We then test the universality of this mechanism by a statistical analysis of promoter sequences from transcriptomes of phylogenetically distant bacteria under conditions of supercoiling variations (i) by gyrase inhibitors, (ii) by environmental stresses, or (iii) inherited in the longest-running evolution experiment. In all cases, we identify a robust and significant sequence signature in the discriminator region, suggesting that supercoiling-modulated promoter opening underpins a ubiquitous regulatory mechanism in the prokaryotic kingdom based on the fundamental mechanical properties of DNA and its basal interaction with RNA polymerase.DNA supercoiling acts as a global transcriptional regulator that contributes to the rapid transcriptional response of bacteria to many environmental changes. Although a large fraction of promoters from phylogenetically distant species respond to superhelical variations, the sequence or structural determinants of this behavior remain elusive. Here, we focus on the sequence of the “discriminator” element that was shown to modulate this response in several promoters. We develop a quantitative thermodynamic model of this regulatory effect, focusing on open complex formation during transcription initiation independently from promoter-specific regulatory proteins. We analyze previous and new expression data and show that the model predictions quantitatively match the IMPORTANCE In this study, we highlight the role of the discriminator as a global sensor of supercoiling variations and propose the first quantitative regulatory model of this principle, based on the specific step of promoter opening during transcription initiation. It defines the predictive rule by which DNA supercoiling quantitatively modulates the expression rate of bacterial promoters, depending on the G/C content of their discriminator and independently from promoter-specific regulatory proteins. This basal mechanism affects a wide range of species, which is tested by an extensive analysis of global high-throughput expression data. Altogether, ours results confirm and provide a quantitative framework for the long-proposed notion that the discriminator sequence is a significant determinant of promoter supercoiling sensitivity, underpinning the ubiquitous regulatory action of DNA supercoiling on the core transcriptional machinery, in particular in response to quick environmental changes. Escherichia coli promoters are not targeted by any known TF  to which they respond by quick and global modifications of their transcriptional program. Inspired by early studies, current mechanistic models of this regulatory action are mostly based on transcription factors (TFs) that bind at specific promoters and interact with RNA polymerase (RNAP). However, more than half of known TF , and entknown TF , 3 but nknown TF  dependinknown TF .in vitro and in vivo , i.e., the over- or underwinding of the double helix by the action of topoisomerase enzymes and architectural proteins –9. In ba– in vivo –14. Mechin vivo –, it coulin vivo ––9. Accorn vivo ––, 16–19.In spite of its importance, no sequence or structural signature was ever clearly identified in support of the latter property. A possible reason is that SC affects transcription at many successive steps of the process, e.g., open complex formation , 21, profis promoter, which are both strongly SC sensitive and subject to stringent control  in both in vitro (in vivo (in vivo SC levels are taken from reference tyrTd mutant) indeed strongly shifts the opening curve to the left  or SC increase (topA). This feature was reproduced using the experimentally measured SC levels of these strains  and a reduced number of adjustable parameters was a key advantage. The approximations used in the modeling of this specific step as well as those other contributing factors may explain the slight discrepancies with the data , but the clear overall agreement supports the notion that the proposed mechanism is the primary contributor in the SC sensitivity of promoters controlled by the discriminator sequence.pheP promoter of E. coli, which is SC sensitive  of superhelical variations on mutant promoters of protein-coding genes with different features. Two families of synthetic promoters were constructed . For all promoters, the left panel shows bacterial growth measured by OD600 and the right panel shows the expression level measured by luminescence. (A) E. coli bacteria carrying plasmids with pheP WT promoter in rich medium. (B) pheP AT-rich promoter. (C) pelE WT promoter in minimal medium with only 100 μg/ml novobiocin concentration tested. (D) pelE GC-rich promoter. (E) pelD WT promoter. (F) pelD AT-rich promoter. The OD600 discrepancies at shock time or after several hours in some cases  are optical artifacts due to the opening of the recorder and/or the formation of sediments disrupting the measurements (the luminescence does not vary correspondingly among replicates). Download FIG S2, PDF file, 0.2 MB.Bacterial growth and mutant promoter expression monitored in Copyright © 2021 Forquet et al.2021Forquet et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the E. coli cells in a microplate reader after treatment by novobiocin, which relaxes the chromosomal DNA by inhibiting gyrase and, to a lesser extent, topoisomerase IV  Bacterial growth measured by OD600 and pelE expression level measured by luminescence in minimal medium. A novobiocin shock was applied in mid-exponential phase (100 μg/ml concentration). (B) Same for pelD. The patterns of bacterial growth, promoter expression, and response to novobiocin are consistent between the two systems  for both pelE and pelD. (C) Absolute expression levels for pelE promoter in absence of shock. As expected, the multicopy plasmid-borne fusion is expressed at a higher level than the chromosome-borne fusion. (D) Comparison of expression fold changes in response to relaxation for pelE promoter, demonstrating a quantitatively identical effect of novobiocin in the two constructions, in spite of the difference of absolute expression. This observation confirms that the fold-changes observed on the plasmid-borne constructions are indeed due to a regulatory effect of DNA relaxation  rather than a plasmid-specific effect . The relaxation activity of novobiocin was previously demonstrated in D. dadantii cells on the plasmids employed (pelD promoter. (C to F) Computed from the final luminescence time points in panels A and B. Download FIG S1, PDF file, 0.1 MB.Bacterial growth and promoter expression monitored in a microplate reader for employed .  but is a quantitative effect that is progressively modulated by the G/C% and equally affects promoters with a naturally low G/C%, such as pelE, as expected from our modeling. It affects promoters of diverse biological functions and regulation complexities and is detectable under different physiological conditions . Based on these observations on a few selected promoters, and since the proposed mechanism of open complex formation is involved in RNAP-promoter interaction independently from additional regulatory proteins, we now enlarge the scale of the analysis to entire genomes.These results show that the effect of the discriminator on the SC sensitivity is not specific to G/C-rich ones , which may contribute to their supercoiling response; therefore, we looked for a statistical relation rather than a prediction valid for all analyzed promoters.We first looked at the variability of discriminator G/C contents among mRNA promoters in various species based on available TSS maps . These d10.1128/mSystems.00978-21.4FIG S3E. coli, S. Typhimurium, D. dadantii, and M. pneumoniae, only σ70 promoters were considered, whereas only σA promoters were considered for S. elongatus. All promoters were aligned at their −10 site, except for S. elongatus and M. pneumoniae for which promoters were aligned at their annotated TSS, and the signal shifted 4 nt in the case of M. pneumoniae. The distributions are similar with the two methods. Download FIG S3, PDF file, 0.09 MB.Variability of promoter discriminator A/T content in the investigated organisms. Proportion of promoters depending on their A/T content in the discriminator defined as the sequence of variable length between −10 element and +1 positions (A) or the 5-nt sequence centered around position −2 (B) used throughout the manuscript. For Copyright © 2021 Forquet et al.2021Forquet et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 70 promoters of Salmonella enterica and looked at their average A/T% profile . For heat/cold shock, and S. elongatus, P values were not provided and were replaced by thresholds on log2-fold-change values (0.5) to generate subsets of act/rep genes of comparable sizes as in other datasets. A/T% contents are compared with Student tests, in 5-nt windows centered at position −2 in the discriminator region. The sensitivity gain is computed as the increase in the proportion of accurately predicted genes (among significantly activated or repressed genes) compared to a null (random) model. Download Table S2, PDF file, 0.08 MB.Compilation of investigated species, conditions, and results. The SC change refers to the direction of SC variation induced by the condition . The correlation condition from laxation . For theCopyright © 2021 Forquet et al.2021Forquet et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Since the investigated mechanism relies on highly conserved molecular actors, RNAP and topoisomerases, it might affect a broad range of bacterial species. We therefore tested the validity of our observations in all organisms where a transcriptome obtained after DNA relaxation was available together with an accurate TSS map (from independent studies). The list of references of the employed data is summarized in 10.1128/mSystems.00978-21.10TABLE S3E. coli, (B) S. Typhimurium, (C) D. dadantii, (D) S. elongatus, and (E) M. pneumoniae. Genome-wide TSS maps were collected from the literature . Download Table S3, XLSX file, 0.3 MB.List of detailed promoter sequences for (A) terature , and a sterature , imposinCopyright © 2021 Forquet et al.2021Forquet et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the E. coli with DNA microarrays after norfloxacin shock in two alternate topoisomerase mutant strains . The P value of the resulting linear regression is indicated. Act refers to significantly upregulated promoters, Rep to significantly downregulated promoters, Non to not significantly affected promoters. Colored shaded areas represent 67% confidence intervals, while error bars represent 95% confidence intervals. Δσ> 0 refers to DNA relaxation, whereas Δσ< 0 refers to an increase in negative SC. Data are for (A) E. coli, (B) D. dadantii, (C) S. elongatus, and (D) M. pneumoniae. In enterobacteria (A and B), only σ70 promoters were considered, and aligned at the −10 element, whereas in the two other species (C and D), all promoters were aligned at their annotated TSS, resulting in a poorer definition of the signal and positional shifts. All references are provided in FIG S4, PDF file, 0.4 MB.Relation between discriminator sequence and response to SC variations in all investigated conditions and organisms. For each expression dataset, the left panel corresponds to average A/T% profiles of promoters along 5-nt-centered windows depending on their transcriptomic response, while the right panel corresponds to the