using Metascape (http://metascape.org) [105]. These analyses were per-
formed on the top 500 significant (q-value < 0.05) up-regulated or down-
regulated genes classified based on log₂ fold-changes (log₂ FC) issued from
the RNA-seq analyses. Alternatively, genes assigned to regions with
differential H3K27ac ChIP-seq signal were also used. GOBPs were further
clustered using MonaGO (https://monago.erc.monash.edu/) [106] based on
the average Resnik similarity between terms using 1.5 as cutoff [107].

Pre-ranked Gene-Set Enrichment Analyses (GSEA) were performed using the GSEA software (v3.0) [108] with the following settings: 1000 permutations, “weighted” as enrichment statistic and log₂ FC as metric for ranking genes. The following gene sets were used : 61 liver fibrosis-associated genes of human MASH patient livers and in vitro and in vivo mouse models for liver fibrosis [47]; 163 genes defining a fibrogenic MF-HSC program of in vivo mouse models for liver fibrosis and reversal [45] and 71 liver fibrosis matrisome genes coding ECM proteins of human fibrotic livers (http://matrisomedb.pepchem.org/ [46]) (Supplementary Table 3). For the murine HSCs data, human ortholog genes were first retrieved using gProfiler (https://bitcs.ut.ee/gprofiler/orth) [109]. In addition to enrichment plots, the normalized enrichment score (NES) and the FDR were also retrieved and displayed in the figures.

HSC gene expression specificity indexes

Normalized relative log expression (RLE) data (CAGE-seq) from 126 human differentiated primary cells in basal condition were downloaded from the FANTOM5 website (https://fantom.gsc.riken.jp/5/sstar) [110] and used to define HSC gene expression specificity indexes. Specificity indexes correspond to log₂ FC of average gene expression between MF-HSCs and other cell types.

Statistical analyses

Statistical analyses were performed using the GraphPad Prism software (v9.4.1, GraphPad). Specific tests and corrections for multiple testing that were used are indicated in the figure legends. Statistical significance was displayed as follow *q* < 0.05, **q** < 0.01 and ***q** < 0.001. Moreover, green and red colors were used to indicate a significant increase or decrease, respectively, while gray color was used to indicate no statistically significant changes.

DATA AVAILABILITY

All RNA-seq and ChIP-seq data generated in this study have been deposited into the Gene Expression Omnibus (GEO) database under accession number GSE243107. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD045860 for the characterization of the LX-2 O-GlcNAcome and the identification of BNC2 and TEAD4 O-GlcNAcylated amino-acid residues.

REFERENCES

1. Schuster R, Younesi F, Ezzo M, Hinz B. The role of myofibroblasts in physiological and pathological tissue repair. Cold Spring Harb Perspect Biol. 2023;15:a041231.

2. Rockey DC, Bell PD, Hill JA. Fibrosis-a common pathway to organ injury and failure. N Engl J Med. 2015;372:1138-49.

3. Distler JHW, Győrfi A-H, Ramanujam M, Whitfield ML, Königshoff M, Lafyatis R. Shared and distinct mechanisms of fibrosis. Nat Rev Rheumatol. 2019;15:705-30.

4. Henderson NC, Rieder F, Wynn TA. Fibrosis: from mechanisms to medicines. Nature. 2020;587:555-66.

5. Pakshir P, Noskovicova N, Lodyga M, Son DO, Schuster R, Goodwin A, et al. The myofibroblast at a glance. J Cell Sci. 2020;133:jcs227900.

6. Bobowski-Gerard M, Boulet C, Zummo FP, Dubois-Chevalier J, Gheeraert C, Bou Saleh M, et al. Functional genomics uncovers the transcription factor BNC2 as required for myofibroblastic activation in fibrosis. Nat Commun. 2022;13:5324.

7. Santos A, Lagares D. Matrix stiffness: the conductor of organ fibrosis. Curr Rheumatol Rep. 2018;20:2.

8. Tsuchida T, Friedman SL. Mechanisms of hepatic stellate cell activation. Nat Rev Gastroenterol Hepatol. 2017;14:397-411.

9. Walton KL, Johnson KE, Harrison CA. Targeting TGF-β mediated SMAD signaling for the prevention of fibrosis. Front Pharmacol. 2017;8:461.

10. Scarpa M, Grillo AR, Brun P, Macchi V, Stefani A, Signori S, et al. Snail1 trans-
scription factor is a critical mediator of hepatic stellate cell activation following
hepatic injury. Am J Physiol Gastrointest Liver Physiol. 2011;300:G316-26.

