Abstract
The intestinal epithelium maintains an important barrier throughout life. It consists of several epithelial cell lineages that are derived from LGR5+ intestinal stem cells. Although epigenetic regulation of embryonic stem cell differentiation is well established, its role in adult stem cell systems such as the intestinal epithelium is still undefined. Yet, targeting of epigenetic regulatory enzymes may be relevant for new therapeutics, for example in cancer treatment. Here, we combine a newly established organoid toolbox with an epigenetic probe library to identify epigenetic regulators of intestinal epithelial biology. We discover several probes that alter intestinal epithelial biology including those targeting HDACs, EP300/CREBBP, LSD1, and type I PRMTs. We conclude that epigenetic modifiers are primarily involved in mediating maturation of the epithelium rather than dictating specific cell lineage differentiation. Furthermore, we show that inhibiting type I PRMTs, which leads to epithelial maturation, blocks the growth of adenoma but not normal organoid cultures. Thus, epigenetic probes are a powerful tool in defining biological processes and demonstrate therapeutic potential.
Introduction
The intestinal epithelium, a single layer of cells, faces the challenge of both providing a barrier against pathogens while also being responsible for the uptake of nutrients and water. One of the hallmarks of intestinal epithelium is the rapid turnover of 3-5 days, which is driven by LGR5+ intestinal stem cells (ISCs) that reside at the bottom of crypts. ISCs are continuously dividing and give rise to progenitor cells that in turn differentiate into specialized intestinal epithelial cell (IEC) lineages such as absorptive enterocytes and secretory lineages such as mucus-producing goblet cells, antimicrobial-producing Paneth cells, hormone-secreting enteroendocrine cells, and chemosensory tuft cells1. Classically, this system is tightly controlled by pathways such as WNT, NOTCH, and BMP that allow for ISCs to retain their stemness and proliferate, whilst NOTCH-mediated lateral inhibition controls differentiation1. IEC lineage frequencies can rapidly change in response to pathogenic cues and cytokines2 and different mature lineages have the capacity to de-differentiate following ISC damage3,4,5.
Intestinal organoids were introduced more than a decade ago as a near-physiological 3D in vitro model to study the intestinal epithelium6. The intestinal organoid model allows to manipulate the complex interplay of IECs in vitro and to distinctively study the epithelial contribution to complex physiological pathologies7. Organoids grow in a heterogeneous population that is quite sensitive to handling and culture conditions. Therefore, development of quantitative analysis methods for reproducible quantification of a whole organoid population instead of relying on representative example data points is crucial8. Indeed, this has led to specialized studies such as using light-sheet microscopy to elegantly define symmetry breaking9, using single-cell RNA sequencing (scRNA-seq) to describe epithelial responses to immune cues10,2, or analysis of single organoids in microcavity arrays11. However, these techniques are costly and the required instrumentation and data analysis pipelines are not widely available to all researchers. Therefore, there is a great need for the development of quantitative but cost-efficient tools based on standard laboratory equipment that can be scaled to larger setups and tailored to individual research questions.
The chromatin landscape is a determining factor in the regulation of gene expression. Epigenetic marks such as methylated DNA, histone tail modifications, and nucleosome positioning are crucial regulators of embryonic stem cell differentiation12, differentiation of progenitors to mature cell lineages e.g. in the immune system13, and conversely involved in cancer development14. While the epigenetic landscape strongly differs between fetal and adult intestine15,16, the role of epigenetic modifiers and subsequently epigenetic marks in adult ISCs and IECs is not fully defined. Both a permissive chromatin structure and regulation of IEC lineage differentiation by transcription factors, and a control of gene expression patterns by the chromatin states itself have been proposed as conflicting models (extensively reviewed by Elliot et al.17). The classic NOTCH-mediated lateral inhibition model of ISC-to-IEC differentiation has been attributed to a broadly permissive chromatin landscape, supporting the idea of regulation by transcription factors as the most defining factor18. This is in line with a critical role of specific transcription factors, such as Atoh1, Neurog3, or Sox4, for IEC effector lineage differentiation19,20,21,22. However, other studies suggest that ISC differentiation and the de-differentiation of lineage-defined IECs back to ISCs are mediated by changes in DNA methylation and chromatin accessibility23,15,24,25. In support, intestine-specific genetic deletion of the Polycomb Repressive Complex 2 (PRC2) core subunit EED, which controls tri-methylation of H3K27 (H3K27me3) associated with heterochromatin, leads to disturbed IEC lineage differentiation and dramatic physiological consequences in mice26,27,28. Disturbed IEC lineage differentiation is similarly seen after intestine-specific genetic deletion of the H9K3me3-specific SET Domain Bifurcated Histone Lysine Methyltransferase 1 (Setdb1)29. These examples indicate that epigenetic regulation by several modifiers could be essential for the homeostatic regulation of the adult intestinal epithelium.
Given the critical role of epigenetic regulation in embryonic development, the investigation of the role of epigenetic modifiers in the intestinal epithelium depends mostly on mouse models with intestine-specific conditional genetic deletion, which have the disadvantage of a limited throughput. Organoid work has recently been proposed as an alternative approach30. Screening for epigenetic modifier effects became feasible when a chemical probe library targeting epigenetic modifiers with high selectivity and specificity became available recently31. This probe library is curated by the Structural Genomics Consortium, is continuously expanding, and is openly available.
Here, we provide an organoid quantification toolbox suitable for screening experiments and designed to be used in laboratories with a standard infrastructure. To widely investigate the role of epigenetic modifiers in the intestinal epithelium, we combined this toolbox with the chemical epigenetic probe library and discovered several mediators of IEC biology that could be interesting candidates for a mechanistic investigation. Specifically, we find new regulators of organoid size related to ISC frequency, as well as new regulators of IEC differentiation. Emerging from our discoveries and follow-up work, we propose that epigenetic modifiers are primarily involved with the intestinal epithelial maturation processes rather than defining specific cell lineage differentiation or ISC stemness maintenance. Finally, we explore the potential of some of these probes for treatment of intestinal cancer by application on intestinal tumor organoids.
