp9-v1.xlsx
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Supplementary file 10
Subgroup statistical analysis of liver transcriptome in diabetic and non-diabetic individuals.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp10-v1.xlsx
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Supplementary file 11
Progressive gene markers associated with liver fibrosis in MASLD patients.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp11-v1.xlsx
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Supplementary file 12
Genes involved in the transition from fibrosis-free steatosis to fibrosis.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp12-v1.xlsx
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Supplementary file 13
Associations between GTPase-related genes and liver histological grades.
- https://cdn.elifesciences.org/articles/109534/elife-109534-supp13-v1.xlsx
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MDAR checklist
- https://cdn.elifesciences.org/articles/109534/elife-109534-mdarchecklist1-v1.pdf
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Global molecular landscape of early MASLD progression in human obesity
eLife 14:RP109534.
https://doi.org/10.7554/eLife.109534.3
