Colonic stem cell from severe ulcerative colitis maintains environment-independent immune activation by altering chromatin accessibility and global m⁶A loss

Abstract

Ulcerative colitis (UC) is a chronic inflammatory disease of colon, which is characterized by cryptarchitectural distortion. Alternation of colonic stem cell (CoSC) contributed to the occurrence of UC, yet the regulatory mechanisms remain unclear. To investigate the dysregulation of transcriptional and post-transcriptional regulation, we performed RNA-seq, ATAC-seq, and m⁶A meRIP-seq analysis of the cultured CoSCs that were isolated from UC patients. The transcriptome analysis revealed distinct expression signatures of UC patients in mild and severe stages. We observed abnormal activation of immune and extracellular matrix-related genes in patients affected by severe UC. The chromatin accessibility at the promoter regions of these genes was also specifically increased in the severe stage. In addition, we identified that a global loss of RNA m⁶A modification in the severe stage was accompanied by higher expression of the m⁶A demethylase FTO. The aberrant activation of a large number of immune and extracellular matrix-related genes, including IL4R, HLA-DPA1, and COL6A1, was related to both the gain of chromatin accessibility and the loss of m⁶A in severe UC patients. Our finding revealed an environment-independent immune activation of CoSCs in UC and provided FTO as a potential therapeutic target.

Keywords

ulcerative colitis / colonic stem cell / chromatin accessibility / m ⁶A / FTO

References

[1] Kobayashi T, Siegmund B, Le Berre C, et al. Ulcerative colitis. Nat Rev Dis Primers 2020;6:74.

[2] Baumgart DC, Le Berre C. Newer biologic and small-molecule therapies for inflammatory bowel disease. N Engl J Med 2021;385:1302–15.

[3] Lynch WD, Hsu R. Ulcerative Colitis. Treasure Island (FL): StatPearls, 2022.

[4] Laass MW, Roggenbuck D, Conrad K. Diagnosis and classification of Crohn’s disease. Autoimmun Rev 2014;13:467–71.

[5] Harpaz N, Ballentine S, Colombel JF, et al. Microscopic heterogeneity in ulcerative colitis: implications for microscopic measurement of disease activity. Gut 2020;69:401–2.

[6] Sinopoulou V, Gordon M, Akobeng AK, et al. Interventions for the management of abdominal pain in Crohn’s disease and inflammatory bowel disease. Cochrane Database Syst Rev 2021;11:CD013531.

[7] Greuter T, Rieder F, Kucharzik T, et al. Emerging treatment options for extraintestinal manifestations in IBD. Gut 2021;70:796–802.

[8] Ungaro R, Mehandru S, Allen PB, et al. Ulcerative colitis. Lancet 2017;389:1756–70.

[9] Rodriguez-Colman MJ, Schewe M, Meerlo M, et al. Interplay between metabolic identities in the intestinal crypt supports stem cell function. Nature 2017;543:424–7.

[10] van der Flier LG, Clevers H. Stem cells, self-renewal, and differentiation in the intestinal epithelium. Annu Rev Physiol 2009;71:241–60.

[11] Falloon KA, Fiocchi C. Current therapy in inflammatory bowel disease: why and how we need to change? EMJ innov. 2022;6:40–9.

[12] Xuan T-Y, Tan J, Sun H-H, et al. Cyclocarioside OQ, three novel seco-dammarane triterpenoid glycosides from the leaves of Cyclocarya paliurus. Nat Prod Res 2021;35:167–73.

[13] Ray G, Longworth MS. Epigenetics, DNA organization, and inflammatory bowel disease. Inflamm Bowel Dis 2019;25:235–47.

[14] Czarnewski P, Parigi SM, Sorini C, et al. Conserved transcriptomic profile between mouse and human colitis allows unsupervised patient stratification. Nat Commun 2019;10:1–11.

[15] Kazakevych J, Sayols S, Messner B, et al. Dynamic changes in chromatin states during specification and differentiation of adult intestinal stem cells. Nucleic Acids Res 2017;45:5770–84.

[16] Miraldi ER, Pokrovskii M, Watters A, et al. Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 cells. Genome Res 2019;29:449–63.

