[1] Z. Zhao et al., “Epitranscriptomics in liver disease: Basic concepts and therapeutic potential,” J. Hepatol., vol. 73, no. 3, pp. 664–679, Sep. 2020, doi: 10.1016/j.jhep.2020.04.009.
[2] M. Eslam, A. J. Sanyal, J. George, and International Consensus Panel, “MAFLD: A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease,” Gastroenterology, vol. 158, no. 7, pp. 1999-2014.e1, May 2020, doi: 10.1053/j.gastro.2019.11.312.
[3] J. S. Rodríguez-Sanabria, R. Escutia-Gutiérrez, R. Rosas-Campos, J. S. Armendáriz-Borunda, and A. Sandoval-Rodríguez, “An Update in Epigenetics in Metabolic-Associated Fatty Liver Disease,” Front. Med., vol. 8, 2022, Accessed: Dec. 18, 2022. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fmed.2021.770504
[4] P.-H. Park, R. W. Lim, and S. D. Shukla, “Involvement of histone acetyltransferase (HAT) in ethanol-induced acetylation of histone H3 in hepatocytes: potential mechanism for gene expression,” Am. J. Physiol.-Gastrointest. Liver Physiol., vol. 289, no. 6, pp. G1124–G1136, Dec. 2005, doi: 10.1152/ajpgi.00091.2005.
[5] A. Page et al., “Alcohol directly stimulates epigenetic modifications in hepatic stellate cells,” J. Hepatol., vol. 62, no. 2, pp. 388–397, Feb. 2015, doi: 10.1016/j.jhep.2014.09.033.
[6] A. Ajoolabady, H. Aslkhodapasandhokmabad, Y. Zhou, and J. Ren, “Epigenetic modification in alcohol-related liver diseases,” Med. Res. Rev., vol. 42, no. 4, pp. 1463–1491, 2022, doi: 10.1002/med.21881.
[7] J. R. Edwards et al., “Chromatin and sequence features that define the fine and gross structure of genomic methylation patterns,” Genome Res., vol. 20, no. 7, pp. 972–980, Jul. 2010, doi: 10.1101/gr.101535.109.
[8] M. B. Stadler et al., “DNA-binding factors shape the mouse methylome at distal regulatory regions,” Nature, vol. 480, no. 7378, Art. no. 7378, Dec. 2011, doi: 10.1038/nature10716.
[9] M. Zeybel et al., “Differential DNA methylation of genes involved in fibrosis progression in non-alcoholic fatty liver disease and alcoholic liver disease,” Clin. Epigenetics, vol. 7, no. 1, p. 25, 2015, doi: 10.1186/s13148-015-0056-6.
[10] S. K. Murphy et al., “Relationship between methylome and transcriptome in patients with nonalcoholic fatty liver disease,” Gastroenterology, vol. 145, no. 5, pp. 1076–1087, Nov. 2013, doi: 10.1053/j.gastro.2013.07.047.
[11] R. Loomba et al., “DNA methylation signatures reflect aging in patients with nonalcoholic steatohepatitis,” JCI Insight, vol. 3, no. 2, p. e96685, doi: 10.1172/jci.insight.96685.
[12] S. Horvath et al., “Obesity accelerates epigenetic aging of human liver,” Proc. Natl. Acad. Sci. U. S. A., vol. 111, no. 43, pp. 15538–15543, Oct. 2014, doi: 10.1073/pnas.1412759111.
[13] T. M. Stubbs et al., “Multi-tissue DNA methylation age predictor in mouse,” Genome Biol., vol. 18, no. 1, p. 68, Apr. 2017, doi: 10.1186/s13059-017-1203-5.
[14] N. Köhler et al., “Kupffer cells are protective in alcoholic steatosis,” Biochim. Biophys. Acta Mol. Basis Dis., vol. 1868, no. 6, p. 166398, Jun. 2022, doi: 10.1016/j.bbadis.2022.166398.
[15] A. Bertola, O. Park, and B. Gao, “Chronic plus binge ethanol feeding synergistically induces neutrophil infiltration and liver injury in mice: a critical role for E-selectin,” Hepatol. Baltim. Md, vol. 58, no. 5, pp. 1814–1823, Nov. 2013, doi: 10.1002/hep.26419.
[16] P. Godoy et al., “Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME,” Arch. Toxicol., vol. 87, no. 8, pp. 1315–1530, Aug. 2013, doi: 10.1007/s00204-013-1078-5.
[17] S. Tierling et al., “High-resolution map and imprinting analysis of the Gtl2-Dnchc1 domain on mouse chromosome 12,” Genomics, vol. 87, no. 2, pp. 225–235, Feb. 2006, doi: 10.1016/j.ygeno.2005.09.018.
[18] P. Boyle et al., “Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling,” Genome Biol., vol. 13, no. 10, p. R92, Oct. 2012, doi: 10.1186/gb-2012-13-10-r92.