proportion of activated promoters depending on their A/T% in a 5-nt window around position −2 in the discriminator region . We nevertheless looked for sequence signatures comparable to those observed previously. In Synechococcus elongatus, where SC was shown to be a major determinant of circadian oscillatory genomic expression  associated with DNA relaxation, the response was analyzed in the enterobacteria E. coli and D. dadantii  before 2,000 generations and a second mutation  before 20,000 generations both lead to an inheritable increase of negative SC (P = 0.005), or after 20,000 generations (P = 0.011)  , where 4In this work, we propose a simple thermodynamic model of open complex formation that quantitatively accounts for transcriptional regulation by SC based on the discriminator sequence. Our analysis confirmed and gave a quantitative content to the long-proposed notion that the discriminator sequence is a significant determinant of promoter supercoiling sensitivity. The statistical analysis of promoter sequences, carried out in various species and experimental conditions, highlights the widespread relevance of this mechanism in the genome-wide response to transient or inheritable variations of SC levels.70-dependent promoter sequences in E. coli yields a significant negative statistical relation between the A/T% at the discriminator and the RNAP binding score at the −10 element  and computed the proportion of accurate predictions among the observed differentially expressed genes (activation or repression). Compared to a null (random) model, this proportion is improved by around 10 to 15% of the responsive genes in the investigated relaxation and environmental stress assays  response , deservegppA, spoT, and relA) does not exhibit any significant response  is detected in the discriminator . Data are from reference FIG S5, PDF file, 0.06 MB.Simultaneous regulation by SC and ppGpp at the discriminator. (A) Average A/T% profiles of Copyright © 2021 Forquet et al.2021Forquet et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the E. coli , and these promoters exhibit a similar  sequence signature at the same location , suggesting a synergistic action of these two mutations  and individually cloned into pUCTer-luc plasmids (see luc). E. coli strain MG1655 cells were then transformed with these plasmids using a standard electroporation procedure.Sequences 230, 329, and 313 nt upstream of the E. coli cells carrying the plasmids with the different promoters were recovered from glycerol stock (−80°C) and grown overnight (about 16 h) on LB agar plates at 37°C. The obtained colonies were further transferred to liquid cultures overnight (about 16 h), with shaking at 200 rpm under selective antibiotic pressure . LB medium was used for bacteria carrying plasmids with pheP-derived promoters, whereas M63 minimal medium supplemented with 0.2% glucose was used for bacteria carrying plasmids with pelE- and pelD-derived promoters. Cells were washed , and then outgrowth cultures were performed in the same medium without antibiotics, stopped during exponential phase, and diluted for a final optical density at 600 nm (OD600) of 0.1 in a 96-well microplate. Each well  contained the chosen medium supplemented with d-luciferin . The microplate was placed in a humidity cassette and grown at 37°C until stationary phase was reached in a microplate reader (Tecan Spark). The OD600 and luminescence were measured every 5 min, preceded by a 45-s shaking step . During mid-exponential phase for pheP and early exponential phase for pelE and pelD, the microplate was taken out and DNA relaxation was transiently induced by injecting 5 μl of novobiocin  using a multichannel pipette. Data files produced by the microplate reader were parsed using a Python home-made script, and the response to DNA relaxation was computed by comparing the luminescence values (in triplicates) of the novobiocin-shocked strain compared to the same strain injected with water (novobiocin solvent) 60 min after shock. The employed firefly luciferase has a short lifetime, between 6 min in B. subtilis , P values were not provided and were replaced by a threshold on log fold change values (±0.5), generating subsets of act/rep genes of sizes comparable to those in other data sets. For enterobacteria, only σ70-dependent promoters were retained and aligned at their −10 site to reduce statistical noise. Some of them also bind other σ factors, but σ70 is predominant in exponential phase where the analyzed samples were collected. The A/T% content was computed along 5-bp sliding windows . Curves of Transcriptomes obtained after DNA relaxation by antibiotics, inheritable supercoiling variations, or environmental stresses were collected from the literature, as were genome-wide TSS maps . A scan database ), which  windows  and C. P windows . For S. finition  and exhigression  or by a romoters  and 6. Ak is the transcription rate, k0 is the basal  rate, s is the precise 14-nt sequence of the denatured region in the open complex  , and an k0, is achieved, whereas the promoter is mostly closed when DNA is strongly relaxed.The opening energy, dynamics , where turements  and withurements  but is nP0(s)>0]  and the Based on these hypotheses, the expression fold change of a promoter during an SC variation (in the regimen where it is not fully activated) depends only on and is independent of the precise  value of For the modeling of the data in the data  and G, bin vitro and in vivo transcription, respectively, the latter value being kept constant for all subsequent in vivo calculations. These low values are likely due to the strongly simplified description of the solvent  and DNA  used in that software and should be considered effective parameters for the computation rather than quantitative concentrations.For each promoter, the denaturation energy is computed with TwistDNA  using tha posteriori by the good agreement with in vitro and in vivo expression data . The validity of the computation is justified ion data  and 2. Nion data  and E, aIn vivo SC levels used in the computations of E. coli strains with norfloxacin), and E. coli).pheP-, pelE-, and pelD-derived promoters were reproduced (Expression fold changes in response to relaxation measured in microplates with produced  with a rcultures .For the computation of the genome-wide contribution to the relaxation response (see Discussion), transcription rates from all promoters are normalized by their sum under each condition before computing fold changes, without any cutoff value . This procedure results in the activation of a fraction of promoters (since the G/C-rich promoters represent a weaker proportion of total transcripts after the relaxation, A/T-rich promoters appear activated; see See 10.1128/mSystems.00978-21.1TEXT S1pelD-luc and pelE-luc transcriptional fusions in D. dadantii chromosome. Download Text S1, PDF file, 0.04 MB.Insertions of Copyright © 2021 Forquet et al.2021Forquet et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


Gli3 does not increase target gene expression, enhancer acetylation or accessibility, as it does post-HH signaling. Furthermore, GLI repression is established independently of HH signaling, but after its onset. Collectively, these surprising results challenge current GLI pre-patterning models and demonstrate that GLI repression is not a default state for the HH pathway.The Hedgehog (HH) pathway regulates a spectrum of developmental processes through the transcriptional mediation of GLI proteins. GLI repressors control tissue patterning by preventing sub-threshold activation of HH target genes, presumably even before HH induction, while lack of GLI repression activates most targets. Despite GLI repression being central to HH regulation, it is unknown when it first becomes established in HH-responsive tissues. Here, we investigate whether GLI3 prevents precocious gene expression during limb development. Contrary to current dogma, we find that GLI3 is inert prior to HH signaling. While GLI3 binds to most targets, loss of  GLI repression has been presumed to be the default transcriptional state and important for pre-patterning tissues. Challenging current models, the authors show that GLI3 repression is inert in the limb bud before the onset of Hedgehog signaling. The processing of GLI-A and GLI-R are both directly dependent on processes localized within the primary cilium and defects in ciliary components lead to characteristic misregulation of HH target genes in a large group of birth defects termed ciliopathies6. While GLI-A directly activates a subset of HH targets, most genes are ‘de-repressed’ by the loss of GLI-R alone; they do not require GLI-A-mediated activation for their expression. The importance of de-repression is exemplified in the developing limb where the phenotype of Sonic Hedgehog (Shh) null limb buds is dramatically improved in Shh;Gli3 mutants9. In this context, the loss of GLI3-mediated repression, even in the absence of pathway activation, is sufficient to restore expression of most GLI target genes and many aspects of limb growth and patterning.The Hedgehog (HH) signaling pathway is one of the major developmental regulators of tissue-specific development and differentiation. GLI proteins mediate transcriptional responses to the pathway in a strikingly cell-specific fashion. In the absence of HH pathway activation, GLI transcriptional repressors (GLI-R) prevent the activation of HH target genes, while upon exposure to HH, GLI proteins undergo alternative post-translational processing into transcriptional activators (GLI-A)10. As a regulator of tissue patterning, GLI3-R spatially and temporally restricts expression of HH targets, preventing sub-threshold activation of the pathway in HH-responsive tissues12. Consequently, GLI transcriptional repression has primarily been studied in tissues with ongoing HH signaling. Although not experimentally addressed, it is widely assumed that GLI3-R also regulates the expression of its target genes prior to the initiation of HH signaling16. Before pathway activation, GLI3 has been proposed to have a pre-patterning role in the very early limb by repressing Hand2, thus limiting its expression to the posterior limb bud, where it is required to activate Shh expression, thereby establishing anterior-posterior polarity17. Curiously, Hand2 and Gli3 are co-expressed in early limb buds, prior to their segregation into distinct posterior and anterior domains14, raising the question of whether GLI3-R is capable of repressing Hand2 at this time.GLI3 represses transcription, at least in part, by epigenetically regulating a subset of its own enhancers. Its properties include reduced enrichment levels of the active enhancer mark H3K27ac and reduced chromatin accessibility at a subset of HH-responsive GLI3 binding regions (GBRs), termed HH-responsive GBRs, that likely mediate the majority of HH-specific transcriptionGli3 as they are after the initiation of HH signaling, even though a subset of these genes appear competent to be repressed at this stage. Contrary to our initial hypothesis, we conclude that the GLI3-R isoform is transcriptionally inert in early limb buds, a finding that is incompatible with the pre-patterning model for limb polarity16. Interestingly, most genes that are later repressed by GLI3 are expressed in pre-HH limb buds, suggesting that rather than preventing their activation, GLI3 