Results
Developing a toolbox to quantify growth and cellular composition of intestinal organoids
Rapidly after its establishment by Sato et al.6, the use of (intestinal) organoids has been become a standard in the method repertoire. However, the analytical tools available to a broad community, especially for screening purposes, remain limited or labour intensive. We thus initiated a small intestinal (SI) organoid system that, similar to the original work, starts with freshly isolated crypts that self-organize into budding organoids by day 4 (96h after seeding), which can be split and propagated (Fig. 1a, Supplementary Fig. S1a). We next designed a setup to daily acquire bright-field z-stack images of the whole extracellular matrix (Matrigel droplet) in a well, followed by automatic segmentation and quantification of all individual organoids based on the open-source tools ImageJ/Fiji and Ilastik (Fig. 1b). Based on edge detection in each stack layer, this workflow can be used to robustly quantify organoid size (object area) and classify, e.g. by determining intensity, organoid phenotypes over time (Fig. 1c, Supplementary Fig. S1b,S1c). The workflow is robust to changes in morphology, stitching artefacts, and can be adjusted to image data from different automated microscopes. As the object classification by Ilastik is not dependent on deep learning and extensive training data, it can easily be adapted to new phenotypes and changes in experimental conditions. In addition to determining organoid size, the cellular composition is of critical interest. We therefore selected transcripts that are specific for individual IEC lineages10, and performed qRT-PCR on these within a 24h-96h time course (Fig. 1d). Except for the enterocyte marker Alpi, we generally find an increase in lineage-specific gene expression over time cumulating at 96 h (Fig. 1d). This corresponds to the transition from spheroids, consisting mainly of progenitors, to mature budding organoids that contain more differentiated lineages, as was shown previously9. To provide a secondary technique to quantify cellular composition, we conducted flow cytometry of commonly used IEC surface markers and observed modest changes between 48h and 96h organoids (Fig. 1e, Supplementary Fig. S1d, S1e). Previous studies have identified compounds that target WNT (CHIR and IWP2) and NOTCH (VPA and DAPT) signaling pathways and alter the cell composition in organoids32. In verification of our methods, we find drastic changes in cellular composition after treatment with these compounds, both analyzed by qRT-PCR (Fig. 1f) and flow cytometry (Fig. 1g, Supplementary Fig. S1f). Remarkably, we observed that incubation with CHIR + VPA followed by DAPT + IWP2 increased the expression of tuft cell marker genes in addition to the previously described effects on goblet cells and enteroendocrine cells (Fig. 1f). In summary, we developed an easy to use and cost-efficient toolbox for the analysis of (intestinal) organoids that is suitable to detect changes in organoid growth and cell composition.
Organoid screen of probes against epigenetic modifiers identifies established drugs targeting cancer growth
Next, we applied our organoid toolbox for screening of a chemical probe library that targets epigenetic modifiers to modulate the epigenome31. Organoids generated from 4 individual mice were grown in the presence of 39 inhibitors, with valproic acid (VPA) or DMSO vehicle serving as controls. Samples were imaged daily and expression of 12 transcripts specific for IEC lineages10 was analyzed at the 96h endpoint (Fig. 2a). We observed that some of probes significantly affected organoid growth as determined by area (Fig. 2b, 2c, Supplementary Fig. S2a-S2c, S3a). Integration of the primary readouts revealed a strong correlation of organoid size and expression of the ISC marker gene Lgr5 (Fig. 2d). We found three probes that reduced both organoid size and Lgr5 mRNA expression, namely the pan-Poly (ADP-ribose) polymerase (PARP) inhibitor olaparib and two histone deacetylase (HDAC) inhibitors LAQ824 (Dacinostat), a pan-HDAC inhibitor, and CI-994 (Tacedinaline), an HDAC1-3 & HDAC8 inhibitor (Fig. 2b-2d, Supplementary Fig. S2b, S2c)33,34,35. These findings are in agreement with a study that showed reduced growth and Lgr5 gene expression in CI-994 treated organoids36. Olaparib-treated organoids would sufficiently grow to perform flow cytometry. This allowed us to use Lgr5 -EGFP expressing reporter organoids to confirm the reduced Lgr5 gene expression levels. Indeed we found markedly fewer GFP-high/GFP-positive cells in olaparib-treated compared to control organoids (Fig. 2e, Supplementary Fig. S2d). Finally, we treated Adenomatous polyposis coli (Apc) knockout organoids, which are a model for intestinal cancer, with the two HDAC inhibitors and olaparib and found that these probes also limited growth in these tumor cultures (Fig. 2f). Together, this is well in line with the design goal of these probes to limit cellular growth to target cancer cells.
Inhibition of EP300/CREBBP enhances organoid size and Lgr5 expression
We next focused on probes that increased organoid size (Fig. 2b-2d). We found that both SGC-CBP30 and I-CBP112 significantly increased the organoid area (Fig. S3a) and this increase was seen in objects that were classified as “Organoids” and thus was not dependent on the occurrence of large spheres (Fig. 3a, Supplementary Fig. S3b). In support of the sensitivity of our assay, both probes have the same targets: EP300/CREBBP. E1A Binding Protein P300 (EP300, P300) and Creb-binding protein (CREBBP, CBP) are closely related bromodomain-containing acetyltransferases that serve as transcriptional co-activator for numerous transcription factors37,38,39. Both SCG-CBP30 and I-CBP112 specifically target the bromodomain-binding domain, which thus renders these proteins unable to bind acetylated lysines. The observed increase in organoid size was surprising since both inhibitors have been designed to cause growth restriction in cancer cells40,41,42. Comparing SGC-CBP30/I-CBP112-treated organoids with the DMSO vehicle control, organoid morphology appeared normal but we observed a reduction of putative goblet/Paneth cells as determined by cytosolic Ulex Europaeus Agglutinin I (UEA1) staining (Fig. 3b, Supplementary Fig. S3c). We next tested whether these probes would expand the LGR5+ cell population in Lgr5 -EGFP organoids and found a modest increase upon treatment, either alone or in combination with our positive control CHIR (Glycogen synthase kinase 3 (GSK3) inhibitor), an activator of canonical WNT signaling (Fig. 3c, Supplementary Fig. S3d,S3e). The additive effect on LGR5+ expansion by SGC-CBP30 or I-CBP112 with CHIR suggests that EP300/CREBBP inhibition does not result in increased WNT/β-Catenin signaling. In support, we neither observed differences in organoid size when organoids were grown with low EGF concentrations (1ng/ml), nor were the probes able to replace R-Spondin in the culture medium or overcome treatment with the WNT inhibitor IWP2 (Fig. 3d, Supplementary Fig. S3f, S3g). To determine which genes are under the control of EP300/CREBBP in the intestinal epithelium, we performed mRNA sequencing on untreated vs. I-CBP112 treated organoid cultures. In accordance with a transcriptional co-activator role for EP300/CREBBP, We found 53 genes upregulated and 110 genes downregulated using a log2 fold change cutoff of 0.5 and padjust ≤ 0.01 (Fig. 3e). Furthermore, we found negative enrichment of target gene signatures of transcription factors known to interact with either EP300 or CREBBP (Fig. 3f). Remarkably, Lgr5 was the most significantly upregulated gene in our dataset, substantiating our previous results (Fig. 3e). In support, gene set enrichment analysis (GSEA) with a LGR5+ stem cell gene set43 showed positive correlation (Fig. 3g). The second most significantly upregulated gene is Egr1 (Fig. 3e), which is an inducible transcription factor that is involved in cell proliferation44. The expansion of ISCs or progenitors appears to come at a cost to the differentiation of other cell lineages. We observed reduced UEA1 staining and downregulation of goblet cell markers such as Muc4 and Ccl6 following EP300/CREBBP inhibition (Fig. 3b, 3e). This is further supported by the negative correlation with secretory cell gene sets by GSEA (Supplementary Fig. S3h). Conversely, this is in line with positive enrichment of Gene Ontology biological process (GO:BP) terms such as smoothened signaling pathway and tissue morphogenesis (Supplementary Fig. S3i, S3j). Irrespective of the exact mechanism, we demonstrate that the surprising increase of organoid size after inhibition of EP300/CREBBP bromodomains may be explained by upregulation of Lgr5, Egr1, and genes associated with developmental processes, at the cost of IEC differentiation.
