Background: Tracking dynamic protein–chromatin interactions in vivo is key to unravel transcriptional and epige‑ netic transitions in development and disease. However, limited availability and heterogeneous tissue composition of in vivo source material impose challenges on many experimental approaches. Results: Here we adapt cell‑ type‑ specific DamID ‑ seq profiling for use in Drosophila imaginal discs and make FLP/ FRT‑ based induction accessible to GAL driver‑ mediated targeting of specific cell lineages. In a proof ‑ of‑ principle approach, we utilize ubiquitous DamID expression to describe dynamic transitions of Polycomb‑ binding sites during wing imaginal disc development and in a scrib tumorigenesis model. We identify Atf3 and Ets21C as novel Polycomb target genes involved in scrib tumorigenesis and suggest that target gene regulation by Atf3 and AP‑ 1 transcription factors, as well as modulation of insulator function, plays crucial roles in dynamic Polycomb‑ binding at target sites. We establish these findings by DamID ‑ seq analysis of wing imaginal disc samples derived from 10 larvae. Conclusions: Our study opens avenues for robust profiling of small cell population in imaginal discs in vivo and pro‑ vides insights into epigenetic changes underlying transcriptional responses to tumorigenic transformation. Keywords: DamID, Wing imaginal disc, Polycomb, Scrib Background Several experimental approaches to overcome these Understanding the in vivo dynamics of DNA binding challenges have been developed. For example, chromatin by chromatin regulatory proteins is key to elucidate the immunoprecipitation (ChIP) protocols use fluorescence- molecular basis of cell behaviours ranging from differ - activated cell sorting (FACS) or immunoprecipitation entiation to adaptation and plasticity. The model system (IP)-based methods to isolate Drosophila cell popula- Drosophila has contributed tremendously to our under- tions from tissues [1–4]. These approaches, however, still standing of chromatin dynamics during developmental require a significant amount of input material for repro - transitions, stem cell differentiation and also tumorigen - ducible results, which has prevented these methods esis. Yet, like other in vivo model systems, the small size from being used in contexts where small source tissues, and the heterogeneous fate composition of Drosophila such as imaginal discs, are routinely isolated by manual tissues still pose challenges to the detailed tracking of dissection. Alternatively, recent publications establish DNA binding sites in different cell populations and line - cell-type-specific DamID profiling in Drosophila brains ages in vivo. [5–8]. PCR-amplified tracking of adenine methylation (m6A) conferred by DamID to GATC sequence motifs and the absence of IP steps significantly reduces the *Correspondence: firstname.lastname@example.org‑freiburg.de input material required for DamID . Moreover, m6A Center for Biological Systems Analysis, Albert‑ Ludwigs‑ University is only generated in cell types expressing DamID con- Freiburg, Habsburgerstrasse 49, 79104 Freiburg, Germany structs; therefore, DamID protocols do not necessitate to Full list of author information is available at the end of the article © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/ publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 2 of 17 physically isolate cell populations from complex tissues of insulator function plays crucial roles in dynamic PcG [5, 6]. Thus, DamID is a very attractive technology to pro - behaviour. We establish these findings by DamID-seq file small and even rare cell populations in vivo. analysis of wing imaginal discs samples derived from as We wanted to adapt the inducible FRT/FLP-out DamID little as 10 larvae. We furthermore describe a versatile system described for Drosophila brains  to cell-type- GAL4-driven cell lineage-specific DamID system that specific profiling in imaginal discs. These small tissues can be used for DamID-seq profiling in many Drosophila have a rich history as model to study developmental tissue. patterning, tumorigenesis and regeneration  but are mostly accessed by manual dissection for experimental Results analysis. We wanted to establish versatility of targeting Establishment of versatile GAL4‑dependent control of cell DamID expression to specific cell types by enabling the lineage‑specific DamID use of GAL4 driver lines available in these tissues. While To establish DamID in WIDs, we employed a transgenic the TaDa-DamID system [6, 8] also utilizes cell-type-spe- fly line carrying an inducible Dam or Dam-Pc fusion con - cific targeting by GAL4 drivers, TaDa depends on acute struct [5, 7]. Briefly, a full-length Hsp70 promoter is sepa - expression patterns of a chosen GAL4 driver at the time rated from the Dam or the Dam-Pc coding sequence by of analysis. In contrast, we aimed to target DamID to spe- a cassette containing a transcriptional terminator flanked cific cell lineages enabling tracking of DNA binding sites by FRT sites, which prevents transcription of Dam or in parental and descendant