repression later regulates their spatial expression after HH signaling initiates. Overall, this work demonstrates that GLI3-mediated repression of target genes is not a default state for the Hedgehog pathway; instead GLI repression is established during limb development at a time point after HH induction.We initially hypothesized that GLI3-mediated repression would be important prior to HH expression for preventing premature activation of target genes by reducing H3K27ac enrichment at enhancers. Despite GLI3 binding to a majority of the same sites it occupies in the post-HH limb bud, we found that it does not regulate activation of its enhancers or their chromatin accessibility until after the activation of the HH pathway and does so independently of HH signaling. In addition, GLI target genes are not upregulated in limb buds lacking Gli1 was at 24 somites (24 S), where 58% of embryos at this stage had detectable Gli1 expression, while Shh was not detected until 25 S (86%) , a stage slightly earlier than previous reports  Fig. . As 24 S14. We then compared endogenous GLI3FLAG binding using CUT&RUN21 in pre-HH  and anterior E10.5 (32-35 S) limb buds, a post-HH time frame when HH signaling is firmly established in the posterior limb, but before there are morphological changes in Shh−/− limb buds22. In pre-HH limb buds, GLI3 bound to most regions that were bound in post-HH limb buds 10  and enriched for H3K4me2 (98%) in the early limb 28. As many distal regions lacked H3K4me1 enrichment prior to HH induction, we observed a slight preference for GLI3 to be bound to promoter proximal regions  of post-HH (32-35 S) WT and Gli3−/− limbs , including well known signature HH target genes such as Ptch1, Hand2, Hoxd13 and Grem1  were significantly upregulated with loss of Gli3 in the early limb  (n = 7). At early post-HH stages (24–26 S), Hand2 expression is variable in WT and Gli3−/− limb buds, with most WT limb buds expressing anterior Hand2 at levels comparable to those seen posteriorly. However, comparably lower levels of anterior Hand2 expression were present even in some Gli3−/− limb buds  might broadly regulate HH targets in pre-HH limb buds. We predicted a set of 74 high confidence GLI target genes by identifying genes downregulated in uds Fig. 40. Theres10 Fig. . This H3s10 Fig. . Most HHs10 Fig. .Hand2 locus in the early limb , are highly enriched for H3K27me3 and completely lack acetylation at E9.25, suggesting they may not be competent for de-repression upon loss of Gli3 at this stage  limb buds at HH-responsive GBRs10, compared to posterior WT limbs which have active HH signaling and lack GLI repression. To determine if this was caused by GLI repression, as opposed to a lack of GLI activator, we performed ATAC-seq in posterior E10.5 Shh−/−;Gli3−/− limb buds. Loss of Gli3 in the absence of Shh resulted in accessible chromatin at HH-responsive GBRs similar to posterior WT controls, confirming that chromatin compaction is GLI3 repressor-dependent  when HH signaling is firmly established and an intermediate timepoint, E10 (28–30 S), 8–12 h after the initiation of HH signaling. We first examined Hand2, whose expression becomes posteriorly restricted in the limb around 26 S  WT controls, in addition to pre-HH WT limbs, which contain accessible chromatin and lack active GLI repression. Alternatively, if GLI-repression was still in the process of being established, the accessibility of HH-responsive GBRs would be similar to that of pre-HH and E10 (28–30 S) WT limbs. Consistent with an absence of GLI repression, the chromatin at HH-responsive GBRs in posterior E10 (28–30 S) Shh−/− limb buds was not yet compacted. In fact, chromatin was significantly more accessible in E10 (28–30 S) Shh−/− limb buds compared to both E9.25 (21–23 S) WT limbs and posterior E10 (28–30 S) WT limbs, which have active HH signaling and little to no nuclear GLI3-R10  in Shh−/− compared to WT, such as HH-responsive GBRs near HH targets Ptch1 and Hhip   Shh−/− limbs supports that GLI2-R is unlikely to functionally compensate for the lack of GLI3 repression in early limbs (see discussion). Collectively these results support that GLI repression is not fully established even up to 12 h after HH signaling would have normally become active . Here, GLI3 ciliary localization remained at comparable levels to E9.25 limbs, as well as in anterior and posterior E10.5 (32–35 S) limbs, after GLI repression is established  limb buds, endogenous GLI3Hand2 is one of the best characterized GLI3 repression targets in the limb bud, however in opposition to this, HAND2 and GLI3 have recently been shown to physically interact and synergistically activate targets in developing facial structures45. Similar to their co-occurrence in craniofacial tissue45, they are co-expressed throughout the pre-HH limb, prior to HAND2 and GLI3 segregating into distinct domains post-HH signaling14. Instead of being responsible for repressing targets prior to HH signaling, we questioned whether GLI3 might initially co-activate targets with HAND2, in a manner similar to that in facial tissue. Interestingly, while GLI3 bound to HAND2 binding regions both pre- and post-HH signaling near many genes . It is possible that other factors could be present at some ATAC-seq footprints in Shh−/− limbs overlapping HAND2 motif regions, thus masking our ability to detect a loss of HAND2 footprints. We also note that Tbx2 and Tbx3 are not reduced in pre-HH Gli3−/− limb buds as would be expected if GLI3 had a significant role in co-activating these HAND2 target genes , until nearly 12 h after HH signaling would have normally been activated   Fig. .Shh−/− limb buds, it also suggests that GLI repression is likely established temporally during limb development through HH-independent mechanisms. Rather than preventing initial expression of HH targets, most genes repressed by GLI3 in later limb development are initially expressed in pre-HH limb buds , but not E10 (28–30 S) Gli3Xt-J (Jackson Cat# 000026) and Shhtm1amc null (Jackson Cat# 003318) alleles69 were maintained on a Swiss Webster background. The Gli33XFLAG allele, with an N-terminal 3XFLAG-epitope70, was maintained on a mixed background. Mice were housed in a facility with 12 h light/12 h dark cycles and temperatures of 65-75 °F (~18–23 °C) with 40–60% humidity. For all genomic experiments, with the exception of Shh−/−;Gli3−/− ATAC samples which had to be individually genotypes, fresh E9.25 (21–23 S) and E10.5 (32–35 S) forelimb buds were pooled from multiple litters of the specified genotype. For ATAC-seq experiments with WT forelimbs , and Shh−/− forelimbs , posterior halves of forelimbs from 2 to 3 embryos were dissected and pooled prior to cell dissociation. For RNA-seq, E9.25 whole forelimbs or E10.5 anterior halves of WT and Gli3−/− forelimbs were pooled from 3 to 4 embryos and dissociated in TRIzol (Invitrogen Cat# 15596026). Purified RNA was library prepped using NEBNext Ultra II RNA library Prep Kit for Illumina (NEB Cat# E7775) and sequenced with an average of 60 million reads/sample; 3–4 biological replicates were used for each stage and genotype.Experiments involving mice were approved by the Institutional Animal Care and Use Committee at the University of Texas at Austin (protocol AUP-2019-00233). The 7. Briefly embryos were fixed for 16 h at 4 °C in 4%PFA/PBS and rehydrated through methanol/NaCl washes. Embryos were treated with proteinase K (10 μg/ml) for ~15 min, post-fixed, and prehybridized at 70 °C for 1 h before being hybridized with 500 ng/ml digoxigenin-labeled riboprobes at 70 °C overnight. Samples were then incubated with RNase A, washed, blocked and incubated with a 1:5000 dilution of anti-digoxigenin antibody conjugated with alkaline phosphatase (AP) (Roche) overnight at 4 °C. Extensive washing in 1XMBST was followed by incubation in NTMT prior to signal visualized with BM purple. Fluorescent in situ hybridization was performed using HCR v3.0 Molecular Instruments reagents and was performed according to manufacturer’s protocol71 except that probes were incubated overnight with a concentration of 16 nM. Embryos were embedded in Ultralow gelling temperature agarose (Sigma A5030), cleared in Ce3D + +72 and visualized on a Zeiss LSM 710 confocal microscope and are shown as maximal intensity z-stack projections.Conventional in situ hybridization done as previously describedFor M2 FLAG ciliary staining, freshly dissected, unfixed embryos were sucrose protected (30% sucrose) at RT and embedded in OCT. 12 μm cryosections were immediately fixed in 100% methanol for 3 min at −18 °C. For GLI2 ciliary staining, embryos were fixed with 4% PFA/PBS at RT for 1 h, prior to sucrose protection and embedding. Sections were permeabilized in 0.1% PBST at RT for 15 min. then blocked in 3% BSA , 5% Normal Goat Serum (Jackson Immunoresearch #005-000-121) in 0.1% PBST for 1 h. at RT. Primary antibodies were incubated in blocking solution overnight at 4 °C at 1:500 dilutions: M2 FLAG , Arl13b , GLI2 (a gift from Jonathon Eggenschwiler), Gamma tubulin (Sigma #T6557). Secondary antibodies were incubated in block at 1:500 dilutions at RT for 1 h: goat anti-mouse Alexa568 , goat anti-rabbit Alexa488  and donkey anti-guinea pig Alexa594 (Jackson ImmunoResearch 706-585-148). After washes, sections were then stained with 1:5,000 DAPI in PBS for 8 min at room temperature prior to being coverslip mounted with Prolong Gold mounting media (Invitrogen #P36930). Images were taken with a Zeiss LSM 710 Laser Confocal at 63x. Images were quantified using Zen Blue Lite software to notate category designations. Cilium length measurements were made in FIJI (Image J) normalized to a Zeiss scale bar graphic.Western blots were blocked in 5% milk for 30 min., then incubated with primary antibodies for 1 h at room temperature in 3% milk: 1:4000 M2 Flag (Sigma #F3165) and 1:2000 B-actin . Secondary antibodies were incubated for 1 h at room temperature in 3% milk: 1:5000 Donkey anti-mouse (Jackson #715-035-150), Donkey anti-rabbit (Jackson #711-005-0152). Blots were developed using Amersham ECL Prime .15 with the following modifications. Cells were dissociated with 100μg/ml Liberase (Roche 05401119001) and fixed for 15 min in 1% formaldehyde. After cell lysis, chromatin was sheared in buffer containing 0.25% SDS with a Covaris S2 focused ultrasonicator. Antibodies used for ChIP experiments include H3K27ac  and H3K4me2 . Libraries were generated using the NEBNext Ultra II library preparation kit with 15 cycles of PCR amplification (NEB E7645) and sequenced to a depth of >40 million reads per sample, using two biological replicates. Peaks were called using CisGenome version 2.1.073. The read numbers were adjusted by library size and log2 transformed after adding a pseudo-count of 1. The differential analyses were performed using Limma version 3.1474; peaks with an FDR < 0.05 were considered to have differential enrichment.ChIP-Seq was performed on whole E9.25 (21-23 S) forelimbs pooled from 30-40 embryos as previously describedn = 3, n = 3). Posterior forelimbs from E10 (28–30 S) or E10.5 (35 S) were pooled from Shh−/− embryos . ATAC used components from the Nextera DNA Library Preparation Kit (Illumina) as described previously75 with the following variations. Limb buds were dissociated in 100μg/ml Liberase (Roche 05401119001) and lysed for 10 min. at 4 °C, rotating, in a nuclear permeabilization buffer  and then centrifuged for 10 min. at 300 g, 4 C. Nuclei were resuspended, counted using Trypan Blue, and 30,000 nuclei from each WT and Shh−/− sample were put into each transposase reaction. The transposase reaction (30 µL volume) was carried out for 1 h at 37 °C with gentle agitation. Libraries were generated using 11 cycles of PCR amplification with NEB high fidelity 2x master mix (New England Biolabs), cleaned up with SparQ PureMag beads (QuantaBio) and sequenced on an Illumina NovaSeq using PE150 or SR100 to a depth of at least 40 million reads. Peaks were called using MACS2 with a fixed window size of 200 bp and a q-value cutoff of 0.05. Read counts from the peak regions were obtained and differential analysis was performed using DESeq276. Peaks with FDR < 0.05 in the differential test were considered to have significant chromatin accessibility difference.WT E9.25 (21-23 S) forelimbs or posterior forelimbs from E10 (28–30 S) or E10.5 (35 S) forelimbs were dissected and pooled from WT embryos .Footprinting analysis was performed using HINT39 with the following modifications. 