Overall, our study demonstrates selective constraint of promoter G4s and reinforces their stimulative role in gene expression.G-quadruplexes (G4s) regulate DNA replication and gene transcription, and are enriched in promoters without fully appreciated functional relevance. Here we show high selection pressure on putative G4 (pG4) forming sequences in promoters through investigating genetic and genomic data. Analyses of 76,156 whole-genome sequences reveal that G-tracts and connecting loops in promoter pG4s display lower or higher allele frequencies, respectively, than pG4-flanking regions, and central guanines (Gs) in G-tracts show higher selection pressure than other Gs. Additionally, pG4-promoters produce over 72.4% of transcripts, and promoter G4-containing genes are expressed at relatively high levels. Most genes repressed by TMPyP4, a G4-ligand, regulate epigenetic processes, and promoter G4s are enriched with gene activation histone marks, chromatin remodeler and transcription factor binding sites. Consistently,  Extensive computational analyses of genomic datasets from cancerous cell lines or tumor samples and genomes of various primates reveal selective constraint of promoter G4 elements and their contribution to active gene expression. The regulatory elements affected by variations mainly include promoters, enhancers and transcription factor binding sites that are involved in transcription machinery assembly and chromatin remodeling, eventually altering gene expression.Genome-wide association studies (GWAS) have revealed that over 88% of the genetic variations associated with different diseases are located in the noncoding regions of the human genome2. In addition, levels of non-B-DNA structures, such as G-quadruplexes (G4s), are altered in cancer cells and tissues compared to normal counterparts, and the formation and stabilization of these special DNA structures can determine transcription levels of target genes, which provides insights for therapeutic interference of cancer development4.A promoter is generally considered as a DNA sequence with a certain length upstream of the transcription start sites (TSSs) of a gene and is a functional element associated with various regulatory factors. Gene expression can be either activated or inhibited by the presence of transcription factor binding sites, DNA methylation and histone modification statuses in promoters3+N1-12G3+N1-12G3+N1-12G3+5. The four consecutive G-tracts are the most essential elements for G4 structure formation, and self-recognized Gs from these G-tracts can form stacked G-tetrads that are stabilized by Hoogsteen bonds and central cations. G4 stability is determined by many factors, including G-tract length, G-tetrads, loop length and structure, and composition of the sequences within and flanking G4s7. Due to the highly stable structures, G4s can impede the progression of DNA replication by promoting replication-fork collapse8, leading to DNA double-strand breaks and increased genomic variations. Meanwhile, G4 structures also obstruct the processes of RNA transcription and protein translation9. RNA G4s are important regulators of pre-mRNA processing, including splicing and polyadenylation10. G4 motifs are frequently identified in exons, especially in regions close to splicing sites; consistently, splicing quantitative trait loci (sQTLs) are enriched in G4 sequences, which is evolutionarily conserved across many species11.A G4 motif is composed of at least four G-tracts, each of which includes three or more guanines (Gs), and three loops linking adjacent G-tracts typically with 1-12 nucleotides (nts) in each, leading to the consensus motif sequence of G13. Helicases, such as PIF1 and BLM that unfold G4 structures, can improve genome stability15, while other non-helicase G4-interacting proteins may either prevent or augment G4-associated genome disturbance16. The substitutions of any G in a G-tract, especially the central one(s), by another nucleotide can cause detrimental effects on G4 stability and are associated with pathological consequences, such as the alterations in untranslated regions (UTRs)17 and telomeres18. Overall, G4s are causally associated with genome instability, and G4 loci in genome generally have high variation density and disruptive sites with low minor allele gene frequencies (MAFs), supporting their evolution under negative selection19.The impediment of G4 structures to various chromatin-related processes explains their potence in impairing genome stability. Putative G4s (pG4s) are enriched at the loci of germline and somatic mutations, and frequently cause DNA sequencing errors20). G4 loci exhibited heightened selective pressure and the middle Gs of 3G G-tracts in UTRs has a similar degree of selective pressure to missense variations in protein-coding regions17. Another study reported the selective pressure of pG4s at different genic positions, including promoters; however, the contributions of G-tracts or loops, which are essential elements for G4 folding, have not been individually examined21. Due to distinct sequence features, it is still possible that G-tracts and loops are subject to different selective pressures.Previous studies suggested that pG4s represent a type of drivers in genome evolution , G4s were shown to enhance promoter activity, but for unknown reasons the 2.5 kb regions flanking TSSs were excluded from the analysescis-eQTL variants, histone modifications, and transcript factor (TF) and chromatin remodeler binding sites. Together, our results revealed the biological relevance of promoter pG4 sequences, especially their interior G-tracts, and reinforced the critical roles of chromatin secondary structures in the regulation of gene expression.Based on previous studies, we asked whether different parts of a G4 in promoters are subject to distinct selective pressures, which may affect chromatin state and determine promoter activity. To answer these questions, we combinatorially analyzed multiple large-scale genomic and genetic datasets from different databases to evaluate the evolutionary constraint on promoter pG4 sequences in the human genome, and enrichment of functional relevance. We discovered that promoter pG4 sequences are under negative selection and G-tracts in pG4 sequences are exposed to heightened selective pressures compared with whole pG4s. Additionally, promoter G-tracts are enriched with 7 to analyze the human reference genome hg38, we discovered a total of 707,816 pG4s, of which about 97.9% had their lengths fall between 15-76 base pairs (bps). Each pG4 sequence contains 4-8 G-tracts, 410,844 G4s include only 4 G-tracts, and the median stability score was 19.47 based on Quadron analysis  of a gene, and can bind various regulatory proteins, such as transcription factors (TFs) and RNA polymerase, to initiate RNA synthesis. The length of a promoter is typically 100–1000 bps, and can be extended based on the abundancy of TF binding sites. We designated the (−1000 to 0) region of each gene as its “1 kb” (1 kilobase pairs) promoter, when the first nucleotide upstream of the TSS was designated as −1. Meanwhile, we also defined the 200 bps upstream of each TSS as its “0.2 kb” promoter, or active promoter Fig. , based oOne gene may have several promoters due to different transcription initiation sites of its multiple transcripts. In 19,955 protein-coding genes from GENCODE database, we totally identified 41,259 promoters, because 10,999 of these genes contain two or more promoters. Next, in the 17,343 promoters of 13,657 genes, we discovered 38,680 pG4 sequences. The average length of pG4 motifs in promoters ranges from 15 to 91 bps, and the median value of the thermostability score was 21.94  and template strand pG4s (tpG4s), respectively , a G4 probe , or BG4-associated Cut & Tag method (Cut&Tag-BG4). All these methods indicated that G4s are highly enriched in the vicinity of TSSs despite their relatively different peak positions. In three BG4-related studies, two datasets obtained by BG4 in K562 and HepG2 cellsths Fig. . However200 Fig. .32. If promoter pG4s experience heightened selective pressure, we expect that the variants with altered promoter pG4 sequences will show relatively low allele frequency compared to those of the variants with non-pG4 promoters.Assuming that pG4s are biologically functional, they should exhibit patterns of high selection constraint in evolutionary terms. To test this hypothesis, we evaluated the single nucleotide variants (SNVs) within promoter pG4 motifs using the whole genome sequencing data of 76,156 individuals from the public gnomAD release (version 3.1.2)32 . Overall, the reduced allele frequencies of G-tract variants in these pG4s of 1 kb and 0.2 kb promoters relative to those in non-pG4s imply the effects of negative selection.G-tracts with three or more consecutive Gs are essential for G4 formation. We compared the SNVs affecting the G-tracts of pG4s to those in the G-tracts of non-pG4 (≥3 G) within promoters that belong to the transcript subset with estimated overall constraint levels matching our promoter containing transcripts. This subset of reference transcripts with non-pG4 G-tracts was selected using the upper 90% bound of the observed versus expected (LOEUF) metric, as published by gnomADAllele frequencies across the genome can be affected by local sequence contexts and constrained by functional elements of nearby linkage selection. Therefore, we partitioned each pG4 in 1 kb promoters into G-tracts, loops and bilateral regions (including upstream and downstream of pG4), to analyze their individual allele frequencies. In all-, stable- and unstable-pG4s, G-tracts exhibited the lowest allele frequency, and loops showed the highest, while the allele frequencies of the regions upstream and downstream of pG4s were lower than those of the sequences in non-pG4 promoters Fig. . Similar33. Using this model, we compared the ratios of observed versus expected polymorphic sites in pG4s and their partitioned sections based on the genomic data of 503 individuals from the European subpopulation of 1000 genomes project (the EUR)34.Next, to understand the selective pressure of pG4s in promoters, we applied a background model of neutral evolution to evaluate SNP distributions in 1 kb and 0.2 kb promoters under the neutral theory of molecular evolution, which was used to explain >81% of variability in substitution probabilities for noncoding genomic regions based on the local hepta-nucleotide context of a given position35. We predict that the stability of promoter G4s may be under evolutionary selective pressure and contributes to promoter activity. Therefore, in the following analyses, we also considered the relative stability of pG4s as shown in Supplementary Fig. The stability of a G4 structure depends on the lengths of loops and G-tracts, as well as nucleotide compositions on both sides of the G436. Importantly, we discovered that the high variation rates were attributed to the mutations in the loops, while the G-tracts are relatively conserved.Overall, the ratios showed a similar pattern between 1 kb and 0.2 kb promoters Fig. . First, 37 that can be used in any genomic context to analyze evolutionary selection patterns of different genomic loci. This test typically compares the numbers of polymorphisms and fixed variants between two sets of regions, including a test group that is assumed to have evolved under selection and a control group with neutral evolution. In HKA analysis, we first used the abovementioned EUR DNA-seq data with >30× sequencing depth to get single nucleotide-polymorphic variants, and single nucleotide-fixed variants in human and orangutan genomes obtained by genome-wide alignment. Subsequently, evolutionary selectivity was assessed by calculating the odds ratios (ORs) of SNPs in pG4 sequences versus non-pG4 regions of pG4-containing promoters.To assess the evolutionary pressure of pG4 sequences in promoters, we examined the evolutionary signature of promoter pG4s using the Hudson-Kreitman-Aquadé (HKA) testP < 1.2 × 10−20, 1.58 × 