Bivalent genes, of which promoters are marked by both H3K4me3 (trimethylation of histone H3 on lysine 4) and H3K27me3 (trimethylation of histone H3 on lysine 27), play critical roles in development and tumorigenesis. Monomethylation on lysine 4 of histone H3 (H3K4me1) is commonly associated with enhancers, but H3K4me1 is also present at promoter regions as an active bimodal or a repressed unimodal pattern. Whether the co-occurrence of H3K4me1 and bivalent marks at promoters plays regulatory role in development is largely unknown.Eed (Embryonic Ectoderm Development) or Suz12 (Suppressor of Zeste 12) in mESCs (mouse embryonic stem cells), the core components of Polycomb repressive complex 2 (PRC2) which catalyzes H3K27 trimethylation, generates an artificial H3K27me3-H3K4me1 transition at partial bivalent promoters, which leads to up-regulation of meso-endoderm related genes and down-regulation of ectoderm related genes, thus could explain the observed neural ectoderm differentiation failure upon retinoic acid (RA) induction. Finally, we find that lysine-specific demethylase 1 (LSD1) interacts with PRC2 and contributes to the H3K27me3-H3K4me1 transition in mESCs.We report that in the process of lineage differentiation, bivalent promoters undergo H3K27me3-H3K4me1 transition, the loss of H3K27me3 accompanies by bimodal pattern loss or unimodal pattern enrichment of H3K4me1. More importantly, this transition regulates tissue-specific gene expression to orchestrate the development. Furthermore, knockout of These findings suggest that H3K27me3-H3K4me1 transition plays a key role in lineage differentiation by regulating the expression of tissue specific genes, and H3K4me1 pattern in bivalent promoters could be modulated by LSD1 via interacting with PRC2.The online version contains supplementary material available at 10.1186/s13578-023-01017-3. In 2006, Bernstein et al. firstly identified a subset of genes, known as bivalent genes, of which promoter regions are marked by both a repressive histone mark trimethylation of histone H3 on lysine 27 (H3K27me3) and an active histone mark trimethylation of histone H3 on lysine 4 (H3K4me3) in mouse embryonic stem cells (mESCs) . SubsequPolycomb repressive complex 2 (PRC2) is responsible for catalyzing the trimethylation of H3K27, which comprises four core members, including catalytic subunit Enhancer of Zeste homologue 2 (EZH2) or its paralogue EZH1, the cofactor of catalytic subunit Embryonic Ectoderm Development (EED), the stabilizing factor Suppressor of Zeste 12 (SUZ12) and the nucleosomes binding factor retinoblastoma-binding protein 4/7 (RBBP4 or RBBP7) . H3K4 isEed, Ezh2, Suz12 (genes coding for members of the PRC2 complex), Set1a, Set1b, Mll1 or Mll2 (genes coding for the COMPASS complex) in mice results in early embryonic lethality [The bivalent genes are thought to play key roles in embryonic development, cell differentiation and tumorigenesis. Studies in stem cells and mouse animal models have shown that core members of the COMPASS and PRC2 complexes, which are closely linked to bivalent promoter formation, have significant influences on embryonic development , 12, 13.ethality . Mutatioethality , 16. Conethality . Remarkaethality –21. Seveethality .H3K4me3 usually features the transcriptional active promoters, while H3K4me1 is enriched at primed enhancers to fine-tune its activity. The MLL3/MLL4 complexes are able to mono-methylate H3K4, together with H3K27 acetylation, to form an active enhancer landscape , 13, 23.In this study, we found that H3K4me1 in combination with H3K4me3 can represent most traditional bivalent promoters, implying most traditional bivalent promoters are actually trivalent promoters marked by both H3K4me1 and bivalent marks. Furthermore, during tissue and cell development, the bivalent promoters undergo a H3K27me3-H3K4me1 transition: the loss of H3K27me3 accompanies by bimodal pattern loss or unimodal pattern enrichment of H3K4me1, and this transition regulates tissue-specific genes expression. We proposed a model where PRC2 interacts with LSD1 to specify early neural ectoderm development in mESCs by inducing the H3K27me3-H3K4me1 transition at the promoter of bivalent genes that regulate meso-endoderm and neural ectoderm fate. Finally, our results assign a new role to H3K4me1, a conditional repressor at bivalent promoters during development, and further uncover a layer of epigenetic regulation mediated by PRC2 and LSD1 through the H3K27me3-H3K4me1 transition at bivalent promoters during lineage specification at early stages of development.Considering that it has been previously reported that both H3K4me1 and H3K27me3 are present at the promoter , and areConsidering the significant role of H3K4me1 at promoters, promoter CGIs were repartitioned into three clusters based on the distribution of all three histone methylation marks. We subsequently combined public data of DNA methylation, gene expression level and tissue/cell type-specific expression pattern to fully characterize the three different clusters of promoters in more detail. Cluster 1 promoters show low enrichment of both H3K4 and H3K27 modifications  gene encodes a component of the synaptonemal complex, which formed between homologous chromosomes in the prophase of meiosis [GAPDH, one of the enzymes involved in carbohydrate metabolism [GAPDH gene shows high enrichment of H3K4me3 and low levels of DNA methylation  [To further investigate the biological significance of the genes among three different clusters, Gene Ontology (GO) enrichment analysis was performed. Genes involved in cluster1 show enrichment in classical reproduction-related pathways such as gamete generation and spermatogenesis   , such as EZH2、SUZ12 and RBBP5, and HDMTs (histone demethylases), such as KDM1A. The results showed that distribution patterns of histone-modifying enzymes across the whole genome are consistent with corresponding histone modifications , mouse embryonic fibroblasts (MEFs) and the included pluripotent stem cells (iPSCs) reprogrammed from hBJs and MEFs , 37. NotTaken together, the H3K27me3-H3K4me1 transition is conserved in mammals, and closely linked with the specification of the cell fate and lineages priming at early stages of development. Furthermore, the H3K27me3-H3K4me1 transition is reversible upon cell fate reprogramming.p < 0.05, t-test; Fig. FOXF2, that can transcriptionally activate several other lung-specific genes to ensure lung function [PTF1A and DEPDC7, which specifically instructs pancreas and liver development, respectively [Next, we sought to investigate how the H3K27me3-H3K4me1 transition affects the tissue development through regulating expression of bivalent genes. Tissue-specific bivalent genes were filtered out from the Genotype-Tissue Expression (GTEx) database in a way reported by Teng et al.  and subjfunction , shows lectively –42  on enhancer region is associated with transcriptional activation . The hisCollectively, our results showed that dynamic landscapes of three histone modification events, including H3K27me3-H3K4me1 transition, existence of enhancer H3K27ac and enhancer H3K4me1, as well as promoter-enhancer interaction, work together to regulate the expression of tissue-specific bivalent genes in multiple tissues and lineage priming.Eed and Suz12 in mESCs.To confirm that H3K27me3-H3K4me1 transition determines the expression of bivalent genes in the process of tissue specification, we generated an artificial H3K27me3-H3K4me1transition through inactivating Eed−/− and Suz12−/− OG2 mESCs  knockdown mESCs or UTX  knockout mESCs were subjected for analysis. In http://string-db.org/)  Fig. B. Consisg/) Fig. C. Taken Eed−/− and Suz12−/− mESCs. ChIP-seq of LSD1 was performed in wild-type OG2-mESCs, Eed−/− and Suz12−/− OG2 mESCs. Notably, in Eed−/− and Suz12−/− mESCs, LSD1 chromatin binding level was decreased at bivalent promoter CGIs of bimodal-loss group  was observed throughout the mouse brain [PARD3) is a gene implicated in later aspects of neural tube development [Eed−/− and Suz12−/− mESCs upon RA treatment  were generated through CRISPR-Cas9 , two core components that were integral and shared components of all the COMPASS family complexes, shows decreased H3K4me3 and a marginal decreased H3K27me3 on the promoter of multiple ESC specific genes [mCherry driven by this synthetic promoter [Previous studies have shown that there is regulatory crosstalk between H3K4 methylation and H3K27me3. The dynamics of these histone modifications orchestrates the expression of genes. The catalytic activity of PRC2 was greatly reduced on the histone tail which is already modified by H3K4 methylation , and thuse genes , 64. A sed by RA . Howeveric genes . Anotherpromoter . In currpromoter . RemarkaPoised enhancers, marked by both positive (H3K4me1) and repressive H3K27me3) histone modifications, are another class of bivalent regions7me3 hist, implicaEed, Suz12 and Ezh2 in mice result in early lethality [EZH1 or EZH2 null H1 cells (hESCs) spontaneously differentiate towards the meso-endoderm at the cost of neural ectoderm differentiation [Suz12−/− mESCs were unable to form neurons [Eed in the mouse brain leads to postnatal lethality, impaired neuronal differentiation, and malformation of the dentate gyrus [Eed or Suz12. In this modal, genes related to meso-endoderm differentiation acting as self-tissue specific genes were up-regulated while genes related to ectoderm-differentiation acting as other tissue-specific genes were down-regulated owing to this new artificial transition, which probably further lead to spontaneous meso-endoderm differentiation in prime Eed−/− and Suz12−/− mESCs [Eed−/− and Suz12−/− mESCs through epigenetic mechanisms.Core members of PRC2 complex are essential for embryonic development , 69, 70.ethality . Previountiation . Additio neurons . Loss ofte gyrus . These r/− mESCs , and ectEed−/− and Suz12−/− mESCs, the genes undergoing an artificial