100,000 cells from E9.25 (21–23 S) forelimbs were dissociated, bound to Concanavalin beads and incubated overnight at 4 °C on a nutator with primary antibodies. Antibodies were used at the following concentrations: 1 μg/mL of H3K4me1 (Abcam #ab8895) and 5 μg/mL of H3K27me3 (abcam #ab195477). The following day, samples were incubated at room temperature for 30 min. with secondary antibody, 1:100 Donkey anti-rabbit (Jackson ImmunoResearch #711-005-152). CUT&Tag transposases used were gifted from the Henikoff lab and EpiCyphr . Libraries were generated using NEB high fidelity 2x master mix with 14 PCR cycles and cleaned up to remove adapters using SparQ PureMag beads (QuantaBio). Samples were sequenced on an Illumina NextSeq 500 instrument using PEx75 to a depth of depth of 3–5 million reads. Peaks were called using SEACR78.Experiments were performed as described previously79.Experiments were performed according to EpiCypher’s CUTANA CUT&RUN protocol (EpiCypher #15-1016) with the following modifications. 200,000–300,000 cells from E9.25 (21–23 S) forelimbs and anterior E10.5 (32–35 S) forelimbs were incubated overnight at 4 °C on a nutator with 1:250 FLAG primary antibody (Sigma #F3165), 1:100 HDAC1 (Abcam ab7028) or 1:200 HDAC2 (Abcam ab7029). The following day, samples were incubated at room temperature for 30 min. with secondary antibody, 1:100 Donkey anti-mouse (Jackson ImmunoResearch #715-035-150) or Donkey anti-rabbit (Jackson ImmunoResearch #711-005-152), followed by three washes in Digitonin wash buffer. CUTANA pAG-MNase was then incubated with samples for 10 min at room temperature and then the MNase reaction was performed for 2 h at 4 °C on a nutator. Libraries were generated using NEBNext Ultra II DNA Library Prep Kit with 14 PCR cycles and cleaned up to remove adapters using AMPure XP beads (Beckman Coulter) or SparQ PureMag beads (QuantaBio). Samples were sequenced on an Illumina NextSeq 500 instrument using PEx75 to a depth of depth of 3–5 million reads. Peaks were called using MACSSingle limb pairs were collected, saved in TRIzol and later pooled (~4 limb bud pairs/replicate) for each developmental stage, E9.25 (21–23 s) and E10.5 (33–35 S). After RNA extraction, libraries were prepped with NEBNext Ultra II RNA Library Prep Kit. Samples were sequenced on a NovaSeq S1, SR100, with a minimum of 40 million reads/sample. Reads were aligned with HISAT2 and DESeq2 was used for DEG analysis.N = the number of reads overlapping with any of the gene’s exons and M = the number of reads overlapping with the body of the gene. Limma version 3.1474 was used to identify differential intron retention rate between E9.25 and E10.5 Gli3−/− limbs, FDR cutoff< 0.05.The formula (1-N)/M was used to calculate the intron retention rate for each gene, where Shh−/− RNA-seq compared to WT limb buds7  (GSE58222 [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58645]). HH-responsive GBRs were intersected with poised enhancer modifications from H3K4me1 ChIP-seq (GSE86690), H3K4me2 ChIP-seq and E10.5 ATAC-seq datasets10 (GSE108880) from E10.5 WT limb buds. HH-responsive GBRs enriched for poised enhancers modifications, and thus increasing the probably of being active enhancers, were given a higher weight than ones without modifications. Genes identified as downregulated in Shh mutant limbs were intersected with H3K4me3 (GSE86698) E10.5 WT limb datasets. Genes enriched for these active promoter marks were given more weight. The weighted HH-responsive GBRs and weighted genes were then intersected based on proximity, up to 500 kb, and within the same topologically associated domain (TAD) (GSE96107). Genes were identified as a putative HH targets based on their proximity to HH-responsive GBRs and the number of HH-responsive GBRs, such that a gene with multiple HH-responsive GBRs, enriched for poised enhancer marks, would rank higher than a gene with HH-responsive GBRs found more distally. [https://github.com/Boksunni/Predicted-HH-List-Generation] [https://zenodo.org/badge/latestdoi/379668324].To identify GLI target genes, we developed a weighted system which incorporated HH-responsive GBRs, poised chromatin modifications, active promoter marks and genes identified as being significantly downregulated in E10.5 Individual pairs of 21–35 S limb buds were dissected and dissolved in Trizol. RNA was extracted from the samples using SuperScript IV VILO Master Mix with ezDNase Enzyme (Invitrogen #11766050). Primer sequences used:Gapdh F: GGTGAAGGTCGGTGTGAACG R: CTCGCTCCTGGAAGATGGTGGli1 F: CCCAGCTCGCTCCGCAAACA R: CTGCTGCGGCATGGCACTCTPtch1 F: GACCGGCCTTGCCTCAACCC R: CAGGGCGTGAGCGCTGACAAFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Supplementary Data FilesSupplementary Data 1-5Reporting Summary


Resistance to DNA damaging chemotherapies leads to cancer treatment failure and poor patient prognosis. We investigated how genomic distribution of accessible chromatin sites is altered during acquisition of cisplatin resistance using matched ovarian cell lines from high grade serous ovarian cancer (HGSOC) patients before and after becoming clinically resistant to platinum-based chemotherapy.Resistant lines show altered chromatin accessibility at intergenic regions, but less so at gene promoters. Clusters of cis-regulatory elements at these intergenic regions show chromatin changes that are associated with altered expression of linked genes, with enrichment for genes involved in the Fanconi anemia/BRCA DNA damage response pathway. Further, genome-wide distribution of platinum adducts associates with the chromatin changes observed and distinguish sensitive from resistant lines. In the resistant line, we observe fewer adducts around gene promoters and more adducts at intergenic regions.Chromatin changes at intergenic regulators of gene expression are associated with in vivo derived drug resistance and Pt-adduct distribution in patient-derived HGSOC drug resistance models.The online version contains supplementary material available at 10.1186/s13148-021-01105-6. Platinum-based chemotherapeutics, such as cisplatin and carboplatin, are clinically important first line therapies in the treatment of a wide variety of solid cancers , 2. ThesEpigenetic mechanisms play a key role in the development of platinum-resistance and drug tolerance . Cells sUsing matched chemosensitive and chemoresistant ovarian cancer cell lines isolated from high-grade serous ovarian cancer patients before and following treatment, we examined the relationship between chromatin accessibility, platinum-DNA adduct distribution, and chemotherapy resistance. To examine further the potential functional significance of any changes observed, we have correlated chromatin changes, identified using ATAC-seq, between the lines at gene promoters, CpG islands, clusters of cis-regulatory elements (COREs) and other genomic regions with changes in gene expression.The platinum atom of cisplatin can form covalent bonds to the N7 positions of purine bases and the density of GG dinucleotides is considered the main factor influencing distribution of platinum adducts . HoweverWe developed Pt-exo-seq to map platinum adducts, genome-wide by adapting methods for exonuclease mapping of transcription factor binding sites -21. Pt-eWe studied chromatin conformation by ATAC-seq in three isogenic pairs of sensitive and resistant human ovarian cell lines. PEO1 and PEO4 were both isolated from the same patient diagnosed with high-grade serous ovarian cancer (HGSOC) following platinum-based chemotherapy, but before and after the clinical development of resistance . PEA1 ant test FDR < 0.001. Substantial alterations to the landscape of chromatin accessibility were found in all three resistant lines compared to their sensitive counterparts, affecting 297 windows (0.009%) of the genome in the A2780 pair . FPKM values  were calculated for each window, in each replicate. Individual windows were called differentially accessible based on a log2 fold change in coverage in the sensitive-to-resistant lines > ±2 and moderated Thus, the resistant HGSOC tumor cells isolated at time of recurrence with non-responsive disease show similarity, suggesting common chromatin changes occurring during the acquisition of resistance in vivo. The HGSOC lines were in turn different in their chromatin accessibility to the non-HGSOC, in vitro resistance derived, A2780-CP70 pair, potentially reflecting their different origin and in vitro method of drug selection.Each of the windows showing differential accessibility between sensitive and resistant lines by ATAC-seq was annotated by genomic class using HOMER . Those tIn all three sensitive/resistant pairs, there was under-representation of promoter-TSS, CpG island and exonic windows in the sets of differentially accessible windows, suggesting fewer chromatin changes at these genomic regions during the acquisition of resistance Fig. . In all t test FDR < 0.05. The summary of differentially expressed (DE) genes and their direction of change for each pair of cell lines is shown in Additional file PARP9, SPHK1, DDX60L and BCAM, while only 5 genes were consistently downregulated CCDC80, TLE4, THBS1, MAP1A and VEPH1. Using alternative methods of analysis  only THBS1 was consistently defined as downregulated using all methods. We confirmed the differential expression of THBS1 and PARP9 using RT-QPCR  . ATAC-seq data at example genes upregulated or downregulated are shown in Additional file In order to relate gene expression with chromatin accessibility, ATAC-seq coverage was measured in the 2 Kb region spanning the transcription start site (TSS) of the genes defined as differentially expressed using rank product analysis in each cell line pair  Fig. a, b. GenThus, these trends are consistent with an accessible promoter being associated with a permissive state for gene transcription and an inaccessible promoter with transcriptional silencing. However, relatively few changes in chromatin accessibility were observed in the resistant lines at promoter regions Fig.  and thoshttps://www.biorxiv.org/content/10.1101/222562v1). The results of this are summarized in Additional file The differences detected in chromatin conformation between sensitive and resistant lines frequently occurred at intergenic regions. Given the minimal association between chromatin accessibility changes at gene