10−14, 3.30 × 10−8, respectively) , while unstable-pG4 was not . For G-tracts in pG4s, the ORs are generally greater than 1 in both 1 kb and 0.2 kb promoters, while the ORs in stable- and unstable-pG4s were lower and higher, respectively, than all-pG4s. For loops, the results were mixed. In both 1 kb and 0.2 kb promoters, the ORs of loops in stable-pG4s were greater than 1 and also higher than all- and unstable-pG4s, while the ORs of loops in unstable-pG4 were less than 1 and lower than all- and stable-pG4s  Fig. . In 0.2 G4s Fig. . These rThe assessment of HKA depends on population size. Therefore, we further analyzed the SNPs from 2,504 individuals of the 1000 Genomes project with >30× sequencing depth to get single nucleotide-polymorphic variants. The distribution trend of ORs in pG4s and G-tracts of different pG4 groups versus non-pG4 regions in both 1 kb and 0.2 kb promoters Fig.  was consOverall, these results indicated that both pG4s and their G-tracts are under heightened selective pressure, and pG4 G-tracts are more powerful elements than entire G4 sequences in the HKA test to evaluate selection pressure on pG4s, which is due to the higher frequency of mutations in the loop regions of pG4s.38. To determine which type of selection is applicable for pG4s, we used the Kolmogorov-Smirnov test to compare the site frequency spectra of pG4 sequences, G-tracts and loops versus non-pG4 sequences, in which we obtained significant HKA test results in 1 kb and 0.2 kb promoters. Importantly, we observed relatively high prevalence of polymorphic variants at low MAFs for pG4s, G-tracts and loops in both 1 kb and 0.2 kb promoters , which measures the relative enrichment of rare variants in specific types of variants that can explain difference in mutation rates based on local sequence contexts42.We only considered the SNPs in G-tracts consisting of three Gs and assessed the selection pressure of these Gs at different positions in a G-tract. Gs at central positions in G-tracts of 1 kb and 0.2 kb promoters were enriched with rare variants that are present in only one allele of the individuals in gnomAD Fig. . The enr44. Unlike alternative splicing of genes, the selection and activity alteration of promoters are pre-transcriptionally regulated. Alternative promoters represent a major context- and tissue-specific mode of transcriptional regulation and contribute to transcript diversity. In many cases, the overall expression of a cancer-relevant gene remains unchanged in cancer cells versus their normal counterparts, but its different promoters may show distinct activities, leading to increased expression of cancer-promoting transcripts. Therefore, compared to differential gene expression, promoter activity can be more accurate in predicting the clinical outcomes of cancer patients44. We hypothesized that pG4 sequences in promoters may affect the selection and activity of promoters, based on their selective constraints. Although G4 motifs in the promoters of many oncogenes, such as MYC, KIT and KRAS, could reportedly inhibit their gene expression, it remained unclear whether G4s in all or most promoters had similar regulatory activity.Over 10,000 protein-coding genes in human cells are regulated by two or more promoters. For each gene, the alternative use of different promoters can determine the position and length of its 5’-UTR, first exon, 3’-UTRs, and even CDSWe define the genes that have at least one pG4 in each of its annotated promoters as constitutive genes, and the genes that have both pG4-containing and non-pG4 promoters as alternative genes. For the majority of constitutive genes, different promoters of each gene shared the same pG4 sequence(s) ; meanwhile, 1586 and 813 constitutive genes for 1 kb and 0.2 kb promoters, respectively, harbored different pG4s Fig. . AlternaP < 0.05) than the first and third Gs, and showed similar scores to those of missense variants , an RNA sequencing approach that can quantify nascently transcribed RNA, to determine TMPyP4-caused changes in gene expression. We used the G4-CUT&Tag and TT-seq datasets from this study (GSE178668) to interrogate the relationship between G4 formation and gene expression. Among 23,674 tested genes, 2135 were downregulated in response to TMPyP4 treatment versus the control, but no upregulated gene was detected (FDR < 0.05)  Fig. . HoweverInterestingly, when analyzing G4 occupancy, we observed an increase of 1297 G4 signals after TMPyP4 treatment versus the control, without any detectable G4 signal loss at other loci Fig. . Importacis-eQTLs47. To evaluate the contributions of G4 motifs in promoters to gene expression, we analyzed the enrichment of cis-eQTL-associated SNPs in pG4s versus non-pG4 sequences in both 1 kb and 0.2 kb promoters. We categorized the promoter cis-eQTLs into four groups, including Lead , Causal 48, TF (SNPs residing in any binding site of 655 transcription factors), and Nominal . With the cis-eQTLs in non-pG4 sequences of 1 kb promoters as controls, we observed that all four groups of cis-eQTLs were highly enriched in the pG4s of 0.2 kb promoters, while the first three cis-eQTLs, but not the Nominal, were also in enriched in the pG4s of 1 kb promoters, although their corresponding enrichments were markedly lower than those in 0.2 kb promoters  represent the SNPs in genome associated with gene expression levels, while eQTLs located near the gene-of-origin are defined as ers Fig. . Furtherm 1 Fig. . These rNext, to evaluate the potential regulatory roles of G4 motifs in gene expression, we analyzed the overlapping of pG4s with the interaction sites of 655 TFs based on their ChIP-seq data in HepG2 and K562 cells obtained from ENCODE database. We discovered 10.87 and 1.18 TF binding sites in each kb of sequences for both 1 kb pG4 and non-pG4 promoters, respectively, while for 0.2 kb pG4 and non-pG4 promoters, 16.56 and 2.95 TF binding sites per kb of sequences were identified, respectively Fig. . suggest28. Zyner, et al. reported that G4-containing promoters were enriched with H3K4me3 at different differentiation stages of embryonic stem cells49. Interestingly, based on our analysis, TMPyP4-repressed genes were also enriched in those that regulate histone modifications  of the BG4-recognized promoter regions enriched by the chromatin remodelers and TFs were remarkably higher than those of the enriched sites in the promoter pG4s Fig. . The res21; however, based on the analysis of the variations of all populations in the 1000 genomes comprised of 2504 individuals, we did not find significant difference between non-G4 regions and pG4 sequences in 0.2 kb promoters  in gene expression. We have mapped a significantly greater amount of ers Fig. , suggesters Fig. . ConsistBCL2 and KRAS was activated by the G4s in their promoters53, while OCT4 and ARID1A were repressed55. The discrepancy among these reports could be due to the following reasons. First, many early studies employed plasmid-based reporter assays to evaluate the effects of G4s on gene expression, which obviously could not reflect the behavior of G4s in a genomic context. Second, in these studies, the judgment of G4-mediated regulation was based on the effects of G4 stabilizers on gene expression, which could easily lead to the conclusion that G4 formation would cause gene repression. In our analyses, TMPyP4 could repress a large number of genes without upregulating any gene  and genomic coordinates were identified by Quadron software7 using its default arguments. Over 75% overlap of pG4 and promoter is defined as promoter pG4. 1 kb promoters, 0.2 kb promoters, pG4s and genomic coordinates were obtained using a custom python script. This approach yielded a total of 26,247 genes with 64,311 pG4s in 1 kb promoters and 12,328 genes with 20,764 pG4s in 0.2 kb promoters.TSS is defined by the transcript start site of the basic isoform of each gene in Genecode (v38) that uses the human genome hg38. The 1 kb promoter is defined by the 1 kb sequence upstream of the TSS of each transcript. The 0.2 kb promoter or active promoter is defined by ENCODEWe defined the pG4s with predicted stability scores of >19 as stable-pG4, and those with scores ≤19 as unstable-pG4. All these pG4s were designated as all-pG4s.https://gnomad.broadinstitute.org/downloads), and filtered to exclude those marked with low complexity regions and segmental duplication regions. Thus, only those variants that were identified by the FILTER as PASS with a mutation type of SNP were reserved. Those filters were generated by bcftools . Additionally, only the variants from the alleles with their sequences clearly identified in over 80% of the whole cohort  were selected for analyses, which could reduce the variations caused by sequencing depth in the gnomAD v3.1 dataset32. The obtained dataset of variants with relatively high confidence was further analyzed by bedtools (version 2.30.0) intersected with the -u and -b flags through overlapping the dataset with the genomic coordinates for 1 kb promoters, 0.2 kb promoters, 1 kb promoter pG4s, 0.2 kb promoter pG4s, CDSs and non-pG4 regions.Variants from the gnomAD release 3.1 were obtained (URL: 32 (https://gnomad.broadinstitute.org/downloads#v2-constraint). This process consisted of transcript constraint matching between pG4 and non-pG4 sequences according to the information of loss of function variants metric (LOEUF) for each transcript . We then removed telomeres (20 kb at the 5 ‘and 3’ ends), repetitive regions and sequences that do not present in the accessibility mask (version 20160622) filter of the 1000 Genomes Project. Variants in the European subpopulation of 1000 Genomes project (EUR) were identified for use in downstream analysis.The sequence context model was constructed according to the method developed by Aggarwala et al.33 based on the heptamer sequence context was used to calculate posterior substitution probabilities for intergenic noncoding regions. Cumulative substitution probabilities for each mutation site in a heptamer context were calculated by summing over all substitution probabilities for a given heptamer context, such as the sequence ATCGTCC showing the cumulative substitution probabilities of ATCCTCC, ATCATCC and ATCTTCC. The expected substitution probabilities in the regions, such as pG4s, were calculated by summing over the cumulative substitution probabilities of the heptamer contexts at each site. The ratio of observed versus expected substitution sites was calculated by the numbers of variants divided by the expected substitution probabilities in each region.Next, the statistical framework to model substitution probabilities as previously publishedTo get the values in the control zone, we first randomly selected the sequences in non-pG4 promoters using the same number and average length of pG4s, and determined their polymorphic sites. Then, we repeated this random selection and polymorphic site evaluation cycle for 10,000 times. For the obtained data, we removed the top and bottom 2.5% values, and the rest was used to build the control zone as the bootstrapped 95% confidence interval for the ratio.21. Specifically, we used fixed single-nucleotide variants between human and orangutan, and SNPs from high coverage genomic sequence data (>30× sequencing depth) for the 2504 unrelated samples in the 1000 Genomes Project61. A total of 95,738,090 fixed single-nucleotide variants between human and orangutan genomes were retrieved from the vertebrate MULTIZ 100-way alignment63. The total observed allele number was at least 80% of the maximum number of sequenced alleles and considered as plausible alleles. We discarded singletons and doubletons in the plausible alleles as they might possibly represent false positives. Fixed and polymorphic sites were intersected with 1 kb promoters and 0.2 kb promoters, respectively. Non-pG4s regions in all 1 kb and 0.2 kb promoters regardless pG4-containing statuses were used as background variations. We compared the observed counts of fixed and polymorphic sites at pG4 sequences to those expected values based on the non-pG4 