populations—independent of Dam fusion proteins (Fig. 1a). Ubiquitous or cell-type- whether the GAL4 driver used was still active in descend- specific expression of a FLIP recombinase (FLP) medi - ant cells. Furthermore, while the FRT/FLP-out DamID ates site-directed recombination of flanking FRT sites has been suggested to be compatible with GAL4-depend- and removal of the terminator cassette, allowing expres- ent targeting , its cell-type specificity and experimen - sion of Dam or Dam fusion proteins [5, 7]. Indeed, only tal feasibility have not yet been tested. Finally, we sought upon ubiquitous expression of a heat-shock-induced to establish a proof of principle that a limiting amount FLP, we observed the characteristic DNA smear formed of manually dissected imaginal disc material is sufficient by the methylation-dependent PCR products ampli- to sensitively detect changes in DNA binding activity in fied from genomic DNA (gDNA) extracted from WIDs development and disease. (Fig. 1b, Additional file 1: Fig. S1A). In addition, genotyp- More specifically, we asked whether DamID may be ing PCR confirmed the genomic elimination of the ter - suitable to track the epigenetic regulator Polycomb (Pc) minator cassette from the DamID constructs only after in wing imaginal discs (WIDs) during different develop - FLP induction (Additional file 1: Fig. S1B, B′). Combined, mental stages and tumorigenic transformation. Polycomb these observations indicate that the terminator cassette is the founding member of the Polycomb group (PcG) prevents transcription of Dam or Dam-Pc proteins in family of proteins who form different complexes, such WIDs and that their expression can be efficiently induced as the Polycomb Repressive Complexes 1 and 2 (PRC1 by the presence of FLP. and PRC2). PcG proteins epigenetically silence genes We wanted to optimize this inducible DamID system required for fate specification, cell cycle progression and for flexible cell-type-specific targeting by the rich reper - tissue growth by modulating multiple histone modifi - toire of GAL4 driver lines available. We thus screened a cations [11–15]. Previous studies demonstrated that number of UAS-FLP constructs from different sources PcG protein binding sites change dynamically through- for their ability to mediate efficient removal of the FRT- out early embryonic development and suggested that a flanked transcriptional terminator cassette. Moreover, number of Pc target genes, like JAK/STAT cytokines of we specifically searched for a UAS-FLP line that did not the unpaired (upd) family, may be silenced by Pc to sup- show leaky expression in the absence of a GAL4 driver press tumorigenesis [16–20]. In fact, a significant overlap to prevent unspecific removal of the terminator cassette. between PcG target genes and genes upregulated in neo- Indeed, combining a UAS-FLP( JD2) transgene  with plastic WIDs mutant for the epithelial polarity regulator the inducible DamID system caused GAL4-independed scribbled (scrib) has been described . However, direct removal of the terminator cassette (Additional file 1: Fig. experimental evidence for dynamic Pc-binding at co-reg- S1B′). In contrast, a UAS-FLP(EXEL) transgene  did ulated candidate genes is still outstanding. not induce removal of the terminator cassette in WIDs in We report here the co-regulation of multiple oncogenic the absence of a GAL4 driver (Additional file 1: Fig. S1B″). genes by dynamic Pc-binding, while also identifying at Only combining a DamID;UAS-FLP(EXEL) line with a least two novel Pc target genes involved in scrib tumori- rotund(rn)GAL4 driver caused partial removal of the ter- genesis. We furthermore suggest that gene regulation by minator cassette in WIDs, consistent with the restricted Atf3 and AP1 transcription factors as well as modulation expression of rnGAL4 in the central domain of the disc La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 3 of 17 a b Myc STOP cassette Hsp Dam FRT sites 800 Myc Hsp Dam Pc STOP cassette ptc > FLP;Dam-Pc ptc > FLP;Dam PtcWg adult wing c d g h i AP P AP MycMyc g’ h’ i’ WT ptc > FLP; Dam-Pc e f g’’ h’’ i’’ DAPI DAPI AP e’ f’ pH3max-proj pH3max-proj f’’ e’’ Dcp-1 max-proj Dcp-1 max-proj Fig. 1 Establishing cell lineage‑specific DamID in wing imaginal discs. a Schematic representation of the FLP ‑inducible Dam and Dam‑Pc constructs used in this study. b Characteristic DNA smear formed by DamID methylation‑ dependent PCR products on agarose gel. Lanes 1–2: wing imaginal disc ( WID) samples, where FLP expression has not been induced. Lanes 3–4: WID samples from genotypes ubiquitously expressing FLP after induction by a heat shock (hsflp). me ‑PCR‑NC refers to negative PCR controls lacking DNA template. c, d WIDs stained for expression of the Myc‑tag if Dam (c) and Dam‑Pc (d) were induced by ptcGAL4‑ driven expression of UAS‑FLP(EXEL). Expression of the Myc‑tagged fusion proteins was boosted by a heat shock (see Experimental procedures). A and P refer to anterior and posterior compartments, respectively. e–e″ Wild‑type WID stained with DAPI (e), and for pH3 (e′) and Dcp‑1 (e″). Maximum projections