11. Jayachandran A, Königshoff M, Yu H, Rupniewska E, Hecker M, Klepetko W, et al. SNAI transcription factors mediate epithelial-mesenchymal transition in lung fibrosis. Thorax. 2009;64:1053-61.

12. Chen L, Chen R, Kemper S, Charrier A, Brigstock DR. Suppression of fibrogenic signaling in hepatic stellate cells by Twist1-dependent microRNA-214 expression: Role of exosomes in horizontal transfer of Twist1. Am J Physiol-Gastrointest Liver Physiol. 2015;309:G491-G499.

13. Ning X, Zhang K, Wu Q, Liu M, Sun S. Emerging role of Twist1 in fibrotic diseases. J Cell Mol Med. 2018;22:1383-91.

14. Liu X, Xu J, Rosenthal S, Zhang L, McCubbin R, Meshgin N, et al. Identification of lineage-specific transcription factors that prevent activation of hepatic stellate cells and promote fibrosis resolution. Gastroenterology. 2020;158:1728-1744.e14.

15. Jokl E, Mullan AF, Simpson K, Birchall L, Pearmain L, Martin K, et al. PAK1-dependent mechanotransduction enables myofibroblast nuclear adaptation and chromatin organisation during fibrosis. Cell Rep. 42, 113414 (2023).

16. Mia MM, Singh MK. New insights into hippo/YAP signaling in fibrotic diseases. Cells. 2022;11:2065.

17. Delgado ME, Cárdenas BI, Farran N, Fernandez M. Metabolic reprogramming of liver fibrosis. Cells. 2021;10:3604.

18. Bernard K, Logsdon NJ, Ravi S, Xie N, Persons BP, Rangarajan S, et al. Metabolic reprogramming is required for myofibroblast contractility and differentiation. J Biol Chem. 2015;290:25427-38.

19. Khomich O, Ivanov AV, Bartosch B. Metabolic hallmarks of hepatic stellate cells in liver fibrosis. Cells. 2019;9. https://doi.org/10.3390/cells9010024.

20. Trivedi P, Wang S, Friedman SL. The power of plasticity—metabolic regulation of hepatic stellate cells. Cell Metab. 2021;33:242–57.

21. Wang S, Liang Y, Dai C. Metabolic regulation of fibroblast activation and proliferation during organ fibrosis. Kidney Dis. 2022;8:115-25.

22. Chatham JC, Zhang J, Wende AR. Role of O-linked N-acetylglucosamine protein modification in cellular (patho)physiology. Physiol Rev. 2021;101:427-93.

23. Very N, Vercoutter-Edouart A-S, Lefebvre T, Hardivillé S, El Yazidi-Belkoura I. Cross-dysregulation of O-GlcNAcylation and PI3K/AKT/mTOR axis in human chronic diseases. Front Endocrinol. 2018;9:602.

24. Swamy M, Pathak S, Grzes KM, Damerow S, Sinclair LV, van Aalten DMF, et al. Glucose and glutamine fuel protein O-GlcNAcylation to control T cell self-renewal and malignancy. Nat Immunol. 2016;17:712-20.

25. Marshall S, Nadeau O, Yamasaki K. Dynamic actions of glucose and glucosamine on hexosamine biosynthesis in isolated adipocytes: differential effects on glucosamine 6-phosphate, Udp-N-acetylglucosamine, and Atp levels *. J Biol Chem. 2004;279:35313-9.

26. Yang X, Qian K. Protein O-GlcNAcylation: emerging mechanisms and functions. Nat Rev Mol Cell Biol. 2017;18:452-65.

27. Wang F, Chen L, Zhang B, Li Z, Shen M, Tang L, et al. O-GlcNAcylation coordinates glutaminolysis by regulating the stability and membrane trafficking of ASCT2 in hepatic stellate cells. J Clin Transl Hepatol. 2022;10:1107-16.

28. Li R, Ong Q, Wong CC, Chu ESH, Sung JJY, Yang X, et al. O-GlcNAcylation inhibits hepatic stellate cell activation. J Gastroenterol Hepatol. 2021;36:3477-86.

29. Aguilar H, Fricovsky E, Ihm S, Schimke M, Maya-Ramos L, Aroonsakool N, et al. Role for high-glucose-induced protein O-GlcNAcylation in stimulating cardiac fibroblast collagen synthesis. Am J Physiol Cell Physiol. 2014;306:C794–C804.

30. Kisseleva T, Brenner D. Molecular and cellular mechanisms of liver fibrosis and its regression. Nat Rev Gastroenterol Hepatol. 2021;18:151-66.