regions in 1 kb and 0.2 kb promoters. Fisher’s exact test was used to evaluate the significance of an odds ratio of fixed variants to polymorphic sites, as well as the 95% confidence interval of odds ratios.The HKA test was used to analyze whether a pG4 region in a 1 kb promoter or 0.2 kb promoter conforms to neutral evolution. The HKA test was slightly modified by referring to the method developed by Guiblet, et al.64, and presented as a hierarchical clustering dendrogram when plotted by iTOL65 (https://itol.embl.de/). We individually analyzed the fixed single-nucleotide variants between the human genome and each of these 11 primate genomes by genome-wide alignments, and obtained SNPs from the 2504 unrelated samples in the 1000 Genomes Project to perform HKA test.A phylogenetic tree was constructed based on the information of 12 selected primates in the vertebrate MULTIZ 100-way alignment results from the UCSC database66 based on the SNPs from the 2504 unrelated samples in the 1000 Genomes Project61. We used non-pG4 regions in all promoters (including both pG4 and non-pG4 promoters) as controls.Tajima’s D, Fu and Li’s D and Fu and Li’s F tests were performed using the R package PopGenome41 was applied for its positional constraint analysis. Our model was constructed based on a reported MAPS model (https://bitbucket.org/biociphers/g4-paper-2019/src/master) with slight modifications by referring to a method developed by Lee, et al.17. Our MAPS model was also trained by regressing the percentage of singleton trinucleotide-related synonymous variants residing in protein-coding sequences of the human genome over the mutation rate of all types of trinucleotide sequences. Mutagenesis vulnerability of methylated cytosine was also evaluated for CpG dinucleotides. The filtering criteria of Variant frequency analysis was used to filter all variants for the positional constraint analyses.For each individual nucleotide position in a G-tract consisting of three Gs in a pG4 sequence, mutability-adjusted proportion of singletons (MAPS) metricFor G-tracts with more than three Gs, the location of disrupted sites for each G-tract can be different and create complex situations. Therefore, we only considered the G-tracts composed of three Gs in our analysis. MAPS values were also determined for the set of variants with VEP predicted missense, or with predicted loss-of-function (pLoF) in genomAD. pLoF variants were defined as nonsense, frameshift, and canonical splice site shift  variants. We calculated the MAPS for eight categories of pG4 variants: (1) all genes, (2) constitutive genes in which each transcript’s promoter includes at least one pG4, (3) alternative genes that contain both pG4 and non-pG4 promoters, (4) stable-pG4s, (5) unstable-pG4s.45. Median TPMs for each isoform were extracted for all-pG4- or non-pG4 containing 1 kb promoters and 0.2 kb promoters of each alternative gene, and the promoters driving the highest pG4- or non-pG4 isoform expression were selected. An isoform was considered expressed if its TPM in the tissue was greater than 1. The ratio of the isoform driven by pG4 1 kb or 0.2 kb promoter versus all expressed isoforms of an alternative gene in each tissue was then calculated.A gene with each promoter containing at least one pG4 was defined as a constitutive gene, and a gene that has both pG4-containing promoter(s) and non-pG4 promoter(s) was defined as an alternative gene. The median expression of each annotated isoform (as measured in units of TPMs) across tissues was obtained from GTEx v830) were used to explore the relationship between G4 structures and gene expression. TT-seq pipeline was referred to the method developed by Schwalb, et al.67 with slight modifications. Reads were demultiplexed and mapped with STAR 2.7.10a68 to the hg38 genome assembly. Samtools69 was used to qualify SAM files, where by alignments MAPQs smaller than 7 (-q 7) were skipped and only proper pairs  were selected. Next, the read counts across each gene were quantified by HTSeq 2.070 with the union mode, and DESeq2 was used to perform the differential gene expression analysis71. To estimate the fold changes based on the Drosophila spike-in RNA, size factors were calculated on the counts of the Drosophila genes and applied to the human gene counts prior to fold change estimation with DESeq2.A published TMPyp4-treated CUT &Tag dataset and a TT-seq dataset  version 2.2.6 with default parameters and a q-value cutoff of 1 × 10−5. For the ChIP-seq data in K562 and HepG2 cells (GSE145090), we selected the overlapping peaks that were 5 out of 8 and 6 out of 9 replicated samples in K562 and HepG2 cells, respectively, using bedtools with version v2.30.073. For CUT&Tag and other ChIP-seq data (GSE133379), the overlapping peaks were also selected in two replicate samples using bedtools. The all G4 peaks were annotated with bedtools intersect command. The different peaks analysis was performed using R package DiffBind and DESeq2. The genes with differential expression and genes with different G4 occupancy were identified by FDR < 0.05, and 252 genes were shared in those two groups of genes. GO enrichment analysis was performed using the clusterProfiler (v4.2.2) package74.ChIP-seq and CUT&Tag reads were aligned to the human genome (UCSC hg38) with Bowtie2 version 2.4.2. Samtools was used to qualify SAM files, whereby the alignments with MAPQ smaller than 10 (-q 10) and secondary and partial alignments (-F 2308) were skipped. Next, replicate reads were marked and removed using SAMBAMBA version 0.6.675 (v0.23.2) with its default parameters, and transcript levels were quantified using the Salmon algorithm (v1.10.0). We defined the summation of transcript count values of the same promoter as the promoter activity, and then used DESeq2 to perform differential promoter activity analyses.RNA-seq reads were filtered using fastp softwarehttps://gtexportal.org/home/datasets) that collected nominally significant cis-eQTLs. Among them, the Lead cis-eQTLs variants of each gene were determined as those with the lowest P values for that gene in each individual tissue, while the Causal cis-eQTLs variants were identified as those showing causal relationship with gene expression when screened by CaVEMaN, a computational model48. We overlapped the sets of the Lead, Causal and nominally significant variants with promoter pG4 and non-pG4 regions of promoters. Next, we quantitatively compared the significant cis-eQTL variants per region with the tested non-significant SNPs residing in the same region to calculate the proportion of cis-eQTLs versus non-cis-eQTL SNPs. A two-sided Fisher exact test was employed to evaluate the enrichment statistics of the cis-eQTLs in pG4 G-tracts and pG4s versus non-pG4 regions in pG4-containing promoters.We obtained the variant-gene pairs from the GTEx release version 8 . For chromatin remodelers, 79 datasets comprised of 59 unique marks in K562 (with 20 repeated datasets), and 63 datasets comprised of 56 unique marks in HepG2 (with 7 repeated datasets). For TFs, 490 datasets were comprised of 338 unique marks in K562 (with 152 repeated datasets), and 578 datasets were comprised of 505 unique marks (with 73 repeated datasets). To maximize the robustness of our analysis, only peaks called with an irreproducible discovery rate ≥540 were used for the following enrichment analysis. Randomization  for endogenous G4s or control sites and statistical analysis was performed using GAT76 in the workspace (either 1 kb or 0.2 kb promoter regions). For marks that had been mapped in multiple different experiments, their merged peaks were considered.Genomic binding sites for histone modification marks, chromatin remodelers and TFs aligned to GRCh38/hg38 Reference Genomes were downloaded from ENCODEData were analyzed and statistical evaluation was performed using and Python (version 3.10.9) and R (version 4.2.2) languages. Significant differences were denoted by asterisks. Detailed statistical tests or descriptions are provided in the custom codes in Code availability.Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


Malus domestica) and plum (Prunus spp.). By contrast, ethylene inhibits anthocyanin biosynthesis in pear (Pyrus spp.), but the underlying molecular mechanism remains unclear. In this study, we identified and characterized an ethylene-induced ETHYLENE RESPONSE FACTOR (ERF) transcription factor, PpETHYLENE RESPONSE FACTOR9 (PpERF9), which functions as a transcriptional repressor. Our analyses indicated PpERF9 can directly inhibit expression of the MYB transcription factor gene PpMYB114 by binding to its promoter. Additionally, PpERF9 inhibits the expression of the transcription factor gene PpRELATED TO APETALA2.4 (PpRAP2.4), which activates PpMYB114 expression, by binding to its promoter, thus forming a PpERF9-PpRAP2.4-PpMYB114 regulatory circuit. Furthermore, PpERF9 interacts with the co-repressor PpTOPLESS1 (PpTPL1) via EAR motifs to form a complex that removes the acetyl group on histone H3 and maintains low levels of acetylated H3 in the PpMYB114 and PpRAP2.4 promoter regions. The resulting suppressed expression of these 2 genes leads to decreased anthocyanin biosynthesis in pear. Collectively, these results indicate that ethylene inhibits anthocyanin biosynthesis by a mechanism that involves PpERF9-PpTPL1 complex-mediated histone deacetylation of PpMYB114 and PpRAP2.4. The data presented herein will be useful for clarifying the relationship between chromatin status and hormone signaling, with implications for plant biology research.Ethylene induces anthocyanin biosynthesis in most fruits, including apple ( PpMYB114 and PpRAP2.4 expression via histone deacetylation.The ethylene-activated PpERF9-TOPLESS complex suppresses pear anthocyanin biosynthesis by inhibiting  IN A NUTSHELLBackground: The red coloration of many fruits is the result of anthocyanin accumulation. The biosynthesis of anthocyanins is influenced by internal and environmental factors, including phytohormones. Ethylene induces anthocyanin biosynthesis in most fruits, including apples, grapes, and plums. However, ethylene inhibits anthocyanin biosynthesis in pears with different genetic backgrounds. Preliminary studies suggest that the transcription factors PpETHYLENE RESPONSE FACTOR9 and PpRELATED TO APETALA2.4 (PpRAP2.4) act upstream of PpMYB114, which encodes a key transcription factor in anthocyanin biosynthesis in pears. PpERF9 and PpRAP2.4 play important roles in ethylene-mediated inhibition of anthocyanin biosynthesis, and PpERF9 function is associated with histone modifications.Question: What is the relationship between ethylene and the PpERF9 and PpRAP2.4 transcription factors? How do PpERF9 and PpRAP2.4 regulate anthocyanin biosynthesis in pear fruit? How does PpERF9 regulate anthocyanin biosynthesis in pear fruit through histone modification?Findings: Our 4 major findings are the following: (i) ethylene induces the expression of PpERF9 while inhibiting the expression of PpRAP2.4 in pear fruit. (ii) PpERF9 directly inhibits PpMYB114 expression by binding to its promoter, thereby repressing anthocyanin biosynthesis in pear fruit. (iii) PpRAP2.4 activates PpMYB114 expression by binding to its promoter, thereby inducing anthocyanin biosynthesis in pear fruit, while PpERF9 directly represses the expression of PpRAP2.4, thus forming a PpERF9-PpRAP2.4-PpMYB114 regulatory circuit. (iv) PpERF9 interacts with the co-repressor PpTOPLESS1 via EAR motifs to form a complex that removes the acetyl group on histone H3, maintaining low levels of acetylated histone H3 in the promoter regions of PpMYB114 and PpRAP2.4. Acetylated H3 is associated with the suppressed expression of these 2 genes and decreased anthocyanin biosynthesis in pear fruit.Next step: It will be interesting to explore how ethylene inhibits the expression of PpMYB10, which encodes another important transcription factor regulating anthocyanin biosynthesis in pear fruit.Anthocyanins are important specialized metabolites responsible for the development of different-colored  