of a confocal stack are shown in E’ and E’’ to reveal all signals. f–f″ WID from ptc > FLP;Dam‑Pc expressing larvae stained with DAPI (F) and for pH3 (f′) and Dcp‑1 (f″). Maximum projections of a confocal stack are shown in f′ and f″ to reveal all signals. g–g″ WIDs from indicated genotypes stained for patched (Ptc). A and P refer to anterior and posterior compartments, respectively. h–h″ WIDs from indicated genotypes stained for wingless ( Wg). D and V refer to dorsal and ventral compartments, respectively. i–i″ Adult wings from indicated genotypes 24 h after eclosion. All scale bars: 100 µm Dam (unind) Dam-Pc (unind) Dam Dam-Pc mePCR-NC ptc > FLP;Dam-Pcptc > FLP;Dam WT La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 4 of 17 whether expression of Dam by the low basal activity (Additional file 1: Fig. S1B″). This region was visualized of the Hsp70 promoter at 21 °C is suitable for DamID using the G-trace system (Additional file 1: Fig. S1C) , profiling by maintaining wing disc cell viability, we which maps cell lineage history and real-time expression monitored the occurrence of mitosis and apoptosis by of a GAL4 driver of choice. To prove that Dam and Dam- immunodetection of phospho-H3S10 (pH3) and the Pc fusion proteins are really expressed in a cell-type-spe- activated effector caspase Dcp-1, respectively. No dif- cific and GAL4/UAS-FLP(EXEL)-dependent manner, we ferences in mitotic or apoptotic activity between the sought to visualize expression of the Myc-tag encoded by anterior and posterior compartment could be observed both constructs [5, 7]. To this end, we induced removal of when larvae were maintained at 21 °C and the termi- the terminator cassette by crossing a stable DamID;UAS- nator cassette was removed under the control of ptc- FLP(EXEL) line to a patched(ptc) GAL4 driver. ptcGAL4 GAL4/UAS-FLP(EXEL) (Fig. 1e–f″). Furthermore, is active in a row of cells anterior to the anterior–poste- immunodetection of developmental regulators such rior compartment boundary in WIDs (Additional file 1: as Ptc itself (Fig. 1g–g″) or wingless (Wg) (Fig. 1h–h″) Fig. S1C′). However, most of the anterior compartment revealed appropriate patterning activity, and adult derives from cells that had expressed ptc earlier dur- wings arising from these discs displayed only sub- ing development (Additional file 1: Fig. S1C′). Thus, the tle alterations, such as extra vein tissues (Fig. 1i–i″). early removal of the terminator cassette during develop- Combined these results suggest that inducible DamID ment under the control of ptcGAL4 is expected to cause profiling does not interfere with WID viability and expression of Myc-tagged Dam and Dam-Pc proteins in developmental progression and thus presents an excel- all cells of the anterior WID compartment. Notably, Dam lent option for cell-type-specific mapping of DNA and Dam-Pc proteins expressed under the control of the binding sites in WIDs in vivo. heat-shock promoter are present at undetectable levels if flies were kept at 21 °C. However, if boosted by a heat shock (see Experimental procedures), high expression of DamID and ChIP profiles of Polycomb‑binding sites the Myc-tag could be detected specifically in the anterior correlate compartment, if FLP expression was induced by ptcGAL4 To provide a proof of principle that DamID sensitively (Fig. 1c, d). Importantly, Myc-tag expression was com - detects differences in DNA binding activity in vivo, we pletely absent in the posterior compartment. Similarly, wanted to compare Pc-binding profiles between wild- when DamID was induced using the posterior compart- type (WT) and scrib tumourous wing discs (Fig. 2a, ment driver engrailed(en)GAL4, boosted expression of a′, Additional file 1: Fig. S2). We used scrib as a classic the Myc-tag was exclusively detected in the posterior example of a polarity-deficient tumour suppressor gene compartment (data not shown). These results indicate  for which genetic interactions with and defects in that UAS-FLP(EXEL) allows for the specific and flexible Polycomb silencing have been reported . induction of cell-type-specific DamID in WIDs under the We first induced ubiquitous expression of Dam and versatile control of cell-type-specific GAL4 drivers. Dam-Pc in whole larvae using a FLP under the control High expression levels of Dam are known to interfere of a heat-shock promoter (hsflp ). We isolated and ampli with DamID specificity  and viability  (Addi - fied methylated genomic DNA from WIDs of 10 WT or tional file 1: Fig.S1D, E). Therefore, to understand scrib third-instar larvae expressing either Dam alone or (See figure on next page.) Fig. 2 DamID and ChIP profiles of Polycomb ‑binding sites correlate. a–a′ Wild‑type WID (a) and scrib WID stained with DAPI (cyan) and phalloidin (red). Scale bar: 100 µm. b Characteristic DNA smear formed by DamID methylation‑ dependent PCR products obtained from hsflp‑induced samples isolated from WT WIDs (lane 1 and 2) or scrib WIDs (lanes 3 and 4). c Box plot comparing the distribution of the Pc‑binding intensities (log ) at individual GATC fragments (normalized to Dam) in WT and