Arabidopsis, while H1 is known to influence heterochromatin and DNA methylation, its contribution to transcription, molecular, and cytological chromatin organization remains elusive.Chromatin provides a tunable platform for gene expression control. Besides the well-studied core nucleosome, H1 linker histones are abundant chromatin components with intrinsic potential to influence chromatin function. Well studied in animals, little is known about the evolution of H1 function in other eukaryotic lineages for instance plants. Notably, in the model plant Arabidopsis linker histones. We show that H1-deficient plants are viable yet show phenotypes in seed dormancy, flowering time, lateral root, and stomata formation—complemented by either or both of the major variants. H1 depletion also impairs pluripotent callus formation. Fine-scale chromatin analyses combined with transcriptome and nucleosome profiling reveal distinct roles of H1 on hetero- and euchromatin: H1 is necessary to form heterochromatic domains yet dispensable for silencing of most transposable elements; H1 depletion affects nucleosome density distribution and mobility in euchromatin, spatial arrangement of nanodomains, histone acetylation, and methylation. These drastic changes affect moderately the transcription but reveal a subset of H1-sensitive genes.We provide a multi-scale functional study of Arabidopsis with distinct roles in euchromatin and heterochromatin and a dual causality on gene expression. Phenotypical analyses further suggest the novel possibility that H1-mediated chromatin organization may contribute to the epigenetic control of developmental and cellular transitions.H1 variants have a profound impact on the molecular and spatial (nuclear) chromatin organization in The online version of this article (10.1186/s13059-019-1767-3) contains supplementary material, which is available to authorized users. Drosophila, Neurospora crassa, and mammalian cells, H1 deposition is part of a crosstalk with the epigenetic landscape, notably DNA methylation [Tetrahymena, yeast, fungi, worms [Drosophila, it impairs viability [Linker histones H1) belong to the major constituents of plant and animal chromatin besides the core nucleosomal histones. H1 variants (collectively referred to as H1 thereafter) appeared early during evolution as lysine-rich, proto-linker histones found in the ancestral eukaryotes such as protists but not Archaea  belong t, 3. In ahylation –12 and hhylation . Given ti, worms –19. Howeiability , 20. TheArabidopsis thaliana possesses only three canonical H1 variants, and the reduced number of isotypes makes it suitable for analyzing H1 functions [Arabidopsis genome, spanning both heterochromatin and euchromatin chromosomal domains [Arabidopsis, primarily but not exclusively, in heterochromatin and in all sequence contexts  [In the plant kingdom, H1 gene variants can be traced back to the earliest land plants . The flounctions , 22. Twounctions –25. Unliunctions , no gameunctions ) where iunctions . The gen domains , 29. Spe domains . They ar domains . H1 variHG, CHH) , 31, 32.HG, CHH) .Arabidopsis produced pleiotropic phenotypes [Overall, however, the functional influence of H1 on chromatin organization and function in plants remains elusive, and our understanding of H1 function on cell specification and development is limited. Whereas H1 downregulation by RNAi knockdown in enotypes , where penotypes , double enotypes , 31.Arabidopsis, which was so far overlooked. It regulates chromatin compaction and organization at the nuclear level, being required for heterochromatin formation and influencing on the spatial distribution of nanoscale, compact domains in euchromatin. Mutant analyses reveal that H1 restricts nucleosome mobility and influences H3K9 acetylation, H3K27, and H3K4 methylation levels. We also show that H1 regulates nucleosome distribution and provides distinct nucleosome density over expressed genes correlating with their expression levels. Interestingly, these nucleosomal states and large-scale chromatin structures mediated by H1 are not epistatic to transcriptional regulation for a vast majority of loci. A small fraction of genes and TEs is, however, H1-sensitive suggesting that chromatin level regulation primes over other transcriptional controls for these loci. Finally, the analysis of mutant and complemented mutant lines unveils a new role of H1-mediated organization during development.Here, we report that H1 has a profound role not only in heterochromatin but also in euchromatin in H1 genes were previously introgressed producing double and triple mutants which are viable [H1 triple (T-DNA) mutant plants, thereafter called 3h1, were previously characterized at the molecular level and do not show detectable levels of H1 transcripts nor H1 proteins [3h1 appeared at first normal with regular shoot and root apparatus indicating that organogenesis coordinated by the apical meristems are not affected. We investigated the development of 3h1-mutant plants in further details and noticed subtle phenotypes at developmental transitions. We noticed for example that a large fraction of 3h1 seeds underwent a prolonged dormancy after harvest -based chromatin regulation [T-DNA insertion mutant alleles for the three e viable , 31, 32.e viable , where pe viable  cannot bproteins . 3h1 appgulation , 35. Firgulation .Fig. 1H13h1 with a higher occurrence of high-degree (tertiary and quaternary) clusters, associated with complex arrangements, collated stomata, or atypical division patterns in early stages. These features were not found in the wild type and were restored upon the introduction of H1.1 and H1.2 transgenes  measuring the fraction of chromatin condensed in conspicuous foci or domains [3h1 nuclei have a stark reduction in heterochromatin content with a RHF of less than 5%, were significantly larger than the wild type and failed to form the typical 6–8 heterochromatic chromocenters (CCs) normally seen in most wild-type somatic nuclei  repeats of the five chromosome pairs, and a subset of two to four CCs comprising ribosomal (rDNA) repeats is associated with the nucleolus [3h1 nuclei in the absence of conspicuous chromocenters, we carried out 3D fluorescence in situ hybridization (FISH) on intact nuclei embedded in acrylamide gel pads [3h1-mutant nuclei, while rDNA repeats were localized in the remaining compact CCs, as in the wild-type nuclei . We focused the analysis on nucleosomal coverage and distribution in the regions characteristic for heterochromatin and indexed as chromatin states CS8 and CS9 in the established nomenclature [Arabidopsis, CS8 and CS9 are enriched in DNA methylation, H3.1 histone variant, and H3K27me1 and H3K9me2 modifications. In our analyses, the typical CS8 and CS9 regions have a consistent 12–15% reduction in nucleosomal density in the 3h1 nuclei  and unusually long (> 300 nt) fragments, while the average nucleosome repeat length (NRL) was globally shorter by 10 nt in the 3h1 mutant  which are typically confined to chromocenters. Strikingly, RNA-seq profiling of wild-type and 3h1 seedlings did not show changes for the vast majority of TE transcript levels. This demonstrates that H1-mediated heterochromatin condensation into larger compact CCs is dispensable for silencing of the majority of heterochromatic TEs  representing about 1.5% of TAIR10-annotated TEs were significantly reactivated in 3h1 plants. A third of them corresponded to LTR/gypsy elements that are mainly distributed within the pericentromeric regions  spatially distributed along variable intervals  compared to a highly regular distribution in wild-type chromatin. This trend was reversed in mutants complemented by a tagged H1.1 variant  [3h1 chromatin. Comparative density profiles for each chromatin state showed enhanced or diminished average density levels relative to the element boundaries, as for instance, the CS1- and H3K27me3-marked CS4 states, respectively   is compa3h1 may possibly arise from decreased structural constraints facilitating nucleosome redistribution compared to in wild type. To address the question of whether nucleosome turnover is increased in the absence of linker histone, we carried out fluorescence recovery after photobleaching (FRAP) analyses on the root epidermal nuclei expressing an RFP-tagged H2B reporter [3h1 background. The analysis showed a ~ 2.5 times faster recovery in H1-depleted chromatin compared to in the wild-type nuclei  cells   Fig. a.Fig. 4H3h1 transcriptomes determined that about 701 genes are misregulated as compared to wild-type plants  expressed genes. This observation poses questions particularly when considering the strong, genome-wide alterations in nucleosome occupancy and euchromatin mobility in the 3h1 mutant to analyze the cytological distribution and abundance of DNA methylation. Albeit cytological imaging would not capture locus-specific alterations as those described previously in H1-depleted tissues by molecular profiling [3h1-mutant root nuclei were similar to that in wild-type nuclei  corresponds to genes misregulated in the PRC2 mutant clf (curlyleaf), compromised in histone H3-Lys27 methyltransferase activity [3h1 seedlings. This indicates that altered H3K4me3 and H3K27me3 landscapes may rather be a consequence of altered chromatin structure affecting targeting or spreading of histone modifications, or both.We previously described that natural eviction of H1 in spore mother cells (SMC) precedes a breadth of global chromatin changes at the structural and epigenetic levels during the somatic-to-reproductive transition . These irofiling , 32, theactivity   in a study published during our manuscript revision [3h1 define a functional category of loci where H1-mediated chromatin organization primes over other controls of transcriptional silencing. Those TEs are mostly pericentromeric and enriched in LINE, gypsy, and copia elements. DNA methylation at pericentromeric TEs is