[17] Zhang K, Hocker JD, Miller M, et al. A single-cell atlas of chromatin accessibility in the human genome. Cell 2021;184:5985–6001.e19.

[18] Zhao, BS, Roundtree IA, He C. Publisher correction: post-transcriptional gene regulation by mRNA modifications. Nat Rev Mol Cell Biol 2018;19:808.

[19] Yang Y, Hsu PJ, Chen YS, et al. Dynamic transcriptomic m(6)A decoration: writers, erasers, readers and functions in RNA metabolism. Cell Res 2018;28:616–24.

[20] Wang X, Yamamoto Y, Wilson LH, et al. Cloning and variation of ground state intestinal stem cells. Nature 2015;522:173–8.

[21] Thomsen MK, Bakiri L, Hasenfuss SC, et al. JUNB/AP-1 controls IFN-gamma during inflammatory liver disease. J Clin Investig 2013;123:5258–68.

[22] Wang JR, Shen CQ, Zhang JP, et al. TEAD4 is an immune regulating- related prognostic biomarker for bladder cancer and possesses generalization value in pan-cancer. DNA Cell Biol 2021;40:798–810.

[23] Economides KD, Capecchi MR. Hoxb13 is required for normal differentiation and secretory function of the ventral prostate. Development 2003;130:2061–9.

[24] Pineault KM, Song JY, Kozloff KM, et al. Hox11 expressing regional skeletal stem cells are progenitors for osteoblasts, chondrocytes and adipocytes throughout life. Nat Commun 2019;10:3168.

[25] Koller FL, Hwang DG, Dozier EA, et al. Epithelial interleukin-4 receptor expression promotes colon tumor growth. Carcinogenesis 2010;31:1010–7.

[26] Todaro M, Perez Alea M, Scopelliti A, et al. IL-4-mediated drug resistance in colon cancer stem cells. Cell Cycle 2008;7:309–13.

[27] Di Stefano AB, Iovino F, Lombardo Y, et al. Survivin is regulated by interleukin-4 in colon cancer stem cells. J Cell Physiol 2010;225:555–61.

[28] Olen O, Erichsen R, Sachs MC, et al. Colorectal cancer in ulcerative colitis: a Scandinavian population-based cohort study. Lancet 2020;395:123–31.

[29] Bloemendaal ALA, Buchs NC, George BD, et al. intestinal stem cells and intestinal homeostasis in health and in inflammation: a review. Surgery 2016;159:1237–48.

[30] Biton M, Haber AL, Rogel N, et al. T Helper cell cytokines modulate intestinal stem cell renewal and differentiation. Cell 2018;175:1307–1320.e22.

[31] Boulias K, Greer EL. Biological roles of adenine methylation in RNA. Nat Rev Genet 2022;24:143–60.

[32] Song T, Lv S, Li N, et al. Versatile functions of RNA m6A machinery on chromatin. J Mol Cell Biol 2022;14:mjac011.

[33] Liu J, Dou X, Chen C, et al. N (6)-methyladenosine of chromosome-associated regulatory RNA regulates chromatin state and transcription. Science 2020;367:580–6.

[34] Abakir A, Giles TC, Cristini A, et al. N(6)-methyladenosine regulates the stability of RNA:DNA hybrids in human cells. Nat Genet 2020;52:48–55.

[35] Li Y, Xia L, Tan K, et al. N(6)-Methyladenosine co-transcriptionally directs the demethylation of histone H3K9me2. Nat Genet 2020;52:870–7.

[36] Wei J, Yu X, Yang L, et al. FTO mediates LINE1 m(6)A demethylation and chromatin regulation in mESCs and mouse development. Science 2022;376:968–73.

[37] McFadden MJ, Sacco MT, Murphy KA, et al. FTO suppresses STAT3 activation and modulates proinflammatory interferon-stimulated gene expression. J Mol Biol 2022;434:167247.

[38] Liu Y, Liang G, Xu H, et al. Tumors exploit FTO-mediated regulation of glycolytic metabolism to evade immune surveillance. Cell Metab 2021;33:1221–1233.e11.

[39] Kim H, Lee YS, Kim SM, et al. RNA demethylation by FTO stabilizes the FOXJ1 mRNA for proper motile ciliogenesis. Dev Cell 2021;56:1118–1130.e6.