GSK-LSD1 broadly affects IEC composition
So far, we have used organoid size as a probe selection criteria. Additionally, we performed qRT-PCR on 12 genes associated with specific cell lineages10. We found that, after our positive control VPA, treatment with GSK-LSD1 leads to the largest perturbation of the IEC lineage marker profile by calculating the euclidean distance of the gene expression xfold changes relative to DMSO treatment (Fig. 4a, Supplementary Fig. S4a, S4f-S4k). Flow cytometry screening of inhibitor treated organoids showed primarily moderate changes in surface marker expressions (Supplementary Fig. S4b, S4c). Although GSK484 and SGC0946 caused the most perturbation in surface marker populations, they showed little effect by qRT-PCR and thus we did not pursue these probes further (Supplementary Fig. S4d,S4e). Treatment with GSK-LSD1 markedly reduced gene expression of Paneth and goblet cell markers, but caused an increase in enteroendocrine and tuft marker genes, particularly Gfi1b (Fig. 4b). This supports our recent work in which we found that Lysine-specific Demethylase 1A (LSD1, KDM1A) is required for Paneth cell differentiation. Instead, genes associated with enteroendocrine progenitors such as Neurod1 and Neurog3 are upregulated45. In addition, we found that LSD1 contributes to goblet cell differentiation that is important for defense against infections46. Paneth cells are commonly gated as SSChi_CD24+ population in flow cytometry47. In line with a strong reduction of the Paneth cell marker genes Lyz1 and Defa22, we find this population significantly reduced in GSK-LSD1 treated organoids (Fig. 4b, 4c). Furthermore, we observed that the pattern of CD24+ expressing cells in GSK-LSD1 treated organoids differs from control organoids in flow cytometry, with increase of a SSClo_CD24hi population (Fig. 4c). This pattern change was even more pronounced in SI crypt IECs from Villin-Cre+ Lsd1f/f mice, which conditionally lack Lsd1 in IECs, compared to wild type (WT) littermates (Fig. 4d). Similar gating has previously been associated with enteroendocrine cells and their progenitors47, which thus fits with our previous work45. However, upon performing intracellular flow cytometry staining for the canonical tuft cell marker DCLK1, we found that also a DCLK1hi population fell within this gate and is increased in Lsd1 -deficient crypts (Fig. 4e). In support, there was a modest yet significant increase of DCLK1+ cells in duodenal tissue sections as well as colon sections from Lsd1 -deficient mice compared to WT mice (Fig. 4f, Supplementary Fig. S4l). Together, this example highlights that the epigenetic probe library contains inhibitors that are able to completely mimic the phenotype that is seen upon genetic deletion in vivo.
BET inhibition reduces relative abundance of tuft cells
Secretory cell lineage differentiation, such as goblet and Paneth cells, is well studied and is generally thought to involve NOTCH-mediated lateral inhibition. Tuft cell differentiation, however, is less defined. Therefore, we next focused on the BRD/BET inhibitors (+)-JQ1 and bromosporine in our marker gene expression dataset (Fig. 4a) as treatment with these led to a strong reduction of tuft cell marker genes Dclk1, Trpm5, and Gfi1b (Fig. 5a). Organoids treated with these probes also had altered expression in some of the other IEC lineage marker genes, but the downregulation of tuft cell marker genes was consistent and prominent (Supplementary Fig. S5a). Although probe A-366, an inhibitor of Euchromatic histone-lysine N-methyltransferase 1 and 2 (EHMT1/2, GLP/G9A) also reduced tuft cell marker genes, two other EHMT1/2 inhibitors, UNC0638 and UNC0642, did not (Fig. 4a, Supplementary Fig. S4k). (+)-JQ1 inhibits Bromodomain-containing protein 2 (BRD2), BRD3, BRD4, and BRDT while bromosporine is a pan-bromodomain inhibitor. In our hands, these two probes did not affect overall organoid growth in the 96h course of the screen experiment (Supplementary Fig. S4a, S5b), but (+)-JQ1 tratment affected organoid morphology when inhibitor treatment was continued after passaging (Fig. 5b). Others have reported that (+)-JQ1 treatment strongly reduced the efficiency of crypts to form organoids48. Tuft cell quantification after treatment of Hpgds2 -tdTomato reporter organoids indeed confirmed a complete lack of tuft cell differentiation in organoids treated with either (+)-JQ1 or bromosporine (Fig. 5b, 5c), suggesting that BRD proteins are necessary for the tuft cell lineage. BRD2, BRD3, BRD4, and BRDT are mutual targets of (+)-JQ1 and bromosporine, however BRDT is not expressed in SI crytps or organoids (Supplementary Fig. S5c). Of note, the BRD2/BRD4 inhibitor PFI-1 did not cause marked changes in tuft cell marker gene expression in our screen (Supplementary Fig. S5d), it thus may be worthwhile to investigate the role of BRD3 in tuft cell differentiation in future studies.