independent of the RNA-dependent DNA methylation (RdDM) pathway but requires the DNA methyltransferase CHROMOMETHYLTRANSFERASE2 (CMT2) and the chromatin SWI/SNF remodeler DECREASE in DNA METHYLATION1 (DDM1) [Arabidopsis, H1 may play a dual role in restricting methylation through antagonizing DDM1 function [Drosophila [We showed that H1 plays an unexpected role in heterochromatin regulation in revision . In the revision . Collect1 (DDM1) . At peri1 (DDM1) . This ex1 (DDM1) . This gl1 (DDM1)  suggestsfunction  but alsofunction . In animfunction  or throuosophila , 61.Arabidopsis, further indicating the existence of an H1-independent control of such heterochromatic structures. It would be interesting to investigate whether other structural proteins such as H1-related, GH1-containing proteins [Chromocenters containing ribosomal DNA repeats are not affected by the loss of H1 variants in proteins  contribuproteins  is self-3h1 nuclei indicating that not only structural compaction but also peripheral location is uncoupled from heterochromatin silencing in Arabidopsis. Interestingly, this role in chromocenter formation is not a shared, inherent feature of linker histones among eukaryotes. Indeed, depletion of the H1c and H1d mammalian variants in mouse embryonic stem cells leads to chromocenter clustering but not decondensation [Drosophila, H1 also promotes the condensation of pericentromeric heterochromatin, but this observation is specific to salivary gland cells which are singular due to their polytenic chromosomes [Drosophila and mouse somatic cells, chromocenter condensation seems instead to be controlled by the D1 and HMGA1 multiAT hook proteins, respectively [In addition, dispersed heterochromatin regions still locate peripherally in the ensation . In Drosomosomes . In bothectively . Therefo3h1 nuclei is highly reminiscent of euchromatin organization in tumorigenic nuclei losing fractal property of organization [We have shown that global chromatin decondensation in H1-depleted nuclei correlates with a greater heterogeneity in the spatial distribution of chromatin domains in the nucleoplasm. Yet, compact nanodomains as those visualized by high- and ultrastructural microscopy imaging still form in the absence of H1. This is consistent with the intrinsic property of chromatin fibers to fold along various configurations even in the absence of linker histones: in vitro assembly assays have shown that nucleosomal arrays can be formed in the absence of H1, yet with a lesser degree of molecular organization, forming “ladder” or “puddle” type of arrangements . Thus, onization .Arabidopsis H1 influences nucleosomal spacing, resulting in deregulated density over defined genomic regions. Notably, H1 depletion affects the structural differentiation, in terms of nucleosomal density, of chromatin states. This effect is distinct from that of a loss-of-function of a general chromatin factor regulating nucleosome assembly such as the histone chaperone CAF-1  or unexpectedly long (> 200 bp). This indicates a relaxed control of nucleosome distribution, with a more permissive (i.e. less constraint) nucleosome positioning [3h1 is thus likely responsible for the observed local variations in chromatin compaction and spatial heterogeneity.At the molecular level, we observed that itioning . H1 incoitioning . The queitioning . Ultimatitioning –69. The Arabidopsis resembles that of animal un-/de-differentiated cells. This proposal invites future studies on the role of Arabidopsis H1 variants in regulating transcriptional dynamics notably during cellular differentiation or in response to environmental cues.How this fine-scale topology connects with chromatin function, notably gene expression, requires a biophysical appraisal of chromatin organization. Emerging models linking molecular-to-spatial (macromolecular) levels of chromatin dynamics are very attractive to explain the emergence of transcriptional dynamics during cellular differentiation . Notably3h1 along a large fraction of genes , yet this reduced nucleosome occupancy did not trigger a significant upregulation in 3h1 plants. This was true for the genomic regions associated with H3K27me3 , hence 3h1 plants with an apparent relaxed control of several developmental transitions. These deficiencies can be interpreted as instable transcriptional states releasing seed dormancy, flowering, lateral root organ initiation, root hair, and stomatal cell fate earlier than in the wild-type counterparts, although the severity of such developmental phenotypes might be attenuated by the buffering capacity of developmental processes inherent to plants [These findings challenge a common view that transcription is influenced by nucleosome density, coming from the observation that nucleosome occupancy is inversely correlated to gene expression level  chromatin decondensation and reduced H3K27me3 abundance resemble the chromatin status of H1-depleted spore mother cells (SMC) at the somatic-to-reproductive fate transition [To a certain extent, chromatin organization and properties in ansition , 72;  and in animals  althoughArabidopsis. Our analyses of the impact of H1 depletion in Arabidopsis unveil a remarkable evolutionary convergence of H1 function in plant and animal kingdoms despite a diversification of the H1 variants family [Drosophila and mammalian cells, H1 depletion generates large-scale chromatin alteration including blurring of topological domains, relaxation of nucleosome distribution periodicity, and impaired propagation of the epigenetic marks [Arabidopsis, the misregulation of a small proportion of genes in the absence of H1 is intringuing when considering the massive structural chromatin alterations. While it suggests conserved functional principles for H1, it also unveils a dual, causal relationship between transcriptional controls and H1-mediated chromatin organisation. Our findings prompt revisiting the common view that chromatin compaction and nucleosome density influence gene expression in a major way. This idea may only hold true for a subset of H1-sensitive genes and TEs. But generally, transcriptional regulation remains epistatic to H1-mediated chromatin organization for a majority of heterochromatin and euchromatin loci. Based on these findings, we propose that chromatin organization in the absence of H1 loses versatility and is less prone to rapid reprogramming at the structural, epigenetic and transcriptional level. This property is not challenged under controlled growth conditions hence requires testing in modular and variable environments. We suggest a working model, in which linker histones act as fine-scale chromatin architects with a dual function in (i) the facilitation of transcriptional reprogramming  and (ii) securing stable transcriptional landscapes buffering against variability. Our findings echo with recent models in mammalian cells implicating higher-order folding chromatin topology as an independent route influencing transcriptional dynamics [This work explored for the first time the functional relationship between fine-scale chromatin organization at the molecular and spatial levels, transcriptional control Fig. , and phes family . In Drosic marks , 88–90. dynamics  and in fdynamics .Arabidopsis thaliana plants used in all experiments were in the Col-0 background unless it is specified otherwise. The h1.1h1.2h1.3 (3h1) mutant was described before [3h1 and wt are presented in Additional file www.agrisera.com/en/artiklar/h1-histone-h1.html). Complemented mutant lines were generated by transforming 3h1 via floral dip method [3h1 was complemented with either two main  or all three H1 variants to generate the following lines: 3h1-comp1,2= h1.1h1.2h1.3;H1.1-RFP;H1.2-GFP (line #KR276), 3h1-comp1,2,3 = h1.1h1.2h1.3;prom. H1.1::H1.1-RFP;prom. H1.2::H1.2-CFP;prom. H1.3::H1.3-GFP (lines #KR264 and #KR265). For FRAP experiments, the UBQ10::H2B-RFP [3h1, and in the subsequent generations, by genotyping the 3h1/UBQ10::H2B-RFP and wt segregants were identified.The d before , 28. Thep method  with H1-p method , 28. The:H2B-RFP  was cros−1 m−2 for routine experiments. Growth of calli and scoring of lateral root production were tested under continuous light . When the flowering stage was necessary, the 10-day-old seedlings were transferred into the soil and grown at 19–21 °C with a 16-h day/8-h night photoperiod.Seeds were surface sterilized and rinsed in sterile water before transferring onto germination medium . They were placed on the medium using toothpicks to ensure a uniform distribution, stratified 2–4 days at 4 °C, and transferred into a plant growth incubator  with long-day photoperiod  and light flux around 120 μM sThe nuclei area, heterochromatin , and immunostaining analyses were carried out essentially as described  with minFor heterochromatin analysis, the slides were rinsed in SSC2X then PBS before staining with DAPI 1 μg/mL in Vectashield (Vector Laboratory). For immunostaining, the protocol essentially followed previously described steps . As primt test  for comparing wild-type and mutant samples.Sixteen-bit images were acquired using a Leica TCS SP5 confocal laser scanning microscope (CLSM)  using a × 63 GLY lens (NA 1.4) for heterochromatin and immunostaining analyses. Exposure times, illumination intensities, zoom factor, scanning speed, and pinhole were kept identical for the image series in an experiment. For RHF measurements, signal intensities were recorded in manually drawn ROIs capturing chromocenters and normalized over the whole nucleus intensity using Fiji . For immThe nuclei were isolated from leaves of 35-day-old rosettes grown under a 16-h/8-h photoperiod. Nuclei extraction and embedding in acrylamide gel pads on the slide were done as described . CentromThe nuclei were isolated from dissected cotyledons of 5-day-old seedlings grown under a 16-h/8-h photoperiod. Nuclei extraction, fixation, and hybridization with pAL1-derived and