31. Higashi T, Friedman SL, Hoshida Y. Hepatic stellate cells as key target in liver fibrosis. Adv Drug Deliv Rev. 2017;121:27-42.

32. Olsen AL, Bloomer SA, Chan EP, Gaça MDA, Georges PC, Sackey B, et al. Hepatic stellate cells require a stiff environment for myofibroblastic differentiation. Am J Physiol-Gastrointest Liver Physiol. 2011;301:G110-G118.

33. Du K, Hyun J, Premont RT, Choi SS, Michelotti GA, Swiderska-Syn M, et al. Hedgehog-YAP signaling pathway regulates glutaminolysis to control hepatic stellate cell activation. Gastroenterology. 2018;154:1465-79.e13.

34. Chen Y, Choi SS, Michelotti GA, Chan IS, Swiderska M, Karaca GF, et al. Hedgehog controls hepatic stellate cell fate by regulating metabolism. Gastroenterology. 2012;143:1319-29.e11.

35. Buchholz M, Kestler HA, Holzmann K, Ellenrieder V, Schneiderhan W, Siech M, et al. Transcriptome analysis of human hepatic and pancreatic stellate cells: organ-specific variations of a common transcriptional phenotype. J Mol Med. 2005;83:795-805.

36. De Minicis S, Seki E, Uchinami H, Kluwe J, Zhang Y, Brenner DA, et al. Gene expression profiles during hepatic stellate cell activation in culture and in vivo. Gastroenterology. 2007;132:1937-46.

37. Marcher A-B, Bendixen SM, Terkelsen MK, Hohmann SS, Hansen MH, Larsen BD, et al. Transcriptional regulation of hepatic stellate cell activation in NASH. Sci Rep. 2019;9:2324.

38. Mannaerts I, Leite SB, Verhulst S, Claerhout S, Eysackers N, Thoen LFR, et al. The Hippo pathway effector YAP controls mouse hepatic stellate cell activation. J Hepatol. 2015;63:679-88.
using Metascape (http://metascape.org) [105]. These analyses were per-
formed on the top 500 significant (q-value < 0.05) up-regulated or down-
regulated genes classified based on log₂ fold-changes (log₂ FC) issued from
the RNA-seq analyses. Alternatively, genes assigned to regions with
differential H3K27ac ChIP-seq signal were also used. GOBPs were further
clustered using MonaGO (https://monago.erc.monash.edu/) [106] based on
the average Resnik similarity between terms using 1.5 as cutoff [107].

Pre-ranked Gene-Set Enrichment Analyses (GSEA) were performed using the GSEA software (v3.0) [108] with the following settings: 1000 permutations, “weighted” as enrichment statistic and log₂ FC as metric for ranking genes. The following gene sets were used : 61 liver fibrosis-associated genes of human MASH patient livers and in vitro and in vivo mouse models for liver fibrosis [47]; 163 genes defining a fibrogenic MF-HSC program of in vivo mouse models for liver fibrosis and reversal [45] and 71 liver fibrosis matrisome genes coding ECM proteins of human fibrotic livers (http://matrisomedb.pepchem.org/ [46]) (Supplementary Table 3). For the murine HSCs data, human ortholog genes were first retrieved using gProfiler (https://bitcs.ut.ee/gprofiler/orth) [109]. In addition to enrichment plots, the normalized enrichment score (NES) and the FDR were also retrieved and displayed in the figures.

HSC gene expression specificity indexes

Normalized relative log expression (RLE) data (CAGE-seq) from 126 human differentiated primary cells in basal condition were downloaded from the FANTOM5 website (https://fantom.gsc.riken.jp/5/sstar) [110] and used to define HSC gene expression specificity indexes. Specificity indexes correspond to log₂ FC of average gene expression between MF-HSCs and other cell types.

Statistical analyses

Statistical analyses were performed using the GraphPad Prism software (v9.4.1, GraphPad). Specific tests and corrections for multiple testing that were used are indicated in the figure legends. Statistical significance was displayed as follow *q* < 0.05, **q** < 0.01 and ***q** < 0.001. Moreover, green and red colors were used to indicate a significant increase or decrease, respectively, while gray color was used to indicate no statistically significant changes.

DATA AVAILABILITY

All RNA-seq and ChIP-seq data generated in this study have been deposited into the Gene Expression Omnibus (GEO) database under accession number GSE243107. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD045860 for the characterization of the LX-2 O-GlcNAcome and the identification of BNC2 and TEAD4 O-GlcNAcylated amino-acid residues.