transition owing to the decrement of LSD1 chromatin binding level at their promoters. Similarly, in Lsd1−/− mESCs, an artificial H3K27me3-H3K4me1 transition also was observed at promoters of L-bimodal-loss bivalent genes. These findings are consistent with previous studies demonstrating that LSD1 is necessary for neural ectoderm development. In cortical progenitors, LSD1 forms a complex with the transcriptional corepressor CoREST, that opposes Notch signaling pathway to promote cortical neuronal differentiation [Eed−/−, Suz12−/− and Lsd1−/− mESCs are required.In addition, we found that LSD1 plays a critical role in this transition process. In this study, consistent with previous reports, we demonstrated the interaction of LSD1 with EED and SUZ12 in mESCs , 48. Thintiation . Similarntiation . And losntiation . In addintiation , 60, 75.Eed−/− and Suz12−/− mESCs. Furthermore, our results provided more insights into the understanding of cell fate decisions mediated by PRC2 complex and LSD1 via regulating histone modifications of bivalent promoters during lineage specification.Our report defines a novel role for H3K4me1 at bivalent promoters, as a conditional repressor to regulate tissue-specific gene expression during development. More importantly, we propose that LSD1-EED/SUZ12 axis, influences early cell fate determination through regulating the H3K27me3-H3K4me1 transition in mESCs. In summary, we provide more evidences to explain epigenetically how neural ectoderm differentiation is impaired in Eed−/− and Suz12−/− OG2 cell lines were kindly provided by Professor Guangjin Pan in Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences. These mouse ESCs were maintained on gelatin-coated plate in DMEM/ high glucose  supplemented with 15% FBS , NEAA , GlutaMAX , Sodium Pyruvate , 1 μM PD0325901 , 3 μM CHIR99021 , 100 μM β-mercaptoethanol , 1000 units/mL mLIF . All cells were maintained at 37 °C, 5% CO2.All the WT, For neural ectoderm differentiation, mESCs were adapted on gelatin-coated plate in N2B27 medium , 50% Neurobasal , N2 , B27 , NEAA , GlutaMAX , Sodium Pyruvate , 100 μM β-mercaptoethanol ) + 2iL , 3 μM CHIR99021 , 1000 units/mL mLIF ) for a minimum of 4 days, and then the cells were seeded on gelatin-coated 6-well plate in N2B27 medium for 48 h. Afterwards, cells were treated with 500 nM retinoic acid (RA) in N2B27 medium for 4 days before subjected for subsequent analysis. Medium was changed every 2 days.The sequence CCTGAGAGGTCATTCGGTCA in exon 3 of LSD1 was selected as the CRISPR target as previously described  and subcThe whole cell extracts were prepared by RIPA buffer  and then subjected to SDS–polyacrylamide gel electrophoresis (PAGE). Proteins in the gel were then transferred to PVDF membranes , and incubated with primary antibodies over-night at 4 °C. HRP-conjugated secondary antibodies were used and bands were visualized with the enhanced chemiluminescence detection (ECL) . All uncropped western blots can be found in Additional file Gapdh was used for qRT-PCR normalization of mouse sample. For statistical analysis of this experiments, all the data were measured in three repeats and results are presented as mean ± SD and tested with t-test (GraphPad Prism software) to calculate the p values between unpaired samples. All primer sequences are listed in Additional file Total RNA was extracted with Total RNA Kit I , and reverse transcribed with RevertAid First Strand cDNA Synthesis Kit , and then Quantitative real-time PCR qPCR was performed with ChamQ Universal SYBR qPCR Master Mix  following the manufacturer’s recommendations. mESC cells were lysed in hypotonic lysis buffer ) and incubated on ice for 15 min. The lysates were centrifuged for 10 min at 1,530 g and the supernatant (cytoplasmic extract) was discarded. The pelleted nuclei were washed once with hypotonic lysis buffer and then resuspended in hypertonic buffer ) with 100U/mL DNase I  for 45 min at 4 °C, and spun down at 13,800 g for 10 min at 4 °C. The supernatant (nuclear extracts) was diluted twofold with IP buffer  and then precleared by the Protein-G magnetic beads  for 1 h with rotation at 4 °C. Then the supernatant was incubated with IgG  or specific antibodies , LSD1 ) overnight with rotation at 4 °C, followed by incubation with Protein-G magnetic beads for 2 h with rotation at 4 °C. The immune-complex were then washed with IP buffer ) for five times. Bound proteins were then eluted in sample buffer  and subjected to western blot analyses. The information for antibodies used is listed in Additional file 6 cells were cross-linked with 1% formaldehyde in 10 mL PBS for 10 min at room temperature. The reaction was stopped by the addition of glycine (125 mM). After washing with PBS, cells were lysed and then rotated in Lysis Buffer 1 ) for 10 min at 4 °C and then spun down at 1150 g for 5 min at 4 °C. The cells were then resuspended and rotated in Lysis Buffer 2 ) for 10 min at RT and spun down at 1150 g for 5 min at 4 °C.For H3K4me1-, H3K4me3- and H3K27me3-ChIP experiments, 5 × 10After lysis, cells were sonicated in shearing buffer  to shear DNA to lengths between 200 and 1000 base pairs by Covaris M220 with 5% duty factor for 10 min at 4 °C. The sonicated samples were added 1% Triton X-100 and 150 mM NaCl and then were diluted with shearing buffer to 1 ml and spun down at 20,000 g for 10 min at 4 °C. The supernatant was precleared by the Protein-G magnetic beads  for 1 h with rotation at 4 °C and then incubated with IgG  or specific antibodies , H3K4me3 , H3K27me3 ) overnight with rotation at 4 °C, followed by incubation with Protein-G magnetic beads for 2 h with rotation at 4 °C. The immune-complex were then washed with IP buffer , High Salt Wash Buffer , LiCl Wash Buffer  and TE + 50 mM NaCl Buffer for two times at 4 °C. The immune complex were then eluted with elution buffer  by shaking at 1,400 rpm at 65 °C for 30 min. Supernatant was de-crosslinked in elution buffer by incubating at 65 °C for 16 h, and then treated with 20 mg/mL RNaseA at 37 °C for 2 h and 20 mg/mL Proteinase K  at 55 °C for 2 h. DNA was purified by phenol–chloroform-isoamyl alcohol  and ethanol precipitation, and subjected for further analysis including RT-PCR and ChIP-seq. All primer sequences are listed in Additional file 6 mESC cells were fixed and harvested as described above. 3ug LSD1 antibody  was used for each IP assay in weak IP buffer  followed by incubation with Protein-G magnetic beads for 2 h with rotation at 4 °C. The immune-complex were then washed four times with weak IP buffer and then once with TE buffer before elution.For LSD1-ChIP, 5 × 10Eed−/−, Suz12−/−, Lsd1−/− mESCs and LSD1 ChIP-seq data for WT, Eed−/−, Suz12−/− mESCs generated during our study are available at Gene Expression Omnibus: GSE217249. Public sequencing data used in this study were obtained from multiple sources: GEO dataset (http://www.ncbi.nlm.nih.gov/geo), Encode dataset (https://www.encodeproject.org/), Roadmap . See Additional file The ChIP-seq data of H3K4me1, H3K4me3, H3K27me3, as well as RNA-seq data for WT, q-value ≤ 0.05. Bigwig files adjusted by input data and enrichment profiles across genomic regions of interest were all generated using deeptools (version 3.5.1). Filtered bam files of Encode human and mouse histone data were download and converted to bigwig files which adjusted by input data using deeptools (version 3.5.1).FastQC (version 0.11.9) was used to access the base quality of raw data and trim_galore (version 0.0.1) was used to trim the adaptor and low-quality reads with parameters -q 25 -phred 33 -length 40 −e 0.1 -stringency 3. After quality control, the remaining reads were mapped to the reference genome (mm10) with bowtie2 (version 2.4.5). Only uniquely mapped reads were kept and duplicates were removed by sambamba (version 0.8.2). Peaks were called using MACS2 (version 2.2.7.1) with the significance cut-off K-means clustering algorithm was used to reveal three major groups of promoter CGIs based on the all three histone modifications in human and mouse ESCs. Signals of other ChIP-seq data and DNA methylation data were then normalized to these grouped promoter CGIs for incorporate analysis. For Eed/Lsd1 WT/KO histone data, k-means clustering algorithm was performed based on H3K4me1 histone signals in CpG islands and its upstream and downstream 1 kb regions respectively. Then by combining the groups clustered based on these two types of regions, the final two H3K4me1 unimodal and bimodal inter conversional groups were defined.Promoter CpG islands were defined as CGIs (200–5000 bp) which overlapped with TSS ± 1 kb regions. H3K4me1, H3K4me3, H3K27me3 histone signals were normalized to these promoter CGIs with extended upstream and downstream 1 kb regions by R package ‘EnrichedHeatmap’ (version 1.24.0). p value ≤ 0.05). While genes that displayed ± 1.2 log2FC  between WT and Eed/Lsd1 KO mouse ESC cells were considered as significantly differentially expressed.FastQC (version 0.11.9) was used to access the base quality of raw data and trim_galore (version 0.0.1) was used to trim the adaptor and low-quality reads with parameters -q 25 -phred 33-length 40 -e 0.1 -stringency 3. After quality control, the remaining reads were mapped to the reference genome  with hisat2 (version 2.2.1). Reads mapping to multiple locations were filtered out. Read count extraction and normalization were performed using HTSeq (version 2.0.2). Differential gene expression analysis was performed using R package ‘DESeq2’ (version 1.34.0). For human ESC and differentiated tissues, differentially expression genes were defined with the certain threshold: ± 2 log2FC . GO term and Reactome Pathway enrichment were calculated. Adjusted Tissue-specific genes were selected as previous paper   and independent variables . R packages ‘caret’ (version 6.0) and ‘epiDisplay’ (version 3.5.0.2) were used for this process and R package ‘forestplot’ (version 2.0.1) was used to plot forest