F28D6-derived (180-bprepeats) probes  were perg, 6 min; resuspended in HBB; applied to 40/75% Percoll gradient; centrifuged at 400g, 40 min; collected; and washed three times with HBC. The integrity of the extracted nuclei was monitored using DAPI staining and fluorescence microscopy. The quantity of nuclei was measured by qPCR with primers targeting nuclear DNA.The nuclei were isolated from 3-week-old seedlings frozen in liquid nitrogen as previously described  with the2, 0.01 mM PMSF, 1× Complete EDTA-free Protease Inhibitors (Roche)) with 1.5 μL  of micrococcal nuclease (Thermo Fisher), and 2 μL  of RNase A (Thermo Fisher) at 8 °C for 90 min with gentle mixing. The reaction was stopped by adding an equal volume of 2× Lysis buffer with EDTA . The samples were lysed by incubation at 37 °C for 60 min with shaking (1000 rpm). DNA was purified using phenol-chloroform extraction, precipitated with isopropanol and sodium acetate, and resuspended in water.Digestion was performed by incubating the nuclei suspended in DB buffer  stain. The mononucleosomal band was excised, frozen, and squeezed by 3 cycles of spinning and rehydration on centrifuge column. DNA was purified and concentrated using Agencourt AMPure XP beads (Beckman Coulter). Barcoded libraries were synthesized from 100 ng of mononucleosomal DNA using Ion Xpress™ Plus gDNA Fragment Library Preparation Kit and Ion Xpress™ Barcode Adapters. DNA was end-repaired prior to adapter ligation and size selection, and amplification steps were omitted. The resulting libraries were quantified with Ion Library Quantitation Kit, pooled, and used to prepare the template by clonal PCR with Ion PI™ Template OT2 200 Kit v3 on Ion OneTouch™ 2 System. Sequencing was performed on Ion PI™ chip v2 and Ion Proton™ sequencer using Ion PI™ Sequencing 200 Kit v2 .promUBQ10::H2B-RFP marker [3h1-mutant plants by crossing. Both wild-type and triple-mutant segregants were analyzed. Measurements were done on the root tips of 2-week-old seedlings grown as previously described. One sample was prepared at a time: the root was excised and delicately mounted  in 0.5× MS between the slide and coverslip (precleaned with EtOH), sealed with transparent nail polish, and let to equilibrate upside down for 10 min on the microscope platform before measurements. The imaging chamber was set at a constant temperature of 20 °C (higher/fluctuating temperatures induce nuclei juggling). Bleaching and imaging were done using an APO PL × 40 oil immersion objective, NA 1.3, over a single plane capturing an optical section of ~ 2 μm encompassing a single nucleus (pinhole opening to 5 AU) with a 256 × 256 pixels image format, threefold zoom factor. Bleaching was performed in euchromatin within ROI of 1 μm diameter using five or more pulses until near-total bleach was obtained , and post-bleach images were recorded using 5–7% laser transmission for excitation, in a series of 10 time points, 1-s interval, followed by 10 time points, 60-s interval. For analyzing the fluorescence recovery, images were first corrected for nuclear drifts occurring during acquisition, using a rigid registration approach in Fiji  lengths were scored using manual vector tracing in Fiji, reported at scale . For micIncreased root hair number was visible under growth conditions as described above for lateral root scoring. The phenotype was however increased using MS medium supplemented with 5% sucrose, and this medium was used to take the micrographs in Fig. Fresh epidermal peals of 14-day-old cotyledons were mounted in water. Images of the adaxial surface were recorded with DIC microscopy, and stomatal clusters were scored following as described .The experiment was designed as described previously  with minCotyledons from 7-day-old seedlings grown under a 16-h/8-h photoperiod were excised, transferred onto callus induction medium , and let to develop for 5 weeks under a 16-h/8-h photoperiod. Callus size (area) was determined from images using manually drawn contours in Fiji . Graphs Seeds were surface sterilized in 70% ethanol 0.05% SDS for 3 min and rinsed with 90% ethanol before drying and plating on MS medium supplemented with 0.5% sucrose and 0.9% agar. Eight-day-old seedlings were used for chromatin extraction protocol as described previously . Forty m3h1 from 3-week-old seedlings pooled and frozen in liquid nitrogen. Ribosomal RNA was removed using RiboMinus Plant Kit (Thermo Fisher), and ERCC RNA Spike-In Mix 1 (Thermo Fisher) was added. Libraries were prepared with Ion Total RNA-Seq Kit v2 and Ion Xpress RNA-Seq Barcode 1-16 Kit according to the user guide. Sequencing template was generated with Ion PI™ Template OT2 200 Kit v3 on Ion OneTouch™ 2 System. Sequencing was performed on Ion PI™ chip v2 and Ion Proton™ sequencer using Ion PI™ Sequencing 200 Kit v2 .RNA was isolated using modified TRIzol method  in threeBase calling and adapter trimming were performed automatically by Torrent Suite software. Residual rRNA and ERCC reads were identified and filtered out using bbsplit and filterbyname scripts from BBTools suite . Reads wQuantitation to ARAPORT11 transcripts was performed in Partek Flow (Partek Inc.) using Partek E/M (expectation-maximization) algorithm. This allows inclusion of multi-mapping reads into quantitation. The algorithm quantitates unique alignments first and then iteratively divides the value of each multi-mapping read between possible loci based on the quantitation value in their close vicinity. The quantitation was performed on gene level . The read counts were normalized by the trimmed mean of M values (TMM) method  followedArabidopsis genes into quintiles of expression, the quantified values from Partek E/M algorithm (described above) were normalized using reads per kilobase of million (RPKM) mapped reads method [3h1 datasets.To divide s method . The expBoxplots were generated with tables of RPKM values using the “boxplot” function in R language.3h1 versus all genes in Arabidopsis thaliana genome across chromatin states, genomic coordinates for chromatin state (CS) locations across the genome were downloaded from published data [3h1 were taken from TAIR 9 to be consistent with CS coordinates. Then, for each gene, the percentage of overlapped chromatin states was calculated, and for the final graph, the summary of all analyzed genes was presented.To calculate the distribution of genes upregulated in hed data . The genBase calling and adapter trimming were performed automatically by Torrent Suite software. Reads were aligned with TMAP 5.0.13. Soft clipping was turned off, end repair was allowed, and all alignments for multi-mapping reads were reported. Other settings were set according to the Torrent Suite defaults. Multi-mapping read positions were resolved using MMR  with defPeak calling was performed on reads reaching terminal adapter with length range between 147 and 220 nt using a set of custom-made Python scripts. First read centers were piled up, and then the highest coverage positions were selected using greedy algorithm. Ends of the longest read used to define a position were used as peak boundaries. A peak was called only if its boundaries were not overlapping those of the neighboring peak. NRLs were defined by calculating peak-to-peak distances from peak calling results. The frequency of distances was calculated as a percentage of all measurements and binned into three groups. Histograms were plotted in Microsoft Excel.Quantile-normalized wiggle occupancy files were generated with DANPOS2  using thWig files were converted to BigWig format using UCSC wigToBigWig  and usedNucleosome occupancy was calculated as the number of reads, adjusted to a length of 80 nt summed over bins of 10 nt, smoothed by 20-nt running window, and quintile normalized.h1.3-mutant allele from Ler background into our h1.1h1.2h1.3 (Col-0) line, some residual Ler sequences were still present, mainly neighboring the H1.3 gene. To avoid interference from those sequences in our analyses, we identified their precise genomic coordinates using SNP and coverage analyses by comparing to sequenced genome of parent Ler h1.3 line. We used those coordinates to generate bed files and filter out all reads overlapping residual Ler sequences using bedtools intersect [Despite series of five backcrosses after introduction of ntersect .Additional file 1:Figures S1–S17. (DOCX 7482 kb)Supplementary Additional file 2:Table S1. Quantifications of chromatin cytology corresponding to Figs. Additional file 3:Table S2. Transposable element (TE) expression in 3h1. Available as an Excel table. (XLSX 64 kb)Additional file 4:Table S3, Table S5, Table S6. Classes of TEs upregulated in 3h1; Gene Ontology (GO) analysis of genes which are misregulated in 3h1 mutant; Expression of histone modifying enzymes in 3h1. (PDF 903 kb)Additional file 5:Table S4. Gene expression in 3h1. Available as an Excel table. (XLSX 4453 kb)Additional file 6:Review history. (DOCX 57 kb)