Inhibition of type I PRMTs results in higher relative abundance of secretory cells and inhibits growth of tumor organoids
So far, we focused on inhibitors that caused reduced IEC differentiation. However, two probes stood out because they increased the expression of genes associated with Paneth-, goblet-, and enteroendocrine cells (Fig. 4a). Of these two, the pan-PARP inhibitor olaparib also had a marked effect on median organoid size and abundance of LGR5+ stem cells (Fig. 2b-2e). The other probe is MS023, which is an inhibitor of type 1 protein arginine methyltransferases (PRMTs) such as PRMT1, PRMT3, PRMT4 (CARM1), and PRMT849 (Fig. 6a). Of note, two other PRMT inhibitors in our probe library, SGC707 and MS049 that inhibit PRMT3 and PRMT4/PRMT6 respectively, did not cause similar effects (Supplementary Fig. 6a). Although MS023-treated organoids were slightly smaller than control organoids and Lgr5 gene expression was slightly reduced, frequency of Lgr5 -EGFP stem cells was not significantly affected, and organoids treated for 96h would renew normally after splitting (Supplementary Fig. S2a, S6b, S6c). The upregulation of secretory cell marker genes by the inhibitors was reflected by relative cell abundance of the respective lineages in MS023-treated versus control organoids. SSChi_CD24+ Paneth cells appeared more frequent in MS023 treated organoids in our flow cytometry screen (Fig. 6b, Supplementary Fig. S4c), and quantification of MUC2+ goblet cells showed a trend in the same direction (Fig. 6c). Furthermore, we treated enteroendocrine cell reporter organoids with MS023 and found an increased frequency of Neurog3 -RFP+ cells compared to the DMSO control (Fig. 6d). To get a more detailed overview of how MS023 affects organoids, we performed mRNA sequencing of untreated vs. MS023 treated organoids. We found 462 genes upregulated and 457 genes downregulated with a log2 fold change cutoff of 0.5 and padjust ≤ 0.01 (Fig. 6e). Importantly, GSEA of cell-lineage specific gene sets confirmed that MS023-treated organoids have a transcriptome that is enriched for secretory cell lineages (Fig. 6f). However, GSEA also indicated an enrichment for genes associated with enterocytes (Fig. 6f). Thus, rather than specifically affecting secretory cells, MS023 treated organoids seem to have increased maturation of all IEC cell lineages, potentially at the cost of progenitor cells. This is in agreement with positive enrichment of GO:BP terms related to nutrient uptake and response to microbials (Supplementary Fig. S6d, S6e), which are associated with mature enterocytes and Paneth cells respectively. DNA repair, which is a well established function of type I PRMTs50, was among the negatively correlated GO:BP terms (Supplementary Fig. S6d, S6f). We found that PRMT1 was the type I PRMT with the highest gene expression level in SI crypts and organoids (Supplementary Fig. S6g). Enhanced PRMT levels are found in various malignancies and high PRMT1 expression is negatively correlated with survival in colon cancer51,52. Furthermore, Prmt1 gene expression was highest in ISC, transit-amplifying cells, and early enterocyte progenitors compared to fully differentiated lineages in a published IEC scRNA-seq dataset10 (Supplementary Fig. S6h). We therefore hypothesize that inhibition of type I PRMTs leads to maturation of IECs, which aligns with the observation that differentiated cells have lower Prmt1 levels. To test if PRMT type I inhibition could thus be used therapeutically to force progenitors, such as those found in WNT-driven tumors, to mature or differentiate, we treated Apc-deficient organoids with MS023 or the PRMT1-specific inhibitor TC-E5003. MS023 treated adenomas were smaller and darker than adenomas treated with DMSO control, and TC-E5003 treatment almost completely hindered their growth (Fig. 6g). Yet, these probes did not cause growth inhibition of wild type organoids nor did they reduce cell viability (Fig. S6h, Supplementary Fig. S6i). In summary, we show that inhibition of type I PRMT leads to more mature organoids and has the potential to force cell maturation in intestinal tumor organoids, making it an attractive candidate to pursue in subsequent studies.
Discussion
Work with heterogeneous organoid cultures can be challenging in aspects of reproducibility and quantification. The toolkit we present in this article enables reproducible results across biological replicates using standard equipment and is suitable for screening setups. We established a quantification workflow that is based on the open source tools ImageJ and Ilastik, which is a simple yet robust alternative to recent stand-alone software options53,54 and could easily be adapted to different tissue organoids. In addition, qRT-PCR and flow cytometry of IEC lineages is sufficiently sensitive for initial screening and was subsequently confirmed by additional methods such as reporter organoids. We used this screening setup to test a set of 39 chemical probes targeting epigenetic modifiers and identified probes that strongly affected organoid size or IEC lineage composition. These new regulators of intestinal epithelial biology are highly interesting candidates for further mechanistic studies.
Probes targeting EP300/CREBBP were designed as cancer therapeutics42. Thus, we were surprised to find that inhibition of P300/CREBBP led to an increase of organoid size, which was supported by an expansion of LGR5+ cells and reduction of differentiation (Fig. 3). EP300/CREBBP mediate acetylation of histone H3K27 at enhancer elements and promoters, and can act as a transcriptional co-activator with numerous transcription factors37,38,55,39,56. In support of a general activating role for EP300/CREBBP, we found that the majority of genes altered by I-CBP112-treatment were down-regulated (Fig. 3e), and many of these genes are established targets of EP300/CREBBP-associated transcription factors (Fig. 3f). EP300/CREBBP is a well established coactivator of signaling cascades that control cell proliferation, including WNT, NFκB, or MYB signaling. Although we cannot rule out that altering these interactions might contribute to enhanced organoid growth, transcriptional signature associated with β-Catenin (Ctnnb1), NFκB-subunit RelA, or MYB were downregulated after treatment with the EP300/CREBBP inhibitor I-CBP112 (Fig. 3f). Furthermore, a synergistic effect with GSK3 inhibition but inability to overcome low R-Spondin or WNT inhibition suggests that EP300/CREBP inhibition is not affecting WNT signaling (Fig. 3c,3d). It is difficult to separate the epigenetic modifier (H3K27 acetylation) from the transcriptional co-activator role of EP300/CREBBP, especially since a recent study showed a central role for the bromodomain and HAT domain also for the EP300/CREBBP transcription factor binding capacity57. Nevertheless, underlining the critical role of the bromodomain, plt6 -mice that carry a mutation in the EP300KIX domain which specifically prevents interaction of EP300 with the transcription factor MYB, displayed reduced cell proliferation in the intestine58. The EP300/CREBBP bromodomain is critically required for H3K27 acetylation at enhancer elements, a mark of active enhancers, and its inhibition leads to reduced expression of enhancerproximal genes59,39. In the adult small intestine, Sheaffer et al. described a gain of H3K27Ac at dynamically methylated enhancer sites in differentiated IECs but not LGR5+ ISC24. Furthermore, Kazakevych et al. found that H3K27Ac positive distal elements were a good indicator for cell identity and differentiation status whereas genes positively regulating proliferation were transcribed in most IEC types15. EP300/CREBBP has previously been shown to be required for differentiation of embryonic stem cells, muscle cells and adipocytes60,61,62. In turn, Ebrahimi et al. recently described that EP300/CREBBP maintains transcription of fibroblast-specific genes and EP300/CREBBP bromodomain inhibition can promote cellular reprogramming to pluripotency by facilitating the silencing of the somatic gene expression program, accompanied by decrease in promoter- and enhancer-associated H3K27 acetylation63. We provide evidence that EP300/CREBBP inhibition in the intestinal epithelium can promote proliferation rather than preventing it. It appears plausible that EP300/CREBBP bromodomain activity is critically required to enable transcription of IEC differentiation genes and that in its absence the intestinal epithelium remains immature, accompanied by an enhanced proliferative capacity.