promoters and altered gene expression, we asked how changes to chromatin conformation at intergenic regions associated with transcriptional changes. The algorithm CREAM  was used to define clusters of cis-regulatory elements (COREs) in each of the lines, based on peaks called by MACS2 from the merged alignment file produced from the 3 ATAC-seq replicates from each cell line  and a strong enrichment for differentially expressed genes near COREs gained . In contrast, there was significant enrichment for differentially expressed genes near COREs lost in PEO4  and to those gained . While there was no significant enrichment for differentially expressed genes near COREs which were gained in CP70 from A2780, similar to PEO4, there was a 2.9-fold enrichment for differentially expressed genes near COREs lost in CP70 from A2780 .We further tested the association between loss or gain of COREs by testing for enrichment of differentially expressed genes in the 100 Kb surrounding lost/gained COREs. Figure We further explored the chromatin accessibility data in the PEO pair as a representative case to confirm that the lost/gained COREs identified in this analysis could be associated with known enhancers. We obtained published data on chromatin states in the PEO1 cell line  calculatp< 0.05, OR 4.8, CI 1.02–16.0, Fisher’s exact test), highlighting a potential mechanism through which altered CORE landscapes may drive the acquisition of resistance to DNA damaging agents. Furthermore, SOX9 has been recently identified as maintaining chemoresistance in ovarian cancer cells through altered accessibility at its associated enhancer [Thus, the resistant line in all three pairs show enrichment of differential expression of genes near COREs with altered chromatin accessibility, and that COREs are associated with cell line specific enhancer elements defined by chromatin states. Importantly, within the set of genes near COREs gained in PEO4, we observe enrichment for genes involved in the Fanconi anemia/BRCA DNA damage response (DDR) pathway  that areenhancer , was amoWe examined in more detail the expression of the genes included in the BIOCARTA_ATRBRCA curated list of genes involved in BRCA related cancer susceptibility. As shown in Fig. To analyse sites of platinum adducts at high resolution, we have adapted methods for exonuclease mapping of transcription factor binding sites and developed Pt-exo-seq , 21. A sCisplatin forms covalent bonds to the N7 positions of purine bases leading to DNA cross-links. The majority of intrastrand cross-links are 1,2-d(GpG) cross-links, followed by a small number of 1,2-d(ApG) cross-links . We woult test FDR < 0.05. These were then separated into two groups based on direction of change in damage in PEO4 compared to PEO1; increased or decreased frequency of DNA adducts. Hierarchical clustering was then performed on the normalized read counts, using these windows, which separated the PEO1 16 µM replicates from the PEO4 replicates treated at 16 µM and 32 µM, with the PEO4 replicates clustering by dose , and doses inducing equal amounts of damage (16 µM vs 32 µM respectively). Mean Pt-exo-seq signal was calculated for each window, across three replicates for each line and treatment condition, and used to calculate a log2FC in damage, after taking account of the untreated controls, between PEO1 and the 32 µM treated PEO4 cells. We found 1170 1 Kb windows showing a log2FC > ± 2 and moderated Thus, even though cisplatin is a relatively non-specific DNA damaging agent and might be expected to induce adducts at purine dinucleotides throughout the genome, there are clear and consistent differences in distribution of Pt-adducts between these matched sensitive and resistant ovarian cell lines and this is independent of the overall level of adducts formed.p < 0.001, t-test) .Chromatin accessibility was investigated in relation to cisplatin-DNA adduct formation. ATAC-seq coverage was calculated for each of the 1 Kb windows previously defined by Pt-exo-seq as having different levels of Pt-DNA adducts between PEO1 16 µM and PEO4 32 µM replicates. Thus, we aimed to assess changes in chromatin accessibility between PEO1 and PEO4 at regions that showed differential DNA Pt-adduct levels. Regions incurring fewer Pt-adducts in PEO4 showed a significant reduction in accessibility as measured by ATAC-seq in PEO4 compared to PEO1 (st) Fig. b. SurpriOdds ratios were then calculated for enrichment of each class of genomic element in the sets of regions showing increased or decreased damage in the 32 µM treated PEO4 samples compared to PEO1 Fig. c. SignifTo examine this further, we compared Pt-exo-seq data at COREs shared between PEO1 and PEO4, those only in PEO1  and those only in PEO4 (i.e. gained in PEO4)    Fig. c. The sast) Fig. d. These st) Fig. e, suggesEREG, implicated in stimulation of cell proliferation [CCL5 in the interferon gamma gene set, which has been shown to promote invasion and migration of ovarian cancer cells [Cytotoxic, DNA targeting, chemotherapies, such as platinum based chemotherapy, have major impact on improving patient survival and are still the first line of treatment for many cancers. However, drug resistance is a major clinical problem leading to poor survival for patients. A diverse range of drug resistance mechanisms have been observed experimentally . Howeverferation  and CCL5er cells . HoweverOur data argue that epigenetic changes at COREs during development of drug resistance is a common underlying mechanism that can drive expression changes in pathways such as those involved in homologous recombination repair leading to drug resistance. These findings reflect the genome wide chromatin reorganization that underlies resistance to endocrine therapies in breast cancer, or the emergence of repressed chromatin states at intergenic, repetitive regions in non-small lung cancer cells exposed to lethal concentrations of EGFR kinase inhibitors, as well as cisplatin , 35. SucIntegrative genome-wide epigenomic and transcriptomic analyses of in vitro derived platinum-sensitive and platinum–resistant ovarian lines identified key distal enhancers associated with platinum resistance and identified SOX9 as a critical CORE regulated transcription factor that plays a critical role in chemoresistance in vitro of ovarian cancer cell lines . We alsoTHBS1 was the only gene consistently defined as downregulated in resistant lines using all methods of analysis of the RNAseq data. THBS1 is an important component of the extra-cellular matrix and has an important role in cancer development and regulating tumor cell behaviour . AlthougIn order to explore further the influence of chromatin organization in cisplatin resistance we developed and applied Pt-exo-seq, a method for mapping cisplatin adducts genome-wide at base pair resolution. Genome-wide adduct formation distinguished the resistant PEO4 line from its isogenic-sensitive counterpart, and was associated with the altered chromatin landscape we detected, highlighting altered adduct formation and distribution as a defining characteristic of these resistant ovarian cancer cells. Regions of the genome with fewer observed adducts in the resistant line were primarily around gene promoters, while regions with increased adducts were enriched for intergenic loci, and included COREs which were reprogrammed in the resistant lines.A limitation of the Pt-exo-seq study is that cells were treated for 5 h to allow platinum cross-links to form before DNA isolation for Pt-exo-seq and some DNA repair of the mono- or cross-linked adduct is likely to have occurred in this time. Transcription-coupled nucleotide excision repair may be responsible for the reduced persistence of adducts near gene promoters; a view supported by the difference in adduct formation at transcription start sites (TSS) of genes showing equally high expression in PEO1 and PEO4 . Duplicate reads were removed using the MarkDuplicates tool from the Picard tools suite (https://github.com/broadinstitute/picard). Reads were then aligned to the genome using Bowtie2 [Chromatin was extracted and digested as described . Librari Bowtie2  with the Bowtie2 . Fragmen Bowtie2 .Libraries were prepared using the NEBNext Ultra Directional RNA Library Prep Kit for Illumina (NEB), according to the manufacturer’s instructions. Using the featurecounts function from the RSubread package in R , alignedLibraries for Pt-exo-seq were produced using a novel protocol for detecting platinum damage genome wide adapted from , 21. Folhttp://broadinstitute.github.io/picard/). The filtered, deduplicated reads were used in analyses which involved clustering the samples based on chromatin accessibility profiles.Counts of ATAC-seq reads in genomic windows were calculated using the featurecounts function from the RSubread package . RPKM vap values for changes in accessibility for each window. Windows were annotated according to their nearest genomic feature using HOMER [ATAC-seq reads were assigned to 1 Kb windows and these were filtered by requiring a depth > 1 RPKM and < 1 SD above the median depth for all windows analysed. Coverage was normalized using the voom method to estimate the mean-variance relationship of the log of these counts. This generated a precision weight for each window which could be analyzed using the limma Empirical Bayes analysis pipeline  in orderng HOMER . Enrichmt test.Coverage around the TSS’ of genes was calculated in the same way as for the sliding window approach described above, except windows were 2 Kb centered around the TSS of genes of interest. Once FPKM values had been calculated, they were compared between cell lines using Students https://www.biorxiv.org/content/early/2017/11/21/222562), which calls clusters of cis-regulatory elements based on the genomic distribution of peaks. Genes within 100 Kb of called COREs were extracted from the Ensembl database using biomaRt. T statistics and odds ratios for differential expression of these genes were calculated from the RNA-seq data produced in this study.ATAC-seq peaks were called in each ATAC-seq replicate using MACS2. These peaks were then used as input for the CREAM package  in the read. Windows were filtered by intersection with the regions passing filters in the ATAC-seq data for the PEO pair . Differentially damaged windows were defined as having a Log2 fold change in Pt-exo-seq signal:background ratio > ± 2 and an FDR corrected ng HOMER . Enrichmt test.PEO4 cells were exposed to 20 µM Vorinostat for 24 h, nuclei isolated and incubated in micrococcal nuclease at a final concentration of 0.02 units/µl, in MNase buffer (New England Biolabs) at 37 °C for 10 min before purification using the MinElute PCR cleanup kit (Qiagen). Isolated DNA was run on TapeStation High Sensitivity DNA tape (Agilent). The relative area of each peak was then used to calculate enrichment for mono-, di- and tri-nucleosome associated fragments. Differences in peak area were tested using students Cells were treated with cisplatin for 5 h, before DNA was extracted. Platinum adduct levels were measured by ICP-MS using an Agilent 7900 mass spectrometer equipped with a micromist nebulizer and a cooled spray chamber . The assay was calibrated using platinum standards  and quality control samples with known platinum concentrations . Platinum adducts were measured as previously described  in no-gaAdditional file 1. Supplementary Figures and Tables.