plot.t-test. Tests involving comparisons of number or ratio were performed using χ2 test, ***p value < 0.001, **p value < 0.01, *p value < 0.05; ns, not significantly, p value > 0.05.All statistical analysis were performed by R (version 4.1.3). Tests involving comparisons among multi-groups were performed using Kruskal Wallis rank-sum test or ANOVA, comparisons between two-groups were performed using Wilcoxon rank-sum test or Bioinformatics tool URLs:https://www.r-project.org/R: http://bowtie-bio.sourceforge.net/index.shtmlBowtie: https://github.com/ncbi/sra-toolsSRA ToolKit: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastQC: https://lomereiter.github.io/sambamba/Sambamba: http://www.htslib.org/SAMTools: http://bedtools.readthedocs.org/en/latest/BEDTools: http://deeptools.readthedocs.org/en/latest/deepTools: https://github.com/taoliu/MACSMACs tools: http://daehwankimlab.github.io/hisat2/HITSAT2: https://htseq.readthedocs.io/en/master/HTSeq: https://www.broadinstitute.org/igv/igvtoolsIGV Tools: https://www.broadinstitute.org/igv/IGV browser: http://genome.ucsc.edu/UCSC browser: Additional file 1: Figure S1. H3K4me1 in combination with H3K4me3 is able to partition promoters and predict bivalent promoters. A Heatmaps showing histone modifications patterns of promoter CGIs and their shores based on the distribution patterns of traditional bivalent marks (H3K4me3 and H3K27me3) (left) or non-traditional bivalent marks (H3K4me1 and H3K4me3) (right), respectively in hESCs; Sankey Diagram showing considerable overlaps between the clusters defined based on the two different bivalent marks combinations described above. B Heatmaps and average line plots showing histone modifications patterns and average methylation patterns of promoter CGIs and their shores in mESCs. Each line represents a single CpG island. Right panel: Heatmaps and boxplots showing gene expression and tissue-specific score (Tau) of three different clusters in mESC. C Histograms showing GO-term enrichment for genes involved in three clusters as in (B) in mESCs. D Heatmaps and average line plots showing H3K4 and H3K27 methylation related HMTs  patterns of promoter CGIs and their shores of three clusters as in (B) in mESCs, respectively. Each line represents a single CpG island.Additional file 2: Figure S2. Bivalent promoter CGIs undergo H3K27me3-H3K4me1 transition during development. A Heatmaps showing H3K4me1, H3K4me3 and H3K27me3 patterns of promoter CGIs and their shores in bivalent cluster in hESC, normal human lung fibroblasts (NHLF) and other human tissues . Each line represents a single CpG island. B Heatmaps showing H3K4me1, H3K4me3, H3K27me3 and H3K27ac patterns at enhancers in hESC and lung. Each line represents a single enhancer. C Heatmaps showing H3K4me1, H3K4me3 and H3K27me3 patterns of promoter CGIs and their shores in bivalent cluster in mESC and mouse tissues . Each line represents a single CpG island. D Heatmaps showing H3K4me1, H3K4me3 and H3K27me3 patterns of promoter CGIs and their shores in bivalent cluster between mouse fibroblasts (MEF) and their reprogrammed iPSC cells. Each line represents a single CpG island.Additional file 3: Figure S3. H3K27me3-H3K4me1 transition influences tissue-specific gene expression. A Top panel: boxplots showing the differences of H3K4me1, H3K4me3 and H3K27me3 on promoters of tissue specific genes between hESC and liver. All Other: all the other tissues without liver. Bottom panel: boxplots showing the differences of H3K4me1, H3K4me3 and H3K27me3 on promoters of tissue specific genes between hESC and pancreas. All Other: all the other tissues without pancreas. Lung: tissue-specific bivalent genes of lung; Liver: tissue-specific bivalent genes of liver; Pancreas: tissue-specific bivalent genes of pancreas; Spleen: tissue-specific bivalent genes of spleen. Significance was examined with t-test. *p value < 0.05, **p value < 0.01, ****p value < 0.0001, ns: not significantly. B Top panel: boxplots showing levels of H3K4me1 and H3K27ac of tissue-specific bivalent genes at enhancers in liver. All Other: all the other tissues without liver. Bottom: boxplots showing levels of H3K4me1 and H3K27ac of tissue-specific bivalent genes at enhancers in pancreas. All Other: all the other tissues without pancreas. Lung: tissue-specific bivalent genes of lung; Liver: tissue-specific bivalent genes of liver; Pancreas: tissue-specific bivalent genes of pancreas; Spleen: tissue-specific bivalent genes of spleen. Significance was examined with t-test. *p value < 0.05, ****p value < 0.0001, ns: not significantly. C Boxplot showing interaction score between promoter and enhancer of liver or spleen tissue-specific and all other (liver or spleen not included) tissue-specific bivalent genes in liver or spleen .Additional file 4: Figure S4. An artificial H3K27me3-H3K4me1 transition regulates the ESCs differentiation. A Western blotting analysis of H3K27me3 modification in WT and Suz12−/− OG2 mESCs. H3 as the loading control; heatmap showing H3K27me3 patterns of promoter CGIs and their shores in WT, Eed−/− (replicate) and SUZ12−/− OG2 mESCs. Each line represents a single CpG island. OG2: WT OG2 mESCs; OG2 Eed−/−: Eed−/− mESCs; OG2 Suz12−/−: Suz12−/− mESCs. B Heatmaps and average line plots showing H3K4me1, H3K4me3 and H3K27me3 patterns at promoter CGIs and their shores of two different groups (H3H4me1 biloss group and bigain group) in WT, Eed−/− (replicate) and SUZ12−/− OG2 mESCs. Each line represents a single CpG island. H3K4me1 Biloss: H3K4me1 bimodal-loss group; H3K4me1 Bigain: H3K4me1 bimodal-gain group. OG2: WT OG2 mESCs; OG2 Eed−/−: Eed−/− mESCs; OG2 Suz12−/−: Suz12−/− mESCs. C Boxplot showing expression alteration (log2Foldchange) of bivalent genes in H3K4me1 biloss group and H3K4me1 bigain group in Suz12−/− OG2 mESCs. Significance was examined with Wilcoxon rank-sum test, ****p value < 0.0001. H3K4me1 Biloss: H3K4me1 bimodal-loss group; H3K4me1 Bigain: H3K4me1 bimodal-gain group. D Histogram showing the percentage of H3K4me1 biloss group genes and H3K4me1 bigain group genes in the up-regulated (Up) or down-regulated (Down) genes in Suz12−/− OG2 mESCs compared with WT OG2 mESCs, respectively. Significance level was determined using χ2 tests, p value  = 1.159e-07. H3K4me1 Biloss: H3K4me1 bimodal-loss group; H3K4me1 Bigain: H3K4me1 bimodal-gain group.Additional file 5: Figure S5. LSD1 interacts with core members of PRC2 and plays a significant role in the artificial H3K27me3-H3K4me1 transition in mESCs. A Heatmaps and average line plots showing histone modifications patterns of promoter CGIs and their shores in WT, Mll2-shRNA and Mll3-shRNA mESCs. Each line represents a single CpG island. B Heatmaps and average line plots showing histone modifications patterns of promoter CGIs and their shores in WT and Utx−/− mESCs. Each line represents a single CpG island. C Heatmaps and average line plots showing LSD1 patterns at promoter CGIs and their shores of up-regulated (Up) and down-regulated (Down) genes of bimodal-loss group in WT and Suz12−/− OG2 mESCs. Each line represents a single CpG island. OG2: WT OG2 mESCs; OG2 Suz12−/−: Suz12−/− mESCs. D Boxplot showing the differences of normalized LSD1 coverage at bivalent promoter CGIs between WT (OG2) and Suz12−/− (OG2 Suz12−/−) mESCs of up-regulated (Up) and down-regulated (Down) genes in bimodal-loss group. Significance was examined with t-test, ****p value < 0.0001.Additional file 6: Figure S6.Lsd1 knockout induces similar effects as Eed or Suz12 knockout. A Western blot analysis of LSD1 protein in each indicated cell lines . ACTIN as the loading control. B Heatmaps and average line plots showing H3K4me1, H3K4me3 and H3K27me3 patterns at promoter CGIs and their shores of two different groups in WT and Lsd1−/− OG2 mESCs. Each line represents a single CpG island. H3K4me1 L-Biloss: H3K4me1 L-bimodal-loss group; H3K4me1 L-Bigain: H3K4me1 L-bimodal-gain group. OG2: WT mESCs; OG2 Lsd1−/−: Lsd1−/− mESCs. C Veen diagram showing considerable overlaps of the promoter CGIs undergoing the artificial transition betweent Lsd1−/− and Eed−/− mESCs. Lsd1 KO Trans CGIs: the promoter CGIs undergoing an artificial transition in Lsd1−/− mESCs; Eed KO Trans CGIs: the promoter CGIs undergoing an artificial transition in Eed−/− mESCs. D Boxplot showing expression alteration (log2Foldchange) of bivalent genes in H3K4me1L- biloss group and H3K4me1 L-bigain group in Lsd1−/− OG2 mESCs. Significance was examined with Wilcoxon rank-sum test, ****p value < 0.0001. H3K4me1 L-Biloss: H3K4me1 L-bimodal-loss group; H3K4me1 L-Bigain: H3K4me1 L-bimodal-gain group. E Histogram showing the percentage of H3K4me1 L-bimodal-loss group genes and H3K4me1 L-bimodal-gain group genes in the up-regulated (Up) or down-regulated (Down) genes in Lsd1−/− mESCs compared with WT mESCs, respectively. Significance level was determined using χ2 tests, p value  = 4.438e-14. H3K4me1 L-Biloss: H3K4me1 L-bimodal-loss group; H3K4me1 L-Bigain: H3K4me1 L-bimodal-gain group. F Images showing the morphology of mESC cells after RA treatment. WT mESCs is OG2 mESCs with GFP expression controlled by Oct4 promoter (Oct4: GFP). Scale bar, 100 μm. OG2: WT mESCs; OG2 Lsd1−/−: Lsd1−/− mESCs.Additional file 7: Figure S7. Uncropped scans of western blots. A Uncropped scans of western blots for Fig. Eed−/− and OG2-Suz12−/−). H3 as the loading control. Red box indicates the location of goal protein. KD Kilodaltons. B Uncropped scans of western blots for Fig. C Uncropped scans of western blots for Fig. D Uncropped scans of western blots for Fig. S6A. LSD1 expression level was analyzed in each indicated cell lines . ACTIN as the loading control. Red box indicates the location of goal protein.Additional file 8: Table S1. The information for antibodies used in this study.Additional file 9: Table S2. The sequence of all primers used in this study.Additional file 10: Table S3. The information of all public sequencing data used in this study.