Transcriptional repression needs to be rapidly reversible during embryonic development. This extends to the Hedgehog pathway, which primarily serves to counter GLI repression by processing GLI proteins into transcriptional activators. In investigating the mechanisms underlying GLI repression, we find that a subset of GLI binding regions, termed HH-responsive enhancers, specifically loses acetylation in the absence of HH signaling. These regions are highly enriched around HH target genes and primarily drive HH-specific transcriptional activity in the mouse limb bud. They also retain H3K27ac enrichment in limb buds devoid of GLI activator and repressor, indicating that their activity is primarily regulated by GLI repression. Furthermore, the Polycomb repression complex is not active at most of these regions, suggesting it is not a major mechanism of GLI repression. We propose a model for tissue-specific enhancer activity in which an HDAC-associated GLI repression complex regulates target genes by altering the acetylation status at enhancers. Transcriptional repressors are instrumental in establishing developmental lineages and preventing improper gene expression. Long-term repression is accompanied by stable modifications to DNA and chromatin that prevent rapid transcriptional changes. In contrast, transient repression is rapidly reversible, providing a mechanism for controlling gene activation during the dynamic process of embryogenesis. This control is especially important for spatially restricting gene expression until signal transduction mechanisms alleviate repressor activity. This is exemplified by the Hedgehog (HH) signaling pathway, which ensures proper spatiotemporal regulation of its target genes through the coordination of bifunctional GLI proteins. Activation of HH signaling results in the processing of GLI proteins into transcriptional activators, which are otherwise proteolytically modified into truncated transcriptional repressors in the absence of HH ligand .The importance of balancing opposing GLI functions is illustrated in the limb bud, where Sonic Hedgehog (SHH) signaling alleviates GLI repression in a spatiotemporal manner to regulate growth of the digit-forming autopod. HH expression initiates in the posterior, distal limb, and forms a gradient along the posterior-anterior axis. Consequently, GLI activators are enriched in the posterior limb bud where many cells are exposed to HH ligands, while an inverse domain of GLI repressors in the anterior limb bud serve to spatially restrict the boundary of HH target gene expression . The pre-/-Shh limb buds , which have a nearly complete absence of digits and a severe reduction in limb size. The phenotype is markedly improved in -/-;Gli3-/-Shh double mutants which lack SHH and the main transcriptional repressor, GLI3, and are therefore devoid of most or all GLI activity (both activation and repression)  . In partression) . The traression) .The mechanisms underlying GLI repression are unknown but could in principle function either by excluding GLI activator binding or by recruiting co-repressors . AlthougUsing a genomic approach and the developing limb as a model, we sought to determine if GLI proteins repress HH target genes through altering the chromatin environment at GLI binding regions (GBRs). We hypothesized that GLI repressors regulate gene expression by inactivating enhancers. Consistent with this, we find that GLI repression regulates enhancer modification status, and thus, activity through the de-acetylation of Histone H3K27. This repression occurs independently of Polycomb activity. Enhancers regulated in this fashion correspond to known GLI limb enhancers, are highly enriched around HH target genes, and primarily drive tissue-specific enhancer activity within HH-specific expression domains. Based on these findings, we propose that GLI repressors inhibit gene expression by altering enhancer activity, providing an explanation for the labile nature of GLI repression.Gli3 allele , when high levels of HH target gene expression are observed. We used an endogenously FLAG tagged 3 allele  to ident3 allele . Altoget3 allele . Nearly 3 allele   null E10.5 forelimbs, prior to overt phenotypes in Shh nulls, and comparing H3K27ac enrichment to that in WT limbs ; however a subset of 2,113 WT H3K27ac enriched regions had acetylation that was significantly reduced or completely lost in the absence of HH signaling  are located in close proximity to the promoters of genes (2 kb upstream to 1 kb downstream of TSS), compared to 26% (91/349) of HH-responsive GBRs . Most prGli1 and Ptch1  to promote transcriptional activation of several HH target genes, most notably nd Ptch1 . If PRC2nd Ptch2 . In contnd Ptch2  and 2 . Shh-/- limbs compared to WT controls . All tested regions retained H3K4me1 enrichment in Shh-/- limb buds  of H3K4me2+ HH-responsive GBRs are near promoters). Since we found that most HH-responsive GBRs in wildtype limb buds are enriched for H3K4me1 (see results above), we asked if this mark was altered at HH-responsive GBRs in response to HH signaling. We performed ChIP on WT and imb buds . We conc-/-Shh limb buds are properties consistent with ‘poised’ enhancers, which retain H3K4me1 and accessible chromatin in the absence of H3K27ac . We hypothesized that loss of H3K27ac at most HH-responsive enhancers in the absence of HH signaling is due to constitutive GLI repression preventing acetylation of GLI enhancers. Thus, in -/-;Gli3-/-Shh limbs, we predicted H3K27ac should be maintained at HH-responsive enhancers. Alternatively, if GLI activator is required, H3K27ac would remain absent or reduced as it does in -/-Shh limbs  at HH-responsive GBRs, in which loss of an HDAC-GLI repressor complex leads to acetylation. To test this, we cultured limb buds in the presence of the HDAC inhibitors FK228  or SAHA Having identified distinct classes of GBRs that respond differently to HH signaling, we next addressed the biological significance of these properties. To this end, we used the VISTA enhancer database to identify a total of 305 Stable and 23 HH-responsive GBRs that had been tested for enhancer activity in transgenic embryos . While nThese results suggest that Stable GBRs act as general enhancers that drive expression in multiple tissues, while HH-responsive GBRs mediate tissue-specific expression. To test this in another biological context, we treated HH-responsive NIH3T3 cells with and without the HH agonist purmorphamine, identified H3K27ac enriched regions by ChIP-Seq, and assessed the H3K27 acetylation status of different classes of limb GBRs. Strikingly, only 12% (42/349 GBRs) of HH-responsive limb GBRs are acetylated in response to HH signaling in NIH3T3 cells. An additional 18% (63/349 GBRs) of HH-responsive limb enhancers have stable acetylation in NIH3T3 cells, while most lack any activity. In contrast, 70%  of Stable GBRs in the limb are still active in NIH3T3 cells in both untreated and HH stimulated cells . We conc-/-Shh embryos, indicating a loss of enhancer activity. Many previously validated GLI limb enhancers have HH-responsive H3K27ac, including those regulating Grem1, Ptch1 and Gli1 , or developmentally (initiation of Shh expression) results in a loss of GLI repression and accompanying HDAC activity  to those Stable GBRs that have no HH response  of all GLI-bound, active enhancers. The rest are Stable GBRs with an unclear role in HH transcriptional regulation. Although these enhancers do not have significantly reduced levels of H3K27 enrichment in response . Consistet genes . They aret genes . In contet genes . One poset genes .Ptch1 and Grem1, are regulated by multiple GLI enhancers  embryos. For ATAC-seq, fresh pairs E10.5 (35 somite) posterior forelimb buds were dissected from individual embryos.Experiments involving mice were approved by the Institutional Animal Care and Use Committee at the University of Texas at Austin (protocol AUP-2016–00255). The eviously  and wereepitope,  was mainTo inhibit HDAC1/2, E10.5 embryos (32–35S) were dissected in warm limb bud culture media  and explPtch1 and Gli1 compared to controls . NIH3T3 cells (ATCC CRL-1658) were authenticated and purchased directly by vendor, and tested negative for Mycoplasma.NIH3T3 were authenticated by and purchased from ATCC (NIH3T3 CRL-1658). They have tested negative for Mycoplasma. Cells were seeded on 6 cm plates with 5 × 10^5 cells and grown for three days until completely confluent. They were then switched to low serum (0.5%) and treated with 400 nM purmorphamine (Stemgent 04–0009) or 0.01% DMSO (vehicle control) for 2 days. Under these conditions, a representative purmorphamine-treated sample had substantial elevation of the canonical HH target genes 2, 1 mM EDTA, 1 mM DTT, 0.5% TritonX-100, incubated on ice for 10 min, and centrifuged for 5 min @ 5000 g. The cytoplasmic fraction (supernatant) and nuclear pellet were each resuspended in loading dye and boiled. Western blots were incubated with the following primary antibodies for 1 hr at room temperature in 3% milk: 1:4000 M2 Flag (Sigma 3165),1:4000 H3 , 1:1000 GAPDH , 1:1000 H3K27ac (Abcam Ab4729), 1:2000 B-actin . Secondary antibodies were incubated for 1 hr at room temperature in 3% milk: 1:5000 Donkey anti-mouse (Jackson 715-035-150), Donkey anti-rabbit (Jackson 711-005-0152).Whole limb buds from a single litter were lysed for 1 hr at 4C. For fractionation, 500,000 cells from limb buds were then dissociated with 100 ug/mL Liberase (Roche 05401119001), resuspended in CSKT buffer  were processed separately by dissociating limb buds with 100 ug/mL Liberase (Roche 05401119001), crosslinked for 10 min, lysed and then sheared. Samples were sheared on a Diagenode BioRuptor for six cycles on high, 30 s on/off and processed through shearing while genotyping in parallel for -/-;Gli3-/-Shh and wildtype littermates . Sheared chromatin was then incubated with H3K27ac antibody (Diagenode C15410196) overnight and Protein A magnetic beads (Diagenode C03010020) the following day for 2 hr. Chromatin-bound beads were washed, eluted and de-crosslinked and purified using MicroChIP DiaPure columns (Diagenode C03040001).Samples were processed for ‘MicroChIP’ using the Diagenode True MicroChIP kit (Cat #C01010130) with the following modifications. Briefly, individual limb pairs (~100 k cells) of wildtype, The ChIP-seq raw datasets from this study have been deposited in GEO (GSE108880) (see Source Data for -/-Shh limb buds (or control and purmorphamine-treated NIH3T3 cells), the peaks were merged to determine how many WT, WT input, -/-Shh and -/-Shh input reads overlapped with the peak region. The read numbers were adjusted by library size and log2 transformed after adding a pseudo-count of 1. The differential analysis between WT and WT input used limma  or generated using the MicroPlex library prep kit (Diagenode C05010012) and sequenced to a depth of >40 million reads per sample for both ChIP and ‘MicroChIP’ experiments, using two biological replicates. Peaks were called using CisGenome version 2.1.0 . To idened limma . The FDR-/-Shh embryos (n = 2). ATAC used components from the Nextera DNA Library Preparation Kit (Illumina) as described previously  or eviously  with theng limma . In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:In this manuscript, Lex et al. investigate chromatin modifications in wildtype and Shh mutant undergoing constitutive Gli repression in limb buds. From these data they propose a model to explain dynamic, tissue-specific derepression of genes. Strengths of the approach include: 1) the potential physiological relevance of using a well-studied in vivo system where there is a clear role for Hh signaling and derepressive mechanisms, 2) using a genome wide approach and 3) using the in vivo data of Hh-responsive enhancers (from VISTA datasets) to show that a subset of the putative enhancers are bona fide. The revised paper makes an important contribution to the literature.The manuscript investigates an important question about how chromatin modifications solidify the dynamic and quick gene expression changes necessary for development and investigates one of the most complicated mechanisms at play- derepression.Decision letter after peer review:eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by Marianne Bronner as the Senior and Reviewing Editor. The reviewers have opted to remain anonymous. The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Thank you for submitting your article "GLI transcriptional repression regulates tissue-specific enhancer activity in response to Hedgehog signaling" for consideration by Summary:This manuscript examines how GLI transcriptional repression regulates tissue-specific enhancer activity in response to Hedgehog signaling. The authors investigate chromatin modifications in wildtype and Shh mutant with constitutive Gli repression in the limb buds. From these data they propose a model to explain dynamic, tissue-specific derepression of genes.Essential revisions:While the reviewers found the manuscript potentially interesting, they also noted that it was difficult to read and evaluate. In general, the paper needs extensive revision with clear definitions and further support for the conclusions. Overlaying the VISTA enhancer database with the ATAC-seq (and ChIP) data would be important. In addition, you need to provide evidence that the Gli3-HDAC mechanism proposed is direct. As the necessary revisions are quite extensive, we include the full reviews for your information.Reviewer #1:Smo is via the release of Gli2/3-mediated suppression of target gene transcription. To further find mechanistic support, this paper probes Gli Binding Regions (GBRs) and finds that GBRs enriched around Hh target genes can lose acetylation in the absence of Shh due to the presence of the repressor forms of Gli3. Although the mechanisms remain unresolved the results indicate that Histone Deacetylases interact with the repressor form of Gli3.The Vokes lab has provided important contributions cementing the finding that a significant part of the transcriptional regulation downstream of Shh-/- (or perhaps even better Smo-/-) limb buds would yield a better collection of relevant GBDs, that would provide a better correlate for the ChIP-seq that is appropriately performed in WT and Shh-/- limb buds. Although some elements of this approach are presented in Figure 4, these results are presented as supporting, and not as driving the question.The initial identification of Gli3 binding regions is in WT, E10.5 limb buds, when "high levels of HH target gene expression are observed". According to the release of inhibition model, under these conditions it would be predicted that the Gli3R is not bound to its targets, and would thus not be ChIP-ed. It would seem to me that differential Gli binding in A vs. P or WT vs. Ptch1 as the prototypical target for HH signaling is reasonable but Gremlin is more complex, as in the developing neural tube its expression is restricted to the roof plate, and thus at best inhibited by Shh signaling, while in the gut there appears little evidence that it is under Shh control. In general, there is very little resolution whether the observed correlation is general (supported by Gli1 and Ptch1) or limb bud-specific.Using Figure 4D-F: I don't see the indicated p-values as described in the legend.Figure 4G-I: What are the "Selected GBRs", and what genes are they associated with?Smo agonist. I do wander about the choice of Purmorphamine, as much more specific small molecules (e.g. SAG) but in particular Shh should be used in these experiments, as Smo downstream of Ptch1/2 not necessarily equates Ptch1/2 inactivation via Shh.Purmorphamine is better described as a Reviewer #2:Shh mutant (undergoing constitutive Gli repression) limb buds. From these data they propose a model to explain dynamic, tissue-specific derepression of genes. Strengths of the approach include: 1) the potential physiological relevance of using a well-studied in vivo system where there is a clear role for Hh signaling and derepressive mechanisms, 2) using a fairly agnostic genome wide approach and 3) using the in vivo data of Hh-responsive enhancers (from VISTA datasets) to show that a subset of the putative enhancers may be bona fide. However, it is difficult to evaluate whether the data support the conclusions through much of the manuscript due to the lack of definitions and details.In their manuscript, GLI transcriptional repression regulates tissue-specific enhancer activity in response to Hedgehog signaling, Lex et al. investigate chromatin modifications in wildtype and The manuscript investigates an important question about how chromatin modifications solidify the dynamic and quick gene expression changes necessary for development and investigates one of the most complicated mechanisms at play- derepression. As written, the analysis of the VISTA enhancer database provides the only compelling evidence that the authors are, in fact, identifying relevant enhancers. The demonstration that these sites are sensitive to an HDAC inhibitor is consistent with the model that GliR recruits an HDAC but leaves open the possibility of indirect HDAC recruitment. Thus, the manuscript provides further evidence that enhancer-promoter interactions to induce transcription work as the field understands and the advance is that these data may demonstrate it in an in vivo setting.1) The authors need to define and prove that the GBRs they are examining are, in fact, enhancers. While this possibility is consistent with the data, as currently stands the manuscript does not provide sufficient definitions and analysis for this determination to be made. For example, as stands it is possible that GBRs represent some other type of element, such as a gene body or the 3' ends of genes, or no element at all. As the authors examine H3K4me1/2, they may have appropriate data available.2) The authors need to define terms such as "around genes", "in close proximity to the promoters", "significantly clustered around", "selecting regions that would not overlap with promoters" as is standard in the field. Without clear definitions of proximity and how the associated genes are being called, these data are not interpretable/overinterpreted. One example: subsection “HH-responsive GBRs are distal enhancers containing high quality GLI motifs”, as explained this paragraph says that the stable GBRs are more likely than Hh-responsive GBRs to be near promoters; however, this does not mean that the rest of the Hh-responsive GBRs are enhancers – – and by the section title “Hedgehog signaling does not regulate other histone modifications at enhancers”, the authors are clearly referring to these distal elements as enhancers.Shh mutants to wildtype suggests these may not be active enhancers. In fact H3K4me2 only marked "a subset of Hh-responsive GBRs"- why don't they have H3K4me2? If the thought is that they might represent weak enhancers, they should show bidirectional PolII. Related to this in the subsequent section using ATAC-seq, why are the GBRs less accessible under the wild type "activated" condition?3) In the section "Hh signaling does not regulate other histone modifications at enhancers", H3K4me1 should shift to H3K4me2 at the active enhancers. The fact that H3K4me2 does not change from 4) The statement stating that the data suggest "a model in which loss of an HDAC-GLI repressor complex leads to acetylation" is a bit misleading as it sounds like the only possibility. In fact, the data suggest a correlation that would likely be true regardless of mechanism and the subsequent culturing with FK228 are again consistent with the model but do not prove it as the GLI-HDAC interaction need not be direct.5) The authors should examine and report the ATAC-seq and chromatin data specifically at the enhancers from the VISTA enhancer database and discuss in regards to their model. Essential revisions:While the reviewers found the manuscript potentially interesting, they also noted that it was difficult to read and evaluate. In general, the paper needs extensive revision with clear definitions and further support for the conclusions. Overlaying the VISTA enhancer database with the ATAC-seq (and ChIP) data would be important. In addition, you need to provide evidence that the Gli3-HDAC mechanism proposed is direct. As the necessary revisions are quite extensive, we include the full reviews for your information.Reviewer #1:-/- (or perhaps even better Smo-/-) limb buds would yield a better collection of relevant GBDs, that would provide a better correlate for the ChIP-seq that is appropriately performed in WT and Shh-/- limb buds. Although some elements of this approach are presented in Figure 4, these results are presented as supporting, and not as driving the question.