Thermococcus kodakarensis, we genetically alter the chromatin landscape and quantify the resultant changes in gene expression, including unanticipated and significant impacts on provirus transcription. Global transcriptome changes resultant from varying chromatin landscapes reveal the regulatory importance of higher-order histone-based chromatin architectures in regulating archaeal gene expression.Histone proteins compact and organize DNA resulting in a dynamic chromatin architecture impacting DNA accessibility and ultimately gene expression. Eukaryotic chromatin landscapes are structured through histone protein variants, epigenetic marks, the activities of chromatin-remodeling complexes, and post-translational modification of histone proteins. In most Archaea, histone-based chromatin structure is dominated by the helical polymerization of histone proteins wrapping DNA into a repetitive and closely gyred configuration. The formation of the archaeal-histone chromatin-superhelix is a regulatory force of adaptive gene expression and is likely critical for regulation of gene expression in all histone-encoding Archaea. Single amino acid substitutions in archaeal histones that block formation of tightly packed chromatin structures have profound effects on cellular fitness, but the underlying gene expression changes resultant from an altered chromatin landscape have not been resolved. Using the model organism  Most Archaea encode histone proteins to organize their DNA into a protein:DNA complex known as chromatin . The orgThe polymerization of histone dimers in Archaea does not resemble the nucleosome-nucleosome interactions observed in eukaryotes . Alignmearchaeal histone-based chromatin polymers (AHCPs) to denote histone:DNA superstructures wherein each additional histone dimer can wrap ∼30 bp of DNA to extend the AHCP  and Histone B (HTkB), but strains encoding only a single histone variant are viable , only HTkA with a single amino acid substitution G17D , or retain HTkB in WT form and express HTkAG17D . Substitution of just a single residue, G17, is sufficient to disrupt AHCP formation in vivo beyond ∼90 bp by inhibiting the close association of adjacent gyres of AHCPs . Strain TS622 was constructed from TS600 by allelic substitution of TK1413 (HTkA) for TK1413G17D (HTkAG17D). TS620 was constructed by markerless deletion of TK2289 (HTkB) from TS622. Allelic substitution of TK1413 for TK1413G17D was confirmed by PCR amplification of the TK1413 loci and subsequent sequencing while deletion of TK2289 was confirmed via diagnostic PCR with primers flanking TK2289. Cultures were grown at 85°C in artificial seawater (ASW) supplemented with 0.5% (w/v) tryptone, 0.5% (w/v) yeast extract (ASW-YT), trace mineral solution and vitamin mixture (nutrient rich medium). ASW contains, per l, 20 g NaCl, 3 g MgCl2⋅6H2O, 6 g MgSO4⋅7H2O, 1 g (NH4)2SO4, 200 mg NaHCO3, 300 mg CaCl2⋅2H2O, 0.5 g KCl, 420 mg KH2PO4, 50 mg NaBr, 20 mg SrCl2⋅6H2O, and 10 mg Fe(NH4)2(SO4)2⋅6H2O. The trace mineral solution  contains, per l, 0.5 g MnSO4 6H2O, 0.1 g CoCl2⋅6H2O, 0.1 g ZnSO4⋅7H2O, 0.01 g CuSO4⋅5H2O, 0.01 g AlK(SO4)2⋅12H2O, 0.01 g H3BO3, and 0.01 g Na2MoO4⋅2H2O. The vitamin mixture (200×) contains, per l, 0.2 g niacin, 0.08 g biotin, 0.2 g pantothenate, 0.2 g lipoic acid, 0.08 g folic acid, 0.2 g p-aminobenzoic acid, 0.2 g thiamine, 0.2 g riboflavin, 0.2 g pyridoxine, and 0.2 g cobalamin. When present, sodium pyruvate was added at 5 g per l, agmatine sulfate to 1 mM and 6- methyl purine (6MP) to 100 μM (ASW-YT-Pyr). Elemental sulfur (S∘) was added at 2 g per l in liquid media (ASW-YT-) but was replaced by polysulfides in solid media. Polysulfide solution (500×) contained, per l, 66.7 g sodium sulfide (Na2S⋅9H2O) and 3 g sulfur. Gelrite was added to 1% (w/v) to solidify media.escribed . While Tcessible . Thus, tg and immediately frozen at −80°C. Cell pellets were resuspended in 1.0 mL of MNase buffer  per 0.2 g of cell mass and ground to homogeneity with a mortar and pestle. Homogenized cells were mechanically lysed by repeated liquid nitrogen freezing and subsequent grinding five times. Whole cell lysate was gently clarified at 1,700 × g for 5 min and the chromatin containing clarified lysate was RNase A digested  for 1 h at 37°C. ∼1,500 U of micrococcal nuclease (New England Biolabs) was added to chromatin and aliquots (∼100 μl) of digested DNAs were extracted by the addition of 300 μl of 10 mM Tris-HCl pH 8.0 and 400 μl of phenol/chloroform/isoamyl alcohol (25:24:1). Following thorough emulsion and centrifugation at 10,000 × g for 5 min, ∼200 μL of the DNA-containing aqueous layer was precipitated by the addition of an equal volume of 1 M Tris-HCl pH 8.0 and 2.6 volumes of 100% ethanol proceeding a 1-h incubation at −80°C. DNAs were pelleted in a 4°C centrifuge at 10,000 × g for 30 min and subsequently resolved in a 4% agarose gel.Chromatin isolation and micrococcal nuclease digestions were adapted from g at 4°C for 15 min yielding an RNA-containing aqueous layer which was added to 500 μl of isopropanol and incubated at room temperature for 10 min. Centrifugation at 10,000 × g for 15 min at 4°C produced an RNA pellet that was washed with 1 ml of 75% ethanol and subsequently resuspended in 88 μl of RNase-free H2O, 10 μl of DNaseI buffer, and 1 μl of DNaseI (New England Biolabs) to digest residual DNA (37°C for 30 min). Replicate samples were prepared identically.TS600 and TS622 were grown in triplicate to an optical density (measured at 600 nm) of ∼0.5 in liquid ASW-YT-S and used to inoculate (1:100) 300 ml of ASW-YT-Pyr per strain and allowed to grow to an optical density (measured at 600 nm) of ∼0.05 to encourage chromatin reprograming. Cultures were rapidly chilled and pelleted at 8,000 × g for 5 min and then resuspended in 1.0 mL of Trizol (Invitrogen) with a 10-min incubation at room temperature. A total of 200 μl of chloroform was added followed by centrifugation at 10,000 × ® rRNA Depletion Kit (E6310), cDNA libraries were constructed with New England Biolabs NEBNext® UltraTM Directional RNA Library Prep kit for Illumina® (E7420s) and NEBNext® Multiplex Oligos for Illumina®  according to the manufacturer’s procedure. The multiplexed libraries were sequenced at Cofactor Genomics using one high output NextSeq Illumina® run for single-end reads with a minimum read length of 75 bp and with a requested >40 million reads per sample.Using ∼120 ng of RNA that was depleted of rRNA, following the NEBNextTM Magnetic Kit), library construction (NEBNext® UltraTM RNA Library Prep kit) and 150 bp paired-end sequencing on a HiSeq Illumina® platform.A total of 1.5 μg of RNA was processed at Novogene for Prokaryotic RNA-seq, specifically for rRNA depletion (Ribo-ZeroT. kodakarensis (KOD1) reference genome using bowtie  reference genome using bowtie2 . Pacific Biosciences (PacBio) libraries were constructed following the Pacific Biosciences Template Preparation and Sequencing Protocol. The library was then sequenced on a PacBio Sequel instrument using Polymerase 3.0 Chemistry and diffusion loading for 600 min. The data was analyzed using PacBio SMRT Analysis tools.T. kodakarensis strains that encode histone variants known to impact AHCP length. While strains retaining only a single histone isoform are viable, reducing the concentration of total histone proteins . Modification of TK1413 to generate a G17D variant of HTkA in an otherwise native background, including the presense of WT HTkB, generated strain TS622. TS620 combines both genetic modifications, thereby generating a strain supported by only the synthesis of a single, G17D HTkA variant. Deletion of TK2289, encoding HTkB, was confirmed in TS600 and TS620 by PCR amplification of the flanking genomic regions and sequencing to confirm the exact desired endpoints of the genomic modification  digestion. MNase digestion provides a rapid, genome-wide mechanism to define minimal units of chromatin - such as the nucleosome in Eukarya. Digestion of chromatin isolated from TS600 confirmed the previously observed wildtype AHCP protection patterning – a prominent ∼60 bp DNA fragment along with larger DNA fragments in increasing ∼30 bp increments (up to ∼300 bp) – indicative of varied-length AHCPs  Figures. The perG17-encoding strains, suggesting histone dimers form tetramers, protecting ∼60 bp of DNA, and an additional dimer interacts to form a hexamer, protecting ∼90 bp of DNA, but that larger associations of histone dimers are restricted due to clashes between adjacent gyres of AHCPs and loss of potential electrostatic interactions across the adjacent gyres , the chromatin from strain TS620 displayed a dramatically different MNase protection pattern . Discretnt gyres . Thus, aG17D did not interfere with superhelix formation of the HTkB isoform. Given that chromatin from strain TS620 demonstrates HTkAG17D alone cannot make up the superhelix, it is likely that all the larger regions of DNA protection in strain TS622 result from superstructures formed entirely of HTkB. It is possible that at least some of the smaller (60–90 bp) protected DNA fragments result from chromatin structures composed entirely of HTkAG17D or from HTkB/HtkAG17D heteromers; additionally, it is possible HTkAG17D may cap or terminate the superhelices composed of HTkB resulting in protection of >90 bp fragments  and TS620  provides a quantitative measure of the regulation normally afforded by AHCPs in T. kodakarensis  highlights the impact of histone variants on AHCPs that can regularly form with a mixture of HTkB and HTkAG17D (2-average fold change (Log2FC) against log-average counts per million (LogCPM)  . The T. nscripts . Our seqG17D (TS620) for HTkAWT (TS600) as the only histone in T. kodakarensis resulted in significant (> 2-fold) changes in the steady-state abundance of 234 genes, representing ∼11% of the entire transcriptome. The lack of extended AHCPs in TS620 resulted in increased abundance of 100 genes and decreased abundance of another 134 genes , TK1960 (increased ∼5.8 fold) and TK1961 (increased ∼4.8 fold) in strain TS620. The likely increased abundance of functional RPA proteins suggests inhibiting extended AHCPs may permit some DNA regions to locally unwind or melt, thereby requiring more RPA to protect the increased abundance of single-stranded DNA  and chemotaxis proteins (che operon) appear particularly sensitive to AHCPs and are likely dependent on AHCPs for proper regulation (T. kodakarensis archaellum-encoding fla operon (TK0038-TK0049) and chemotaxis-encoding che operon (TK0629-TK0639) were ∼7.1–65-fold and ∼2.7–10-fold less abundant in strain TS620 compared to strain TS600  and TS600 , we noted only approximately half as many transcripts (49) of decreased abundance in TS622, while just 18 transcripts were enriched , TKV2 (TK0381-TK0421), TKV3 (TK0575-TK0614), and TKV4 (TK1342-TK1378)  . Compariy stable , 2017. Tkarensis . An obse regions . Several regions  (TK0582) regions . Most inThe observed complete absence of these TKV2 transcripts prompted further evaluation of the genome of TS620 . Whole-gT. kodakarensis and the regulation afforded by AHCP structures likely extends to most histone-encoding Archaea (Histone proteins encoded in most Archaea are the primary proteins responsible for genome organization . Despite Archaea .T. kodakarensis strains that can (TS600 and TS622) and cannot generate extended AHCPs (TS620) reveal significant differences most likely associated directly with altered chromatin