scrib DamID‑seq samples. Pc‑binding intensities averaged over two biological replicates are shown. d Heat‑scatterplot showing the correlation of Pc‑binding intensities (log ) at individual GATC fragments (normalized to Dam) in WT and scrib . (Pearson’s correlation, r = 0.47). e ChIP‑ chip Pc‑binding profiles (modENCODE) from three different sources (S2 cells, DmBG3 cells and embryo) and DamID‑seq profiles mapped to individual GATC fragments obtained in this study ( WT and scrib WID) visualized across the BX‑C cluster (demarcated by dotted lines). GATC motifs mapping to the genome sequence are indicated below. e′ Pearson’s correlations for a comparison of Pc‑binding intensities in ChIP ‑ chip profiles (modENCODE) from three different sources (S2 cells, DmBG3 cells and embryo) and Pc‑binding intensities WT and scrib WID DamID‑seq Pc profiles at GATC fragments mapping to microarray probe sequences. f Percentage of genomic sites in scrib compared to WT WID that lose (loss), acquired new (gain) and had no change (no change) in Pc‑binding visualized for each chromosome and the whole genome. Loss, gain and no‑change transitions were determined by transitions between ‘enriched’, ‘ intermediate’ and ‘depleted’ Pc‑binding states classified by a three ‑state HMM analysis. Note that the no‑change category contains GATC fragments that were classified as ‘enriched’, ‘intermediate’ and ‘depleted’ for Pc‑binding and thus includes Pc target and non‑target genes La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 5 of 17 WT scrib a b a’ DAPI c d 0 0 r = 0.47 log Pc-binding WT WT scrib 100kb -2 S2 -2.9 Embryo WID-WT -2 WID-scrib -2 GA e’ DamID f 1 chr 2L chr 2R chr X WID-WT WID-scrib 10% 8% 16% scrib vs WT 20% 24% 19% S2 11% 0.40 18% 65% 70% 68% 0.25 Embryo chr 3L chr 3R chr 4 11% 3% 8% 13% 38% 16% loss 71% gain no change 76% 76% 58% Dam Dam; scrib scrib log Pc-binding DamID-seq log Pc-binding scrib 2 La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 6 of 17 a Dam-Pc fusion protein (Fig. 2b) and generated NGS Pc-binding states and analysed transitions between these libraries using protocols devoid of additional PCR ampli- states when comparing scrib to WT WIDs (see Experi- fication steps to avoid PCR biases (see Experimental mental procedures, Additional file 1: Fig. S4B, Addi- procedures). tional files 2: SF2 and 3: SF3) [26, 32–35]. As expected, The PCR-free NGS library preparation from 20 WIDs we obtained three possible clusters that described the generated sequencing profiles with relatively low cor - changes between the two profiles, namely (1) ‘no change’, relation coefficients across replicates (Additional file 1: which defined GATC fragments that did not vary in their Fig. S1F), likely due to high noise in profiles. However, Pc-binding classification between WT and scrib profiles, assessment of multiple reproducibility parameters, such irrespective of whether these sites were bound by Pc in as correlation coefficients (Additional file 1: Fig. S1F), WT and scrib WIDs or not; (2) ‘loss’ defined GATC frag - hierarchical clustering approaches using 94 DamID-Seq ments, which were bound by Pc in WT but not in scrib profiles (Additional file 1: Fig. S3A) and autocorrelation discs; and (3) ‘gain’ defined GATC fragments, which were of neighbouring GATC sites at Lag 2 (Additional file 1: not bound by Pc in WT but in scrib WID samples. This Fig. S3B) , revealed that technical replicates within analysis revealed that about 11% of ‘intermediate’ and genotypes are always more similar to each other than ‘enriched’ Pc-binding states present in WT were lost in 1 1 replicates across genotypes. Thus, PCR-free DamID-seq scrib WIDs and about 18% of scrib ‘intermediate’ and libraries can reproducibly reveal DNA binding profiles ‘enriched’ Pc-binding states were arising de novo (Fig. 2f ). for small in vivo tissue samples. This suggests that Pc-binding dynamics are altered in a While a subset of PcG target genes was previously loci-specific manner in scrib discs. reported to be upregulated in scrib WIDs , we found To learn more about the effects that gain and loss of that total levels of H3K27 modifications were compara - Pc-binding may have on transcriptional activity of Pc tar- 1 1 ble between WT and scrib WIDs (Additional file 1: Fig. get genes in scrib discs, we related DamID Pc-binding S4A). Our DamID-seq profiles confirmed that Pc-binding sites to previously published WT and scrib WID tran- at individual sites (as defined by any genomic sequences scriptome dataset . To this end, we extracted the flanked by Dam-targeted GATC motifs, also referred to presumptive regulatory region spanning across the tran- as GATC fragments hereafter) was not globally altered in scriptional start site (TSS)(− 2.5 kb ~ + 1 kb) of all genes 1 1 scrib (Fig. 2c). Indeed, when the genome-wide distribu- differentially expressed in scrib (Fig. 3a) and recovered tion of Pc-binding intensities at these sites was compared, all included GATC fragments, hereafter referred to as the correlation between WT and scrib discs (Pearson’s transcription-associated GATC fragments (taGATCf) correlation, r = 0.47, Fig. 2d) was only slightly lower than (Additional file 1: Fig. S4C). We compared changes in for biological replicates (Pearson’s correlation r = 0.51, Pc-binding (gain, loss or no change) at an individual Additional file 1: Fig.S1F). Importantly, broad binding taGATCf with changes in the transcription levels of the of Pc to the Bithorax complex (BX-C) observed in Pc- associated differentially expressed gene (Fig. 3a). When DamID profiles could also be detected in Pc ChIP profiles comparing WT and scrib WIDs, many transcriptional from S2 cells, DmBG3 cells and whole embryo (Fig. 2e) changes at differentially expressed genes whose presump - [27, 28]. The Pearson’s correlation coefficients calculated tive regulatory region contained at least one Pc-bound for a comparison of the genome-wide Pc-binding intensi- taGATCf occurred in the absence of changes to Pc-bind- ties at individual GATC fragments in our Pc-DamID-seq ing (data not shown). In numerous instances, however, a and the corresponding GATC fragments in individual gain or loss of Pc-binding at any one taGATCf was linked Pc ChIP-chip profiles ranged from 0.25 to 0.4 (Fig. 2e′). to a gain or loss in transcript levels of the associated gene This finding is in agreement with previous comparisons (Fig. 3b, Additional file 1: Fig. S4D, Additional file 4: of the two techniques [29–31] (for example Pearson’s cor- Table S1). Surprisingly, we found that, in some cases, relation r = 0.37 in ). Our analysis thus indicates that gain in Pc-binding could occur in the context of upregu- DamID-seq is a suitable method to reveal DNA binding lated transcription (group I) and loss of Pc-binding could profiles of Polycomb in WID in vivo. occur when transcription was downregulated (group IV) (Fig. 3b). While this unexpected behaviour appears to Polycomb‑binding is altered only at a subset of target sites contradict the established role of Pc as promoter of gene in scrib wing discs silencing, we speculate that, instead, additional regula- To understand whether alterations in Pc-binding at spe- tory inputs at these target sites dominate target gene cific target genes may contribute to tumour phenotypes expression or, alternatively, that the bulk of transcrip- in scrib disc, we performed a three-state hidden Markov tional changes and changes in Pc-binding states may arise model (HMM) analysis of Pc-binding at individual GATC in two different cell populations. fragments to define ‘depleted’, ‘intermediate’ and ‘enriched’ La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 7 of 17 Pc-binding a b intermediate enriched depleted Ilp8 WT chinmo scrib I III GATC TSS WT -2500 bp +1000 bp Socs36E scrib upd3 GATC Ets21C WT upd3 scrib GATC chinmo Atf3 taGATCf WT Atf3 PlexB Socs36E scrib mRNA level GATC 0 caps WT Ets21C pnt Fas3 scrib GATC dsx WT Ilp-8 DamID-seq data RNA-seq data scrib GATC WT Toll-7 scrib GATC WT dsx II IV scrib GATC WT pnt scrib Pc-binding: gain loss GATC -2.5 kb TSS +1 kb chinmo Socs36E Pc S2 - ChIP-chip upd3 H3K27me3 ChIP-seq Pc ChIP-seq Pc loss in scrib - DamID-seq Atf3 Ets21C Ilp-8 Fig. 3 Polycomb‑binding is altered only at specific loci in scrib wing discs. a Schematic representation of the workflow used to analyse transition in Pc‑binding states on transcription‑associated GATC fragments (taGATCf ) that are mapping to a regulatory region surrounding a TSSs of a gene that was differentially expressed in scrib versus WT RNA‑seq samples. b Graph visualizes the distribution of GATC fragments classified according 1 1 to a gain or loss in Pc‑binding in sc–rib compared to WT profiles, and according to the change in expression level of the gene in sc–rib to whose TSS the GATC fragment had been mapped to. Group I (RNA—upregulated; Pc‑binding—gain); group II (RNA—downregulated; Pc‑binding—gain); group III (RNA—upregulated; Pc‑binding—loss); group IV (RNA—downregulated; Pc‑binding—loss). c Profiles visualize Pc‑binding in WT and scrib WIDs at indicated loci that represent known Pc target genes involved in tumorigenesis, and novel Pc target genes belonging to group II and III loci. Pc‑binding levels on each GATC fragments were classified by a three ‑state HMM analysis to be either ‘enriched’ (red), ‘intermediate’ (orange) and ‘depleted’ (green) and visualized by centring a fragment around individual GATC motifs. GATC fragments not recovered by our DamID‑Seq analysis in either genotype are shown in grey and were excluded for both genotypes in our analysis. Intron–exon structure, TSS and position of GATC motifs are indicated for each gene. Scalebar is 5 kb. d Profiles visualize the presumptive regulatory region 2.5 kb upstream to 1.5 kb downstream of the TSS of indicated genes. Domains bound by Pc in S2 cells (modENCODE) (orange), domains enriched for H3K27me3 (dark grey) and Pc (light grey) by ChIP‑Seq analysis in wing discs  and domains with loss transitions DamID_Seq profiles in scrib (light blue) AAAAAA log mRNA level Pc gain novel Pc target known Pc target La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 8 of 17 Polycomb‑binding at differentiation additional evidence for a role of PcG