REFERENCES

1. Schuster R, Younesi F, Ezzo M, Hinz B. The role of myofibroblasts in physiological and pathological tissue repair. Cold Spring Harb Perspect Biol. 2023;15:a041231.

2. Rockey DC, Bell PD, Hill JA. Fibrosis-a common pathway to organ injury and failure. N Engl J Med. 2015;372:1138-49.

3. Distler JHW, Győrf A-H, Ramanujam M, Whitfield ML, Königshoff M, Lafyatis R. Shared and distinct mechanisms of fibrosis. Nat Rev Rheumatol. 2019;15:705-30.

4. Henderson NC, Rieder F, Wynn TA. Fibrosis: from mechanisms to medicines. Nature. 2020;587:555-66.

5. Pakshir P, Noskovicova N, Lodyga M, Son DO, Schuster R, Goodwin A, et al. The myofibroblast at a glance. J Cell Sci. 2020;133:jcs227900.

6. Bobowski-Gerard M, Boulet C, Zummo FP, Dubois-Chevalier J, Gheeraert C, Bou Saleh M, et al. Functional genomics uncovers the transcription factor BNC2 as required for myofibroblastic activation in fibrosis. Nat Commun. 2022;13:5324.

7. Santos A, Lagares D. Matrix stiffness: the conductor of organ fibrosis. Curr Rheumatol Rep. 2018;20:2.

8. Tsuchida T, Friedman SL. Mechanisms of hepatic stellate cell activation. Nat Rev Gastroenterol Hepatol. 2017;14:397-411.

9. Walton KL, Johnson KE, Harrison CA. Targeting TGF-β mediated SMAD signaling for the prevention of fibrosis. Front Pharmacol. 2017;8:461.

10. Scarpa M, Grillo AR, Brun P, Macchi V, Stefani A, Signori S, et al. Snail1 trans-
scription factor is a critical mediator of hepatic stellate cell activation following
hepatic injury. Am J Physiol Gastrointest Liver Physiol. 2011;300:G316-26.

11. Jayachandran A, Königshoff M, Yu H, Rupniewska E, Hecker M, Klepetko W, et al. SNAI transcription factors mediate epithelial-mesenchymal transition in lung fibrosis. Thorax. 2009;64:1053-61.

12. Chen L, Chen R, Kemper S, Charrier A, Brigstock DR. Suppression of fibrogenic signaling in hepatic stellate cells by Twist1-dependent microRNA-214 expression: Role of exosomes in horizontal transfer of Twist1. Am J Physiol-Gastrointest Liver Physiol. 2015;309:G491-G499.

13. Ning X, Zhang K, Wu Q, Liu M, Sun S. Emerging role of Twist1 in fibrotic diseases. J Cell Mol Med. 2018;22:1383-91.

14. Liu X, Xu J, Rosenthal S, Zhang L, McCubbin R, Meshgin N, et al. Identification of lineage-specific transcription factors that prevent activation of hepatic stellate cells and promote fibrosis resolution. Gastroenterology. 2020;158:1728-1744.e14.

15. Jokl E, Mullan AF, Simpson K, Birchall L, Pearmain L, Martin K, et al. PAK1-dependent mechanotransduction enables myofibroblast nuclear adaptation and chromatin organisation during fibrosis. Cell Rep. 42, 113414 (2023).

16. Mia MM, Singh MK. New insights into hippo/YAP signaling in fibrotic diseases. Cells. 2022;11:2065.

17. Delgado ME, Cárdenas BI, Farran N, Fernandez M. Metabolic reprogramming of liver fibrosis. Cells. 2021;10:3604.

18. Bernard K, Logsdon NJ, Ravi S, Xie N, Persons BP, Rangarajan S, et al. Metabolic reprogramming is required for myofibroblast contractility and differentiation. J Biol Chem. 2015;290:25427-38.

19. Khomich O, Ivanov AV, Bartosch B. Metabolic hallmarks of hepatic stellate cells in liver fibrosis. Cells. 2019;9. https://doi.org/10.3390/cells9010024.

20. Trivedi P, Wang S, Friedman SL. The power of plasticity—metabolic regulation of hepatic stellate cells. Cell Metab. 2021;33:242–57.

21. Wang S, Liang Y, Dai C. Metabolic regulation of fibroblast activation and proliferation during organ fibrosis. Kidney Dis. 2022;8:115-25.

22. Chatham JC, Zhang J, Wende AR. Role of O-linked N-acetylglucosamine protein modification in cellular (patho)physiology. Physiol Rev. 2021;101:427-93.