We recently demonstrated that LSD1 is centrally involved in Paneth and goblet cell differentiation and maturation45,46. Here, we confirm the critical role of LSD1 for IEC lineage differentiation in our unbiased screen and provide support that instead of Paneth/goblet cells there is an expansion of DCLK1+ tuft cells that is associated with a CD24high SSClow population by flow cytometry (Fig. 4). In contrast, we find that treatment with the BRD/BET inhibitors (+)JQ-1 and bromosporine completely block tuft cell differentiation (Fig. 5). Tuft cells are important mediators of intestinal type 2 immunity64,65. Our work matches observations of two studies that found that inhibition of the BET bromodomain in vivo abolished tuft cells48,66. Using a different BRD/BET probe, Nakagawa et al. described that the absence of tuft cells was due to blockade of transit-amplifying cells as their intermediate progenitors66. While Nakagawa et al. also found a reduction of enteroendocrine cells, another study described an increase of pancreatic NEUROG3+ enteroendocrine progenitors following (+)JQ-1 treatment67. Our findings could be the foundation of using these compounds to modulate immune responses, especially when a type 2 response is unfavourable, such as in allergic disease.
Type I PRMT inhibition with MS023 led to a more mature intestinal epithelium without major loss of LGR5+ stem cells (Fig. 6, Supplementary Fig. S6b). PRMT1 was the most highly expressed type I PRMT and is higher expressed in ISCs and progenitors compared to differentiated cells (Supplementary Fig. S6h,S6h). An evolutionary conserved role of PRMT1 in the adult intestine has been proposed earlier as endogenous PRMT1 knockdown reduces the adult ISC population in Xenopus and zebrafish, while transgenic PRMT1 overexpression leads to an increase of ISCs68,69. Furthermore, our observation is very similar to what has been found in the epidermis where PRMT1 is enriched in progenitors and is required to maintain the progenitor population70. Bao et al. proposed that PRMT1 is both involved in the maintenance of progenitor/proliferative genes as well as the repression of ‘differentiation’ genes70. In agreement with the latter, we found increase of all mature IEC lineages after treatment with MS023 (Fig. 6a-6f). PRMT1 has a wide substrate specificity and mediates both arginine methylation of histones such as H4R3, and non-histone proteins71,72. Elevated PRMT1 expression is found in several cancer types and is associated with poor prognosis and chemoinsensitivity73,74 and pharmacological PRMT inhibitors have recently gained interest as drug candidates for cancer treatment50,52. Targeting cancer stem cells (CSCs) in the gut comes with the challenge that following ablation of LGR5+ CSCs, LGR5-cells have the potential to de-differentiate to CSCs75. Therefore, forcing maturation of cancer cells could be an attractive treatment strategy. Indeed, we found that PRMT type I inhibition with MS023 and PRMT1-specific inhibition with TC-E5003 severely impaired growth of Apc-deficient tumor organoids but not normal organoids (Fig. 6g, 6h). A conditional Prmt1 -deficient mouse was recently generated76. Crossing these mice with intestine-specific Villin-Cre or tumor-developing Apcmin mice, could be an elegant way to further study the role of PRMT1 in IEC maturation and to investigate the therapeutic potential of PRMT1 inhibition for the treatment of intestinal cancer.
Highly permissive chromatin and transcriptional regulation of IEC fate as well as gene regulation by differential methylation status have been discussed as opposing models of intestinal epithelial differentiation regulation17. Testing a library of highly selective epigenetics probes, the majority of the tested inhibitors did not massively alter organoid growth or IEC composition, among them inhibitors of well-established epigenetic regulators such as the methyltransferase EHMT1/2 complex (probes A-366, UNC0638, UNC0642)77. However, we found that few probes resulted in pronounced changes and these were associated with a generally less mature (EP300/CREBBP, LSD1 inhibition) or more mature (PRMT type I inhibition) epithelium. We therefore conclude that epigenetic modifiers control intestinal epithelial maturation rather than directly affecting cell lineage choice such as in embryonic stem cell differentiation. Of note, the epigenetic modifiers identified to affect IEC differentiation in our screen share the capacity to both modify histones and to interact with multiple transcription factors. By integrating the investigation of gene expression, open chromatin, and DNA hydroxymethylation in IEC populations with differential expression levels of the transcription factor Sox9, recent elegant work by Raab et al. identified either highly permissive or dynamic chromatin states at given loci relative to transcription factor binding78. Global yet context-dependent changes in 5-Hydroxymethylcytosine, which is associated with epigenetic reprogramming, were also shown in another recent study comparing ISC and IEC progenitors79. Thus, we propose that rather than being mutually exclusive processes, epigenetic and transcriptional regulation are tightly intertwined in the adult intestinal epithelium and collaboratively shape the epithelial differentiation. EP300/CREBBP could be a key link connecting the epigenetic and the transcriptional layers of regulation in the intestinal epithelium. In development of cochlea hair cells, it has been shown that epigenetic marks guide activity of the transcription factor Atoh1, which also is one of master transcription factors in IEC differentiation80. Similarly, epigenetic control of Cdx2 - mediated transcription has been shown in the intestine81,82. Along this line, our results indicate that epigenetic modifiers with the capacity to both mediate histone modifications and act as transcriptional co-activators control the balance between an immature/mature epithelial state. Thereby, they lay basis for a fine-tuned transcriptional regulation and rapid adjustment upon injury or pathogenic challenge.
Methods
Epigenetic modifier inhibitors
The epigenetic modifier inhibitors in the screen experiment were part of the Structural Genomics Consortium Epigenetic Chemical Probes Collection as of March 2016. Probes were reconstituted in DMSO and used at the recommended concentration as listed in Supplementary Table 1. 1mM valproic acid (VPA) was included as positive control. DMSO vehicle control was matched to the highest concentration used per experiment, maximal 10µM. PRMT1-specific inhibitor TC-E5003 (Santa Cruz Biotechnology, # sc397056) was included in follow-up experiments and used at 50µM, equivalent to 10µM DMSO.
Mice
C57BL/6JRj wild type (Janvier labs), Lgr5 -EGFP-IRES-CreERT2 (Jackson Laboratories, stock no: 008875), Villin-Cre83 (kind gift from Sylvie Robine), Lsd1f/f 84 (kind gift from Stuart Orkin), and Apc15lox (Jackson Laboratories, stock no: 029275) mice were housed under specific-pathogen free conditions at the Comparative Medicine Core Facility (CoMed), Norwegian University of Science and Technology, Norway. For the flow cytometry screening experiment, organoids were generated from C57BL/6 mice housed at the Leibniz-Forschungsinstitut für Molekulare Pharmakologie, Germany. Hpgds-tdTomato mice85 were housed at University of Montpellier, France. Neurog3 -RFP mice86 (kind gift from Anne Grapin-Botton) were housed at University of Copenhagen, Denmark. Experiments were performed following the respective legislation on animal protection, were approved by the local governmental animal care committee, and were in accordance with the European Convention for the Protection of Vertebrate Animals used for Experimental and other Scientific purposes.