[19] F. Schmidt et al., “Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction,” Nucleic Acids Res., vol. 45, no. 1, pp. 54–66, Jan. 2017, doi: 10.1093/nar/gkw1061.
[20] K. Gianmoena et al., “Epigenomic and transcriptional profiling identifies impaired glyoxylate detoxification in NAFLD as a risk factor for hyperoxaluria,” Cell Rep., vol. 36, no. 8, p. 109526, Aug. 2021, doi: 10.1016/j.celrep.2021.109526.
[21] P. Ebert and M. H. Schulz, “Fast detection of differential chromatin domains with SCIDDO,” Bioinforma. Oxf. Engl., vol. 37, no. 9, pp. 1198–1205, Jun. 2021, doi: 10.1093/bioinformatics/btaa960.
[22] L. Arrigoni et al., “Standardizing chromatin research: a simple and universal method for ChIP-seq,” Nucleic Acids Res., vol. 44, no. 7, p. e67, Apr. 2016, doi: 10.1093/nar/gkv1495.
[23] U. Raudvere et al., “g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update),” Nucleic Acids Res., vol. 47, no. W1, pp. W191–W198, Jul. 2019, doi: 10.1093/nar/gkz369.
[24] D. Szklarczyk et al., “STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets,” Nucleic Acids Res., vol. 47, no. D1, pp. D607–D613, Jan. 2019, doi: 10.1093/nar/gky1131.
[25] F. Schmidt, F. Kern, P. Ebert, N. Baumgarten, and M. H. Schulz, “TEPIC 2—an extended framework for transcription factor binding prediction and integrative epigenomic analysis,” Bioinformatics, vol. 35, no. 9, pp. 1608–1609, May 2019, doi: 10.1093/bioinformatics/bty856.
[26] A. Mathelier et al., “JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles,” Nucleic Acids Res., vol. 44, no. D1, pp. D110-115, Jan. 2016, doi: 10.1093/nar/gkv1176.
[27] I. V. Kulakovskiy et al., “HOCOMOCO: a comprehensive collection of human transcription factor binding sites models,” Nucleic Acids Res., vol. 41, no. Database issue, pp. D195-202, Jan. 2013, doi: 10.1093/nar/gks1089.
[28] P. Kheradpour and M. Kellis, “Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments,” Nucleic Acids Res., vol. 42, no. 5, pp. 2976–2987, Mar. 2014, doi: 10.1093/nar/gkt1249.
[29] M. D. Robinson, D. J. McCarthy, and G. K. Smyth, “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data,” Bioinformatics, vol. 26, no. 1, pp. 139–140, Jan. 2010, doi: 10.1093/bioinformatics/btp616.
[30] J. Ernst and M. Kellis, “Chromatin-state discovery and genome annotation with ChromHMM,” Nat. Protoc., vol. 12, no. 12, Art. no. 12, Dec. 2017, doi: 10.1038/nprot.2017.124.
[31] A. Kundaje et al., “Integrative analysis of 111 reference human epigenomes,” Nature, vol. 518, no. 7539, Art. no. 7539, Feb. 2015, doi: 10.1038/nature14248.
[32] A. Tarasov, A. J. Vilella, E. Cuppen, I. J. Nijman, and P. Prins, “Sambamba: fast processing of NGS alignment formats,” Bioinformatics, vol. 31, no. 12, pp. 2032–2034, Jun. 2015, doi: 10.1093/bioinformatics/btv098.
[33] A. R. Quinlan, “BEDTools: The Swiss‐Army Tool for Genome Feature Analysis,” Curr. Protoc. Bioinforma., vol. 47, no. 1, Sep. 2014, doi: 10.1002/0471250953.bi1112s47.
[34] A. Frankish et al., “GENCODE 2021,” Nucleic Acids Res., vol. 49, no. D1, pp. D916–D923, Jan. 2021, doi: 10.1093/nar/gkaa1087.
[35] F. Müller et al., “RnBeads 2.0: comprehensive analysis of DNA methylation data,” Genome Biol., vol. 20, no. 1, p. 55, Mar. 2019, doi: 10.1186/s13059-019-1664-9.
[36] M. E. Ritchie et al., “limma powers differential expression analyses for RNA-sequencing and microarray studies,” Nucleic Acids Res., vol. 43, no. 7, pp. e47–e47, Apr. 2015, doi: 10.1093/nar/gkv007.
[37] C.-C. Chen, L.-W. Hsu, K.-D. Chen, K.-W. Chiu, C.-L. Chen, and K.-T. Huang, “Emerging Roles of Calcium Signaling in the Development of Non-Alcoholic Fatty Liver Disease,” Int. J. Mol. Sci., vol. 23, no. 1, p. 256, Dec. 2021, doi: 10.3390/ijms23010256.
[38] X. Chen, L. Zhang, L. Zheng, and B. Tuo, “Role of Ca2+ channels in non-alcoholic fatty liver disease and their implications for therapeutic strategies (Review),” Int. J. Mol. Med., vol. 50, no. 3, p. 113, Jul. 2022, doi: 10.3892/ijmm.2022.5169.