23. Very N, Vercoutter-Edouart A-S, Lefebvre T, Hardivillé S, El Yazidi-Belkoura I. Cross-dysregulation of O-GlcNAcylation and PI3K/AKT/mTOR axis in human chronic diseases. Front Endocrinol. 2018;9:602.

24. Swamy M, Pathak S, Grzes KM, Damerow S, Sinclair LV, van Aalten DMF, et al. Glucose and glutamine fuel protein O-GlcNAcylation to control T cell self-renewal and malignancy. Nat Immunol. 2016;17:712-20.

25. Marshall S, Nadeau O, Yamasaki K. Dynamic actions of glucose and glucosamine on hexosamine biosynthesis in isolated adipocytes: differential effects on glucosamine 6-phosphate, Udp-N-acetylglucosamine, and Atp levels *. J Biol Chem. 2004;279:35313-9.

26. Yang X, Qian K. Protein O-GlcNAcylation: emerging mechanisms and functions. Nat Rev Mol Cell Biol. 2017;18:452-65.

27. Wang F, Chen L, Zhang B, Li Z, Shen M, Tang L, et al. O-GlcNAcylation coordinates glutaminolysis by regulating the stability and membrane trafficking of ASCT2 in hepatic stellate cells. J Clin Transl Hepatol. 2022;10:1107-16.

28. Li R, Ong Q, Wong CC, Chu ESH, Sung JJY, Yang X, et al. O-GlcNAcylation inhibits hepatic stellate cell activation. J Gastroenterol Hepatol. 2021;36:3477-86.

29. Aguilar H, Fricovsky E, Ihm S, Schimke M, Maya-Ramos L, Aroonsakool N, et al. Role for high-glucose-induced protein O-GlcNAcylation in stimulating cardiac fibroblast collagen synthesis. Am J Physiol Cell Physiol. 2014;306:C794–C804.

30. Kisseleva T, Brenner D. Molecular and cellular mechanisms of liver fibrosis and its regression. Nat Rev Gastroenterol Hepatol. 2021;18:151-66.

31. Higashi T, Friedman SL, Hoshida Y. Hepatic stellate cells as key target in liver fibrosis. Adv Drug Deliv Rev. 2017;121:27-42.

32. Olsen AL, Bloomer SA, Chan EP, Gaça MDA, Georges PC, Sackey B, et al. Hepatic stellate cells require a stiff environment for myofibroblastic differentiation. Am J Physiol-Gastrointest Liver Physiol. 2011;301:G110-G118.

33. Du K, Hyun J, Premont RT, Choi SS, Michelotti GA, Swiderska-Syn M, et al. Hedgehog-YAP signaling pathway regulates glutaminolysis to control hepatic stellate cell activation. Gastroenterology. 2018;154:1465-79.e13.

34. Chen Y, Choi SS, Michelotti GA, Chan IS, Swiderska M, Karaca GF, et al. Hedgehog controls hepatic stellate cell fate by regulating metabolism. Gastroenterology. 2012;143:1319-29.e11.

35. Buchholz M, Kestler HA, Holzmann K, Ellenrieder V, Schneiderhan W, Siech M, et al. Transcriptome analysis of human hepatic and pancreatic stellate cells: organ-specific variations of a common transcriptional phenotype. J Mol Med. 2005;83:795-805.

36. De Minicis S, Seki E, Uchinami H, Kluwe J, Zhang Y, Brenner DA, et al. Gene expression profiles during hepatic stellate cell activation in culture and in vivo. Gastroenterology. 2007;132:1937-46.

37. Marcher A-B, Bendixen SM, Terkelsen MK, Hohmann SS, Hansen MH, Larsen BD, et al. Transcriptional regulation of hepatic stellate cell activation in NASH. Sci Rep. 2019;9:2324.

38. Mannaerts I, Leite SB, Verhulst S, Claerhout S, Eysackers N, Thoen LFR, et al. The Hippo pathway effector YAP controls mouse hepatic stellate cell activation. J Hepatol. 2015;63:679-88.
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79. Zummo FP, Berthier A, Gheeraert C, Vinod M, Bobowski-Gérard M, Molendi-Coste O, et al. A time- and space-resolved nuclear receptor atlas in mouse liver. J Mol Endocrinol. 2023;71:e230017.

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77. Louvet A, Labreuche J, Artru F, Bouthors A, Rolland B, Saffers P, et al. Main drivers of outcome differ between short term and long term in severe alcoholic hepatitis: a prospective study. Hepatology. 2017;66:1464–73.