[40] Peng S, Xiao W, Ju D, et al. Identification of entacapone as a chemical inhibitor of FTO mediating metabolic regulation through FOXO1. Sci Transl Med 2019;11:eaau7116.

[41] Wang A, Tao W, Tong J, et al. m6A modifications regulate intestinal immunity and rotavirus infection. Elife 2022;11:e73628.

[42] Zhang T, Ding C, Chen H, et al. m(6)A mRNA modification maintains colonic epithelial cell homeostasis via NF-kappaB-mediated antiapoptotic pathway. Sci Adv 2022;8:eabl5723.

[43] Zong X, Xiao X, Shen B, et al. The N6-methyladenosine RNA-binding protein YTHDF1 modulates the translation of TRAF6 to mediate the intestinal immune response. Nucleic Acids Res 2021;49:5537–52.

[44] Huang Y, Su R, Sheng Y, et al. Small-molecule targeting of oncogenic FTO demethylase in acute myeloid leukemia. Cancer Cell 2019;35:677–691.e10.

[45] Su R, Dong L, Li Y, et al. Targeting FTO suppresses cancer stem cell maintenance and immune evasion. Cancer Cell 2020;38:79–96.e11.

[46] Schreurs R RCE, Baumdick, ME, Sagebiel AF, et al. Human Fetal TNF-α-Cytokine-Producing CD4+ Effector Memory T Cells Promote Intestinal Development and Mediate Inflammation Early in Life. Immunity 2019;50:462–76.e8.

[47] Ahn SH, Shah YM, Inoue J, et al. Hepatocyte nuclear factor 4alpha in the intestinal epithelial cells protects against inflammatory bowel disease. Inflamm Bowel Dis 2008;14:908–20.

[48] El-Salhy M, Umezawa K. Anti-inflammatory effects of novel AP-1 and NF-kappaB inhibitors in dextran-sulfate-sodium-induced colitis in rats. Int J Mol Med 2016;37:1457–64.

[49] Corces MR, Trevino AE, Hamilton EG, et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat Methods 2017;14:959–62.

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

[51] Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013;29:15–21.

[52] Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. GigaScience 2021;10:giab008.

[53] Li H, Handsaker B, Wysoker A, et al; 1000 Genome Project Data Processing Subgroup. The sequence alignment/map format and SAMtools. Bioinformatics 2009;25:2078–9.

[54] Liao Y, Smyth GK, Shi W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res 2019;47:e47.

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

[56] Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (N Y) 2021;2:100141.

[57] Yu G, Wang LG, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012;16:284–7.

[58] Meng J, Lu Z, Liu H, et al. A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. Methods 2014;69:274–81.

[59] Meng J, Cui X, Rao MK, et al. Exome-based analysis for RNA epigenome sequencing data. Bioinformatics 2013;29:1565–7.

[60] Cui X, Wei Z, Zhang L, et al. Guitar: an R/Bioconductor package for gene annotation guided transcriptomic analysis of RNA-related genomic features. Biomed Res Int 2016;2016:8367534.

[61] Zhang Y, Liu T, Meyer CA, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol 2008;9:R137.

[62] Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010;26:841–2.

[63] Heinz S, Benner C, Spann N, et al. Simple combinations of lineage- determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 2010;38:576–89.

[64] Chen Y, Lun AT, Smyth GK. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Res 2016;5:1438.

[65] McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res 2012;40:4288–97.

[66] Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139–40.

[67] Hounkpe BW, Chenou F, de Lima F, et al. HRT Atlas v1.0 database: redefining human and mouse housekeeping genes and candidate reference transcripts by mining massive RNA-seq datasets. Nucleic Acids Res 2021;49:D947–55.

[68] Ramirez F, Ryan DP, Gruning B, et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res 2016;44:W160–5.

[69] Robinson JT, Thorvaldsdottir H, Wenger AM, et al. Variant review with the integrative genomics viewer. Cancer Res 2017;77:e31–4.

[70] Gao T, Qian J. EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species. Nucleic Acids Res 2020;48:D58–64.

[71] de Hoon MJ, Imoto S, Nolan J, et al. Open source clustering software. Bioinformatics 2004;20:1453–4.

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