in regulating their and tumour‑associated targets is altered in scrib discs expression. An analysis of transcript levels in WIDs Our approach indicated the presence of multiple genes mutant for the PRC1 components Psc/Su(z)2  associated with transcriptional upregulation upon revealed that specifically dsx, Toll-7 and the neuronal loss of Pc-binding (group III) and with transcriptional Notch target pnt were upregulated upon loss of repres- repression upon gain of Pc-binding (group II) in scrib sive PcG complex function (Fig. 3c, Additional file 1: (Fig. 3b), which is consistent with the described func- Fig. S4E). This suggests that at least a subset of group II tion of Pc in gene silencing [11–15]. We thus focused genes are bona fide Pc target genes. our subsequent analysis on these genes. Strikingly, however, group III was comprised of many Surprisingly, group II included genes implicated genes implicated in promoting tumorigenic transfor- in axon guidance, for example dsx, Lrt, caps, PlexB, mation, but which had not yet been identified as Pc pdm3, Toll-7 and Fas3 (Fig. 3b), possibly reflecting a target genes. Foremost among them are Ets21C [36, failure to develop wing and thorax sensory neurons. 37], Atf3  and Ilp8 [39, 40]. As reported previously, While all group II genes gained Pc-binding for at least we also found the tumour-associated genes upd3 [16, one taGATCf in scrib discs, we wanted to provide 17], SOCS36E [16, 41, 42] and chinmo [43, 44] to be Pc aa’ a’’ Chromatin factors Histone modifications WT G L WT G L NES Psc H3K9me3 dRING H3K27me3 pho H3K23ac H3K27me2 enriched-WT Gain Loss in scrib Pc E(z) H3K9me1 CTCF H3K27ac pol2 PIWI AGO2 nejire GAF i-cisTarget b b’ Transcription factors Group: II III lola Group II Group III kay Jra ftz ERR NES Dref cnc CG6272 Atf3 AP1 rn i-cisTarget Med Mad jumu CG12299 bol Adf1 Fig. 4 Modulation of Polycomb‑binding and target gene expression is associated with enrichment of specific regulatory elements. a Schematic representation of the workflow used to identify regulatory elements in GATC fragments pooled into categories representing no change (nc), gain (G) or loss (L) of ‘enriched’ Pc‑binding states in scrib DamID‑seq profiles if compared to WT. Note that the ‘no‑change’ category for this conservative analysis only contains GATC fragments that were classified as ‘enriched’ for Pc‑binding and thus excludes the ‘depleted’ and ‘intermediate’ classifications. a′–a″) Regulatory elements identified by i‑cisTarget that either represent enrichment for chromatin‑binding factors (a′) or presence of specific histone modifications (a″) at GATC fragments ‘enriched’ for Pc‑binding (a) that show no change (nc), gain (G) or loss (L) of Pc‑binding in scrib WIDs. Normalized enrichment scores (NES) are visualized as coloured scale. b Schematic workflow used to identify enriched regulatory elements within genomic regions spanning 2.5 kb upstream to 1.5 kb downstream of the TSS in group II and III genes (see Fig. 3b for definition). b′–b″) Enrichment for transcription factors identified by i‑cisTarget in the presumptive regulatory domains of Pc‑targeted genes belonging to group II or III La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 9 of 17 target genes (Fig. 3b, c). Ets21C, Atf3, Ilp8 and upd3 are as H3K27me3 and H3K9me3 modifications (Fig. 4a′, known JNK target genes [37, 45], whereas chinmo and a″). In contrast, regulatory regions exhibiting dynamic SOCS36E are important effectors of JAK/STAT signal - Pc-binding transitions in scrib displayed high NES ling [41, 43]. Importantly, Pc-binding at all but one gene scores for RNA-mediated silencing machineries (Piwi, can also be identified in Pc ChIP profiles from S2 cells Ago2), transcriptional activation by histone acetylation (Fig. 3d). A recent study  suggests that a large num- (Nejire/CBP) or recruitment of RNAPol II (Fig. 4a′), all ber of PRC1 targets involved in proliferation and signal- of which may cooperate with CTCF (Fig. 4a′) in insu- ling, like SOCS36E, may only acquire PRC1-binding but lator-dependent transcriptional regulation and spa- not PRC2-dependent H3K27me3 modifications. We thus tial organization of chromatin [48–52]. Interestingly, specifically asked whether H3K27me3 and Pc may be histone modifications previously observed to occur at found at Ets21C, Atf3 and Ilp8 loci in WT WIDs. To do genes that are expressed, but importantly, at intermedi- so, we compared our data with H3K27me3 and Pc ChIP- ate levels , were also detected at dynamic Pc-bind- seq profiles published by Loubiere et al.  (Fig. 4b). ing sites (Fig. 4a″). This suggests that Pc target genes, Like chinmo , Ets21C and Atf3 carry both H3K27me3 which experience altered Pc-binding in scrib , may be and Pc signatures (Fig. 3d), suggesting that Ets21C and subject to transcriptional modulation rather than abso- Atf3 may be canonical PcG target genes utilizing PRC2- lute repression by Pc. dependent H3K27me3 modifications for transcrip - Next, we wondered whether tumour-associated tran- tional regulation. On the other hand, like SOCS36E , scripts upregulated upon loss of Pc-binding in scrib upd3 only acquires PRC1-binding but lacks H3K27me3 (group III, Fig. 3b) were characterized by a specific (Fig. 3d). Interestingly, neither H3K27me3 nor Pc signa- signature of regulatory elements. We thus repeated tures from previous studies mapped to Ilp8 (Fig. 3d). an i-cisTarget analysis for the presumptive regulatory Despite these different behaviours with respect to region spanning the transcriptional start site (TSS) H3K27me3 modifications, Ets21C, Atf3, Ilp8, SOCS36E, (− 2.5 kb ~ + 1 kb) of genes belonging to group III upd3 and chinmo are all upregulated upon loss of repres- (Fig. 4b). Strikingly, AP-1 (Jra/Kay), Atf3, Cnc and sive PRC1 complex function in Psc/Su(z)2 mutant WIDs, Lola-binding motifs enriched in group III loci (Fig. 4b′, demonstrating a role for Pc in silencing these tissue- Additional file 5: Table S2) and align with the stress- stress-responsive genes in wild-type WIDs (Additional dependent activation of chinmo, Atf3, Ets21C, Ilp8, file 1: Fig. S3D). u Th s, we identify at least three tumour- upd3 and SOCS36E associated with high JNK and JAK/ associated genes as novel bona fide Pc target genes and STAT activity during wound healing, regeneration and imply that the tumour-suppressive function of PcG pro- tumorigenesis [38, 44, 54–57]. teins  integrates with regulation by the two important We repeated an i-cisTarget analysis for group II tumour-promoting pathways JNK and JAK/STAT. genes, whose transcripts were downregulated upon gain of Pc-binding in scrib (Fig. 4b) to ask how Poly- comb may be recruited to these sites. In agreement Modulation of Polycomb‑binding and target gene with the observation that group II genes were enriched expression is associated with enrichment of specific for axon guidance targets, we found that transcription regulatory elements factors specifically expressed in neurons, such as Jumu A question we wanted to address is how epigenetic and CG12299, were enriched in regulatory regions of mechanisms may intersect with changes in signalling group II (Fig. 4b′, Additional file 5: Table S2). Impor- environment of cells, and more specifically, how Pc- tantly, however, wing patterning regulators, such as the binding may be affected by cross-talk with transcrip - transcription factor Rn and the Dpp/TGF-β signalling tion factors that act as effectors of signalling cascades effectors Med and Mad, were also enriched, confirm - activated during tumorigenesis. Thus, to advance our ing that wing differentiation is affected in a Polycomb- insight into how gain or loss of Pc-binding in scrib dependent manner in scrib WID (Fig. 4b′) . These WIDs may be regulated, we analysed GATC frag- data, however, may indicate that transcriptional down- ments classified by the three-state HMM analysis to regulation of genetic circuits involved in neuronal and be ‘enriched’ in Pc-binding, for predicted transcrip- wing disc patterning promotes binding of Pc to these tion factor binding motifs or modENCODE-identified target genes. chromatin domains [27, 46] using i-cisTarget  (see Based on our finding that GATC fragments gaining Experimental procedures). In parallel, we performed Pc-binding in scrib were enriched for CTCF (Fig. 4a′), an i-cisTarget on GATC fragments classified as gain we asked whether insulator elements locate to group II or loss of ‘enriched’ Pc-binding states in scrib WIDs genes. Strikingly, 71% of group II genes contained Fly- (Fig. 4a). As expected, Pc-bound GATC fragments in base-mapped class I and II insulator elements within WT were enriched for PRC1 and PRC2 binding, as well La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 10 of 17 1 1 their gene body. In contrast, insulator features mapped state of scrib discs, we asked whether scrib Pc- to only 19% of group III genes. This suggests that DamID profiles correlated better with developmentally insulator-dependent modulation of Pc function or Pc- younger than with older WIDs, indicative of a failure dependent modulation of insulator function may have to acquire PcG-regulated wing fates during develop- important consequences for Pc-targeted gene expres- ment. We thus compared Pc-DamID profiles from WT 1 1 sion in scrib . and scrib late third-instar WIDs to Pc-DamID pro- files from young WT WIDs isolated 2 days earlier in Polycomb‑binding transitions fail in scrib imaginal discs development (120 h AEL at 21 °C, early third instar) development (Additional file 6: SF4). Strikingly, Pc-DamID profiles Previous studies indicate that abnormal differentia- of scrib WIDs correlated more strongly with young tion in scrib discs may be linked to deregulation of Pc WIDs than with older WIDs (Fig. 5a). Importantly, function . To better characterize the differentiation while the percentage of Pc-‘enriched’ GATC fragments a b WT Early vs WT Late scrib vs WT Early 5% 12% 16% Late 17% Correlation 0.55 scrib Pc-binding 0.50 gain 0.45 loss 71% 79% 0.40 Early no change cd Chromatin factors Transcription factors Psc luna NES dRING Spps Ez z Pc Trl Lsd1 pho CTCF fkh Su(Hw) nub hb 4 sd trsn jigr1 gt croc Atf3 Adf1 ttk Caudal gain loss no change Pc-binding in WT Late