Global changes in transcriptional regulation and RNA metabolism are crucial features of cancer development. However, little is known about the role of the core promoter in defining transcript identity and post-transcriptional fates, a potentially crucial layer of transcriptional regulation in cancer. In this study, we use CAGE-seq analysis to uncover widespread use of dual-initiation promoters in which non-canonical, first-base-cytosine (C) transcription initiation occurs alongside first-base-purine initiation across 59 human cancers and healthy tissues. C-initiation is often followed by a 5′ terminal oligopyrimidine (5′TOP) sequence, dramatically increasing the range of genes potentially subjected to 5′TOP-associated post-transcriptional regulation. We show selective, dynamic switching between purine and C-initiation site usage, indicating transcription initiation-level regulation in cancers. We additionally detail global metabolic changes in C-initiation transcripts that mark differentiation status, proliferative capacity, radiosensitivity, and response to irradiation and to PI3K–Akt–mTOR and DNA damage pathway-targeted radiosensitization therapies in colorectal cancer organoids and cancer cell lines and tissues. Here, using CAGE-seq, the authors show that transcription start site choice in thousands of genes may endow transcripts with distinct post-transcriptional fates, with dynamic switching defining cancer cell proliferation, differentiation and treatment response. It can influence all levels of RNA metabolism, functioning as a first step in regulating post-transcriptional processing and translation efficiency4. This is exemplified by the evolutionarily conserved translation machinery genes, transcribed into mRNAs with a C base at the 5′ end, followed by a terminal oligopyrimidine stretch, called 5′TOP5. These transcripts are distinctively regulated in cellular stresses and various cancers, predominantly through modulation by the phosphoinositide 3-kinase (PI3K)–mammalian target of rapamycin (mTOR) signaling pathway11. mTORC1 signaling phosphorylates and inactivates 4EBP1 and LARP1, both selective regulators of 5′TOP mRNA cap interaction with the eIF4E–eIF4G1 translation initiation complex. In cellular stress, mTOR signaling is lost and therefore 5′TOP mRNA translation initiation is inhibited17. However, there is a gap in knowledge of how patterns of transcription initiation and selection of start sites influence RNA metabolism and cancer progression.Transcription regulation is a defining factor in cancer development. Detection of transcript abundance is diagnostic and reveals mechanisms of malignant transformation. Transcription initiation at the core promoter reflects a fundamental regulatory level, creating dynamic transcript variation through alternative promoter usage in cancers19), defining alternative transcription initiation site usage.We explored the role of transcription initiation dynamics within the core promoter in cancer cell identity and behavior. We uncovered a previously unappreciated dynamic of transcription initiation choice, and demonstrate distinct regulation of transcripts emanating from the same core promoter but with distinct 5′ end nucleotides. This regulation plays a role in cancer cell behavior, differentiation status and response to radiotherapy in colorectal cancer (CRC) models. We identified the PI3K–Akt–mTOR–Myc–p53 regulatory network, previously implicated in ribosome synthesis regulation (reviewed in refs. 20) -related factor TRF2 has been identified to selectively mediate the transcription of TCT promoter genes in Drosophila24; however, despite evolutionary conservation, vertebrate regulation of YC transcription initiation is largely unknown.In the majority of genes, transcription initiates from a well-characterized motif consisting of a pyrimidine  at −1 bp upstream of the transcription start site (TSS) followed by a purine  at the +1 position. This constitutes the canonical ‘YR’ motif in mammals (reviewed in ref. 20) Fig. . Howeverila Fig. 21,22 andDrosophila to vertebrates23. Intermingled R-start and C-start mRNA species can display distinct transcriptional and post-transcriptional RNA dynamics23. Since YC initiation-linked 5′TOP mRNAs are targets of cancer-associated transcript metabolism25, we reasoned that pervasive use of YC initiation in a large number of genes may expand the potential for tumorigenic and prognostic transcriptional changes. Moreover, as the differential initiation dynamics are not restricted to alternative promoters1, but mostly occur within promoters, we postulated that previously unappreciated complexity of transcript isoforms may be generated from the same genes with distinct 5′ end sequence composition and associated post-transcriptional fates in cancers.Recently, we challenged the exclusivity of YC-initiating 5′TOP mRNAs to translation machinery genes by demonstrating that thousands of protein-coding genes carry both YR and YC transcription initiation, often intermingled on promoters. Genes in this group are termed dual-initiating genes and are evolutionarily conserved from 26. Since transcripts initiating from YC dinucleotides are pervasive across the genome23, we reasoned that 5′TOP mRNAs in tumor development may be extended to YC RNAs produced in dual-initiating genes, owing to a potential shared targeting mechanism. To test this hypothesis, we measured 5′-C RNA content in various types of tumors representing different phases of tumorigenesis.Tumorigenesis is often marked by increased activity of mRNA translation machinery, encoded by transcripts bearing the 5′TOP motif and transcribed from YC initiators23. CAGE-seq detects altered transcription initiation patterns in cancer as demonstrated by the promoter of GUCD1 (encoding guanylyl cyclase domain containing 1), a gene associated with enhanced cell proliferation and invasion of cancer cells28. CAGE-seq analysis in published FANTOM5 datasets30 revealed it to be a dual-initiator promoter (DIP) in healthy colon, with both YC and YR initiation present. In CRC samples, however, the YC component of expression is dramatically enhanced at the expense of the YR component  values calculated. Dual-initiating promoters were identified  with consensus clusters of >1 TPM for both 5′-R transcription and 5′-C transcription in the majority of datasets (30 datasets). The ratio of YC to YR transcription was calculated for these DIPs across the datasets and compared between matched cancer and healthy samples. This analysis revealed that the majority (22 out of 30) of cancer cell types had significantly enhanced YC transcription within DIPs (YC-enriched tumors). There was considerable heterogeneity between cancer types, with four cancers with significantly depleted 5′-C transcript usage relative to their matched healthy cell types (YC-depleted tumors) and four cancers with no significant change   mutation status also trended with YC-enriched cancers, with mutation-bearing tumors representing 48%, 25% and 20% of YC-enriched, YC-neutral and YC-depleted tumors, respectively   Fig. . Of note Fig. TP5 mutationABI1 (encoding Abl Interactor 1), a component of the WAVE complex33. In healthy bronchial epithelial cells, and in the well-differentiated lung cancer line (PC9), its expression was predominantly from YR initiation sites. In the undifferentiated lung cancer line (A549), however, initiation switched dramatically towards a dominant YC transcription initiation site .Next, we sought to identify genes specifically upregulated in YC-enriched versus YC-depleted cancers. We identified genes consistently upregulated in YC-enriched cancers over matched healthy tissues (enriched by more than twofold in >75% of samples) and unchanged or depleted in all YC-depleted cancers and vice versa. This analysis identified 132 and 144 genes specific to YC-enriched or YC-depleted cancers, respectively. Gene ontology analysis revealed cell cycle control and proliferation genes upregulated in YC-enriched cancers Fig.  in contr19, and our findings suggest their wider role in regulating YC:YR ratios and differential RNA metabolism from dual-initiating promoters.We asked which DIPs showed a cancer cell type-defining switch in YC:YR TSS usage. We calculated the average YC:YR ratio for each DIP within each cohort, selecting promoters in which the YC:YR ratio dynamically changed from the highest value in YC-enriched cancers, to an intermediate value in YC-neutral cancers, to the lowest value in YC-depleted cancers. A total of 422 genes showed a cohort-dependent trajectory of YC:YR transcription initiation levels  cancer subtypes. Intriguingly, these factors have all been associated with radiotherapy response37 and raise the potential for TSS as a prognostic indicator of tumor subtypes and therapy response.Taking these findings together, a segregation of transcripts between YC-enriched and YC-depleted cancers was seen, with enrichment of YC initiation correlated with poorly differentiated, proliferative  shifted, with YC initiation enriched in tumors responsive to chemo-radiotherapy.To investigate whether 5′-C transcript abundance influences radiotherapy response, we focused on CRC, one of the YC-enriched cancer types Fig. . We perfP = 0.02) enrichment in the YC:YR ratio was found. To determine the contribution of both types of initiation site selection, we compared 5′-C and 5′-R transcript levels separately between the responsive and non-responsive cohorts  versus 3,475 DIPs  in FANTOM5 data). Therefore, we aimed to confirm our observations on CRC organoid samples by CAGE-seq (CRC1–CRC5)  compared to untreated samples, one with moderate response  and two with little response   Fig. . This fin = 6,285, 34% of consensus clusters) and analyzed CTSS expression from all DIPs versus all other promoters   Fig. . This an28) Fig. ; and (4)28) Fig. , in agre28) Fig. . To dete28) Fig. . This an28) Fig. .SND1 , a gene associated with cancer proliferation, angiogenesis, metastasis and the stress response (reviewed in ref. 41). This dual-initiating promoter displayed a significant change in its TSS usage between responsive, moderately responsive and non-responsive organoids, transitioning from predominantly YC TSS in responsive organoids to balanced transcription in moderately responsive organoids and YR-predominant TSS in non-responsive organoids  across all CRC organoid samples and quantified their TSS levels  and faster proliferation rate Fig. , in agre43 Fig. . This, tMotivated by the observation of enrichment of initiation of YC transcripts in radiotherapy-responsive CRC tumors, we asked about the potential effect on 5′-C transcript abundance of radiotherapy itself. We challenged the CRC organoid lines with 25 Gy of radiation and performed CAGE-seq. We compared the relative frequency of YC and YR initiation between irradiated and control samples for each organoid Fig. . This anC9orf85 gene, linked to cellular differentiation44  , TOP-deg (4/5 pyrimidines) and YC-other (≤3/5 pyrimidines) transcripts based on their 5′ ends , permitting post-transcriptional co-regulation with 5′TOP transcripts. Therefore, we investigated the radiotherapy-responsive dynamics of YC-other-initiating transcripts with and without 5′TOP transcripts co-expressed in DIPs . We performed Venn overlap analysis of the pan-cancer, radiosensitivity and irradiation-affected trajectory gene sets , the only transcription factor to have previously been implicated in the regulation of YC transcription24. This analysis revealed that each of these transcription factors showed a similar expression pattern and trajectory among the CRC organoid samples to the radiotherapy response signature gene set   between the latter two gene sets Fig. . This ov7 genes bC9orf85, protein only RNase P catalytic subunit (PRORP), guanosine monophosphate reductase 2 (GMPR2) and SND1) from the radiotherapy responsiveness signature gene set were selected, in which YC and YR initiation are sufficiently spatially separated to allow discriminating primer design. We designed primers to amplify the longest transcripts, which in each case were initiating from 5′-R. These 5′-R transcript levels were subtracted from the total RNA detected by internal primers to indirectly calculate the YC contribution to the total  protocol that separately monitors YC and YR transcription from DIPs. Four candidate genes  with the PI3K–Akt–mTOR pathway inhibitor dactolisib with and without radiotherapy. We used 0.1 µM dactolisib, the previously identified IC50 dose when combined with radiotherapy, but with minimal survival impact alone39  together with untreated (control) and irradiation-treated samples Fig. . Analysi52. Additionally, the