[…] The initial identification of Gli3 binding regions is in WT, E10.5 limb buds, when "high levels of HH target gene expression are observed". According to the release of inhibition model, under these conditions it would be predicted that the Gli3R is not bound to its targets, and would thus not be ChIP-ed. It would seem to me that differential Gli binding in A vs. P or WT vs. Shh-/-Shhor c/cPrxCre;Smo limb buds. However, our current Flag ChIP procedure requires fresh tissue and we need large numbers of synchronously staged embryos for collection . The extensive breeding required to generate this synchronous population of -/-;Gli3FlagShh limb buds for ChIP is not feasible for us with our current procedure (we tried but failed to obtain meaningful enrichment with the Flag antibody using the MicroChIP protocol on single pairs of E10.5 FlagGli3 limb buds).The best way to address this would be with GLI3-Flag ChIPs in FlagGli3 limb buds into anterior and posterior halves  and generated nuclear and cytoplasmic fractions. There are minimal levels of GLI3 protein in the posterior limb bud where HH is active, while the anterior limb nuclei nearly exclusively express repressor specific GLI3. We have added these data as Figure 1—figure supplement 1B and have added the following section to the Results:As an alternative strategy, we dissected E10.5 “Nearly all nuclear GLI3 is present in the anterior half of the limb bud in the repressor form with little or no nuclear GLI3 present in the posterior half . Therefore, the GBRs identified in this study are likely to exclusively represent GLI3 repressor binding regions.”Using Ptch1 as the prototypical target for HH signaling is reasonable but Gremlin is more complex, as in the developing neural tube its expression is restricted to the roof plate, and thus at best inhibited by Shh signaling, while in the gut there appears little evidence that it is under Shh control. In general, there is very little resolution whether the observed correlation is general (supported by Gli1 and Ptch1) or limb bud-specific.Shh null limb buds at Ptch1 . We also focus on the limb specific GLI enhancer, GRE1, near Gremlin , omitting the broader locus.We agree and have edited Figure 1 to highlight domains that have been previously shown to be limb-specific. We have added tracks showing H3K27ac levels in WT and Figure 4D-F: I don't see the indicated p-values as described in the legend.2. We have added the p-values to the legend.Figure 4GI: What are the "Selected GBRs", and what genes are they associated with?Gremlin . The other 4 regions are randomly selected HH-dependent GBRs that were not selected on the basis of being associated with a particular gene and are not near genes. We have added this information in tabular form to the Materials and methods (subsection “Chromatin Immunoprecipitation”). Please note that the referenced figure panels using these primers are now shown in Figure 4G, H and Figure 5D.GBR #1 is GRE1, a limb-specific enhancer previously shown to be associated with Purmorphamine is better described as a Smo agonist. I do wander about the choice of Purmorphamine, as much more specific small molecules (e.g. SAG) but in particular Shh should be used in these experiments, as Smo downstream of Ptch1/2 not necessarily equates Ptch1/2 inactivation via Shh.Gli1 and Ptch1. Compared to vehicle treated control cells, those treated with 400nM of Purmorphamine have 47-fold enrichment of Ptch1 and 697-fold enrichment of Gli1. While we agree that the specificity and quantitative aspects of the response might be improved upon activation with Hedgehog ligand, the robust expression of GLI activator targets under our conditions suggests that there is unlikely to be residual GLI repressor activity. We have added the following information to the Materials and methods section:As part of our quality control, we reserved a portion of the NIH3T3 cells prior to harvesting for ChIP-seq experiments and used them for generating cDNA to determine the induction of Ptch1 and Gli1 compared to controls .”“Under these conditions, a representative purmorphamine-treated sample had substantial elevation of the canonical HH target genes Reviewer #2:[…] 1) The authors need to define and prove that the GBRs they are examining are, in fact, enhancers. While this possibility is consistent with the data, as currently stands the manuscript does not provide sufficient definitions and analysis for this determination to be made. For example, as stands it is possible that GBRs represent some other type of element, such as a gene body or the 3' ends of genes, or no element at all. As the authors examine H3K4me1/2, they may have appropriate data available.We agree that this is an important point. As H3K4Me2 marks only a minority of regions , we reanalyzed published H3K4Me1 ChIP-seq data from E10.5 limb buds from the ENCODE consortium and intersected it with GBRs. The majority of the GBRs (83% of HH-responsive GBRs and81% of Stable GBRs) overlap with H3K4Me1 peaks in addition to being enriched for H3K27ac. Their dual status as H3K27ac+;H3K4Me1+ suggests that these are enhancers. We have added the following statement to the Results:“As H3K27ac is not exclusively localized to enhancers, we also examined the enrichment of histone H3K4me1, a general marker of primed and active enhancers, at these GBRs, using publicly available data (Encode 2012) . In wildtype limb buds, 82% of HH-responsive GBRs are enriched for H3K4 mono-methylation, supporting that these regions are likely to act as enhancers .”2) The need to define terms such as "around genes", "in close proximity to the promoters", "significantly clustered around", "selecting regions that would not overlap with promoters" as is standard in the field. Without clear definitions of proximity and how the associated genes are being called, these data are not interpretable/overinterpreted. One example: subsection “HH-responsive GBRs are distal enhancers containing high quality authors GLI motifs”, as explained this paragraph says that the stable GBRs are more likely than Hh-responsive GBRs to be near promoters; however, this does not mean that the rest of the Hh-responsive GBRs are enhancers – and by the section title “Hedgehog signaling does not regulate other histone modifications at enhancers”, the authors are clearly referring to these distal elements as enhancers.We have extensively revised the entire manuscript to define terminology, including the specific example mentioned above, which now reads:“Although Stable GBRs are not highly enriched at HH target genes, 62% of them  are located in close proximity to the promoters of genes (2kb upstream to 1kb downstream of TSS), compared to 26% (91/349) of HH-responsive GBRs . Most promoter-associated Stable GBRs  are found at promoters associated with CpG islands (defined as a TSS with a CpG region within 5kb upstream to 2.5kb downstream), a quality typically associated with housekeeping genes, and genes that tend to be more broadly expressed and less tissue-specific .”3) In the section "Hh signaling does not regulate other histone modifications at enhancers", H3K4me1 should shift to H3K4me2 at the active enhancers. The fact that H3K4me2 does not change from Shh mutants to wildtype suggests these may not be active enhancers. In fact H3K4me2 only marked "a subset of Hh-responsive GBRs"- why don't they have H3K4me2? If the thought is that they might represent weak enhancers, they should show bidirectional PolII. Related to this in the subsequent section using ATAC-seq, why are the GBRs less accessible under the wild type "activated" condition?We are unsure why H3K2Me2 is only enriched at a minority of GBRs . To address this concern more directly, we visualized the presence of several enhancer markers: ATAC-seq, H3K4Me2, H3K4Me1 in those GBRs that have enhancer activity in transgenic embryos and are, by that criteria, functional enhancers. Over half (61% – 81/144) of the Stable GBRs with enhancer activity in the limb have H3K4Me2 enrichment while 25%(3/12) have H3K4Me2 enrichment in HH-responsive enhancers. To illustrate these findings, we have added Venn Diagrams showing the overlap of enhancer markers in transgenic embryos that drive limb expression  and have also added ChIP-seq plots for a representative enhancer .The data are also summarized for all GBRs in Figure 7A and Figure 7—figure supplement 1. We have added the following section to the Results:“While all GBRs tested in the VISTA database with limb activity are by definition enriched for H3K27ac, 91% of HH-responsive GBRs and 95% of Stable GBRs are also enriched for H3K4me1 . Additionally, all GBRS are enriched for at least two markers of enhancers  while most are enriched for 3-4 of these markers  .”To address the question concerning wild type ATAC-seq accessibility, we compared WT GBRs that have called ATAC-peaks (ATAC+) with WT GBRs that do not have ATAC-peaks (ATAC-) . We have added this information to the Results:“To determine if these regions are likely to be enhancers, we analyzed the co-enrichment of the enhancer markers H3K4me1 and H3K4me2 at ATAC accessible (ATAC+) and inaccessible (ATAC-) HH-responsive GBRs. […] These results suggest that most of the ATAC- regions are likely to correspond to real enhancers though at a somewhat reduced frequency compared to ATAC+ regions.”4) The statement stating that the data suggest "a model in which loss of an HDAC-GLI repressor complex leads to acetylation" is a bit misleading as it sounds like the only possibility. In fact, the data suggest a correlation that would likely be true regardless of mechanism and the subsequent culturing with FK228 are again consistent with the model but do not prove it as the GLI-HDAC interaction need not be direct.We agree, and to address this we performed ChIP-seq to identify HDAC1 binding regions in E11.5 limb buds . A sizable number of HH-responsive GBRs are enriched for HDAC1, which supports the possibility that this interaction might be direct. We