78. Bou Saleh M, Louvet A, Ntandja-Wandji LC, Boleslawski E, Gnemmi V, Lassailly G, et al. Loss of hepatocyte identity following aberrant YAP activation: a key mechanism in alcoholic hepatitis. J Hepatol. 2021;75:912–23.

79. Zummo FP, Berthier A, Gheeraert C, Vinod M, Bobowski-Gérard M, Molendi-Coste O, et al. A time- and space-resolved nuclear receptor atlas in mouse liver. J Mol Endocrinol. 2023;71:e230017.

80. Nagaraju CK, Dries E, Popovic N, Singh AA, Haemers P, Roderick HL, et al. Global fibroblast activation throughout the left ventricle but localized fibrosis after myocardial infarction. Sci Rep. 2017;7:10801.

81. Yoo S-H, Yamazaki S, Lowrey PL, Shimomura K, Ko CH, Buhr ED, et al. PERIOD2:LUCIFERASE real-time reporting of circadian dynamics reveals persistent circadian oscillations in mouse peripheral tissues. Proc Natl Acad Sci USA. 2004;101:5339–46.

82. Berthier A, Gheeraert C, Johanns M, Vinod M, Staels B, Eeckhoute J, et al. The molecular circadian clock is a target of anti-cancer translation inhibitors. J Biol Rhythms. 2023;39:20–34.

83. Martin-Burgos B, Wang W, William I, Tir S, Mohammad I, Javed R, et al. Methods for detecting PER2:LUCIFERASE bioluminescence rhythms in freely moving mice. J Biol Rhythms. 2022;37:78–93.

84. Dubois V, Gheeraert C, Vankrunkelsven W, Dubois-Chevalier J, Dehondt H, Bobowski-Gérard M, et al. Endoplasmic reticulum stress actively suppresses hepatic molecular identity in damaged liver. Mol Syst Biol. 2020;16:e9156.

85. de Chaumont F, Dallongeville S, Chenouard N, Hervé N, Pop S, Provoost T, et al. Icy: an open bioimage informatics platform for extended reproducible research. Nat Methods. 2012;9:690–6.

86. Ligasová A, Koberna K. Quantification of fixed adherent cells using a strong enhancer of the fluorescence of DNA dyes. Sci Rep. 2019;9:8701.

87. Zhang H, Song L, Wang X, Cheng H, Wang C, Meyer CA, et al. Fast alignment and preprocessing of chromatin profiles with Chromap. Nat Commun. 2021;12:6566.

88. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9:R137.

89. Afgan E, Baker D, Batut B, van den Beek M, Bouvier D, Cech M, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46:W537–W544.

90. Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: identification of problematic regions of the genome. Sci Rep. 2019;9:9354.

91. Tu S, Li M, Chen H, Tan F, Xu J, Waxman DJ, et al. MANorm2 for quantitatively comparing groups of ChIP-seq samples. Genome Res. 2021;31:131–45.

92. Ramirez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, et al. deep-Tools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 2016;44:W160–W165.
93. Boleu N, Kundaje A, Bickel P, Li Q. Irreproducible discovery rate. Berkley, CA, available at: https://github.com.

94. Hitz BC, Lee J-W, Jolanki O, Kagda MS, Graham K, Sud P, et al. The ENCODE uniform analysis pipelines. bioRxiv https://doi.org/10.1101/2023.04.04.535623 (2023).

95. Sheffield NC, Bock C. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics. 2016;32:587-9.

96. Leporcq C, Spill Y, Balaramane D, Toussaint C, Weber M, Bardet AF. TFmotifView: a webserver for the visualization of transcription factor motifs in genomic regions. Nucleic Acids Res. 2020;48:W208-W217.

97. Santana-Garcia W, Castro-Mondragon JA, Padilla-Gálvez M, Nguyen NTT, Elizondo-Salas A, Ksouri N, et al. RSAT 2022: regulatory sequence analysis tools. Nucleic Acids Res. 2022;50:W670-676.

98. Hait TA, Amar D, Shamir R, Elkon R. FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map. Genome Biol. 2018;19:56.

99. Mellacheruvu D, Wright Z, Couzens AL, Lambert J-P, St-Denis NA, Li T, et al. The CRApome: a contaminant repository for affinity purification-mass spectrometry data. Nat Methods. 2013;10:730-6.

100. Hu H, Miao Y-R, Jia L-H, Yu Q-Y, Zhang Q, Guo A-Y. AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors. Nucleic Acids Res. 2019;47:D33-D38.

101. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357-9.

102. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011;12:323.

103. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

104. Vandel J, Gheeraert C, Staels B, Eeckhoutje J, Lefebvre P, Dubois-Chevalier J. GIANT: galaxy-based tool for interactive analysis of transcriptomic data. Sci Rep. 2020;10:19835.

105. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.

106. Xin Z, Cai Y, Dang LT, Burke HMS, Revote J, Charitakis N, et al. MonaGO: a novel gene ontology enrichment analysis visualisation system. BMC Bioinform. 2022;23:69.

107. Resnik P. Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res. 1999;11:95-130.

108. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545-50.

109. Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H, et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019;47:W191-W198.

110. Lizio M, Abugessais I, Noguchi S, Kondo A, Hasegawa A, Hon CC, et al. Update of the FANTOM web resource: expansion to provide additional transcriptome atlases. Nucleic Acids Res. 2019;47:D752-D758.

## ACKNOWLEDGEMENTS

The authors are indebted to Nathalie Jouy, Emilie Floquet, Antonino Bongiovanni, and Sarah Gabut from the Biolmaging Center Lille (BIceL) platform (UMR 2014 - US 41, PLBS - Plateformes Lilloises en Biologie et Santé - Univ. Lille, Lille, France) for their assistance. The authors acknowledge the IBiSA network for the support of the UAR 3290 (MSAP) proteomics facility. This work was supported by the Association Française pour l'Etude du Foie (AFEF), Fondation pour la Recherche Médicale (FRM grants EQU202203014645 and EQU202003010299) and the Agence Nationale de la Recherche (ANR) grants "HSCreg" (ANR-21-CE14-0032-01), "MEDICAL" (ANR-21-CE17-0016-02), "PreciNASH" (ANR-16-RHUS-0006-PreciNASH) and "European Genomic

Institute for Diabetes" E.G.I.D. (ANR-10-LABX-0046), a French State fund managed by ANR under the frame program Investissements d'Avenir I-SITE ULNE/ANR-16-IDEX-0004 ULNE. This work also received support from the Université de Strasbourg and the ANR and the French Proteomic Infrastructure (ANR-10-INBS-08 - 03).

## AUTHOR CONTRIBUTIONS

Conceptualization: NV, LD, BS, PL, and JE; Methodology: NV, CB, CG, AB, and JDC; Software: LG and JDC; Validation: NV and CB; Formal analysis: NV, MBS, LG, FB, JMS, LD, JDC, and JE; Investigation: NV, CB, CG, MJ, MBS, FB, JMS, MBG, FPZ, EV, OMC, EW, and JDC; Resources: MBS, SC, DM, LCNW, and LD; Data curation: NV, LG, FB, JMS, JDC, and JE; Writing - original draft preparation: NV and JE; Writing - review and editing: all authors; Visualization: NV, CB, MBS, FB, and JE; Supervision: NV, JDC, and JE; Project administration: NV and JE; Funding acquisition: NV, BS, PL, and JE.

## COMPETING INTERESTS

The authors declare no competing interests.

## ETHICAL APPROVAL

The research was carried out according to the World Medical Association Declaration of Helsinki. All animal studies were performed in compliance with EU specifications regarding the use of laboratory animals and have been approved by the Nord-Pas de Calais Ethical Committee (CEEA-075) (APAFIS#15539). Studies with human samples received ethical authorizations from the Lille University Hospital or Comité de Protection des Personnes Ile de France V and patients provided informed consent.

## ADDITIONAL INFORMATION

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41419-024-06773-9.

**Correspondence** and requests for materials should be addressed to Jérôme Eeckhoutje.

**Reprints and permission information** is available at http://www.nature.com/reprints

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_39.png" /></figure>

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2024
93. Boleu N, Kundaje A, Bickel P, Li Q. Irreproducible discovery rate. Berkley, CA, available at: https://github.com.

94. Hitz BC, Lee J-W, Jolanki O, Kagda MS, Graham K, Sud P, et al. The ENCODE uniform analysis pipelines. bioRxiv https://doi.org/10.1101/2023.04.04.535623 (2023).

95. Sheffield NC, Bock C. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics. 2016;32:587-9.

96. Leporcq C, Spill Y, Balaramane D, Toussaint C, Weber M, Bardet AF. TFmotifView: a webserver for the visualization of transcription factor motifs in genomic regions. Nucleic Acids Res. 2020;48:W208-W217.