radiotherapy responsiveness, irradiation-affected and pan-cancer trajectory gene sets were all significantly enriched for DNA damage-responsive genes  and pyrimidine:cytosine (YC) dinucleotide motifs), showing these to be differentially regulated between matched cancer and healthy tissues but also between cancers of differing differentiation status and radiotherapy sensitivity, allowing highly sensitive segregation of cancers into poorly, moderately and well-differentiated states and CRCs into highly radiotherapy-responsive, moderately responsive and non-responsive cohorts. This reveals that both of these cellular events are marked by global changes in RNA transcript metabolism, mediated by TSS shift.This study demonstrated a previously unexplored layer of gene regulation in cancers, identifying TSS selection as a regulatory interface between transcription and translation-associated post-transcriptional RNA fate. We used CAGE-seq to profile TSS selection in thousands of genes with a range of functions, producing transcripts from the same promoter but with differing 5′ endsOur results demonstrate a distinct molecular signature of radiotherapy responsiveness, characterized not by overall gene expression but rather by the contribution to the transcriptome of TSS isoforms. We showed YC initiation to be severely depleted in response to irradiation, in contrast to the moderately changing YR-initiated transcript level in CRC organoids. This YC class is additionally enriched in radiotherapy-responsive clinical CRC samples and organoids derived from them. Strikingly, when we investigated gene sets with the most dynamic YC initiation components in response to radiation, they marked the same genes enriched in radiotherapy-responsive tumors, suggesting a mechanistic role in radiotherapy response.15, this could be due to stabilization of mRNAs. DNA damage also has been shown to repress the RNA translation and ribosomal genes in yeast58 and in humans through p53 signaling60. Collectively, our results demonstrate the prognostic value of this novel molecular signature that can be cost-effectively monitored to indicate CRC tumor response to radiotherapy and the efficacy of radiosensitizing therapies.We demonstrated that a molecular signature of radiotherapy responsiveness can be measured by custom RT–qPCR and that this approach dissected responsive and non-responsive tumors to chemo-radiotherapy. Furthermore, we showed that radiosensitization of CRC tumors by PI3K–Akt–mTOR and DNA damage pathway blockade resulted in the enrichment of YC-initiating transcripts in radiotherapy-resistant tumors, closer to the level of radiosensitive lines, establishing a mechanistic link between CRC radiosensitivity, the PI3K–Akt–mTOR pathway, the DNA damage pathway and YC transcription initiation. As healthy tissues are generally relatively YC depleted, this approach may also radiosensitize them, with the potential for off-target toxicity needing further investigation. As mTOR inhibition selectively blocks the translation of 5′TOP-containing transcripts5. Since only TSS-resolving technologies can detect the specific effects on 5′-C transcript dynamics, it is feasible that mTOR-mediated targeting of the YC component of DIPs was missed by conventional transcriptomics. Thus, the YC component of thousands of DIPs may be targeted by mTOR signaling-associated translation regulation17. It is noteworthy that the molecular function of the genes with differential 5′-C transcript metabolism extends beyond that of the canonical 5′TOP mRNAs. We show roles as diverse as DNA replication, transcriptional regulation and mitochondrial function, with YC:YR transcripts under distinct regulatory dynamics dependent on cellular context. This highlights the importance of a previously little-appreciated regulation by TSS selection, which permits the co-regulation of a broad range of transcripts, mediated by their shared 5′ end. It also demonstrates the power of CAGE-seq (and other 5′ end-resolving techniques) to decipher transcript identities and intra-promoter transcript dynamics with far greater specificity than RNA-seq.Our results identified a much larger population of genes that produce 5′TOP mRNAs with potential for mTOR signaling regulation than previously described61. Loss of the tumor suppressor p53 was found to enhance 5′TOP mRNA translation through mTORC1 signaling62. DNA damage-responsive Myc signaling was also associated with the regulation of transcript metabolism. Myc inactivation was associated with decreased mRNA translation, in particular, mitochondrial respiration genes63, which were represented in the molecular signature of radiotherapy responsiveness63. Additionally, Myc regulates ribosome biogenesis through the recruitment of RNA polymerase II to ribosomal protein genes to produce 5′TOP mRNAs64. This suggests that Myc may not only regulate the transcription of 5′TOP but also regulate other YC-initiating mRNAs emanating from DIPs. Indeed, we showed that genes of the radiotherapy responsiveness molecular signature are significantly enriched for Myc binding sites, alongside transcriptional effectors associated with Myc and PI3K–Akt–mTOR signaling cascades48. Elk1 has been shown to directly induce MYC gene expression in CRC tumorigenesis46, while target genes of Gabpa are specifically enriched for metabolic, stress response, DNA damage and MYC-regulated oncogenic signatures47. Tbpl1, a general transcription factor previously implicated in the transcriptional regulation of 5′TOP-bearing ribosomal genes24, was also found to be co-regulated with radiotherapy-associated YC transcriptional dynamics. The enriched expression of Tbpl1 in radiotherapy-responsive organoids is in line with enriched 5′-C transcript abundance and suggests a potential role for Tbpl1 in the regulation of radiotherapy-responsive 5′-C transcript metabolism.In this study, we highlight the putative role of the interconnected PI3K–Akt–mTOR, Myc, p53 and DNA damage signaling pathways in radiosensitivity-associated differential YC:YR metabolism. The PI3K–Akt–mTOR signaling pathway modulates 5′TOP mRNA interactions with effector proteins and enhances their translationA striking finding from the investigation of candidate regulators of YC transcription  was that their genes carry both YC and YR initiation products, with the expression of their 5′-C transcripts correlated with radiosensitivity and that of the 5′-C transcripts of the molecular signature genes. This suggests a possible regulatory feedback loop mediated by TSS selection and a potential role for both transcriptional and post-transcriptional regulation through PI3K–Akt–mTOR signaling in YC–YR dynamics. It is likely that both layers of regulation play a role in radiotherapy response-associated 5′-C transcript metabolic changes. Further investigation to unpick the role of transcription versus post-transcriptional mRNA stability in these dynamics will be necessary.Taking these findings together, we propose YC–YR initiation dynamics as a read-out of transcriptional and post-transcriptional mechanisms through which a range of proliferation-associated signaling pathways, including PI3K–Akt–mTOR–Myc, regulate tumor cells. Aberrant PI3K–Akt–mTOR–Myc pathway signaling in radiotherapy-resistant CRCs changes the metabolic landscape of the cell and shifts the balance of transcript abundance from YC to YR initiation. As YR-transcripts are less affected by ionizing radiation-responsive metabolic changes in transcript processing and stability, this represents a reduction in the radiosensitivity of the transcriptome, constituting a survival benefit Fig. . Upon irThis project received approval (code 17-287) from the Human Biomaterials Resource Centre, Birmingham, United Kingdom, under ethical approval from the Northwest–Haydock Research Ethics Committee (reference 15/NW/0079).39. Ethical approval for all tissue collection was encompassed under HBRC application 17-287 .FFPE pre-treatment biopsy specimens from patients with primary colonic or rectal adenocarcinoma were collected with the help of the University of Birmingham Human Biomaterials Resource Centre (HBRC). Anonymized clinicopathological data such as patient demographics, neoadjuvant treatment status, tumor location, TNM stage and histopathological data including tumor regression grade were obtained, again with the help of HBRC. Organoid samples used in this study were generated as described in ref. –1 basement membrane extract type 2 (Bio-techne). Organoids were maintained in 500 μl of IntestiCult medium (STEMCELL Technologies) containing Primocin (Invitrogen) at a final concentration of 100 µg ml–1, which was replenished twice per week, and were kept in 5% C02 at 37 °C.Organoids were split once per week and disassociated to a one-cell suspension using TrypLE before being washed in PBS and re-plated in 24-well plates at a concentration of 50,000 cells per well in 50 μl of 8 mg mlR = time/days (4 or 9) and X was the average count on day 4 or 9.A total of 50,000 cells were plated per line in 24-well plates on day 0, disassociated on day 4 or day 9 and counted again using a Bio-Rad TC20 automated cell counter three times per well. Doubling time was calculated using the following formula:A total of 50,000 cells were plated per line in 24-well plates on day 0. When radiosensitizing drug treatment with dactolisib, omipalisib or VE821  was performed, this was added with a medium change on day 3. Five sequential days of 5 Gy of irradiation commenced on day 4, terminating on day 9. Drugs were replenished with a medium change on day 6 and removed with a medium change on day 9. This was followed by a 5-day recovery period. Cell viability was assessed in triplicate using the CellTiter-Glo 3D Cell Viability Assay (Promega) following the manufacturer’s protocol.65. On day 14, organoids were recovered from basement membrane extract with 500 μl of cell recovery solution (Corning) on ice, washed with PBS–BSA and fixed using 4% PFA. Samples were then loaded into low-melting point agarose (Invitrogen) and imaged with a Zeiss Z1 lightsheet microscope.For morphological assessment of organoids, each line was grown for 14 days in normal culturing conditions and fixed following a high-resolution fixing protocolRNA was extracted from FFPE tissue samples using the RNeasy FFPE Kit (Qiagen) following the manufacturerʼs protocols. RNA was extracted from fresh harvested organoid samples using the miRNeasy mini Kit (Qiagen) following the manufacturerʼs protocols, and RNA quality was analyzed by capillary electrophoresis . All samples had an RNA integrity number of >9.38. Libraries were individually barcoded and pooled for sequencing on the Illumina NextSeq, using the High 75 cycle single-read run operation program. CAGE libraries were generated from CRC organoid RNA samples following a previously published protocol66. To increase the multiplexing capacity and demultiplexing efficiency of libraries sequenced on Illumina two-color instruments , the sequencing adaptors listed in the above protocol were modified to use the standard Illumina barcoding and indexing strategy (instead of the legacy 3-bp barcode at the beginning of read 1), in this case, 8-bp TruSeq Unique Dual Indexes. To attach the Illumina P5/P7 flow cell adaptors and I5/I7 indexes, an additional amplification step of five to six PCR cycles was performed using NEBNext Ultra II Q5 Master Mix (New England BioLabs). The PCR reactions were purified with 1.4× AMPure XP beads (Beckman Coulter) to yield the final libraries. Libraries were sequenced (2 × 50-bp reads) on a NovaSeq 6000 v1.5 SP 100-cycle flow cell.CAGE libraries were generated from FFPE RNA samples following a previously published protocol30.Mapped human CAGE-seq and RNA-seq data were downloaded from the FANTOM5 consortium database67. For all newly generated CAGE samples, reads were trimmed to remove the linker and unique molecular identifier regions (if applicable). Reads were mapped using Bowtie (v. 1.3.1)68, allowing a maximum of two mismatches and only uniquely mapping tags with a MAPQ value of >20. The R/Bioconductor package CAGEr (v. 1.34.0) was used to remove the additional G nucleotide, due to the CAGE protocol, where it did not map to the genome31. All unique 5′ ends of reads were defined as a CTSS and reads were counted at each CTSS per sample. These raw read counts were subsequently normalized based on a power-law distribution based on 106 reads69 and defined as normalized TPM.The human genome assembly (GRCh38/hg38) was downloaded from the UCSC Genome BrowserCTSS that were supported by at least 0.5 TPM in one of the samples were clustered based on a maximum allowed distance