flowers, fruits, leaves, seeds, and other plant tissues . AnthocyPetunia hybrida)  (Pyrus spp.), PpMYB10 and PpMYB114 are 2 major R2R3-MYB TFs that promote anthocyanin biosynthesis  complex. The MBW complex comprises an R2R3-MYB transcription factor (TF), a bHLH TF, and a WD-repeat protein . The R2Rhybrida) , Arabidohaliana) , and apphaliana) . In pearynthesis . In contynthesis , the subynthesis , the miRL module , and somL module .M. domestica), grape (Vitis vinifera), plum (Prunus spp.), and strawberry (Fragaria × ananassa), ethylene usually induces anthocyanin biosynthesis  and plant hormones . Most plynthesis . In applynthesis . Thus, tynthesis  and Arabynthesis , but thePpERF105, leading to the upregulated expression of PpMYB140, which encodes a repressive R2R3-MYB TF. A recent study revealed that PpMYB140 inhibits the expression of anthocyanin biosynthetic structural genes and competes with PpMYB114 to interact with PpbHLH3, thereby inhibiting anthocyanin biosynthesis and the development of red pear fruit  motif, can inhibit the transcription of the downstream target genes . The EARDFR, LEUCOANTHOCYANIDIN DIOXYGENASE (LDOX), and UFGT genes  and disrupts the formation of the MBW complex by recruiting the TPL/TPR co-repressors to form a complex that represses anthocyanin biosynthesis via histone H3 deacetylation at the transcription start sites of GT genes . Moreovebidopsis . Thus, EPpMYB114 expression and constructed a PpERF9/PpTPL1-(PpRAP2.4)-PpMYB114 transcriptional regulatory cascade involving histone deacetylation. Our results reveal a regulatory pattern of ethylene-inhibited anthocyanin biosynthesis in pear, which provides insight into the molecular basis of hormone-regulated anthocyanin biosynthesis in plants.However, it remains unclear whether anthocyanin biosynthetic regulatory genes are negatively regulated by histone deacetylation. In this study, we focused on the repressive effect of ethylene on We recently showed that ethylene inhibits anthocyanin biosynthesis in red pear fruit with different genetic backgrounds ; this fiPpCHI, PpCHS, PpF3H, PpDFR, PpANS, and PpUFGT) were significantly upregulated 3 d after a 1-MCP treatment, while the expression levels of PpANS and PpUFGT were repressed 3 d after ethephon treatment compared with control , respectively, whereas their expression levels were repressed significantly following the ethephon treatment compared with control  motif (i.e. the binding sites of ERF TFs) in the PpMYB114 and PpMYB10 promoter, respectively, indicating that ethylene might regulate PpMYB114 and PpMYB10 expression through ERF TFs. From 21 previously identified ERF TFs  that positively responded to ethylene signaling and were clustered into Class II with EAR motif  assay and an electrophoretic mobility shift assay (EMSA) showed that PpERF9 binds to the GCC-box in the PpMYB114 promoter in vitro  was red, with the exception of the injection site  for transient overexpression analysis and virus-induced gene silencing (VIGS) assays. After a 6-d exposure to strong light, the peel of fruit overexpressing ion site . The peeol fruit . The PpRuit peel . In contion site . The silnd PpANS . These rPpERF9-OX transgenic pear calli were generated. The control (empty vector) and PpERF9-OX pear calli were then treated with strong white light with UV-B  and AT1G22190.1 (AtRAP2.4), respectively . AtSMOS1PpRAP2.4 was upregulated by the 1-MCP treatment, but not by exposure to ethephon  fruit than for the control fruit  were used to perform transient overexpression and VIGS analyses. After a 3-d light treatment, the red coloration of the pear fruit peel surrounding the injection site was more intense for the ol fruit . The antuit peel . The PpM samples . Additioar fruit . Moreoveuit peel . These rPpRAP2.4-OX transgenic pear calli in a coloration assay revealed that PpRAP2.4 overexpression only slightly increased the accumulation of anthocyanins and the expression levels of anthocyanin-related genes after a 5-d light treatment  were much higher in PpRAP2.4-OX pear calli than in the control calli  using an antibody specific for acetylated histone H3 (H3ac). The overexpression of PpERF9 significantly decreased the H3ac level in pear calli  (dissolved in 1% DMSO), which is an effective HDAC inhibitor , increasol calli . Howeverol calli , probablar calli . These rPpRAP2.4 and PpMYB114 in PpERF9-OX pear calli by conducting ChIP-qPCR assays using the anti-H3ac antibody. Previous studies verified that histone acetylation occurs in the proximal promoters and/or exons   . Meanwhiar calli . These rPpRAP2.4 and PpMYB114 in PpERF9-m3-OX pear calli by performing ChIP-qPCR assays. The H3ac levels in the PpRAP2.4 and PpMYB114 promoters did not differ significantly between the control and PpERF9-m3-OX pear calli  domain, and C-terminal WD40 repeats . The delPpTPL1-overexpressing (PpTPL1-OX) transgenic pear calli. The control pear calli (carrying the empty vector) and the PpTPL1-OX pear calli were then exposed to strong white light with UV-B. A 5-d light treatment caused the control pear calli to turn red and accumulate large amounts of anthocyanin, whereas anthocyanins accumulated at lower levels in the PpTPL1-OX pear calli , the proximal promoter, and the exon region of PpRAP2.4 in PpTPL1-OX pear calli  and PpMYB114 in pear.Anthocyanins are crucial for plant defenses against various biotic and abiotic stresses. In fruit, anthocyanins play important roles in signaling, such as serving as visual signals for seed dispersers in ripe fruits . HoweverAtPAP1, which positively regulates anthocyanin biosynthesis, and inducing the expression of the negative regulator AtMYBL2 through the ethylene signaling pathway  in its promoter to regulate its expression. Furthermore, PpERF9 and PpRAP2.4 were positively and negatively regulated by ethylene signaling, respectively. When large amount of ethylene is present, the expression of PpRAP2.4 is repressed by PpERF9, eliminating the competitive binding of PpERF9 and PpRAP2.4 to the PpMYB114 promoter. Thus, ethylene signaling can negatively regulate the transcription of PpMYB114 efficiently through PpERF9-PpMYB114 or PpERF9-PpRAP2.4-PpMYB114 pathways.In the current study, we identified a repressive ERF TF (PpERF9) that responds positively to ethylene signaling . Additiopromoter  to G. Mopromoter  to F, whpromoter  to C, inPpRAP2.4-OX pear calli had a significantly higher growth rate and ethylene release rate than the control calli (PpACS1 promoter (MdACS1 promoter (Musa acuminata), MaERF9 and MaERF11 bind directly to the MaACO1 promoter to regulate gene expression  . To the MaACO1 and expansins to regulate fruit ripening  in Arabidopsis  positively regulates the expression of ethylene-responsive genes  to repress their expression. On the one hand, PpERF9 binds to the PpMYB114 promoter and inhibits its expression via PpERF9-PpTPL1-mediated histone deacetylation, directly inhibiting anthocyanin biosynthesis in pear fruit. On the other hand, PpERF9 represses the expression of PpRAP2.4 via histone deacetylation. PpRAP2.4 induces the expression of PpMYB114 by binding to its promoter. Thus, the PpERF9-PpRAP2.4-PpMYB114 regulatory pathway inhibits anthocyanin biosynthesis in pear fruit. In conjunction with our previous study  was collected from the orchard at the Institute of Horticulture, Henan Academy of Agricultural Sciences, Henan, China. All fruits were covered with lightproof double-layered paper bags at 15 d after full bloom (in March 2020). The bagged fruit was harvested about 140 d after full bloom (in July 2020) and immediately transported to the laboratory. The bagged fruit was randomly divided into 3 groups. The fruit was treated as previously described (−2·s−1) in a growth chamber set at 17°C and 80% relative humidity. After measuring the fruit color, ethylene release rate, TSS, and firmness of each fruit, the exposed side of the fruit peel was collected, immediately frozen in liquid nitrogen, and stored at −80°C until used.“Hongzaosu”  pear fruit (escribed . Briefly530 − A650) − 0.2 × (A650 − A620)]/0.1.Anthocyanins were extracted from 0.1 g pear fruit peel or calli using a methanol:HCl  solution as previously described . The abshttps://www.bio-rad.com/). The qPCR primers were designed using an online tool (https://www.ncbi.nlm.nih.gov/tools/primer-blast/) (−ΔΔCt method (Livak and Schmittgen PpACTIN gene (accession number: JN684184) was used for normalizing data.Total RNA was extracted from individual fruit peel samples according to a modified CTAB method . First-s-blast/) . The relProtein sequence alignments were performed using MEGA version 5.0 software with default parameters. The phylogenetic tree was constructed via the maximum-likelihood method in MEGA version 5.0 software. The protein sequence alignments used to construct the phylogenetic tree are provided in PpERF9, PpTPL1, and PpRAP2.4) were independently cloned into the pCAMBIA1300 vector, which contained the CaMV 35S promoter and GFP gene, resulting in fusion genes driven by the 35S promoter. The intactness of the fusion protein is verified by immunoblotting. Agrobacterium tumefaciens EHA105 cells harboring the vectors were independently infiltrated into the leaves of N. benthamiana transgenic lines containing red fluorescent protein in the nuclei. The empty vector of pCAMBIA1300 was used as the negative control. The fluorescence was observed by confocal laser scanning microscopy .The full-length complete coding sequences (CDSs) (without termination codons) of the target genes (PpERF9 and PpRAP2.4) was fused with the VP16 fragment and inserted into the pGBKT7 (BD) vector (gene-VP16-BD). The resulting vector and the positive (VP16-BD) and negative (BD) controls as well as gene-BD were independently transformed into yeast strain AH109 cells. Positive transformants were selected on a selective synthetic dextrose medium lacking tryptophan and histidine (SD/−Trp/−His). The β-galactosidase activity was measured as previously described  promoter fragments were ligated into the pAbAi vector, whereas the TF gene CDSs were cloned into the pGADT7 vector (gene-AD). The gene-AD vectors were then used to transform Y1HGold cells harboring the pAbAi-bait and then screened on SD/−Leu/AbA medium. Yeast two-hybrid assays were performed using the Matchmaker Gold Yeast Two-Hybrid System Kit (Takara) according to the manufacturer's protocol.Y1H assays were performed using the Matchmaker Gold Yeast One-Hybrid System Kit (Takara) according to the manufacturer's protocol. Briefly, gene  promoter sequence was inserted into the pGreenII 0800-LUC vector (promoter-LUC). Both constructs were used to transform A. tumefaciens strain GV3101 cells harboring the pSoup vector. The firefly luciferase and Renilla luciferase activities were analyzed at 60 h after the infiltration using the Dual-Luciferase Reporter Assay System (Promega) and the GloMax96 Microplate Luminometer (Promega). Three independent experiments were performed to analyze the relative luciferase activities, with 6 biological replicates per experiment.The full-length CDS of each gene was cloned into the pGreenII 0029 62-SK vector (gene-SK). The gene . The EMSA was performed using the LightShift Chemiluminescent EMSA Kit (Thermo Scientific) as previously described . The EMSPpERF9 and PpRAP2.4) was inserted into the pCAMBIA1301 vector for the subsequent expression under the control of the 35S promoter. Pear fruits were infiltrated with A. tumefaciens EHA105 cells containing the recombinant plasmid using a needle-free syringe. The pear fruits were then incubated in darkness firstly and then transferred to a growth chamber for an incubation under continuous white light (60 µmol·m−2·s−1) and UV-B light (1.1 µmol·m−2·s−1).The bagged mature red pear fruits (with no anthocyanin accumulation) were used for infiltrations. For the transient overexpression assay, the full-length CDS of the candidate gene (A. tumefaciens GV3101 cells. The injection solution containing gene-pTRV2 or empty pTRV2 (control) was co-infiltrated with pTRV1 into red pear fruits, which were then incubated in darkness firstly and