97. Santana-Garcia W, Castro-Mondragon JA, Padilla-Gálvez M, Nguyen NTT, Elizondo-Salas A, Ksouri N, et al. RSAT 2022: regulatory sequence analysis tools. Nucleic Acids Res. 2022;50:W670-676.

98. Hait TA, Amar D, Shamir R, Elkon R. FOCS: a novel method for analyzing enhancer and gene activity patterns infers an extensive enhancer-promoter map. Genome Biol. 2018;19:56.

99. Mellacheruvu D, Wright Z, Couzens AL, Lambert J-P, St-Denis NA, Li T, et al. The CRApome: a contaminant repository for affinity purification-mass spectrometry data. Nat Methods. 2013;10:730-6.

100. Hu H, Miao Y-R, Jia L-H, Yu Q-Y, Zhang Q, Guo A-Y. AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors. Nucleic Acids Res. 2019;47:D33-D38.

101. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357-9.

102. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011;12:323.

103. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

104. Vandel J, Gheeraert C, Staels B, Eeckhoute J, Lefebvre P, Dubois-Chevalier J. GIANT: galaxy-based tool for interactive analysis of transcriptomic data. Sci Rep. 2020;10:19835.

105. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.

106. Xin Z, Cai Y, Dang LT, Burke HMS, Revote J, Charitakis N, et al. MonaGO: a novel gene ontology enrichment analysis visualisation system. BMC Bioinform. 2022;23:69.

107. Resnik P. Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res. 1999;11:95-130.

108. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545-50.

109. Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H, et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019;47:W191-W198.

110. Lizio M, Abugessais I, Noguchi S, Kondo A, Hasegawa A, Hon CC, et al. Update of the FANTOM web resource: expansion to provide additional transcriptome atlases. Nucleic Acids Res. 2019;47:D752-D758.

## ACKNOWLEDGEMENTS

The authors are indebted to Nathalie Jouy, Emilie Floquet, Antonino Bongiovanni, and Sarah Gabut from the Biolmaging Center Lille (BIceL) platform (UMR 2014 - US 41, PLBS - Plateformes Lilloises en Biologie et Santé - Univ. Lille, Lille, France) for their assistance. The authors acknowledge the IBiSA network for the support of the UAR 3290 (MSAP) proteomics facility. This work was supported by the Association Française pour l'Etude du Foie (AFEF), Fondation pour la Recherche Médicale (FRM grants EQU202203014645 and EQU202003010299) and the Agence Nationale de la Recherche (ANR) grants "HSCreg" (ANR-21-CE14-0032-01), "MEDICAL" (ANR-21-CE17-0016-02), "PreciNASH" (ANR-16-RHUS-0006-PreciNASH) and "European Genomic

Institute for Diabetes" E.G.I.D. (ANR-10-LABX-0046), a French State fund managed by ANR under the frame program Investissements d'Avenir I-SITE ULNE/ANR-16-IDEX-0004 ULNE. This work also received support from the Université de Strasbourg and the ANR and the French Proteomic Infrastructure (ANR-10-INBS-08 - 03).

## AUTHOR CONTRIBUTIONS

Conceptualization: NV, LD, BS, PL, and JE; Methodology: NV, CB, CG, AB, and JDC; Software: LG and JDC; Validation: NV and CB; Formal analysis: NV, MBS, LG, FB, JMS, LD, JDC, and JE; Investigation: NV, CB, CG, MJ, MBS, FB, JMS, MBG, FPZ, EV, OMC, EW, and JDC; Resources: MBS, SC, DM, LCNW, and LD; Data curation: NV, LG, FB, JMS, JDC, and JE; Writing - original draft preparation: NV and JE; Writing - review and editing: all authors; Visualization: NV, CB, MBS, FB, and JE; Supervision: NV, JDC, and JE; Project administration: NV and JE; Funding acquisition: NV, BS, PL, and JE.

## COMPETING INTERESTS

The authors declare no competing interests.

## ETHICAL APPROVAL

The research was carried out according to the World Medical Association Declaration of Helsinki. All animal studies were performed in compliance with EU specifications regarding the use of laboratory animals and have been approved by the Nord-Pas de Calais Ethical Committee (CEEA-075) (APAFIS#15539). Studies with human samples received ethical authorizations from the Lille University Hospital or Comité de Protection des Personnes Ile de France V and patients provided informed consent.

## ADDITIONAL INFORMATION

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41419-024-06773-9.

**Correspondence** and requests for materials should be addressed to Jérôme Eeckhoute.

**Reprints and permission information** is available at http://www.nature.com/reprints

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