of 20 bp between two neighboring CTSS. These transcriptional clusters were then trimmed on the edges to obtain more robust boundaries of transcriptional clusters by obtaining the positions of the 10th and 90th percentiles of expression per transcriptional cluster. Only transcriptional clusters with higher than 5 TPM expression were considered. Finally, transcriptional clusters across all samples were aggregated if they were within 100 bp of each other to form consensus clusters for downstream analyses.70. Consensus clusters mapping to the promoter region of Ensembl (hg38)-annotated genes were selected for further analysis. Where multiple consensus clusters mapped to the same gene, their TPM expression values were merged.The consensus clusters were annotated to genomic features using the CAGEr function ‘annotate CTSS’ in conjunction with the R/Bioconductor package rtracklayer (v. 1.52.1)CTSS within consensus clusters were segregated on the basis of their +1 or −1 base configuration into YR  and YC  classes. For further segregation of CTSS, the first five bases of the transcript were used. Transcripts with a CYYYY configuration were classed as ‘5′TOP’, transcripts with a C(3Y1R) configuration were classed as ‘5′TOP-deg’ and all other YC-initiating transcripts were classed as ‘YC-other’. For the assessment of internal TOP transcripts, the first 50-bp region of each transcript was assessed by sliding window analysis for an unbroken stretch of five pyrimidines. Transcripts initiating from a YC (excluding 5′TOP) or YR dinucleotide and with an unbroken stretch of five pyrimidines in the first 50 bp were identified as YC internal TOP and YR internal TOP transcripts, respectively. Transcripts with a 5′TOP identity were not assessed for the presence of an internal TOP.71 was used to define differential expression, and the threshold of differential expression was set at an adjusted P value of <0.05. These results were cross-referenced to the consensus cluster information of samples. In cases of more than one consensus cluster mapping to the region, the consensus cluster with the highest expression was chosen to represent the region.The raw read counts were extracted for the consensus clusters and collapsed into total count per consensus cluster. The DESeq2 (v. 1.32.0) R/Bioconductor packagecDNA was generated from sample RNA using the SuperScript VILO cDNA Synthesis Kit (Invitrogen), following the manufacturer’s conditions. RT–qPCR was then performed using the PowerUp SYBR Green system (Applied Biosystems), following the manufacturer’s conditions, and was run on the Real-Time PCR ABI 7900HT machine (Applied Biosystems). RT–qPCR primer sequences are provided in Supplementary Table http://bioinformatics.sdstate.edu/go, accessed 26 July 2023) and Panther classification system for Extended Data Fig. http://pantherdb.org/webservices/go/overrep.jsp, accessed 28 September 2022).Gene ontology analysis was performed using the ShinyGO 0.77 online program (72). Each consensus cluster was centered on the most expressed CTSS (the dominant TSS), and each sequence was scanned from 150 bp upstream and 50 bp downstream. A hit was reported if the scanned region contained a sequence with a 90% match to the position weight matrix. Occurrence was counted for the radiotherapy response signature genes and compared to all other consensus clusters. Significance was assessed using Fisher’s exact test. Obtained P values were considered significant at <0.05.Position weight matrices for Elk1 (MA0028.2), Gabpa (MA0062.1), Myc (MA0147.3) and p53 (MA0106.1) binding site motifs were obtained from converting frequency matrices from JASPAR  unless otherwise stated in the legend. No statistical method was used to predetermine sample size, as this was instead determined by maximum sample availability, which was limited in the case of organoids by numbers successfully generated clinical samples, by ethics agreements and previously published FANTOM5 CAGE data by cancer samples having a suitable match to healthy tissue samples. No data were excluded from the analyses. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment, as all analyses were objective in nature. The only subjective analysis was the organoid morphology scoring in Fig. https://academo.org/demos/venn-diagram-generator) for Fig. https://www.meta-chart.com/venn#/display) for Fig. All figures were generated on GraphPad Prism, Microsoft Excel or Microsoft PowerPoint unless otherwise stated in the legend. Venn diagrams were generated using Academo software (Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41594-023-01156-8.Reporting SummaryPeer Review FileSupplementary Data 1–5Gene lists supporting Figs. 2,3,4 and 6, detailing the pan-cancer, radiotherapy-associated and irradiation-affected trajectory gene lists, together with the radio-responsiveness signature and pan-cancer intersection gene sets, respectively.Supplementary Tables 1–10Supplementary Tables 1–10, individual legends provided in the file.Supplementary codeAnnotated R-script code for dissection of YR, YC only, 5′TOP and internal TOP transcript isoforms.Source Data Figs. 2–4, 6 and 7 and Extended Data Figs. 2, 4 and 9Statistical Source Data.


Short Interspersed Elements (SINEs) are common in the genomes of most multicellular organisms. They are transcribed by RNA polymerase III from an internal promoter comprising boxes A and B. As transcripts of certain SINEs from mammalian genomes can be polyadenylated, such transcripts should contain the AATAAA sequence as well as those called β- and τ-signals. One of the goals of this work was to evaluate how autonomous and independent other SINE parts are β- and τ-signals. Extended regions outside of β- and τ-signals were deleted from SINEs B2 and Ves and the derived constructs were used to transfect HeLa cells in order to evaluate the relative levels of their transcripts as well as their polyadenylation efficiency. If the deleted regions affected boxes A and B, the 5′-flanking region of the U6 RNA gene with the external promoter was inserted upstream. Such substitution of the internal promoter in B2 completely restored its transcription. Almost all tested deletions/substitutions did not reduce the polyadenylation capacity of the transcripts, indicating a weak dependence of the function of β- and τ-signals on the neighboring sequences. A similar analysis of B2 and Ves constructs containing a 55-bp foreign sequence inserted between β- and τ-signals showed an equal polyadenylation efficiency of their transcripts compared to those of constructs without the insertion. The acquired poly(A)-tails significantly increased the lifetime and thus the cellular level of such transcripts. The data obtained highlight the potential of B2 and Ves SINEs as cassettes for the expression of relatively short sequences for various applications. SINEs or short retrotransposons are non-autonomous mobile genetic retroelements shorter than 600 bp that are transcribed by RNA polymerase III (pol III) . SINEs are important factors in genome evolution and function. Their integration into genes including their introns and regulatory regions can affect gene function, i.e., induce deleterious mutations ,5,6. At + class, while SINEs without these signals were considered as T─ SINEs. All 12 T+ SINE families are found exclusively in placentals [+ SINE families. The mechanism of the contribution of polypyrimidine motifs in polyadenylation of Dip and Ves transcripts remains unknown. The same applies to the β-signal in the B2 transcript. A long poly(A) tail (A > 20) in SINE transcripts is strictly required for the retrotransposition mediated by L1 reverse transcriptase [+ SINEs substantially prolong the lifetime of such transcripts in the cell [While studying SINE families from mammalian genomes, we noticed that some of them had the polyadenylation signal AATAAA (PAS) and pol III transcription terminators (TCT ≥ 3 or T ≥ 4) upstream of the poly(A) tail . These Tacentals ,17. Prevacentals . In B2 tacentals ; similarcriptase ,20,21. Ithe cell ,23.+ SINEs. Such cassettes can be used to generate transcripts containing specific protein-binding sites, ribozymes, microRNA targets and competitors, hairpin precursors of siRNAs, etc. So far, tRNA, 5S RNA, 7SL RNA, and VA RNA genes as well as promoters of U6 RNA and H1 RNA have been used in this context [+ SINE-based constructs can be polyadenylated, which increases the level and stability of such RNAs in the cell.The goal of this work was to evaluate the extent to which the β- and τ-signals in B2 and Ves can function autonomously and independently of other SINE parts. In this context, it was of interest to determine whether transcripts of these SINEs could be polyadenylated without the internal promoter (boxes A and B) but with an external pol III promoter. This promoter of the U6 small nuclear RNA is composed of the proximal and distal sequence elements as well as the TATA-box  and init context ,30,31,32EcoRI and BamHI sites.A series of B2 constructs were generated  based onThe purpose of obtaining the constructs presented in The resulting constructs were used to transfect HeLa cells together with the control plasmid pU1 , which iA trinucleotide mutation in box B (B2-mutB) decreased the level of B2 RNA 2.8-fold, whereas an external pol III promoter introduced into this construct (U6-B2-mutB) almost completely restored the transcript level . ReplaceThe data obtained suggest the following. The polyadenylation efficiency is not decreased when the internal pol III promoter of B2 is replaced by an external one (U6 promoter). Deletion of certain extended regions in the B2 sequence does not decrease the cellular level of B2 transcripts or their polyadenylation efficiency. The acquisition of poly(A) tails by B2 transcripts increases their cellular level up to threefold.The latter conclusion was experimentally confirmed by comparing the levels of B2 transcripts with the native (AATAAA) and inactivated (AACAAA) PASs . In all 6 transcription terminator was added downstream of it. The polyadenylation efficiency of such a construct decreased 2.6-fold compared to the original construct B2_T  tails in such transcripts on their cellular level were experimentally tested. A random 55-nt DNA sequence was cloned into the -Δ53+ins  were use-Δ53+ins A. PolyadEcoRI and BamHI sites of U6-B2-Δ53 and U6-B2-ΔA-Δ53 with the functional PASs (AATAAA) or these sequences inactivated by T to C substitutions  as the initial Ves: 80–84% of the full-length transcripts acquired poly(A) tails . A substEcoRI and BamHI sites  tails  tails B. PolyadA) tails B; this c0.5 = 35–40 min), whereas the polyadenylated Ves-Δ68+ins_T and Ves-Δ98+ins_T remained stable within 180 min. Thus, poly(A) tails prolong the life of Ves constructs carrying the long foreign insertion. This results in a higher cellular level of polyadenylated Ves transcripts.The stability of polyadenylated transcripts of Ves-Δ68+ins and Ves-Δ98+ins was tested in experiments similar to those described above for B2-containing constructs. HeLa cells were transfected with one of four plasmids , actinomycin D was added one day later, and cellular RNA was isolated after different periods of time . Here we demonstrated the autonomous function of β- and τ-signals in B2 and Ves, i.e., their dependence on other SINE sequences is low, except for the PAS and the transcription terminator, which are strictly required for the polyadenylation of their transcripts. Deletions of the spacer between the β- and τ-signals could even increase polyadenylation efficiency. Deletion of box A together with the spacer between boxes A and B also had no negative effect on the polyadenylation of transcripts. Transcription of SINEs lacking part of the internal pol III promoter could be driven by an external pol III promoter from the 5′-terminal sequence of the U6 RNA gene. Thus, the promoter type does not affect the polyadenylation capacity of the synthesized RNA. Similarly, the substitution of the entire box B did not affect the polyadenylation efficiency of B2; however, such a substitution decreased the polyadenylation of Ves 1.4–1.6-fold. A similar pattern was observed previously  for 4-ntAccording to our experiments, the external U6 promoter could restore the initial transcription level of B2 with deleted box A or substituted box B. A combination of a U6 promoter with an intact internal promoter increased B2 transcription 1.7–2.3-fold. It is conceivable that independent transcription from both pol III promoters combined to double the transcript level. Surprisingly, a similar construct with Ves containing a U6 promoter showed a threefold decrease in transcription relative to Ves with the original genomic 5′-terminal sequence. It can only be speculated that U6 promoter-binding protein factors interfere with those that bind the internal promoter of Ves. Similarly, the combination of pol III promoter boxes  in the genes of two small nuclear RNAs significantly reduced the activity of such chimeric promoters . This co+ SINE-based expression cassettes was that their transcripts can be polyadenylated leading to an increase in their longevity and cellular level. The experimental data presented demonstrate that B2-based constructs showed a 2.2–3.2-fold increase in expression level compared to similar constructs with mutant PASs  were grown to an 80%-confluent monolayer in 60 mm Petri dishes using DMEM with 10% fetal bovine serum. Cells on each dish were co-transfected with 4 μg DNA of B2 or Ves constructs and 1 μg of the control pU1 mixed with 10 μL of TurboFect reagent  according to the manufacturer’s protocol. (In experiments on the stability of transcripts of B2 and Ves constructs, pU1 was replaced with 1 μg of plasmid with the mouse 4.5SI RNA gene , and the primers . Figure  primers . Hybridi