transferred to a growth chamber for an incubation under continuous white light (60 µmol·m−2·s−1) and UV-B light (1.1 µmol·m−2·s−1). For all the transient transformation assay, the regions surrounding the injection areas were collected for pigment and gene expression analyses.Regarding the VIGS assay, the gene fragment (∼200 to 300 bp) was amplified and cloned into the pTRV2 vector (gene-pTRV2). The resulting vector was inserted into A. tumefaciens strain EHA105 cells harboring specific vectors . The calli were placed on solid MS medium for a 3-d co-culture. The pear calli were screened on solid MS medium supplemented with specific antibiotics at 24°C in darkness for 1 mo. The transformed calli were subcultured every 2 to 3 wk. For the light treatment, freshly subcultured calli were exposed to continuous light in a growth chamber and examined daily. Regarding the 1-MCP treatment, freshly subcultured calli were placed in a clean sealed box containing 0.5 µL/L 1-MCP gas for 16 h. For the TSA treatment, TSA (dissolved in DMSO) was added to solid MS medium for a final concentration of 10 µmol/L. As a control, DMSO was added to the solid MS medium. Because TSA is unstable under light, fresh pear calli were placed on solid MS medium supplemented with TSA or DMSO and incubated under dark for 7 d, after which they were placed under light for an analysis of coloration.Pear calli were transformed as previously described . BrieflyPpRAP2.4-OX pear calli was used. An equal amount of pear calli transformed with the empty pCAMBIA1301 vector was used as the negative control. The pear calli were placed on solid MS medium supplemented with specific antibiotics and allowed to grow. The pear calli weight was measured every 7 d. Additionally, the calli were transferred to fresh MS medium weekly. The relative growth rate was calculated. The experiment was performed twice, with 3 biological replicates each time.To measure the pear calli growth rate, 1 g freshly subcultured PpERF9-OX and PpTPL1-OX (pCAMBIA1301) pear calli were used. The ChIP assays were performed as previously described, with some modifications . Regarding the histone deacetylation analysis, ications . For thePpTPL1 and PpERF9 CDSs were inserted into the p2YN and p2YC vectors. A. tumefaciens GV3101 cells were transformed with the resulting plasmids according to a freeze–thaw method. Equal volumes of the different combinations were mixed for the infiltration of N. benthamiana leaves using a needle-free syringe. Leaves were co-infiltrated with PpERF9-2YN and 2YC as the negative control. The YFP signal was detected using a confocal laser scanning microscope  48 h later.The full-length t-test and a one-way ANOVA (Tukey) were completed using the Statistical Product and Service Solutions program . Detailed statistical analysis data are shown in Student's http://peargenome.njau.edu.cn): Pbr000398.1 (PpERF9), Pbr016049.2 (PpRAP2.4), Pbr042132.1 (PpMYB114), Pbr001905.1 (PpACS1), Pbr018800.1 (PpSAP18), Pbr029366.1 (PpTPL1), Pbr019531.1 (PpCHS), Pbr038148.1 (PpCHI), Pbr034840.1 (PpF3H), Pbr020145.1 (PpDFR), Pbr001543.2 (PpANS), Pbr039986.1 (PpUFGT), Pbr017379.1 (PpbHLH3), and Pbr016663.1 (PpMYB10).Sequence data from this article can be found in an online pear genome database (koad077_Supplementary_DataClick here for additional data file.


Dlx5, and exogenous expression of Myc-Dlx5, but not the activation of either the BMP- or the Wnt-signaling pathway, is sufficient to restore the osteogenic potential of cells depleted of NFIB or MLL1. Moreover, chromatin immunoprecipitation (ChIP) and ChIP-sequencing analysis showed that the NFIB–MLL1 complex mediates the deposition of trimethylated histone H3K4 at both Dlx5 and Cebpa, key regulator genes that function at the early stages of osteogenic and adipogenic differentiation, respectively, in uncommitted C3H10T1/2 MSCs. Surprisingly, the depletion of either NFIB or MLL1 leads to decreased trimethylated histone H3K4 and results in elevated trimethylated histone H3K9 at those developmental genes. Furthermore, gene expression profiling and ChIP-sequencing analysis revealed lineage-specific changes in chromatin landscape and gene expression in response to osteogenic stimuli. Taken together, these data provide evidence for the hitherto unknown role of the NFIB–MLL1 complex in the maintenance and lineage-specific differentiation of C3H10T1/2 MSCs and support the epigenetic regulatory mechanism underlying the stemness and plasticity of MSCs.Despite significant progress in our understanding of the molecular mechanism of mesenchymal stem cell (MSC) differentiation, less is known about the factors maintaining the stemness and plasticity of MSCs. Here, we show that the NFIB–MLL1 complex plays key roles in osteogenic differentiation and stemness of C3H10T1/2 MSCs. We find that depletion of either NFIB or MLL1 results in a severely hampered osteogenic potential and failed activation of key osteogenic transcription factors, such as Dlx5, Runx2, and Osx, following osteogenic stimuli. In addition, the NFIB–MLL1 complex binds directly to the promoter of  Mesenchymal stem cells (MSCs) are multipotent progenitor cells capable of self-renewal and differentiation into multiple lineages, such as osteogenesis and adipogenesis , 2, 3. TOcn, Opn, Alp, and Col1a1  and functions as a DNA-binding transcription factor . Members1/2 MSCs , 49, 50.ntiation , 52. In ntiation , 54. Desntiation .Dlx5 and Cebpa, and helped establish the chromatin landscape consisting of H3K4me3/H3K9me3 in uncommitted C3H10T1/2 MSCs, which is likely to maintain MSCs in a ‘paused for responsive’ state until the relevant environmental signal is reached. Overall, these findings support the idea of epigenetic control in MSC maintenance and plasticity.This study investigated the roles of NFIB and its interacting MLL1 in the osteogenic differentiation of C3H10T1/2 MSCs. The depletion of NFIB or knockdown of MLL1 resulted in severely inhibited differentiation, which could be rescued by the exogenous expression of Myc-Dlx5 but not by activating the Wnt- or BMP-signaling pathways. Furthermore, the NFIB–MLL1 complex targeted critical developmental genes, such as Cebpa and Pparg, and regulates adipogenic differentiation of C3H10T1/2 MSCs  or MLL1 (shMLL1) resulted in the decreased expression of genes involved in adipogenic differentiation. Of particular interest, RNA-seq analysis identified Dlx5, a well-known osteogenic transcription factor gene, as one of those affected by the depletion of NFIB or MLL1 in C3H10T1/2 MSCs prior to adipogenic stimuli . Unlike the increased expression during adipogenic differentiation of C3H10T1/2 MSCs (A and B). On the other hand, no meaningful change in MLL1 expression was observed during the same period (A and B). Next, we investigated whether the NFIB–MLL1 complex is required for the osteogenic differentiation of C3H10T1/2 MSCs. The depletion of endogenous NFIB severely hampered the osteogenic potential of MSCs (A), which is evident by the lack of calcium deposits even after 21 days of incubation in an osteogenic medium. Immunoblot and quantitative reverse transcription-PCR (RT-qPCR) analyses confirmed that the knockdown of NFIB led to inhibited or at least delayed expression of key pro-osteogenic regulators, such as Dlx5, Runx2, and Osx, but it had no significant effect on the level of anti-osteogenic regulator Msx2 (B and C). In addition, the exogenous expression of FLAG-NFIB that lacks 3′ UTR targeted by shRNA restored the osteogenic potential in shNFIB cells, but the overexpression of FLAG-NFIB in the control C3H10T1/2 MSCs (shControl) failed to promote osteogenic differentiation (D and E). Similarly, the knockdown of MLL1 (shMLL1) resulted in the loss of osteogenic potential in C3H10T1/2 MSCs and the inhibited or at least delayed induction of key osteogenic regulators (F–H).We first examined the expression of the NFIB–MLL1 complex during osteogenic differentiation , A and B1/2 MSCs , the exp1/2 MSCs , A and Be period , A and B of MSCs A, which tor Msx2 , B and Cntiation , D and Egulators , F–H.FigMyod1 and Myogenin, was not observed in either shNFIB or shMLL1 cells following myogenic stimuli . Chromatin immunoprecipitation (ChIP) analysis showed that both NFIB and MLL1 were enriched at the Dlx5 promoter but not at a distal region of the Dlx5 gene in the control MSCs . Of particular interest, enrichments at the Dlx5 promoter were observed not only in cells undergoing osteogenic differentiation but also in uncommitted cells . On the other hand, no meaningful binding of the NFIB–MLL1 complex was observed at genes involved in the later stages of osteogenic differentiation, such as Runx2 and Osx .We next investigated whether pro-osteogenic transcription factor genes, such as 1/2 MSCs A. Chromarol MSCs B, left. ed cells B, left. C and D). As expected, the H3K4me3 levels at the promoter of Dlx5, Runx2, and Osx were increased markedly in the control MSCs (shControl) following osteogenic stimuli (C). On the other hand, such increases were not observed in either shNFIB cells or shMLL1 cells . Interestingly, H3K4me3 levels at the Dlx5 promoter were modestly but meaningfully lower in shNFIB and shMLL1 cells than those measured in shControl cells, which suggests that the NFIB–MLL1 complex may target the Dlx5 promoter, even in uncommitted MSCs .We next examined the occupancy of H3K4me3 at the promoter of osteogenic regulator genes because MLL1 is a core component of the MLL1 KMT complex that mediates H3K4me3 , C and D stimuli C. On theL1 cells D, right.Cebpa and Pparg, are marked by both active H3K4me3 and repressive H3K9me3 in C3H10T1/2 MSCs, and the depletion of NFIB or MLL1 disrupts the balance between those two histone modifications . In contrast, no significant changes were detected at the promoters of Runx2 and Osx in shNFIB and shMLL1 cells (F). These data suggest that the NFIB–MLL1 complex directly targets the Dlx5 promoter and deposits active chromatin marks, H3K4me3, which might be critical for maintaining the osteogenic potential of C3H10T1/2 MSCs.The promoters of adipogenic regulator genes, such as ications , 45. Hen stimuli F, left. L1 cells F. These A and D). Immunoblot and RT-qPCR analyses confirmed that replenishing Dlx5 was sufficient for shNFIB (B and C) and shMLL1 cells to regain osteogenic potential (E and F). Of note, the exogenous expression of Myc-Dlx5 appeared to regulate Runx2 expression positively in undifferentiated MSCs, which aligns with previous reports . These results demonstrate that the function of the NFIB–MLL1 complex in osteogenic differentiation is, at least in part, associated with its ability to control Dlx5 expression.We next tested whether the exogenous expression of Dlx5 would be sufficient to restore osteogenic potential in shNFIB and shMLL1 cells to confirm that defective osteogenic differentiation is directly related to the failed activation of Dlx5 upon osteogenic stimuli . Indeed,r shNFIB , B and Cotential , E and F reports , 18  or treatment with 25 mM of lithium chloride (C and D), which are well-established ways to activate the Wnt signaling pathway in cells, promoted osteogenic differentiation of control MSCs, as shown by the stronger ARS staining and higher expression of the key osteogenic regulators in cells with Wnt-activating treatment (A–D). On the other hand, despite marked increases in Ccnd1 expression following Wnt-activating treatments, no meaningful changes were observed in either shNFIB or shMLL1 cells (A and B). Similarly, the control MSCs exhibited accelerated osteogenic differentiation in the presence of recombinant BMP2, but the same treatment failed to overcome the osteogenic defects in shNFIB and shMLL1 cells (E and F). More importantly, unlike Dlx5 showing dramatic increases in expression by activation of the Wnt/BMP signaling pathways, these pro-osteogenic stimuli had no significant effects on the steady-state mRNA level of either Nfib or Mll1, suggesting that the NFIB–MLL1 complex may function independently from the Wnt/BMP signaling pathways in osteogenic differentiation of C3H10T1/2 MSCs (G). We also