landscapes. The impacts are often dramatic, and the regulation is not associated with isolated chromosomal regions but instead is noted genome wide. In strain TS620, where formation of stable and detectable (via MNase digestions) AHCPs was inhibited, >11% of the transcriptome was significantly altered. Approximately equal numbers of transcripts were more or less abundant due to the loss of AHCPs, suggesting that AHCPs can both positively and negatively impact gene expression, depending on the loci. Expression of the che- and fla-operons, as well as many proviral regions were the most dramatically affected by changes in the AHCP formation. In support of AHCP length influencing gene expression in these operons/proviral regions, expression profiles of T. kodakarensis strains encoding WT versions of a single histone isoform displayed no significant differences in the expression of these same operons  was the loss of a portion of TKV2 from the genome. Like many proviral integrations in archaeal genomes, all four proviral regions in the  regions . The com regions . Althougdvantage .The retention of histone proteins in most archaeal clades suggest histone-based chromatin structures provide beneficial regulatory roles that are exploited to provide a level of regulation on gene expression. The varied lengths of AHCPs, the presence of multiple histone isoforms in many Archaea, and the known changes in histone isoform expression in response to environmental changes all present routes to activate, repress and fine-tune gene expression to maximize growth in changing environments . The potThe mechanisms controlling the formation of AHCPs with varying lengths at different loci and in different sequence contexts likely plays an additional role in transcription regulation  and may https://www.ncbi.nlm.nig.gov and can be accessed with accession number GSE151920.The datasets presented in this study have been deposited in the NIH GEO repository at T. kodakarensis strains used in this work. TSd purified proteins, DNAs and RNAs, performed MNase digestions, generated RNA-seq libraries, analyzed data, built structural models, prepared figures, and helped write the manuscript. FU and AB-H analyzed RNA-sequencing datasets, prepared figures and helped write the manuscript. AGe and AGa prepared and sequenced DNA sequencing libraries, analyzed whole genome sequencing results, prepared figures, and helped write the manuscript. TSd and TSt prepared the initial manuscript that was edited, improved, and ultimately approved by all authors. All authors contributed to the article and approved the submitted version.TSd, SF, RV, BB, and TSt designed, constructed, and confirmed the local genotypes of AGe and AGa are employed and funded by New England Biolabs, Inc. New England Biolabs is a manufacturer and vendor of molecular biology reagents, including DNA replication and repair enzymes. This affiliation does not affect the authors’ impartiality, objectivity of data generation or its interpretation, adherence to journal standards and policies or availability of data. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Cancerous tumours contain a rare subset of cells with stem‐like properties that are termed cancer stem cells (CSCs). CSCs are defined by their ability to divide both symmetrically and asymmetrically, to initiate new tumour growth and to tolerate the foreign niches required for metastatic dissemination. Accumulating evidence suggests that tumours arise from cells with stem‐like properties, the generation of CSCs is therefore likely to be an initiatory event in carcinogenesis. Furthermore, CSCs in established tumours exist in a dynamic and plastic state, with nonstem tumour cells thought to be capable of de‐differentiation to CSCs. The regulation of the CSC state both during tumour initiation and within established tumours is a desirable therapeutic target and is mediated by epigenetic factors. In this review, we will explore the epigenetic parallels between induced pluripotency and the generation of CSCs, and discuss how the epigenetic regulation of CSCs opens up novel opportunities for therapeutic intervention. AMLacute myeloid leukaemiaATPadenosine triphosphateBAFBRG1‐ or BRM‐associated factorsBERbase‐excision repairBETbromodomain and extra terminalBRDbromodomainCIMPCpG island methylator phenotypeCSCcancer stem cellDNAdeoxyribose nucleic acidDNMTDNA methyltransferaseEMTepithelial‐mesenchymal transitionESCembryonic stem cellFACsfluorescence‐activated cell sortingGBMglioblastoma multiformeHDAChistone deacetylaseIL6/17interleukin 6/17iPSCinduced pluripotent stem celllncRNAlong noncoding RNALSD1lysine‐specific demethylase 1METmesenchymal‐epithelial transitionmiRNA/miRmicroRNAncRNAnoncoding RNAPDACpancreatic ductal adenocarcinomaPRCpolycomb repressive complexRISCRNA‐induced silencing complexRNAribose nucleic acidSAMS‐adenosyl methionineSWI/SNFSWItch/sucrose nonfermentableTAMtumour‐associated macrophageTETten‐eleven transcription factorTGFtransforming growth factorTMEtumour microenvironmentVEGFvascular endothelial growth factor1As with adult tissues, cancerous tumours also contain a rare subset of cells with stem‐like properties that can function to regenerate the heterogeneous cell populations observed therein. These cancer stem cells (CSCs) are defined by their ability to divide both symmetrically and asymmetrically, to initiate new tumour growth and to tolerate the foreign niches required for metastatic dissemination. As the tumour‐initiating population, CSCs underpin the very nature of malignancy and studying their regulation is essential for understanding tumour formation, metastasis and relapse after therapy.As it is not possible to isolate CSCs based on functional properties, CSC identification can be achieved by FACs sorting based on surrogate cell surface marker profiles and subsequent transplantation into immune‐compromised mice to demonstrate enhanced tumourigenic potential. Using this strategy, CSCs have been identified in most cancers, first in acute myeloid leukaemia (AML) followed by breast cancer and other solid malignancies such as brain, colon and pancreatic cancer, and are purported to account for only a few per cent of the total cell population.Epigenetics refers to a number of mechanisms that control the reversible regulation of gene expression by changing the chromosome without altering the DNA sequence: DNA can be altered epigenetically by methylation and demethylation of CpG nucleotides. Epigenetic changes in the overall structure of chromatin occur through at least three interrelated mechanisms: posttranslational modifications of histones, ATP‐dependent chromatin remodelling and the incorporation (or replacement) of specialised histone variants into chromatin. Finally, noncoding RNA can interact with transcriptional processes to alter gene expression. In addition to 2D processes, epigenetic regulation can also involve higher‐order chromatin organisation including promoter‐enhancer interactions, regulatory DNA loops and 3D chromatin localisation in the cell nucleus Figure .As these epigenetic mechanisms are important mediators of cellular identity, we will explore how the restructuring of such epigenetic barriers reinforces the stem‐like state in both normal cells and cancer, and their relevance to tumour initiation Figure . Further2DNA methylation is a form of gene silencing that occurs mainly to CpG dinucleotides which cluster in CpG islands; areas of high CpG density usually found at promoters. To methylate cytosines, DNA methyltransferases (DNMTs) catalyse the transfer of a methyl group from cofactor S‐adenosylmethionine to the carbon of the cytosine ring to generate 5‐methylcytosine (5mC). This functions to inhibit gene expression either by recruitment of methyl‐CpG‐binding domain proteins which in turn recruit histone‐modifying and chromatin‐remodelling complexes, or by preventing the recruitment of DNA‐binding proteins, that is, transcription factors. To reactivate expression after silencing, 5‐mCs can be oxidised to 5‐hydroxymethylcytosine (5hmC) and back to the unmodified state by TET proteins and base excision repair (BER), to ultimately restore the unmethylated cytosine. TET protein‐mediated demethylation can occur via either passive or active processes. Passive demethylation results from the failure to maintain 5mC marks across cell divisions (5hmC is not a substrate for DNMTs). Active demethylation is enzymatic, whereby TET proteins further oxidise 5hmC marks to 5‐formylcytosine (5fC) and 5‐carboxylcytosine (5caC) then back to the unmodified state. 5fC bases are recognised by thymine‐DNA‐glycosylases (TDG) that excise mismatched pyrimidines that would then be replaced by an unmodified cytosine by the BER pathway. Intermediate marks (5fC or 5caC) are much less abundant than 5hmC but may also have independent functionsMethylation patterns are altered in cancer; localised hypermethylation occurs in CpG islands of the promoters of tumour suppressor genes , silencing their expression.In general, CpG methylation is required for differentiation whereas demethylation is essential for induction of the pluripotent state.Some hypomethylation also continues to be associated with CSCs once the tumour has been established. For example, the CpG region of the CD133 gene promoter, a key gene involved in CSCs, was shown to be hypomethylated in several cancer types including breast, ovarian, colorectal and glioblastoma.However, opposing roles for DMNT1 have been demonstrated as it can both promote or inhibit CSC formation. For example, DNMT1 was required for the initiation of colon cancer.The importance of both hypomethylation and DNMT1 in CSCs suggests that specific localization of 5mC and 5hmC marks is likely to be a more important determinant of cellular identity than the overall global patterning. In particular, hypomethylation at pluripotency loci and hypermethylation at tumour suppressor genes or those involved in differentiation is important in the generation of CSCs.3The amino acid residues located on the N‐ and C‐terminal tails of histones can be modified to influence gene expression, including by acetylation, methylation and ubiquitylation. When occurring on promoters or enhancer regions, these modifications confer chromatin states that affect gene expression by altering the ability of protein complexes to bind. Chromatin formation that permits protein binding and gene expression is known as euchromatin, and repressive is known as heterochromatin. These states are mediated by the Trithorax group (TrxG) proteins and Polycomb group (PcG) proteins, respectively. Modifications are named by the histone type  followed by the amino acid and modification, for example, K4 me3 (trimethylation on lysine 4). The most studied modifications are those that occur to histone 3 found primarily at active enhancers , promoters (H3K4me3) and within the bodies of actively transcribed genes (H3K36me3).Alterations of the histone modification landscape are widely associated with SCs with approximately one‐third of the genome differing in chromatin structure between differentiated cells and ESCs.Although there are many parallels between PSCs and CSCs, histone‐modifying enzymes can have tumour‐specific effects. The subunit of the Polycomb group complex 2 (PRC2) that catalyses H3K27me3 is the histone methyltransferase EZH2 (enhancer of zeste homologue 2). EZH2 inhibition has been shown to be sufficient for tumourigenesis and is