Corynebacterium diphtheriae and Mycobacterium tuberculosis, IdeR is also involved in virulence, whereas in non-pathogenic species such as Streptomyces, it regulates secondary metabolism as well. Although in recent years the focus of research on IdeR has shifted towards drug development, there is much left to learn about the molecular mechanisms of IdeR. Here, we summarize our current understanding of how this important bacterial transcriptional regulator represses and activates transcription, how it is allosterically activated by iron binding, and how it recognizes its DNA target sites, highlighting the open questions that remain to be addressed.Life came to depend on iron as a cofactor for many essential enzymatic reactions. However, once the atmosphere was oxygenated, iron became both scarce and toxic. Therefore, complex mechanisms have evolved to scavenge iron from an environment in which it is poorly bioavailable, and to tightly regulate intracellular iron contents. In bacteria, this is typically accomplished with the help of one key regulator, an iron-sensing transcription factor. While Gram-negative bacteria and Gram-positive species with low guanine-cytosine (GC) content generally use Fur (ferric uptake regulator) proteins to regulate iron homeostasis, Gram-positive species with high GC content use the functional homolog IdeR (iron-dependent regulator). IdeR controls the expression of iron acquisition and storage genes, repressing the former, and activating the latter in an iron-dependent manner. In bacterial pathogens such as  Corynebacterium diphtheriae in an iron-dependent manner and was, therefore, named DtxR (diphtheria toxin repressor) .,11.10,11C. diphtheriae (CdDtxR) and Mycobacterium tuberculosis (MtIdeR). More recently, we characterized the IdeR protein from the erythromycin-producing soil bacterium Saccharopolyspora erythraea (SeIdeR) [The best characterized homologs of DtxR proteins are those from (SeIdeR) . In the 2+-IdeR represses iron uptake, it activates expression of iron storage genes [M. tuberculosis and C. diphtheriae, IdeR/DtxR is required not only to control iron homeostasis, but also to regulate virulence [Streptomyces avermitilis, IdeR regulates iron metabolism as well as other processes such as secondary metabolism and morphology, thus playing a pleiotropic role in the regulation of the cellular metabolism [IdeR proteins regulate genes involved in the acquisition and storage of iron, such as genes coding for siderophore production and transport, iron transporters, ferritins and bacterioferritins, as well as genes involved in iron-sulfur cluster assembly and genes encoding metabolic enzymes with iron cofactors ,14–24. Wge genes ,19. Moreirulence ,25. In ttabolism .2+-activated IdeR thus blocks access to the promoter for RNA polymerase (RNAP) [MtIdeR activates the bacterioferritin and ferritin genes bfrA and bfrB [bfrA activation, activation of bfrB is mediated by four IdeR boxes upstream of its promoter, one of which partially overlaps the −35 region. IdeR activates this gene in an iron-dependent manner by alleviating the repression exerted by the histone-like protein Lsr2 [bfrB gene. IdeR also contributes to a slight Lsr2-independent induction of bfrB, potentially by helping to recruit RNAP [DtxR family iron sensors recognize a 19-bp palindrome of four CCTAA repeats (TTAGGTTAGSCTAACCTAA) that is highly conserved across different organisms ,18,26–28e (RNAP) ,20. In ce (RNAP) ,17. MtIdand bfrB ,15,17,19SeIdeR is also able to recognize what we refer to as a half site, i.e. a 10-bp palindrome consisting of two CCTAA repeats with a 4-bp spacer which binds only a single IdeR dimer. It remains to be investigated if half sites are also found in the genomes of other species utilizing IdeR, and what the function of IdeR is at such sites. Intriguingly, one of the half sites found in S. erythraea is in the promoter region of the nuo gene cluster coding for the respiratory chain NADH dehydrogenase Nuo complex I, a previously unknown potential target gene of IdeR [All previously studied IdeR complexes with DNA contained two dimers bound to the 19-bp target sequence ,27,29–32 of IdeR .In vivo, IdeR proteins respond to Fe2+ [in vitro IdeR can be activated by the transition metals Mn2+, Fe2+, Co2+, Ni2+, Zn2+ or Cd2+, although activation by Mn2+ and Zn2+ is less efficient, while Fe3+ and Cu2+ are not able to activate IdeR [CdDtxR was expressed in Bacillus subtilis (an organism using a Fur protein to sense iron), it responded equally well to Fe2+ and Mn2+ [2+ than it needs to be, having evolved to respond to iron within its natural cellular environment in which different metal ions are present in narrowly defined availability ranges [ to Fe2+ ,33,34. Hate IdeR ,26,35–37and Mn2+ . These oy ranges . Metal sy ranges .2+ is available, namely that of Fe2+-SeIdeR in complex with DNA [2+ or Fe2+ was used for activation [Each IdeR subunit contains two metal-binding sites, named primary and ancillary sites because mutations in the former site have a much more severe effect on IdeR activity than mutations in the latter, although both sites need to be occupied for full IdeR activity ,43,44 F. To datewith DNA . Howevertivation ,27,29,45tivation .2+ [2+ is increased by the sulfur ligands (Met10 and Cys102) in the primary site, as evidenced by comparison with MntR proteins, in which the corresponding binding site contains an aspartate or asparagine in place of Met10 and a glutamate in place of Cys102. Introducing the IdeR-specific metal ligands to an MntR protein and vice versa switched or broadened the metal responsiveness of the respective variant [The ligating residues and coordination geometry make the metal-binding sites suitable, although not exclusively selective for Fe2+ ,46. Sele variant .CdDtxR and MtIdeR, the ancillary site has significantly higher affinity for most metal ions than the primary site and is consequently occupied first [2+ < Fe2+ < Ni2+ < Co2+ < Cu2+ > Zn2+) [2+ ≈ Co2+ > Mn2+ > Ni2+ > Zn2+, with an affinity of ∼0.3–0.5 µM for Fe2+. In contrast, the primary site exhibits the order of affinities Zn2+ ≫ Ni2+ > Fe2+ > Co2+, with an affinity of ∼7–9 µM for Fe2+ [2+, most likely in the ancillary site, which explains why Mn2+ does not efficiently activate IdeR [2+ binds to MtIdeR with an affinity of 36 pM, whereas the second equivalent binds with a dissociation constant of 11 µM [Both in ed first ,47–50. I > Zn2+) . The ancate IdeR ,50. The 2+ was tentative, if correct, Zn2+ would bind to the primary site and Fe2+ to the ancillary site of IdeR when both metal ions are present [in vivo IdeR may be activated by binding one equivalent each of Fe2+ and Zn2+, or by two equivalents of Fe2+, allowing IdeR to respond to a wider range of iron levels, which may have particular relevance for pathogenic organisms such as M. tuberculosis which encounter severe iron limitation inside their host [Escherichia coli and the metal affinities stated above, however, the ancillary site would be ∼90% occupied by Fe, while the primary site would be ∼20% occupied by Fe and only ∼4% by Zn [M. tuberculosis may of course be rather different, especially inside macrophages.Although the assignment of the primary site as the high-affinity site for Zn present . This steir host ,25. Base4% by Zn ,53. Meta2+ binding in the ancillary site induces the correct geometry of the primary site.Binding of two equivalents of metal ions to IdeR is cooperative ,50,54,55MtIdeR and CdDtxR are predominantly monomeric in the apo-form, dimerizing once the first metal ion is bound [However, it was also found that cooperative metal binding depends on IdeR dimerization, suggesting that the cooperativity is inter- rather than intramolecular . While tis bound ,50,54,55CdDtxR crystal structures, the main difference between apo and metallated states in the N-terminal domains is a slight, caliper-like movement of the DBD of one subunit with respect to the other, bringing the two recognition helices closer to each other and, consequently, to the correct distance to fit into successive major grooves of their DNA target sites [CdDtxR does not change significantly upon metallation, while its tertiary structure does [As observed in et sites ,57 , activated IdeR binds to IdeR boxes with an apparent dissociation constant of ∼30–200 nM ,30,47,50onsensus ,27,29–32onsensus ,27,29. Ionsensus  , thus clamping the backbone between the wing and the first helix of the wHTH motif . IdeR foMtIdeR–DNA complex, suggesting that it does not contribute to specificity [SeIdeR and its target sequence revealed that these base interactions formed by IdeR are in fact not required for recognition [Early studies concluded that these IdeR–base interactions must be responsible for target site recognition, although the paucity of base-specific interactions was noted. Gln43 was observed to interact with different bases in different subunits of the cificity . While icificity ,32, the ognition . Insteadognition ,62,63. Tognition ,32,35. Gognition .TAA repeats) are likely to be of particular importance for recognition because they make the minor groove easy to deform [So how does IdeR read DNA shape? Analysis of the DNA conformation in the IdeR–DNA complexes, and prediction of the shape features of sequence variations that are and are not recognized by IdeR, suggest that IdeR reads minor groove width and/or deformability . The TpAo deform . Given to deform ,28,32. WMycobacterium and Corynebacterium. In contrast, the role of IdeR regulation in antibiotic-producing actinomycetes is understudied, despite their importance for antibiotic discovery and production [Although in recent years, research into DtxR/IdeR has focused primarily on drug development , the regoduction ,69. Manyoduction . Moreoveoduction . The mos(i) Which genes does IdeR regulate in free-living, non-pathogenic actinomycetes? Which role does it play in controlling primary and secondary metabolism?(ii) What is the role of IdeR at half target sites which only bind one IdeR dimer?(iii) How does IdeR activate certain genes?(iv) Does IdeR interact with other factors in the cell to accomplish its tasks?(v) What is the source of the metal-binding cooperativity? Is it intra- or inter-molecular? How does metal binding regulate IdeR dimerization?(vi) What exactly is the role of the SH3-like domain in activation in different homologs?2+ as opposed to physiologically relevant competitors? Does Zn2+ play a role in IdeR activation in vivo in different organisms?(vii) What is the affinity of different IdeR homologs for Fe(viii) Which DNA shape features does IdeR recognize and how? How does the DNA mediate cooperative IdeR binding?IdeR regulates iron homeostasis in many bacterial species of medical and industrial importance. It also controls virulence in bacterial pathogens, making it an interesting drug target, while in non-pathogenic, antibiotic-producing actinomycetes, this transcription factor influences multiple cellular processes, including secondary metabolism.IdeR is allosterically activated by binding two iron ions in a process which is mediated by changing protein dynamics and protein–protein interactions. Activated IdeR finds its target sites in the genome using a shape readout mechanism and then represses or activates transcription.Although IdeR has been studied for 30 years, its molecular mechanisms remain incompletely understood. A better understanding of these mechanisms will aid both drug development against IdeR itself and the discovery and production of new antibiotics by genetic manipulation of its host organisms.