examined whether inhibition of the Wnt signaling pathway, which can be achieved by treatment with an NSC668063, a Dvl inhibitor, or the exogenous expression of FLAG-Axin1 rescues adipogenic defects in shNFIB and shMLL1 cells (A and C) as well as the expression of key adipogenic markers, such as Cebpa, Pparg, and Fabp4, suggesting positive regulation of adipogenic differentiation in C3H10T1/2 MSCs (B and D). On the other hand, the same treatments resulted in no meaningful restoration of the adipogenic differentiation potential in shNFIB or shMLL1 cells .Previous results suggested that the functions of the NFIB–MLL1 complex are likely to be related to its DNA binding and histone modification activities  44). Th. Th44). Figs. 5A). As a result, 2903 and 1530 peaks of 6849 NFIB-binding peaks were affected by NFIB or MLL1 knockdown, respectively , and 969 peaks, which were designated as NFIB high-quality binding sites (HQBS), were commonly presented . Using similar approaches, 20,363 MLL1 binding peaks and 19,487 H3K4me3-enriched peaks were identified, of which 6013 and 7798 peaks were localized at proximal promoter region in uncommitted C3H10T1/2 MSCs, respectively . More than 60% of the MLL1 binding peaks (4041/6013) were included in the list of H3K4me3-enriched peaks . Among the 4041 peaks, the depletion of NFIB or MLL1 led to a significant decrease in the H3K4me3 level at 1219 and 3922 peaks, respectively . Of note, 1193 peaks were commonly presented and designated as MLL1 HQBS . Finally, 52 NFIB/MLL1 direct targets listed in 969 NFIB HQBS and 1192 MLL1 HQBS were identified .The genome-wide impact of NFIB or MLL1 depletion on NFIB occupancy in C3H10T1/2 MSCs was analyzed A. As a rC). Surprisingly, both Dlx5 and Cebpa were among those 52 NFIB/MLL1 targets (C and D). Of note, GO analysis of 917 targets exclusive to NFIB HQBS  identified nervous system development (GO:0007399), multicellular organism development (GO:0007275), and learning (GO:0007612) (F). On the other hand, the analysis of 1141 targets exclusive to MLL1 HQBS  failed to identify the GO terms related to differentiation and development (G). Overall, these data suggest that the functions of the NFIB–MLL1 complex in MSCs maintenance are associated with its selective targeting of developmental regulator genes such as Dlx5 and Cebpa in uncommitted C3H10T1/2 MSCs.Gene ontology (GO) analysis of 52 NFIB/MLL1 direct targets (30/52) revealed a strong association with differentiation and development, such as developmental process (GO:0032502), cell differentiation (GO:0030154), neuron development (GO:0048666), and positive regulation of osteoblast differentiation (GO:0045669) C. Surpri targets , C and DFIB HQBS B, blue i0007612) F. On theLL1 HQBS B, yellowelopment G. OveralDlx5 and adipogenic Cebpa are targeted by the NFIB–MLL1 complex and enriched with H3K4me3 in uncommitted MSCs, we next investigated whether lineage-specific osteogenic stimuli affect the 52 NFIB/MLL1 direct targets differently on their NFIB occupancy and H3K4me3 deposition. Comparative ChIP-seq analysis of the 52 direct targets identified 14 genes that exhibited increased NFIB occupancy and elevated H3K4me3 deposition at the promoters following osteogenic stimuli (A and B). More importantly, Dlx5 was included in the list of 14 responsive genes, but adipogenic Cebpa was among those 38 nonresponsive genes (A and B). These findings strongly suggest that the NFIB–MLL1 complex targets in uncommitted MSCs may respond differently to lineage-specific differentiation signals.Considering that osteogenic  stimuli , A and Bve genes , A and BA). GO analysis of those 1962 upregulated OAGs identified GO terms including cell differentiation (GO:0030154), osteoblast differentiation (GO:0001649), and bone mineralization (GO:0030282) . On the other hand, an analysis of 1920 downregulated OAGs revealed enrichment of the genes involved in the cell cycle process . In addition, cluster analysis of 1962 upregulated OAGs identified 553 NFIB-dependent genes and 246 MLL1-dependent genes (A). Further analysis showed that 65.8% (162/246) of MLL1-dependent OAGs were NFIB-dependent (A). Importantly, both Dlx5 and Runx2 were among those 162 NFIB/MLL1-dependent OAGs. . On the other hand, no GO terms associated with osteogenic differentiation were identified from analyses of 391 exclusively NFIB-dependent genes and 84 exclusively MLL1-dependent genes . We also identified 305 NFIB-dependent, 142 MLL1-dependent, and 77 NFIB/MLL1-dependent genes from the analysis of 1920 downregulated OAGs . Unlike the 162 upregulated NFIB/MLL1-dependent genes, the analysis of 77 downregulated genes provided no meaningful information related to osteogenic differentiation .We next conducted RNA-seq analysis of six different samples  to investigate the roles of the NFIB–MLL1 complex on the transcriptome of C3H10T1/2 cells and identify the NFIB/MLL1-dependent genes during osteogenic differentiation  and S7. Fig. S7nt genes A. Furtheependent A. ImportC). GO analyses showed a strong correlation with osteoblast differentiation of 123 OAGs  and an association with fat cell differentiation . On the other hand, no meaningful information related to either osteogenesis or adipogenesis was obtained from the analysis of 39 commonly presented OAGs/AAGs . Lastly, 52 NFIB–MLL1 complex targets from the ChIP-seq analysis were compared with 162 OAGs and 216 AAGs . Surprisingly, only five genes out of 52 direct targets were presented in the list of either 162 OAGs or 216 AAGs. Moreover, both Dlx5 and Cepba, but no other known lineage-specific regulators, were included in those five genes . Collectively, these data show that the function of the NFIB–MLL1 complex in C3H10T1/2 MSCs is, at least in part, to serve as an intrinsic regulatory factor maintaining stemness and plasticity by contributing to chromatin landscape at the critical developmental genes such as Dlx5 and Cebpa.Furtherly, comparative analysis of 162 NFIB/MLL1-dependent OAGs with 216 previously identified NFIB/MLL1-dependent adipogenesis-associated genes (AAGs) was conducted . We founFig. 7Dlx5 and Cebpa, and contribution to the establishment of bivalent chromatin domain consisting of H3K4me3/H3K9me3.Despite the significant advances in understanding the roles of sequence-specific transcription factors and their interacting epigenetic modifiers in lineage-specific differentiation, relatively less is known about how they are integrated into the regulation of stemness and plasticity of MSCs. This study found that NFIB and its interacting MLL1 KMT control the expression of Dlx5 and thus regulate the osteogenic differentiation of C3H10T1/2 MSCs. Moreover, the current study supports the idea that roles of the NFIB–MLL1 complex may not be limited to a specific differentiation pathway but extended to maintaining the stemness and plasticity of MSCs, which involves targeting the developmental genes, such as This study revealed the regulatory function of the NFIB–MLL1 complex in the osteogenic and likely the myogenic differentiation of C3H10T1/2 MSCs, which makes the NFIB–MLL1 complex a factor required for the trilineage differentiation potential of MSCs  and S2 . The inhNfib gene shared by all five isoforms and anti-NFIB antibodies used for ChIP and ChIP-seq analyses also recognize all five isoforms, results from the current study are not enough to determine the isoform dependency of NFIB in regulating the differentiation in C3H10T1/2 MSCs. However, it should be noted that FLAG-NFIB, which successfully rescued defects in lineage-specific differentiation, is derived from the Nfib isoform 3 , raising a possibility that the NFIB–MLL1 complex might play roles in other types of multipotent stem cells such as NSCs. Interestingly, it has been reported that NFIB is highly expressed in NSCs , neurogenesis (GO:0022008), and brain development (GO:0007420) C, raisin in NSCs , and MLL in NSCs , 41. On  in NSCs .Dlx5 and Cebpa promoters, the functions of the NFIB–MLL1 complex are likely related to the establishment of the chromatin landscape in uncommitted MSCs. Of particular interest, ChIP and ChIP-seq analysis revealed decreases in H3K4me3 accompanied by increases in repressive histone mark H3K9me3 at regions of Dlx5 and Cebpa. These data support a previous report suggesting the bivalent chromatin domain comprised of H3K4me3 and H3K9me3 at the adipogenic regulator  or MLL1 (shMLL1) is described elsewhere , 100 μg/ml ascorbic acid 2-phosphate (Sigma), and 0.1 μM dexamethasone (Sigma). The osteogenic medium was changed every 2 days over 21 days, and differentiation was assessed by calcium deposits stained with ARS (Sigma). The stained calcium deposits were monitored using an Olympus BX50 microscope. All images were scanned with an Epson Perfection V500 scanner and processed using Adobe Photoshop CC 2022.Ccnd1 mRNA was monitored to evaluate the activation of the Wnt-signaling pathway. In addition, activation of the signaling pathways during osteogenic differentiation was assessed by increased calcium deposition and the expression of osteogenic marker genes as described previously  or by the exogenous expression of constitutively active β-catenin (S37A). For BMP-signaling pathway activation, cells were cultured in a medium containing 100 ng/ml of recombinant human/mouse BMP2 . The level of endogenous eviously , 56.g. The clarified supernatants were resolved by SDS-PAGE and analyzed by immunoblotting using an ECL plus Western Blotting Detection System (Cytiva) according to the manufacturer’s instructions. Gapdh mRNA. The proteins of interest were analyzed by preparing whole cell lysates using RIPA buffer  supplemented with protease inhibitor cocktail (Roche). After sonication using a VCX130 ultrasonic processor , the sonicated lysates were clarified by centrifugation at 4 °C for 20 min at 13,000ChIP assays were performed as described previously with modifications . BrieflyFor RNA-seq, libraries were prepared from the total RNAs using TruSeq RNA-seq kit (Illumina), quantified using an Agilent Technologies 2100 Bioanalyzer (Agilent Technologies), and then sequenced on Illumina Nova-Seq 6000 platform (Illumina) with paired-end 100 bp read length to obtain at least 60 million reads per sample. The raw reads were subjected to basic quality control using FastQC version 0.11.9, and the adapter sequence was trimmed by Trimmomatic version 0.39 , 70. Thep value of <0.05 with Benjamini-Hochberg multiple testing correction at a 5% FDR and a fold change of >2. GO analysis was conducted using DAVID bioinformatics resources 6.8, and a significant biological process GO term was obtained by applying the thresholds of p < 0.05 (https://ggplot2.tidyverse.org) and modified using Adobe Illustrator 2022.For RNA-seq analysis, the read counts obtained from data processing were first normalized using the trimmed mean of M-values method. The DEGs were then identified using R package edgeR version 3.38 . Signifip < 0.05 . For cluFor ChIP-seq, sonicated chromatins (300–400 μg) were precleared with protein A/G and immunoprecipitated with relevant antibodies . The intThe bamCoverage of deepTools package version 3.5.1 was used to generate ChIP-seq coverage tracks. Read coverage was normalized using read per genome coverage, and the coverage tracks were visualized using Integrative Genomic Viewer , 79. Thehttps://www.graphpad.com). The normality of data was assessed using a Shapiro–Wilk test. For two groups, normally distributed data were analyzed using a two-sided t test, while skewed data were analyzed using a Mann-Whitney test. One-way or two-way ANOVA with a correction for multiple comparisons was performed for three or more groups. Statistical significance was defined as p <0.05.At least three independent experiments were performed as indicated in the figure legends and are presented as the means ± SD or ± SEM. Statistical analyses were conducted using GraphPad Prism software version 9 (All of the data are contained in the article and This article contains The authors declare that they have no conflicts of interest with the contents of this article.