Small intestinal crypt isolation
Small intestinal crypts were isolated as described previously87. The proximal half of the small intestine was rinsed, opened longitudinally, cut to small pieces after villi and mucus were scraped off, washed with PBS until the solution was clear, and incubated in 2mM EDTA/PBS for 30min at 4°C with gentle rocking. Fragments were subsequently washed with PBS and the crypt fraction was typically collected from wash 2-5. All centrifugation steps were carried out at 300 × g.
Organoid culture
Organoids were generated by seeding ca. 250-500 small intestinal crypts in a 50µl droplet of cold Matrigel (Corning #734-1101) into the middle of a pre-warmed 24-well plate. Matrigel was solidified by incubation at 37°C for 5-15min and 500µl culture medium added. Basal culture medium (“ENR”) consisted of advanced DMEM F12 (Gibco) supplemented with 1x Penicillin-Streptomycin (Sigma-Aldrich), 10mM HEPES, 2mM Glutamax, 1x B-27 supplement, 1x N2 supplement, (all Gibco) 500mM N-Acetylcysteine (Sigma-Aldrich), 50ng/ml recombinant EGF (Thermo Fisher Scientific), 10% conditioned medium from a cell line producing Noggin (kind gift from Hans Clevers), and 20% conditioned medium from a cell line producing R-Spondin-1 (kind gift from Calvin Kuo). ENR culture medium was replaced every 2-3 days. Organoids were passaged at 1:3-1:4 ratio by disruption with rigorous pipetting almost to single cells. Organoid fragments were centrifuged at 300 × g, resuspended in 40-50µl cold Matrigel per well, and plated on pre-warmed 24-well plates. Organoids derived from different mice or a repetition at least one passage apart are considered biological replicates. Technical replicates, i.e. separate wells, were carried out in some experiments and were pooled for analysis.
Altering IEC lineage composition in organoids
Protocols to alter the IEC composition in organoids have been described previously32,88,89. Organoids were grown for 48h in ENR or ENR + 3µM CHIR99021 (Sigma-Aldrich) and 1mM valproic acid (VPA). Then, media was replaced by ENR, ENR + 3µM CHIR and 1mM VPA, ENR + 3µM CHIR and 10µM DAPT, or ENR + 10µM DAPT and 2µM IWP2. VPA, DAPT, IWP2 were purchased from Cayman Chemicals. Organoids were harvested 72h after media change.
Organoid screen with Epigenetic Chemical Probes library
Organoids of four biological replicates were passaged to nearly single cells at 1:4 ratio as described above and seeded in 40µl Matrigel droplets in 24-well plates. 250µl/well ENR were added immediately after solidification and 250µl/well ENR + probes at 2x working concentration (see Supplementary Table 1) were added within 30min. For each biological replicate DMSO vehicle controls were carried out in quadruplicates. Media was replaced after 48h. Organoids bright-field images were acquired daily on an EVOS2 microscope and after 96h RNA was harvested.
Reporter organoids
Lgr5 -EGFP organoids were generated as described above from Lgr5 -EGFP-IRES-CreERT2 mice and maintained for no longer than 3 weeks. Organoids were grown in ENR or ENR + 3µM CHIR99021 (Sigma-Aldrich) as indicated and Lgr5 -EGFP+ cells were quantified using a BD LSRII flow cytometer (Becton Dickinson) as percentage of viable cells. Tuft cell reporter organoids were generated from Hpgds-tdTomato mice (expressing tdTomato under the Hpgds promoter) as described above. Hpgds-tdTomato+ cells were quantified by confocal microscopy on an Axio Imager Z1 microscope (Zeiss) as number of cells relative to the organoid area after z-stack projection, determined by nuclear staining. Enteroendocrine cell reporter organoids were derived from the proximal small intestine of Neurog3 -RFP mice (expressing RFP under the Neurog3 promoter) and cultured as described above using recombinant murine Noggin (100ng/ml, Peprotech) and 10% R-Spondin conditioned medium. Neurog3 -RFP+ cells were quantified using a BD FACSAria III flow cytometer (Becton Dickinson) as percentage of viable cells.
Modified organoid growth conditions with EP300/CREBBP inhibition
Lgr5 -EGFP organoids were grown in ENR or ENR + 3µM CHIR for 96h. Media was replaced after 48h. To investigate low growth factor conditions, wild type organoids were grown in ENR or ENR with 1% R-Spondin, ENR with 5ng/ml EGF, or ENR + 2µM IWP2 for 192h. Media was replaced every 48h.
Splitting of organoids after type I PRMT inhibition
Organoids were treated with DMSO or MS023 for 96h, passaged to nearly single cells as described above, and cultured in ENR for additional 96h. Media was replaced every 48h.
Generation of APC-deficient adenomas
Eight week old Apc15lox × Lgr5 -EGFP-IRES-CreERT2 mice were administered 2mg Tamoxifen in corn oil (both Sigma-Aldrich) for 5 consecutive days. Adenomatous polyps developed over the course of a month (ethically approved by the Norwegian Food Safety Authority, FOTS ID: 15888). To generate adenoma organoids, the small intestine was rinsed with PBS, opened longitudinally, polyps were excised, cut into small pieces, and washed in PBS. Next, 5 ml TrypLE express (Thermo Fisher Scientific) was added and incubated for 30min at 37°C while pipetting every 5-10min. After incubation, single cells were obtained by passing the supernatant through a 40µm strainer. Single cells were plated in 50µl cold Matrigel on a pre-warmed 24-well plate, and cultured in basal culture medium lacking R-Spondin-1 (“EN”). EN culture medium was replaced every 2-3 days.
Organoid growth quantification
Organoid bright-field images were acquired on an EVOS2 microscope (Thermo Fisher Scientific) with 2x magnification. At the starting point of the experiment, for each plate an automation setup was generated to acquire z-stacks with 50µm spacing either of a single position or 2-4 tiled images covering height and most area of the Matrigel dome for each well. This automation setup was reused at consecutive timepoints. A custom ImageJ/Fiji v1.52n90,91 macro was used to collect single positions and layers for each well, to save a stack (ImageJ bright-field stack) and projections, and to perform a simple organoid segmentation (“ImageJ workflow”). For the segmentation, a Sobel edge detector was applied to each z-stack layer (ImageJ edge stack), a standard deviation z-projection of the edge stack was generated, and particle analysis with optional manual correction was performed after several binary operations and thresholding. For an improved segmentation that is robust to stitching artefacts, that allows to filter out debris and organoid clusters, and to distinguish different organoid phenotypes, the ImageJ workflow was combined with the interactive machine learning software Ilastik v1.3.292 (“combined workflow”). Training data was taken from the analyzed experiment and excluded from further analysis. In a first step, pixel classification on an intensity summary projection of the ImageJ edge stack was used to separate between background and object outlines. The generated pixel prediction maps were then used as input in a second step of object classification together with minimum projections of the ImageJ bright-field stack. Routinely, the following label classes were used: Organoid, big sphere, small sphere, cluster, debris, background mislabelled as organoid, air bubble, edges of well plate. Objects classfied in the latter three object classes were excluded from all timepoints, objects classfied as debris or cluster were excluded from 72h and 96h timepoints. Representative images were arranged using GNU R packages magick and ggimage.