CEBPA gene selectively deleting p42 are frequent in human acute myeloid leukemia. Here we investigated the individual genomics and transcriptomics of p42 and p30. Both proteins bound to identical sites across the genome. For most targets, they induced a highly similar transcriptional response with the exception of a few isoform specific genes. Amongst those we identified early growth response 1 (Egr1) and tribbles1 (Trib1) as key targets selectively induced by p42 that are also underrepresented in CEBPA-mutated AML. Egr1 executed a program of myeloid differentiation and growth arrest. Oppositely, Trib1 established a negative feedback loop through activation of Erk1/2 kinase thus placing differentiation under control of signaling. Unexpectedly, differentiation elicited either by removal of an oncogenic input or by G-CSF did not peruse C/ebpα as mediator but rather directly affected the cell cycle core by upregulation of p21/p27 inhibitors. This points to functions downstream of C/ebpα as intersection point where transforming and differentiation stimuli converge and this finding offers a new perspective for therapeutic intervention.The transcription factor CCAAT-enhancer binding factor alpha (C/ebpα) is a master controller of myeloid differentiation that is expressed as long (p42) and short (p30) isoform. Mutations within the  CEBPA mutations in AML are common. However, in all cases at least one allele capable of producing p30 is retained  to  package . Peak fi package . BAM fil package . Metagen package  to the r package . TranscrT-test statistics were applied.Where appropriate two-tailed Cebpa cDNA is preceded by a short upstream reading frame that affects the choice of the start codon within the main coding sequence. To achieve isotype-specific expression and detection we replaced this feature with an optimized Kozak initiation site and supplied an N-terminal HA-tag. This allowed exclusive production of p42 without generation of additional p30 protein  [The wt ein Fig. . In tranein Fig. . Prelimiein Fig. . In the ein Fig. . As we hle k.o.) . As thesMyc as well as induction of the differentiation sentinel gene Ngp coding for neutrophilic granule protein  gene coding for a transcription factor, is located at 5q right in the center of the genomic region that is deleted in 5q- MDS and AML [TRIB1 (tribbles1) acts as an oncogene and encodes a protein that has been shown to negatively feedback on C/ebpα [www.bioportal.org) confirmed our results, as both genes were significantly underrepresented in AML with CEBPA mutations compared to cases with a CEBPA wt configuration  and Matk. The Id2 protein displaces E-proteins, which are HSPC-specific bHLH transcription factors from DNA [To study the physiological implications of Egr1 expression we devised another degron system Fig. , becausefrom DNA . Matk isfrom DNA . In GSEAMyc RNA levels. To investigate the impact of Trib1 on differentiation in our cell system, maturation was induced in HoxA9 and HoxA9+Trib1 cells. This was done either by replacing normal cytokines with G-CSF, or by removing the oncogenic stimulus in cells pretransformed with a degradable HoxA9-FKBP through supplementation with dTAG13 . RNA was isolated before treatment after 48 h and 24 h after a recovery period where cells were returned to normal growth conditions without G-CSF/dTAG13. Expression of the differentiation sentinel gene Ngp was determined by qPCR thus allowing a quantitative assessment of differentiation  in combination with a multitude of other transcription factors (e.g. HoxA9) it is likely that the remaining transactivation activity of p30 is sufficient to allow normal regulation of most targets except a few that are hypersensitive towards C/ebpα activity. Interestingly, this phenomenon seems to be more frequent and very recently a similar situation has been discovered for the cartilage master regulator Sox9. Reduction of Sox9 protein had very little effect on chromatin accessibility for most bound loci whereas only a few were hypersensitive towards Sox9 concentrations . The rem disease . In thesormation . Yet, thCebpa to achieve transformation. Rather, proliferation and differentiation pathways seem to intersect further downstream directly at the level of the cell cycle machinery. This is supported by G-CSF induced production of p27 that bypassed transformation directly and induced a state of forced differentiation even in the continuous presence of the transforming event. Generally, the core cell cycle machinery seems to possess a more extended regulatory potential than anticipated. Beyond its role as cell cycle regulator Cdk6 has a nuclear function as transcription factor [Myc, also a central downstream target of HoxA9 [Ccnd1 coding for CyclinD1 is one of the longest known target genes of Myc that is responsible at least in part for the cell cycle promoting activity of this oncogenic transcription factor [Cdkn1a, the p21 parental gene (Fig. Egr1 transcription in conjunction with the transcription factor AP1 [Cdkn2a [Interestingly, HoxA9 did not rely on repression of n factor , 36 and of HoxA9 . In addiof HoxA9 . Ccnd1 cn factor . Mechaniene Fig. . Additioctor AP1  while ac [Cdkn2a  coding fSupplementary MaterialSupplementary Table 1Supplementary Table 2


Nature provides compelling evidence for the involvement of the histone H3K4me3 modification in regulating RNA polymerase II (Pol II) pause-release and elongation.1 The expansion of the functional repertoire of this active chromatin mark and the elucidation of the molecular mechanisms behind it represents a major advance for the field, not least because the epigenetic factors involved are linked to a wide range of human pathologies.A recently published study in 3 However, the exact function of H3K4me3 is still a matter of debate, particularly with regard to the underlying molecular mechanisms responsible for transcriptional gene activation by Pol II. For example, there is also evidence that although transcription and H3K4me3 correlate well, the latter is ultimately not causal nor instructive for gene expression on a genome-wide scale.4The term epigenetics derives from the Greek prefix “epi” and describes an inheritable regulatory state that manifests itself beyond the mere genetic information encoded in the DNA. Epigenetic regulation is imposed, among other things, at the level of DNA-packaging histone proteins, in particular via the reversible, covalent modification of their N-terminal tails. Although these post-translational modifications and their regulatory impact on gene expression were discovered and discussed as early as the 1960s, it was almost another 40 years before one of the leading figures in the field of epigenetics, the recently deceased David Allis, proposed his seminal model for the existence of a “code” of histone modifications. Each histone tail within a nucleosome can be decorated by a variety of (sometimes interdependent) modifications in different combinations. If only the number of publications were used as a benchmark, then lysine 4 tri-methylation in the tail of histone H3 is among the most important ones: as of May 2023, PubMed returns more than 3500 hits for the search term “H3K4me3”. The enzymes directly involved in H3K4me3 are the SET1/COMPASS histone methyltransferase complexes as “writers”, and the histone demethylases KDM5A/B as “erasers”. Since H3K4 methylation is generally associated with transcriptionally active genes, it is not surprising that mutations in SET1/COMPASS or KDM5 have been associated with tumorigenesis.1 To acutely deplete H3K4me3, the authors generated an inducible degron system that allows rapid ablation of either of two core subunits of mammalian SET1/COMPASS complexes, DPY30 or RBBP5, in mouse embryonic stem cells (mESCs). Chromatin immunoprecipitation-coupled sequencing (ChIP-seq) analysis of these cell lines revealed global loss of promoter occupancy by the two COMPASS factors as well as of H3K4me3 within 2 h after degron induction. The rapid turnover of H3K4me3 was dependent on KDM5 demethylase activity: a double knockout of murine Kdm5a and Kdm5b in the DPY30 degron cell line significantly delayed H3K4me3 depletion across all interrogated transcription start site (TSS) regions. Depletion of both COMPASS subunits also resulted in short-term reduction of mRNA synthesis, as demonstrated by time-course experiments using SLAM-seq, a metabolic labeling-sequencing technology for nascent mRNA with single nucleotide resolution. Again, the additional loss of KDM5 activity in DPY30 degron cells led to a significantly delayed decrease in mRNA synthesis. However, the negative effect of H3K4me3 depletion on de novo transcription initiation was neither the result of reduced preinitiation complex (PIC) formation nor reduced recruitment of the Pol II machinery to TSS regions. This finding per se is an important contribution to the long-standing debate as to whether H3K4me3 merely reflects transcriptional activity or whether it has a causal role in its regulation. However, the most important result of this work was yet to come: the rigorous proof that H3K4me3 affects a decisive post-initiation step, namely the arrest of Pol II.In this context, we refer to the aforementioned research article by Kristian Helin’s group, in which the authors present a comprehensive analysis of the effects of H3K4me3 on the early transcription elongation phase using cutting-edge genetic engineering in combination with an impressive arsenal of genome-wide analyses.5 Through sophisticated multi-omics experiments using state-of-the-art technologies, the Helin group was able to demonstrate that H3K4me3 regulates both the pause-release and the elongation of Pol II.1 For example, by combining Pol II ChIP-seq experiments in degron cells with kinetic measurements of ongoing RNA synthesis of chromatin-engaged Pol II, they showed that H3K4me3 depletion led to a significant increase in the mean residence time of Pol II at promoter-proximal pause positions, while the elongation rate, i.e., the speed at which Pol II travels through the gene body, decreased.After recruitment to gene promoters and subsequent PIC formation, Pol II initiates synthesis of a short nascent RNA, but then becomes stalled promoter-proximally before entering the gene body. It is now recognized that pausing is an universal hallmark of Pol II transcription, occurring at all protein-coding genes and other transcribed regions such as enhancers, and that the release of stalled Pol II into productive RNA synthesis is a pivotal checkpoint in gene expression.6 To elucidate the exact role of INTS11, the authors created an additional degron variant of INTS11 in the background of the proximity-tagged RPB1/degron-tagged DPY30 mESC line. Acute loss of INTS11 in these cells led to increased Pol II pausing, broadly decreased productive elongation, and a global decrease in the expression of protein-coding genes and nascent mRNA synthesis. These data suggest H3K4me3-dependent recruitment of INTS11, which is required to enable transcriptional pause-release.To gain more mechanistic insights into this process, the authors used CRISPR/Cas9-based genome editing in COMPASS degron mESCs to proximity-tag the largest Pol II subunit RPB1, which enabled mass spectrometry-based identification of Pol II-associated proteins whose abundance would change upon loss of H3K4me3. One of these proteins is INTS11, the endonuclease subunit of the Integrator complex, which has been aptly described as key modulator of Pol II pausing and elongation before.Taken together, these results lead to a model of H3K4me3-dependent coupling of chromatin “roadwork” with transcriptional “traffic” at the promoter level Fig. . These e