[39] Y. Liu et al., “The correlation and role analysis of COL4A1 and COL4A2 in hepatocarcinogenesis,” Aging, vol. 12, no. 1, pp. 204–223, Jan. 2020, doi: 10.18632/aging.102610.
[40] N. J. Hunt, S. W. S. Kang, G. P. Lockwood, D. G. Le Couteur, and V. C. Cogger, “Hallmarks of Aging in the Liver,” Comput. Struct. Biotechnol. J., vol. 17, pp. 1151–1161, 2019, doi: 10.1016/j.csbj.2019.07.021.
[41] P. Prasun, I. Ginevic, and K. Oishi, “Mitochondrial dysfunction in nonalcoholic fatty liver disease and alcohol related liver disease,” Transl. Gastroenterol. Hepatol., vol. 6, p. 4, Jan. 2021, doi: 10.21037/tgh-20-125.
[42] S. Sookoian et al., “Mitochondrial genome architecture in non-alcoholic fatty liver disease,” J. Pathol., vol. 240, no. 4, pp. 437–449, Dec. 2016, doi: 10.1002/path.4803.
[43] Y. Lou, Y.-D. Chen, F.-R. Sun, J.-P. Shi, Y. Song, and J. Yang, “Potential Regulators Driving the Transition in Nonalcoholic Fatty Liver Disease: a Stage-Based View,” Cell. Physiol. Biochem. Int. J. Exp. Cell. Physiol. Biochem. Pharmacol., vol. 41, no. 1, pp. 239–251, 2017, doi: 10.1159/000456061.
[44] Y.-H. Hung et al., “Super-enhancer signature reveals key mechanisms associated with resistance to non-alcoholic steatohepatitis in humans with obesity.” bioRxiv, p. 2021.08.20.457162, Apr. 25, 2022. doi: 10.1101/2021.08.20.457162.
[45] G. Kang, H.-S. Han, and S.-H. Koo, “NFIL3 is a negative regulator of hepatic gluconeogenesis,” Metabolism., vol. 77, pp. 13–22, Dec. 2017, doi: 10.1016/j.metabol.2017.08.007.
[46] M. Yang et al., “Hepatic E4BP4 induction promotes lipid accumulation by suppressing AMPK signaling in response to chemical or diet-induced ER stress,” FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol., vol. 34, no. 10, pp. 13533–13547, Oct. 2020, doi: 10.1096/fj.201903292RR.
[47] F. Bu et al., “JunB-EGFR Axis is Critical for TGF-β1/P38 MAPK Signaling-Mediated Hepatic Stellate Cells Proliferation in Liver Fibrosis.” Rochester, NY, Apr. 21, 2022. doi: 10.2139/ssrn.4089491.
[48] X. Chen et al., “Dual regulation of HMGB1 by combined JNK1/2-ATF2 axis with miR-200 family in nonalcoholic steatohepatitis in mice,” FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol., vol. 32, no. 5, pp. 2722–2734, May 2018, doi: 10.1096/fj.201700875R.
[49] P. Li, R. Spolski, W. Liao, and W. J. Leonard, “Complex interactions of transcription factors in mediating cytokine biology in T cells,” Immunol. Rev., vol. 261, no. 1, pp. 141–156, Sep. 2014, doi: 10.1111/imr.12199.
[50] Y. Salameh, Y. Bejaoui, and N. El Hajj, “DNA Methylation Biomarkers in Aging and Age-Related Diseases,” Front. Genet., vol. 11, p. 171, 2020, doi: 10.3389/fgene.2020.00171.
[51] M. Bysani et al., “Epigenetic alterations in blood mirror age-associated DNA methylation and gene expression changes in human liver,” Epigenomics, vol. 9, no. 2, pp. 105–122, Feb. 2017, doi: 10.2217/epi-2016-0087.
[52] H. Kirchner et al., “Altered DNA methylation of glycolytic and lipogenic genes in liver from obese and type 2 diabetic patients,” Mol. Metab., vol. 5, no. 3, pp. 171–183, Mar. 2016, doi: 10.1016/j.molmet.2015.12.004.
[53] A. Abderrahmani et al., “Increased Hepatic PDGF-AA Signaling Mediates Liver Insulin Resistance in Obesity-Associated Type 2 Diabetes,” Diabetes, vol. 67, no. 7, pp. 1310–1321, Jul. 2018, doi: 10.2337/db17-1539.
[54] E. Nilsson et al., “Epigenetic Alterations in Human Liver From Subjects With Type 2 Diabetes in Parallel With Reduced Folate Levels,” J. Clin. Endocrinol. Metab., vol. 100, no. 11, pp. E1491-1501, Nov. 2015, doi: 10.1210/jc.2015-3204.
[55] T. Wang et al., “Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction and rapamycin treatment,” Genome Biol., vol. 18, no. 1, p. 57, Mar. 2017, doi: 10.1186/s13059-017-1186-2.