have moved the HDAC inhibitor data  and HDAC 1 ChIP-seq data (summaries and representative plots) to new Figure 5 and describe these findings in the Results:“The increased enrichment of H3K27ac acetylation in HDAC-inhibited anterior limb buds was comparable to that seen in posterior limb buds . […] We conclude that GLI repressors regulate H3K27ac levels at HH-responsive GBRs through HDACs (see Discussion).”We note that many GBRs are not enriched for HDAC1, possibly because it is difficult to identify more transient HDAC1 binding events or because they are not enriched for HDAC1. Even if these regions are enriched for another HDAC, such as HDAC2, it does not ‘prove’ that the interaction is direct and we have modified the model Figure 7B  to signify that the interaction between GLI3 and HDAC is unknown (a gray box with dashed lines between GLI3 and HDAC). We have also added the following to the Discussion:“Our results indicate that HDAC1 is bound to about half of all HH responsive GBRs. […] Although the simplest model is consistent with GLI repressors directly (via a GLI3 and HDAC-containing repression complex), we cannot exclude the possibility that HDAC1 is constitutively bound CRMs in a GLI-independent fashion and the HDAC activity is indirect.”5) The authors should examine and report the ATAC-seq and chromatin data specifically at the enhancers from the VISTA enhancer database and discuss in regards to their model.We performed this analysis and include it as Venn Diagrams . We have included these data in the Results and find that all VISTA GBRs are enriched for at least one other enhancer marker besides H3K27ac (see response to point #3 above) while most are marked multiple markers. We have added the following to the Discussion:Shh-/- embryos, suggesting a loss of enhancer activity.”“We find that a subset of GLI-bound regions has chromatin modifications that change in response to HH signaling. […] However, compared to WT embryos, these regions have reduced or absent levels of histone H3K27 acetylation in In addition, we depict ATAC-seq and the other enhancer markers examined in this study as Figure 7A and Figure 7—figure supplement 1, which is directly above our model , which depicts these regions as ‘Enhancer.’


Bacterial gene expression regulation occurs mostly during transcription, which has two main rate-limiting steps: the close complex formation, when the RNA polymerase binds to an active promoter, and the subsequent open complex formation, after which it follows elongation. Tuning these steps' kinetics by the action of e.g. transcription factors, allows for a wide diversity of dynamics. For example, adding autoregulation generates single-gene circuits able to perform more complex tasks. Using stochastic models of transcription kinetics with empirically validated parameter values, we investigate how autoregulation and the multi-step transcription initiation kinetics of single-gene autoregulated circuits can be combined to fine-tune steady state mean and cell-to-cell variability in protein expression levels, as well as response times. Next, we investigate how they can be jointly tuned to control complex behaviours, namely, time counting, switching dynamics and memory storage. Overall, our finding suggests that, in bacteria, jointly regulating a single-gene circuit's topology and the transcription initiation multi-step dynamics allows enhancing complex task performance. E.g. ts genes . This ists genes –13, whosrs, etc. ,14–19. Ers, etc. .One way to halt a promoter's activity is the intervention of a transcription factor (TF), capable of negative regulation ,21. OtheE. coli are autoregulators [Almost 60% of the TFs produced by gulators –28. Autogulators . The outgulators . The resgulators .In autoregulated genes, the regulatory mode (repression versus activation and the strength of the regulation) is of importance in the resulting changes in mean and cell-to-cell variability in protein numbers ,30–34, aHere, using parameter values extracted from recent single-cell measurements of transcription and translation kinetics in live cells, we designed stochastic models of gene expression controlled by different regulatory modes to explore how the combination of regulation by the action of TFs and regulation of the rate-limiting steps in transcription initiation expands the state space of possible behaviours of autoregulated and externally regulated single-gene circuits. For this, we perform stochastic simulations for varying inducer concentrations, relative durations of the rate-limiting steps in transcription initiation, and binding strengths of the activator/repressor transcription factors (to tune the feedback strength in autoregulation). From these simulations, we assess the mean expression levels at ‘steady state’ (i.e. after a long time period), the cell-to-cell variability in gene expression products and the response times of our circuit in model cells. We note that the term ‘steady state’ here refers to ‘noisy attractors’  because,2.We model gene expression when constitutive (acting as a ‘null model’) and when externally or autoregulated by activator/repressor TFs, which act on transcription initiation. The models are depicted in a, constitutive promoters are always active [From figure 1s active ,41  [In reactions 2.1 and 2.2, the unbinding of activators/repressors occurs at the constant rate ity (kB)   and repressors (in mM). The binding strength of activators/repressors can be tuned by altering factor ‘f’, which is the relative ratio of TF binding rates, which we use as a measure of the feedback strength of autoregulated genes.The inducer concentration is represented by erial in ). MeanwhPON), forming a closed complex, PCC [k2 represents the inverse of the mean time-length for the open complex to be complete, once initiated:In all models, transcription starts with the binding of a free RNAP to an active promoter , the production of activators/repressors is assumed to occur at a basal rate (reaction 2.7):d,e) produce their activators/repressors, which establish a feedback regulatory system (reactions 2.1 and 2.2). Activators/repressors are produced from the RNAs (reaction 2.8):In models where gene expression is regulated by TFs, but feedback reactions are absent b,c, the ed genes d,e produFinally, we assume constant degradation rates of mRNAs (reaction 2.9) and TFs (reaction 2.10) –62:2.9mThe parameter values used here are based on empirical data or have been fitted to physiologically realistic ranges (shown in 2.2.in vivo kinetics of the rate-limiting steps in active transcription (f). Shortly, from measurements of intervals between consecutive transcription events in individual cells (Δt) at different RNAP concentrations, one can infer the mean duration of the events prior to (τprior) and after (τafter) the commitment to open complex formation. This is possible as the value of τprior differs with a concentration of RNAP, while τafter does not [In , a methocription f. Shortldoes not . Here, ft) equals, when regulated by activators or repressors, respectively (equations (2.11) and (2.12)):According to the models, the mean interval between consecutive RNA production events ):In constitutively expressed genes, Δ2.3.To simulate the models, we use SGNSim (Stochastic Gene Networks Simulator) , a simulτafter/Δt between 0.05 and 0.95, to cover the wide diversity of empirical values reported in [t is kept constant, and k1 and k2 are changed according to equations (2.12)–(2.15), depending on the circuit's topology. By keeping Δt constant, the RNA production kinetics (e.g. its noise) is changed due to changing the quantitative relationship between τprior and Δt, rather than due to changing the mean rate of transcription (which would require changing Δt). This is because changes in Δt are limited by biophysical constraints such as intracellular concentration of RNA polymerases, promoter affinity biophysical limitations, etc. while empirical evidence suggests that τprior/Δt can be changed from almost 0 to almost 1 [We focus on how tuning the kinetics of transcription initiation affects the behaviour of the model circuits. For this, instead of changing the mean transcription rate, we alter the fraction of time spent in the events prior to and after initiation of the open complex formation. Namely, we vary orted in ,18. For almost 1 ,18.k1 and k2 for constitutive genes were estimated using equations (2.14) and (2.15). Since, in this model, the kinetics of the steps following initiation of the open complex formation does not depend on the regulatory molecules, k2 of externally regulated genes  is also estimated using equation (2.15).k1 depends on the inducer and TF intracellular concentrations and, thus, is calculated using equation (2.16) for positive regulation and equation (2.17) for negative regulation. To change the induction strength, while maintaining Δt and τprior/Δt constant, we change k1, in accordance with equation (2.16) (for activation) and equation (2.17) (for repression), for both externally regulated and self-regulated genes.The rates τprior, we use τprior/Δt as a means to evaluate the effective influence of transcription initiation on the overall protein expression dynamics . We also explore how the features added by autoregulation, such as memory storage, bimodal activation and oscillations are affected by τprior/Δt, feedback strength and inducer concentration. We use this to determine the optimal parameter values for performing these tasks.For any given set of values of variables (e.g. rate constants), we simulate 1000 individual model cells. From these simulations, we extract the mean and cell-to-cell variability in protein numbers in individual cells at steady state. We also estimate the mean activation time, defined as the time taken to reach half of the protein expression levels at steady state. Finally, as TFs and RNAP numbers only affect 2) (squared mean over standard