Autophagy is an evolutionarily conserved process critical in maintaining cellular homeostasis. Recently, the anticancer potential of autophagy inducers, including phytochemicals, was suggested. Indicaxanthin is a betalain pigment found in prickly pear fruit with antiproliferative and pro-apoptotic activities in colorectal cancer cells associated with epigenetic changes in selected methylation-silenced oncosuppressor genes. Here, we demonstrate that indicaxanthin induces the up-regulation of the autophagic markers LC3-II and Beclin1, and increases autophagolysosome production in Caco-2 cells. Methylomic studies showed that the indicaxanthin-induced pro-autophagic activity was associated with epigenetic changes. In addition to acting as a hypermethylating agent at the genomic level, indicaxanthin also induced significant differential methylation in 39 out of 47 autophagy-related genes, particularly those involved in the late stages of autophagy. Furthermore, in silico molecular modelling studies suggested a direct interaction of indicaxanthin with Bcl-2, which, in turn, influenced the function of Beclin1, a key autophagy regulator. External effectors, including food components, may modulate the epigenetic signature of cancer cells. This study demonstrates, for the first time, the pro-autophagic potential of indicaxanthin in human colorectal cancer cells associated with epigenetic changes and contributes to outlining its potential healthy effect in the pathophysiology of the gastrointestinal tract. Autophagy is implicated in maintaining the balance between the synthesis, degradation, and recycling of cellular components. This process involves the formation of an autophagosome, endowed with a double membrane enclosing cytoplasmic constituents, that subsequently fuses with a lysosome to generate a mature autophagolysosome, in which cellular components are degraded and subsequently released.Both the impairment and excessive activation of autophagy are correlated with multiple pathophysiological states and ageing , and mucThe role of autophagy in cancer is complex and debated. Scientific evidence shows that autophagy allows cell survival under conditions of hypoxia and nutrient deficiency. As these conditions are common in the microenvironment of solid tumours, autophagy can support the survival of cancer cells . Indeed,BECN1, coding for the autophagy regulator Beclin1, leads to increased spontaneous breast, ovarian, and prostate tumorigenesis in murine models [Several studies in mammals have shown the importance of some autophagy genes in tumour suppression. In fact, it was demonstrated that the specific miRNA-mediated loss of autophagy, or the loss of genes responsible for autophagy ,11, may e models . Converse models .Experimental evidence shows that many phytochemicals possess chemotherapeutic properties and some of them have been characterized for their ability to induce autophagy ,16. ThesBeta vulgaris) and fruits of Opuntia species, such as Opuntia ficus indica, are the main sources of these molecules. The antitumoral potential of betalains has been explored through in vitro and in vivo models [Opuntia ficus indica, indicaxanthin (IND), is highly bioavailable in humans in its native form [Betalains are an emerging class of phytochemicals. Their distribution is restricted to nine of the twelve families of the Cariophillales order, and beetroot  [The Caco-2 colon adenocarcinoma cell line was cultured in high-glucose–DMEM medium plus 10% foetal calf serum , 100 U/mL penicillin, 100 µg/mL streptomycin, and 2.5 mg/L amphotericin B  at 37 °C under a 5% CO. (2013) , with so. (2013) .Opuntia ficus indica fruit extracts, as previously reported [IND was isolated from reported . Caco-2 Proteins were extracted and Western blotting was performed as described by Librizzi et al. (2015)  and CaraThe detection and quantitation of acidic vesicular organelles and autophagy markers were evaluated by flow cytometry, as reported by Luparello et al. (2019) . Brieflyhttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/ accessed on 30 January 2023). Subsequently, they were aligned against the reference human genome (GRCh38/hg38) using the bisulphite-specific short-read aligner BSMAP v 2.90 [MspI digestion. Following alignment, the BAM files were sorted and indexed using SAMtools [methylKit and bimodal CpG methylation % profiles were obtained. The general coverage statistics were checked and the samples were filtered based on coverage (minimum coverage <10 and >99.9th percentile of coverage in each sample). The mean coverage obtained on these CpG sites ranged from 22 to 26 between five methylomes.The RRBS approach is now becoming increasingly common because it allows genome-scale DNA methylation analysis in a highly accurate and low-cost manner. Indeed, the fragments obtained from RRBS include most promoters, as well as repeated sequences (which often contain methylated cytosines), which are difficult to profile using conventional approaches. The samples were prepared according to the Diagenode Premium RRBS Kit protocol and sequenced on an Illumina HiSeq platform using 150 bp paired-end reads. The raw FASTQ reads were initially quality-tested using FastQC  to examine the differential methylation profile following cell treatments. As the output, RRBS viewer provides log2 differential methylation data of each CpG contained in the gene region. The region that extended from −1000 to +1000 bp with respect to the transcription start site (TSS) was considered as the “promoter”. To obtain the latest annotation version, the data were downloaded using the Table Browser tool from the UCSC  Genome Browser. For some autophagy genes that possess two TSSs, analyses were also carried out on these additional promoters . The complete list of selected genes and chromosomal ranges corresponding to their promoters and gene bodies are indicated in To cluster the samples based on the similarity in the methylation variation, the heatmap function of R was used and similarity groups were defined.methylKit pairwise comparison using a tile-based method. Tile-based analysis affords higher statistical power due to the aggregation of the signals from multiple CpGs within a defined genomic region. MethylKit differential analysis was performed at the level of 200 bp tiles using default parameters: q-value < 0.01 and minimum coverage in the tile equal to 10. Tiles with |diff.meth| greater than 25% were considered, as lower values were not indicative of substantial differences.To observe the changes in the degree of methylation relative to the regions and, therefore, to better highlight the differences in methylation between the treated and untreated samples, the differentially methylated regions (DMRs) were also determined by genomation, the regions corresponding to the selected tiles were then annotated to assess whether they corresponded to specific regulation elements . The analysis of the tiles was carried out for the regions corresponding to both promoters and gene bodies.Using the R package The isolation of genomic DNA from Caco-2 cells was carried out as described by Longo et al.  and its BECN1 gene promoter after cell treatment with IND, the MSRE-PCR technique was used [BECN1 gene . In this region, those primer couples that allowed the amplification of four fragments, each containing unique CpG sites for HpaII and HhaI methylation-sensitive restriction enzymes, were chosen  was downloaded from the Protein Data Bank . The ProThe default setting of the LigPrep tool implemented in Schrödinger’s software (version 2017-1) was used to prepare IND for molecular docking . All posIFD simulation was performed using the IFD application available ,43 in thThe IFD protocol was performed as follows ,46: IND Finally, the IFD score (IFD score = 1.0 Glide_Gscore + 0.05 Prime_Energy), which accounts for both the protein–ligand interaction energy and total energy of the system, was calculated and used to rank-select the best IFD pose. To assess the complex stability and to dissect the amino acids involved in the interaction, the IFD best-scored output was submitted to 20 ns of molecular dynamics simulation.t-test. p < 0.05 was considered statistically significant. Regarding the methylation difference calculation, the fast Fisher test was performed. q-value < 0.01 indicated a statistically significant difference.The statistical analysis was performed using R, and data are presented as the means  ±  SDs for three independent experiments. Differences between two groups (treated and untreated cells) were assessed by the paired To evaluate the potential effect of IND in inducing autophagy, the expression levels of two autophagic markers, LC3-II and Beclin1, were evaluated by Western blot analysis. The results indicate that the expression of the LC3-II protein increased in a dose-dependent manner in the IND-treated Caco-2 cells compared with the untreated control A. ExposuTo further evaluate the effects of IND on the autophagic process, the quantification of acidic vesicular organelles by flow cytometry was performed D–F. CacoWe previously reported that IND inhibited DNMT activity and induced the epigenetic modulation of silenced oncosuppressor genes in Caco-2 cells . To evalRRBS data analysis confirmed the expected bimodal profile of the methylation rate, where most CpGs displayed either high or low methylation states. The untreated Caco-2 cells showed about 50% demethylated CpG sites and only about 25% strongly methylated sites A. The ceConsidering the four phases of the autophagic process, we selected 60 promoters of 51 phase-specific genes , a major negative regulator of autophagy), is intriguing. In fact, IND10 exposure induced MTOR promoter methylation, while the IND50 and IND100 treatments induced MTOR promoter demethylation, compared with control cells.WDR45.1, while IND100 had a demethylating effect. On the contrary, IND50 had a demethylating effect on the promoter of ATG7, while IND10 and IND100 had methylating effects. MTOR, WDR45.1, and ATG7 showed the presence of a CpG island in the differently methylated region. Of note, an inspection of the epigenetic functional markers of these promoters in untreated Caco-2 cells  showed that all of them exhibited active promoters . Therefore, it is conceivable that the methylating effect could inhibit transcription and could be more effective than the demethylating effect.IND50 had a methylating effect on the promoter of Considering the four phases of the autophagy process, our data show that IND induced promoter methylation changes specifically in the genes involved in the late stages, while the demethylating effect was mainly observed for the genes involved in the fusion stage .q-value of <0.01 obtained for each of the three pairwise comparisons on the promoter regions and on the gene bodies are listed in To highlight the DMRs, a tile-based analysis was also performed in both the promoter regions and the gene bodies. The values with |diff.meth| > 15% at a q-value < 0.01), the genomic coordinates of the tile, the TSS, the difference methylation values, and, if present, the annotated regulatory regions (candidate cis-regulatory elements from ENCODE or regulatory elements from “Open Regulatory Annotation” records) are listed [In e listed ,50.ATG3 upstream region. Moreover, after treatment with IND10 and IND50, a strongly demethylated region was observed in the BCL2.2 gene  promoter.According to the DMC analyses, the tile-based analyses of the promoter regions showed the demethylation of a control element located in the ULK2, EPG5, and VPS11, and confirmed the methylation of MTOR and PIK3R4. In the BECN1 “promoter”, a particularly methylated tile was found downstream of the CpG island. In this region, however, no regulatory elements were annotated.Tile analysis highlighted a previously undetected methylation after treatment with IND with respect to the controls for q-value < 0.01), the genomic coordinates of the tile, the difference methylation values, the annotated regulatory regions (candidate cis-regulatory elements from ENCODE or regulatory elements from “Open Regulatory Annotation” records), and the functional classification are listed.In PIK3R4 gene body, a region with typical features of a distal enhancer. Strong hypermethylation was found also in the TSNARE1 gene body corresponding to an intron or an exon, depending on alternative splicing, and in the ATG9A gene. On the contrary, strong demethylation was found in two regulatory regions located in introns. The first was in the GABARAPL2 gene and the second in the ATG7 gene.Regarding the tile-based analysis of the gene body regions, the highest methylation difference (hypermethylation) was detected in the BECN1 promoter by MSRE-PCR.Considering the importance of Beclin1 in the autophagic process, its increase after cell exposure to IND B and theThe results of the MSRE-PCR experiments are reported in For the 472 site D, the deTo gain more insight into the potential binding capability of IND to the Beclin1 binding site of Bcl-2, molecular modelling studies were performed. A model of the IND-Bcl-2 complex, based on guided IFD simulations starting from the crystal structure of the Bcl-2-Beclin1 complex A, was obThe molecular dynamics simulation of the Bcl-2-IND complex showed that the amino acids of Bcl-2 close to IND were also involved in the binding of Bcl-2 to Beclin1 A–C. In f3K/AKT/mTOR pathway, AMPK activity, and Bcl-2-Beclin1 complex formation. However, the involvement of epigenetic mechanisms in pro-autophagic effects has not yet been investigated.Autophagy is an evolutionarily conserved process by which malfunctioning or senescent cellular constituents are sequestered in autophagolysosomes, then degraded and recycled. This process is crucial for maintaining homeostasis by preserving genome stability  and contDNMT3A expression was upregulated [INK4ap16 [Among phytochemicals, IND, a betalain pigment present in prickly pear fruit, has been extensively studied for its antioxidant, anti-inflammatory, and antiproliferative activities ,24. The egulated . We prevp16INK4a ,24. HoweThe IND concentrations selected for our experiments are compatible with the phytochemical concentrations that can be obtained at the luminal level after ingesting a quantity of yellow cactus pear fruit between 30 g and 300 g .50 of 115 µM [In addition, we previously reported that the treatment of proliferating Caco-2 cells with IND for 48 h caused a concentration-dependent inhibition of cell growth with an ICf 115 µM .50. It is possible that, at 100 µM, indicaxanthin might represent a stress exceeding the threshold of a well-controlled cellular response. The trend of Beclin 1 expression could be the result of a hormetic effect of IND, similar to what has been observed for many phytochemicals and redox-active molecules.Here, under the same experimental conditions, the pro-autophagic potential of IND was demonstrated by the up-regulation of the autophagic markers LC3-II and Beclin1, and the increased production of autophagolysosomes. Beclin 1 protein expression was not correlated with the indicaxanthin concentrations used in our study: in fact, the Beclin 1 protein levels peaked at 50 µM and decreased when 100 µM of IND was used. This unexpected result might be attributed to a reduced ability of cells to respond to the phytochemical when its concentration is close to ICMAP1LC3A, MAP1LC3B, and MAP1LC3C, which are proteins ubiquitously distributed in mammalian tissues [LC3s (microtubule-associated protein 1 light chain 3), encoded by a gene family, an orthologue of ATG8 in yeast, contains three members in humans:  tissues . LC3-I i tissues .Beclin1 is required for the formation of PI3KC3-C2 (class III phosphatidylinositol 3-kinase complexes-C2), and its activity is regulated by its interaction with several proteins at the post-translational level. The best-known interactors are the Bcl-2-like proteins. The dissociation of Beclin1 from Bcl-2 induces autophagy; therefore, the regulation of this association is crucial. Different mechanisms are involved in the dissociation of Beclin1 and Bcl-2/Bcl-XL during autophagy: post-translational modifications, the competitive displacement of Bcl-2 by other Beclin1-binding proteins, or the competitive displacement of Beclin1 by other Bcl-2 interactors . DespiteBy methylomic approaches, we showed that epigenetic mechanisms are also involved in the pro-autophagic activity of IND.Although specific regions, such as some tumour-suppressor genes, are often found to be hypermethylated, it is well known that global DNA hypomethylation is a hallmark of cancer cells . Caco-2 In the context of the autophagic process, these data indicate that IND influenced the methylation level, especially that of those genes that regulate the late stages of autophagy, such as autophagosome fusion and elongation.ATG7, ATG3, and ATG10 encode proteins essential for mammalian autophagy, and are involved in two ubiquitylation-like modifications of target proteins, ATG12-conjugation and LC3-modification. E1-like ATG7, E2-like ATG10, and E3-like ATG5 are involved in the ATG12 conjugation that is essential for the formation of pre-autophagosomes (or phagophores). E1-like ATG7 and E2-like ATG3, whose gene promoters are demethylated by IND, are involved in LC3 lipidation, an event necessary for phagophore expansion and closure, and are also important in the E3-like ATG12–ATG5–ATG16L1 complex formation [Among those genes, ormation ,64.WIPI1 and WIPI2 encode WD40 domain-containing proteins. WIPI1 and WIPI2 (WD repeat-domain phosphoinositide-interacting protein 1 and 2) recruit the E3-like ATG12–ATG5–ATG16L1 complex that directly controls the elongation of the nascent autophagosomal membrane to phagophore assembly sites [Among those genes affected by IND exposure, ly sites .ATG12–ATG5 also binds TECPR1 (Tectonin beta-propeller repeat-containing protein 1), which promotes the fusion of LC3C autophagosomes with lysosomes . This prBCL2 is a gene involved in the nucleation phase, whose expression is regulated by two promoters [BCL2 promoter under normal conditions and oxidative stress in the A549 cell line [We also found an epigenetic effect of IND on genes involved in the early signalling events of autophagy. Among these, romoters ,69. RRBSromoters . Putativell line .Moreover, data analysis performed on gene bodies allowed us to discover regions not belonging to the promoter, which are diversified by IND treatments in terms of DNA methylation. The gene body may contain regulatory regions different from those of the promoter that can influence promoter activity by binding transcription factors. Thus, we highlighted other gene regions that are differently methylated.PIK3R4 and TSNARE1 gene bodies; moreover, hypermethylated tiles were also found in WDR45, EPG5, and ATG9A genes. The hypermethylated tile in the ATG9A gene body contained a putative binding site for EGR1, like the BCL2 gene promoter (demethylated by IND10 treatment), MAPK8.2 promoter (unaffected), and also the SNAP29 gene body (slightly demethylated).Strongly hypermethylated tiles were found, for example, in the ATG7 and GABARAPL2 gene bodies. However, these signatures are not present in untreated Caco-2 cells. In particular, the tile in the ATG7 gene could bind SMARCA4, DUX4 , JUN , and GATA2 . This region is particularly interesting because it has different chromatin states  in different cell types, having signatures ranging from quiescent to strongly transcribed features, or from enhancer to active promoter features in different cell types .On the other hand, hypomethylated tiles, annotated as distal enhancers, were found in the BECN1 gene encoding Beclin1, we showed that at least two regulatory regions displayed changes in DNA methylation in Caco-2 cells after IND treatments: a tile in the second intron (detected by RRBS) that was more methylated, and at least two CpG dinucleotides in the first intron (detected by MSRE-PCR) that were less methylated  cells. Moreover, H19 lncRNA knockdown promoted the interaction between DNA methyltransferase and the BECN1 promoter [BECN1, the tile that was more methylated was located in the region between the positions +700 and +900 bp with respect to the TSS. Moreover, its sequence showed 94% identity with the left monomer of an Alu element (S subfamily), a primate-specific repetitive retrotransposon. Although no functional data are available for this sequence (except for the enhancer signatures highlighted in the roadmap epigenomics database), it is known that transposable elements can have profound effects on genome structure and function and in gene regulation [CD8A gene is composed of an Alu repeat and exhibits both enhancer and silencer activities [Regarding the methylation status of the promoter of the thylated F. Such epromoter . Regardigulation . Alu elegulation . For exativities .BECN1 transcription mainly depended on a different region, located between −1000 and +500 bp with respect to the TSS, where a variety of transcription factors can bind and drive BECN1 expression. Some of them act as activators, such as NFκB, HIF1α, c-Jun/Fos, E2F1, and XBP1, and others as repressors, like Smad2 and STAT3 [BECN1 promoter activity increased remarkably when RELA, encoding RelA/p65, a key subunit of NFκB, was overexpressed [However, the regulation of nd STAT3 ,81,82,83xpressed ,85.Overall, our results show that IND promotes autophagy in intestinal epithelial cancer cells at similar concentrations to the luminal ones after prickly pear fruit consumption. Our results show that the increased expression of autophagy markers is associated with differential DNA methylation, not only in the promoters, but also in the enhancers of the genes. However, other studies are needed to demonstrate the importance of the epigenetic regulatory outcome in the pro-autophagic effects of IND. Finally, molecular modelling data suggest that IND could directly affect autophagy regulators’ activities.Further research can complete the data described and reported here. In particular, it will be interesting to study the global effect of IND on the methylome of Caco-2 cells, highlighting other possible pathways involved in the cell response to IND exposure and completing its nutrigenomic profile. These data can contribute to the characterization of the potential beneficial effects of this dietary phytochemical in the gastrointestinal tract, shedding light on the relationship between diet, IND nutrigenomic effects, and intestinal bowel diseases.