associated with the generation of CSCs in some cancers.As well as methylation, other modifications have shown importance in CSCs. The BMI1 subunit of the polycomb repressive complex 1 (PRC1), which monoubiquitinates histone H2A on lysine 119 (H2AK119Ub1) was able to promote CSCs in leukaemia and GBM.Nearly all the genes involved in ESC identity, including KLF4, Sox2, Oct4 and Nanog, are regulated by super‐enhancers; large genomic regions with very high levels of transcription factors that are highly important in controlling pluripotency and differentiation.Chromatin at promoters and enhancers can be classed as active, repressed or poised. Poised regions are bivalent in terms of histone modifications as they contain both H3K27me (repressive) and H3K4me (activating) histone marks. In ESCs, the majority of bivalency occurs in the promoters of transcription factors and half of bivalent domains have binding sites for pluripotency transcription factors.4The basic unit of chromatin is the nucleosome, which is formed of 147 bp of DNA wrapped around eight histones, two each of histones H2A, H2B, H3 and H4. These appear like beads on a string of chromatin. Gene expression is regulated at the level of chromatin structure in an ATP‐dependant process by chromatin modifiers which act to remove or slide assembled nucleosomes along with the DNA, and can also exchange histone H2A‐H2B dimers with dimers of histone variants. The presence of nucleosomes naturally represses gene expression by preventing the access of transcription factors. The absence or loss of nucleosomes at a transcription start site (nucleosome‐free region [NFR]) allows for assembly of the transcription machinery and rapid activation of gene expression.Sox2 and Oct4, and promote differentiation to mesoderm, but not ectoderm.The chromatin architecture of SCs differs greatly from that of differentiated cells. While small‐scale chromatin changes at DNA regulatory elements occur during transcriptional regulation in most cells, extensive remodelling of chromatin structure is required for cellular differentiation during embryonic development.SNF5 have been identified in epithelioid sarcoma and renal medullary carcinoma.As nucleosome repositioning is required for the generation of the pluripotent state it may be important for the formation of CSCs. This may occur via aberrant functions of ATP‐dependent chromatin remodelers, which has also been linked to cancer, especially in terms of the SWI/SNF complex, which is frequently found mutated.ATP‐dependent chromatin remodelling complexes can also regulate transcription via incorporation of histone variants into nucleosomes.High mobility and exchange of histone variants is a key feature of SCs and this dynamism is thought to contribute to SC plasticity.These findings suggest that the overexpression of labile histone variants such H2A.Z (and in some cases H3.3), coupled with the reduction of stable histone variants such as macroH2A could contribute to cancer cell plasticity and the generation of CSCs.4.1Noncoding RNAs (ncRNA) are transcribed from regions of the genome that do not encode for proteins. The resulting RNA transcripts function to regulate the expression of protein‐coding genes and are therefore essential for control of cellular function and identity. ncRNAs can be divided into two major groups based on their size: small ncRNAs being 200 nucleotides or less, and long ncRNAs (lncRNAs) 200 nucleotides or more. Small ncRNAs can be further subcategorized based on length, function and subcellular localization and include microRNAs (miRNAs) and short interfering RNAs (siRNAs) amongst others.4.2LncRNAs can physically associate with DNA or proteins to either promote or repress gene expression. To promote transcription, lncRNAs can function as guides or scaffolds for the assembly of protein complexes at specific loci. To repress transcription lncRNAs can function as decoys, binding and preventing functions of RNA or protein targets4.3MiRNAs function by recognising small sequences (6‐8 nucleotides) at the 3′‐untranslated region of multiple target mRNAs.The importance of miRNAs in embryonic stem cells (ESCs) has been established by deletion of Dicer1 and DGCR8  in mouse models. Dicer1 deletion is embryonic lethal whereas DGCR8‐deficient mice have impaired differentiation due to failure to silence the stem markers, Oct4, Sox2 and Nanog.Let7 is also known as a negative regulator of CSCs especially in breast cancer where, as expected, let‐7 was linked with differentiation. Inhibition of let‐7 promoted CSC properties, mediated by increased RAS and HMGA2 expression.As well as directly affecting the transcription of pluripotency‐associated genes, miRNAs can also affect CSC biology via cross‐talk with other CSC pathways and epigenetic mechanisms. For example, the maturation of miR‐21 via a DROSHA and RNA helicase p68 complex is induced by TGF‐β signalling.5Many studies have associated EMT with the generation of CSCs.CSC generation by EMT may also be influenced by the localised tissue environment via epigenetic modification. Cells present in the tumour CSC niche such as stromal and immune cells produce cytokines and growth factors which promote CSC characteristics. For example, tumour‐associated macrophages (TAMs) secrete VEGF and IL6, and T cells secrete IL17 and TGF‐β, all of which are able to promote CSCs via EMT.6Many epigenetic aberrations identified as being involved in the generation of CSCs are likely early markers of cancer and therefore potentially useful as early detection methods. Early cancer detection could be achieved by analysis of patient blood samples, which is considered a relatively noninvasive procedure compared to a biopsy. Patient blood can contain cancer cells and cell‐free DNA, which could be identified by characteristic genetic and epigenetic signatures as biomarkers of the cancerous state. Should such a process be found to be accurate, it may facilitate the detection of tumour signatures even when the tumour is not large enough to be picked up on a scan. This technique was originally designed for the identification of large genomic alterations, however, advances in the understanding of epigenetic aberrations in cancer have led to the pursuit of an epigenetic signature that may define the cancerous state. Although these may differ between cancers, epigenetic signatures associated with the generation of CSCs are likely candidates across cancer types due to the requirement for CSC generation in tumour initiation Figure .BCAT1, CDO1, TRIM58 and ZNF177, has been shown to predict lung cancer.Most approaches have focussed on the methylation status of particular gene promoters but it is likely that the methylation status of several promoters would be required to serve as an accurate and reliable signature. For example, hypermethylation of the combination of four genes: An increase in histones and their associated covalent modifications have also been identified in the blood of cancer patients.A useful form of epigenetic cancer detection is ncRNA. Noncoding RNAs are readily secreted by the tumour into the microenvironment and subsequently reach the bloodstream and even distant tissue sites. LncRNAs and miRNAs have been detected in blood, urine and saliva, all of which facilitate diagnostic testing and many have been identified as potential biomarkers.Epigenetic targeting is an exciting area of translational cancer research offering many novel opportunities for therapeutic intervention. Many studies have explored the use of existing and novel compounds to target epigenetic marks to impair cancer cell growth and elicit an improved clinical response Figure .HDAC inhibitors are already widely considered to have anticancer effects and some are in clinical use. For example, SAHA (vorinostat) is approved for T‐cell lymphoma and many others are in clinical trials for various malignancies either alone or in combination with other therapeutics.Targeting of histone modifications may also be achieved through LSD1 inhibitors. Lysine‐specific demethylase 1A (LSD1) removes methyl groups from the histone 3 proteins (H3K4me2/1 and H3K9me2/1) resulting in transcriptional repression or activation, respectively.Histone modification could also be targeted by inhibition of PRC components BMI‐1 or EZH2. BMI‐1 is a key regulatory component of (PRC1) and its function is strongly associated with the maintenance of CSCs.Although an increasing number of ncRNAs are known to be involved in CSCs and cancer in general, and are therefore attractive drug targets, it is considered difficult to specifically target aberrant mRNAs or RNA‐protein interactions with small molecules. Nevertheless, due to the high promise of such targets, a number of studies have attempted therapeutic modulation. The miRNA lin28 which is very important in CSC generation has been found to be targeted by the compound “N‐methyl‐N‐[3‐phenyl]acetamide”, which acts by blocking the interaction between lin‐28 and let‐7. This compound impaired Lin‐28, rescued let‐7 function and resulted in differentiation in ESCs, and reduced tumorsphere formation in cancer cell lines. It is likely that the reduction in tumoursphere formation was caused by differentiation of CSCs.Another way of targeting DNA and RNA moieties is by using peptide nucleic acids (PNA). These are synthetic analogues of DNA that are highly stable and have a hybridization affinity toward natural DNA and RNA, and are therefore a potential approach for the modification of gene expression and epigenetic states.Despite promising results, therapeutic targeting of epigenetic marks should proceed with caution. A therapeutic strategy designed to target epigenetic readers and writers is by definition not a specific approach. Such an intervention would elicit gene expression changes on a global scale and is likely to incur off‐target and adverse effects. Therefore much further investigation is required to identify a clinically viable approach. Epigenetic targeting that focuses on a specific gene or pathway changes  may be more easily targeted to CSC inhibition and therefore have much greater potential for efficacy.7To obtain a greater understanding of the epigenetic mechanisms which drive CSC formation from normal cells.To determine whether the mechanisms of CSC generation differ when deriving from normal or cancerous cells and their associated stem and nonstem subpopulations. Epigenetic mechanisms involved in the emergence of CSCs will likely differ depending on from which population they are derived, that is, from a normal cell, normal stem cell, pre‐CSC or bulk tumour cell, as each of these identities has distinct epigenetic profiles.To ascertain the relative contribution of genetic and epigenetic effects to tumour initiation; can epigenetic events alone initiate the transformed state?To determine which are the most important environmental signals involved in CSC generation and how these can be modified or targeted for prevention and treatment.The epigenetic processes involved in CSC generation have substantial implications for tumour initiation, metastasis and relapse. Many studies have focussed on the elucidation of mechanistic links between epigenetic processes and CSCs, and have identified attractive targets for cancer prevention, diagnosis and therapy. However, as always, these studies have highlighted many areas in which further investigation is required. In particular:The authors note no conflict of interest.