deviation). This quantity is shown to differ with τprior/Δt (e.g. b).We quantify noise in gene expression (variability in e.g. protein numbers over time) by the squared coefficient of variation  of estimation of a given quantity (Q) (e.g. CV2) from the simulations (due to this estimation being performed from a finite set of simulations). U is here quantified by the variance (equation (2.18)), and as expected is shown to differ with Δt .F) of the oscillations as follows, where n is the number of frequency bins in the spectrum, iF is the frequency of the spectrum at bin i of n and iPxx = power spectral density (dB Hz−1) of spectrum at bin i of n:We further quantify the uncertainty  and repressed  genes, as follows, respectively:We also calculate the spread of the amplitudes of each oscillation:3.3.1.a for varying τprior/Δt (while keeping Δt constant). Also, we changed Δt for fixed τprior/Δt values. From the simulations, we extracted the mean and variability (as measured by CV2) of the protein numbers in individual cells at steady state, and the mean activation times. We also estimated the uncertainty in these variables (equation (2.18)). In these simulations, the model is initialized without proteins.Here, constitutive genes are used as a ‘null model’ to assess, by comparison, the effects of external and autoregulation by TFs. Thus, we first characterize the dynamics of this null model. We simulated the model in figure 1τprior/Δt does not affect the protein numbers at steady state (a), as expected, because Δt was not altered. Only the cell-to-cell variability in protein numbers is affected, which is expected because higher τprior/Δt allows more frequent binding and unbinding of the RNAPs to the active promoters in between transcription events (b). As such, the uncertainty in these quantities is not affected . For example, consider two TFs with similar repressing capabilities, with one being able to double the mean duration of the first rate-limiting step, while the other can double the duration of the second rate-limiting step. In this scenario, if e.g. the first rate-limiting step is more longer-lasting than the second, the TF acting on the first step will have a stronger effect on the rate of RNA production. Thus, we hypothesized that the modes of regulation involving TFs  change in sensitivity with τprior/Δt. To test this, we changed inducer concentration and τprior/Δt and assessed the steady state expression levels in each model. Results are shown in Previous studies showed that the sensitivity of a promoter's activity to TFs is affected by itiation ,23 . Meanwhile, the qualitative behaviour depends mostly on τprior/Δt and inducer concentration. Interestingly, the effects of each variable depend on the value of the other. E.g. in figure 3b,d, changing inducer concentration has stronger effects if τprior/Δt is large. Also, changing τprior/Δt has stronger effects for weak inducer levels (figure 3d).Overall, in all cases, the quantitative behaviour of the circuits depends on all three variables . Meanwhile, autoactivation causes this increase to be less monotonic (figure 3b). This would be relevant in the context of gene circuits, allowing sharper state shifting. Meanwhile, in figure 3c,d, for weak inducer levels, increasing τprior/Δt reduces protein numbers at steady state. I.e. it decreases leaky expression (i.e. protein production when in the presence of repressors).Also from figure 33.3.τprior/Δt and autorepression strength. To change the latter, we alter the feedback strength, f (equation (2.3)). In τprior/Δt for three values of f.Repressed genes, especially autorepressed, exhibit leaky expression, which can be detrimental to cell growth rates –79 and fτprior/Δt. Meanwhile, for strong feedback, protein numbers at steady state decrease with τprior/Δt. Owing to the negative autoregulation, induction levels have little to no effects. Overall, this suggests that increasing τprior/Δt along with strengthening the feedback strength is the best strategy for reducing leaky expression in autorepressed circuits.From 3.4.b, autoactivated genes exhibit biphasic behaviour for higher values of τprior/Δt. Next, we explore how to tune the threshold inducer concentration to reach biphasic behaviour as a function of f (equation (2.3)) and τprior/Δt. Results in From figure 3τprior/Δt is too low. In this regard, figure 6a,b shows that having k1 > k2 allows higher expression levels as induction is increased , until a given threshold value, beyond which the opposite occurs, resulting in a biphasic behaviour. From figure 6c,d, this is not observed in autorepressed genes. Interestingly, the feedback strength determines the induction level at which the biphasic behaviour emerges. Overall, these results indicate that the kinetics of transcription initiation can play a key role in autoregulatory networks, even without affecting the mean transcription rate.From 3.5.2 in protein numbers at steady state. Results are shown in Next, we study how the kinetics of transcription initiation and the regulatory mode can be combined to regulate cell-to-cell variability in protein numbers in single-gene circuits with feedback. To quantify this variability, we use CV2 in protein numbers at steady state than externally repressed and autorepressed ones. Also, they are more sensitive to inducer concentrations. Externally activated and autoactivated genes differ in that the latter has more variability in behaviour. Meanwhile, in all cases , τprior/Δt does not tune this variability significantly. Overall, we find the inducer concentration and, secondly, Δt to be the main regulators of cell-to-cell variability in protein numbers at steady state, confirming again the hypothesis that the kinetics of transcription initiation can play a key role in autoregulatory networks, even without affecting the mean transcription rate.From 3.6.τprior/Δt affect activation times . Results are shown in Precise timing of events is essential in complex cellular processes ,82. Typiτprior/Δt. Further, when and only when externally controlled, they are also nearly independent of Δt. Finally, all circuits are affected by the inducer concentration.In general, externally activated and autoactivated genes respond slower than externally repressed and autorepressed ones, in agreement with –87 figu. Meanwhi3.7.f) and the transcription initiation kinetics ). For this, for each case, cells were simulated under different induction levels until reaching their respective steady states (‘ON’ states). We studied the transition from the ON to the OFF state, by simulating cells whose initial condition corresponds to the steady state in protein numbers under full induction. For each model, two curves were generated, from OFF to ON, and from ON to OFF. Results in We explore how the memory storage capacity of autoactivated circuits can be jointly tuned by the feedback strength . Surprisingly, the higher value of k1 caused the ON state to remain, even after removing the inducer (c). Finally, by changing k1 and the feedback strength, a wide range of values of τprior/Δt was covered (0.2 to 0.8). This range is reduced by weakening the feedback strength and/or k1  and feedback strength (f). From the simulations, we obtained histograms of the fraction of cells with a given number of proteins at different time points . Meanwhile, the feedback strength affects the ranges of τprior/Δt that can be reached by tuning k1.3.9.τprior/Δt affects the time for transcript production to initiate, we hypothesize that it can be used to tune the dynamics of oscillations in the protein numbers resulting from autorepressed genes. We simulated these models for various values of k1 and feedback strength and extracted the mean frequency and spread (equation (2.19)) of the oscillations (τprior/Δt (equation (2.22)) reached when changing the feedback strength and k1.Since llations . We alsok1. Overall, longer-lasting transcription initiation results in faster, more spread oscillations. Finally, in the regime of weak induction, τprior/Δt is mainly controlled by the feedback strength while, overall, the ranges of τprior/Δt decrease for increasing k1.Results in 4.E. coli as a model organism  has [in vivo ,18. This[in vivo ). In rec[in vivo –90.Previous studies have thoroughly investigated how negative and positive regulation affects noise (e.g. ) and resOverall, we found that the efficiency with which the models exhibited complex dynamics regulation, such as minimization of leaky expression, biphasic behaviour, regulation of cell-to-cell variability in protein numbers, tuning of activation times, memory storage capacity and bimodal and oscillatory behaviour, was achieved by combining the tuning of the autoregulatory mechanism parameter values with the tuning of the rate-limiting steps in transcription.This suggests that the strategy here used could be of assistance to improve the efficiency of presently existing synthetic genetic circuits. Relevantly, most predictions regarding the changes in kinetics obtained from the simulations could be tested by using such already engineered circuits (e.g. ), by chaWhile too extensive to introduce in the present work, in the future, it will be of interest to focus on specific models and further analyse how the various parameter values combine to generate the complex behaviours here reported, such as biphasic response and behavioural transitions.Evidence suggests that prokaryotic cells evolved several autoregulated genes for time tracking, memory storage and decision making . Given tIn conclusion, our results may assist the engineering of single-gene synthetic circuits with predefined dynamics using the combined tuning of the feedback strength of the proteins and the kinetics of the rate-limiting steps in transcription initiation of the component promoter in order to maximize the circuit's efficiency. Such circuits, if efficient, may become of wide use due to their relative simplicity.