RNA isolation, quantitative RT-PCR and analysis
To harvest RNA, organoids in the Matrigel dome were dissolved in 250µl RNA-solv reagent (Omega Bio-Tek). RNA was isolated using Direct-zol-96 RNA or Direct-zol MiniPrep kit (Zymo Research) according to the manufacturer’s instructions, including DNAse digestion. cDNA was transcribed using High-Capacity RNA-to-cDNA Kit (Applied Biosystems) according to the manufacturer’s instructions. RNA quality and concentration was assessed on an NanoDrop-1000 instrument (NanoDrop). Samples were handled in 96-well plates and transferred with multichannel pipettes. qRT-PCR was carried out in technical duplicates in 384-well plates on a QuantStudio 5 instrument (Thermo Fisher Scientific) using 2x Perfecta ROX,UNG Fast Mix (Quanta Biosciences) and 5ng cDNA per reaction in a total volume of 12µl. Primer-probe combinations were selected based on the Universal Probe Library System (Roche) and are listed in Supplementary table 2, primers were purchased from Sigma-Aldrich. Hprt was used as housekeeping gene. ΔCT values were calculated as ΔCT = CT(housekeeping gene) - CT(gene of interest) (such that higher values indicate higher relative expression); ΔΔCT values referred to the calibrator as indicated, and fold change was calculated as 2ΔΔCT. Target gene “perturbation” was calculated as euclidean distance of the log2 median fold change using GNU R package pheatmap. Defa22 gene expression was below the detection limit for some samples and was therefore omitted from the euclidean distance ranking but is provided in the supplementary information.
Flow cytometry
To obtain single cells, Matrigel in 1-3 wells was disrupted by pipetting, well content was transferred to an Eppendorf tube, centrifuged at 300 × g, and supernatant removed. Then, organoids were incubated with 300µl TrypLE express (Thermo Fisher Scientific) for 37°C for 30min and pipetted up/down with a 1000µ pipet tip prior to and after the incubation. Single cells were stained with Zombie Aqua (Biolegend, 1:1000 in PBS) for 15min at room temperature (RT) for live-dead exclusion. If DAPI instead of Zombie Aqua staining was used for live-dead exclusion, it was added it during the last washing step (1:1000). Samples were incubated with antibody conjugates against CD326-BV605, CD24-PerCp-Cy5.5 or AF647, CD44-BV785, CD117-PE-Cy7 (all Biolegend, see Supplementary Table 3 for detailed list, 1:200 in PBS + 2% fetal calf serum (FCS)), and Ulex Europaeus Agglutinin (UEA)1-Rhodamine (2µg/ml, Vector Laboratories #RL-1062-2) for 20min at 4°C. For intracellular staining, samples were subsequently fixed with 2% paraformaldehyde (PFA) for 15min, and incubated with or without rabbit anti-DCLK1 (Abcam #ab31704, 1:500 in PBS/2% FCS/0.05% Saponin) for 1h at 4°C, followed by incubation with Goat anti-Rabbit IgG-AF405 (Invitrogen, 1:1000 in PBS + 2% FCS + 0.05% Saponin). Samples were analyzed on a BD LSRII instrument (Becton Dickinson) equipped with 405nm, 488nm, 561nm, 647nm laser lines. Single fluorochrome stainings of cells and compensation particles (BD CompBead, Becton Dickinson) were included in each experiment. For analysis, FlowJo software v10.6.2 and GNU R/Bioconductor v3.6.3/v3.10 packages flowCore, CytoML/flowWorkspace, ggcyto, flowViz were used93. If not indicated otherwise, only samples with more than 10000 viable cells in the parent gate were included.
Flow cytometry screening
For the flow cytometry screening experiment, organoids were grown in 96well glass-bottom plates (Cellvis) that were pre-cooled and held on ice during seeding. Organoid fragments in Matrigel (50µl/well) were distributed in pre-cooled plates with an automated pipette, then plates were transferred to a rotary plate shaker for 30sec at 150rpm, before the Matrigel was solidified at 37 °C. With help of a Viaflo 96-channel pipette (Integra Biosciences) 200µl/well ENR without or with inhibitors were added of which 100µl were replaced daily during the 96h time course. To obtain single cells, culture media was removed, 100µl/well TrypLE express (Thermo Fisher Scientific) added and Matrigel disrupted by repeated pipetting with a multichannel pipette. Staining with Zombie Aqua, CD326-BV421, CD24 -PerCp-Cy5.5, CD44-AF647, CD117-PE-Cy7 (all Biolegend, see Supplementary Table 3 for detailed list), and UEA1-FITC (Invitrogen) was carried out as described above. Samples were run on a MACSQuant X instrument (Miltenyi Biotec) equipped with 405nm, 488nm, 647nm laser lines and analyzed as described above. Euclidean distance clustering tree of normalized median population frequencies was generated with GNU R package ggtree94.
Confocal microscopy and quantification
For immunofluorescence staining, organoids were grown in 30µl/well Matrigel droplets in a 8-well microscopy chamber (Ibidi) that was pre-warmed for seeding. After 96h incubation, the organoids were fixed in 4% paraformaldehyde and 2% sucrose for 30min at RT, washed, and permeabilized with 0.2% Triton-X100 in PBS. Free aldehyde groups were blocked using 100mM glycine, followed by blocking buffer (1% BSA, 2% NGS diluted in 0.2% Triton-X100 in PBS) for 1h at RT. The organoids were incubated overnight at 4°C with a primary antibody against KI67 (Invitrogen #MA5-14520; 1:200) or MUC2 (Santa Cruz #sc-15334; 1:200) in blocking buffer, followed by three washes with slight agitation. Next, the organoids were incubated with Goat anti-Rabbit IgG-AF488 (Invitrogen, 1:500), UEA1-Rhodamine (Vector Laboratories #RL-1062-2, 2µg/ml), and Hoechst 33342 overnight at 4°C. After washing, the organoids were mounted using Fluoromount G (Thermo Fisher Scientific), and visualized using a LSM880 confocal microscope (Zeiss). UEA1/MUC2-positive cells were manually counted for ≥5 organoids per biological replicate in a middle plane of a z-stack. Cell numbers are reported relative to the area of the z-stack projection of each organoid, determined by nuclear staining.
Immunohistochemistry & Immunofluorescence staining of paraffin-embedded tissue and quantification
Immediately after euthanizing mice, the intestinal tissues were removed, washed with PBS, fixed in 4% formaldehyde for 48-72h at RT, and embedded in paraffin wax. Staining was carried out on 4µm paraffin sections. The sections were rehydrated and treated with 3% hydrogen peroxide for 10min at RT. Antigens were retrieved by boiling the slides in citrate buffer (pH6) in a microwave for 15min. For immunohistochemistry staining of duodenum sections, the sections were incubated overnight at 4°C with anti-DCLK1 (Abcam #ab31704; 1:1500) in TBS + 0.025% Tween 20 + 1% BSA. Specific binding was detected with Envision-HRP (Dako) and DAB (Dako) and images were acquired on a EVOS2 microscope (Thermo Fisher Scientific) with 10x magnification. DCLK1+ cells were quantified for ≥30 crypt-villus pairs per mouse. Representative images were acquired on a Eclipse Ci-L microscope (Nikon) with 20x magnification. For immunofluorescence staining of colon sections, slides were blocked with PBS + 1% BSA + 2% goat serum + 0.2% Triton X-100 for 1h at RT and incubated overnight at 4°C with anti-DCLK1 antibody (Abcam #ab31704; 1:250) in PBS + 1% BSA +1% goat serum + 0.05% Tween 20. Specific binding was detected with Goat anti-Rabbit IgG-AF488 (Invitrogen, 1:1000) for 1h at 37 °C while nuclei were stained with DAPI (1:1000). Slides were mounted with Fluoromount G (Thermo Fisher Scientific) and images were acquired on a LSM880 confocal microscope (Zeiss) with 20x magnification. DCLK1+ cells were quantified for ≥50 crypts per mouse.
mRNA sequencing
Organoid RNA was isolated and treated with DNAse with Quick-RNA Micro prep kit (Zymo Research) according to manufacturer’s instructions. RNA integrity numbers were found to be ≥7. For the I-CBP112 inhibitor study, library preparation was done using the Illumina TruSeq Stranded protocol. Library concentrations were quantified with the Qubit Fluorometric Quantitation system (Life Technologies) and the size distribution was assessed using a 2100 Bioanalyzer automated electrophoresis system (Agilent). For sequencing, samples were diluted and pooled into NGS libraries in equimolar amounts and sequenced at 75bp single-read chemistry on an Illumina NS500 MO flow-cell on a Ilumina NextSeq 500 instrument (Illumina) by the Genomics core facility (GCF, NTNU). For the MS023 study, library preparation was done using the NEB Next Ultra RNA Library Prep Kit with poly(A) mRNA enrichment and samples were sequenced at 150×2 bp paired-end chemistry on a Illumina NovaSeq 6000 instrument by Novogene (UK) Co.
mRNA sequencing analysis
Read quality was assessed using FastQC v0.11.8, reads were aligned with STAR v2.7.3a to the Mus musculus genome build mm10, and MultiQC v1.7 was used to summarize logs from STAR and FastQC95,96,97. The number of reads that uniquely aligned to the exon region of each gene in GENCODE annotation M18 of the mouse genome was then counted using featureCounts v1.6.498,99. Genes that had a total count less than 10 were filtered out. Differential expression was then determined with GNU R/Bioconductor v3.6.1/v3.10 package DESeq2 v1.26.0 using default settings and shrunken log2foldchange was calculated with the apeglm method100,101. GSEA enrichment was performed using GNU R/Bioconductor v3.6.3/v3.10 package ClusterProfiler v3.14.3 by shrunken log2 fold change and with the shrunken log2 fold change as weights using 10000 permutations102. Gensets for celltype signatures were assembled based on single-cell and bulk RNA-Sequencing data from sorted samples based on datasets by Haber et al.10 (GSE92332) and Munoz et al.43 (GSE33949). Transcription factors interacting with murine or human EP300 or CREBBP were retrieved from protein-protein interactions with an minimum medium experimental confidence level (≥0.4) from STRING-DB v11103. Genesets regulated for the mouse and human version of these transcription factors were retrieved from TRRUST v2104. For human genesets, murine orthologue genes retrieved from Ensembl GRCh38.p13 through GNU R/Bioconductor v3.6.3/v3.10 package biomaRt v2.42.1105 were used for enrichment. Genesets for characterization of Biological Process were directly obtained from the Gene Ontology Consortium106.
Data processing and statistical analysis
Data was processed and statistical analysis was carried out with GNU R v3.6.3 using the packages tidyverse and ggpubr107. Pearson correlation coefficient, and paired or unpaired t-test were calculated as indicated, assuming normal distribution. Median or mean are shown as indicated. In boxplots, the box represent the 25%, 50% and 75% percentiles and whiskers represent 1.5 × IQR.
Data availability
The Imaging data from the initial screen was deposited to the Image Data Resource108 (https://idr.openmicroscopy.org) under accession number idr0092. The qRT-PCR data from the initial screen was deposited along with processed data from follow-up experiments to BioStudies database at EMBL-EBI109 (https://www.ebi.ac.uk/biostudies) under accession number S-BSST447. RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI110 (https://www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-9290 (I-CBP112-treated samples) and E-MTAB-9291 (MS023-treated samples).
Code availability
The ImageJ script used for organoid segmentation is available from https://github.com/jennyostrop/ Fiji_organoid_brightfield_processing and deposited under https://doi.org/10.5281/zenodo.3951126.
Author contributions
JO, MJO designed the study. JO, RZ, MTP, FG, HTL, AD, NP, SR, MJO performed experiments. JO, RZ, MTP, FG, KB, HTL analyzed data. JO, KB, HTL curated data. CA provided critical materials. PJ, KBJ, CA, MJO supervised or provided critical insight. JO, RZ, MJO wrote the manuscript with subsequent input from other authors.
Competing interests
The authors declare no competing financial interests.
Supplementary information
Supplementary Figures S1-S6
Supplementary Table 1: Inhibitors and concentrations used
Supplementary Table 2: qRT-PCR primers and probes used
Supplementary Table 3: Materials, reagents, and software used
Acknowledgements
We thank the imaging (CMIC), animal care (CoMed), and genomics core (GCF) facilities at NTNU for assistance in this work. Funding of this work was provided by the Norwegian Research Council (‘Young Research Talent’ 274760 to MJO and Centre of Excellence grant 223255/F50) and the Norwegian Cancer Society (182767 to MJO). The flow cytometry screen was carried out at Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP) Screening Unit, Berlin, Germany with funding from H2020 CORBEL (PID6031 to JO). ELIXIR Norway and the Centre for Digital Life Norway, funded by the Norwegian Research Council (270068 and 248810/O70) assisted with data management.
References
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- 5.↵
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- 23.↵
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- 57.↵
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- 85.↵
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- 93.↵
- 94.↵
- 95.↵
- 96.↵
- 97.↵
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- 103.↵
- 104.↵
- 105.↵
- 106.↵
- 107.↵
- 108.↵
- 109.↵
- 110.↵