vs scrib Fig. 5 Polycomb‑binding transitions fail in scrib imaginal discs development. a Pearson’s correlations between DamID‑seq Pc profiles obtained from WIDs in early larval stages (Early), late larval stages (Late) and in scrib . b–b′ Percentage of GATC fragments that classify as loss, gain and no change in Pc‑binding states in (b) late ( WT Late) if compared to early ( WT Early) WIDs and (b′) in scrib WIDs if compared to early ( WT Early) WIDs. Loss, gain and no‑change transitions were determined by transitions between ‘enriched’, ‘ intermediate’ and ‘depleted’ Pc‑binding states classified by a three‑state HMM analysis. Note that the no‑change category contains GATC fragments that were classified as ‘enriched’, ‘ intermediate’ and ‘depleted’ for Pc‑binding and thus includes Pc target and non‑target genes. c Relationship of Pc‑targeted GATC fragments that classify as loss, gain and no change in Pc‑binding in ‘scrib if compared to late ( WT Late) WIDs’ versus ‘late ( WT Late) if compared to early ( WT Early) WIDs’ (gain—orange, loss— light blue, no change—grey). The dotted frame highlights sites that lost Pc‑binding in scrib if compared to WT Late samples but should have gained Pc‑binding during normal wing disc development. d Regulatory elements identified by i‑cisTarget that represent enrichment for chromatin‑binding factors and transcription factors at GATC fragments conservatively classified as ‘enriched’ for Pc‑binding in early WID samples and on GATC fragments classified as a gain (Late gain) or loss (Late loss) of Pc‑binding by transitioning in and out of the ‘enriched’ state in late developmental stages if compared to an earlier stage. Normalized enrichment scores (NES) are visualized as coloured scale Early Late-gain Late-loss Early Late-gain Late-loss Pc-binding in WT Early vs WT Late gainloss no change La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 11 of 17 gained in scrib and older WT WIDs stayed relatively and may reflect different distributions at promoters, constant if compared to young WIDs, the percent- introns or intergenic regions. However, it may also age of Pc-‘enriched’ GATC fragments that was lost suggest a link between changes to Pc-binding and was strongly reduced in scrib (Fig. 5b). Furthermore, chromatin accessibility, where chromatin compaction target sites that normally gained Pc-binding during during development may reduce the likelihood of dis- development failed to gain Pc-binding in scrib WIDs tant GATC motifs to be methylated by Pc-Dam fusion (Figs. 2f, 5c). Combined, this suggests that early Pc- proteins. bound sites stay bound as scrib discs progress through development and that sites which should gain Pc- Discussion binding in older scrib discs fail to do so. These results As a consequence of the limited availability and acces- imply that a failure to execute Pc-dependent fate speci- sibility of sample material, in vivo ChIP protocols are fication may contribute to the lack of wing disc differ- technically challenging . Here, we report that DamID entiation in scrib discs. sensitively and reproducibly detects Pc-binding differ - A subsequent i-cisTarget analysis of young WID pro- ences in wing imaginal discs with input samples derived files revealed that Pc-‘enriched’ GATC fragments in from just 10 larvae. We propose that the lower limit young WIDs displayed PRC1 and PRC2-binding, con- necessary for good quality DamID profiles of imaginal firming that they are canonical Pc target sites (Fig. 5d). discs is even less. For example, we specifically omitted GATC fragments that specifically lost ‘enriched’ Pc- PCR amplifications during preparation of NGS librar - binding in late development scored high for bind- ies to avoid oversampling of PCR biases. Consequently, ing sites of the wing differentiation regulators nubbin we eliminated an opportunity to amplify weak signals to (Nub) and scalloped (Sd) (Fig. 5d), reflecting the detectable levels. Indeed, published DamID-seq proto- expansion of the central wing domain. GATC frag- cols report PCR amplification of NGS libraries without ments that gained Pc-binding in late development were adverse effects [5, 8]. enriched in binding sites for Atf3 and Adf1 (Fig. 5d). By targeting an ectopic signature to specific cells, FRT/ Adf1 was recently identified to be critical for recruit- FLP-out DamID circumvents the challenges of in vivo ment and tethering of Pc to target sites . The ChIP approaches that require the researcher to purify enrichment of Atf3 motifs may suggest that Atf3 target cell-type-specific nuclei from complex tissues. For this genes are increasingly silenced as wing discs develop- purpose, previously described cell-type-specific DamID ment progresses, which has indeed been observed for systems rely either on the real-time expression patterns Atf3 expression . This may also have important of GAL4 drivers (TaDa) or on cell-type-specific promot - implications for the reduction in regenerative capacity ers that directly drive the expression of a FLP to achieve previously attributed to Pc silencing of critical tissue- cell-type specificity [5–8]. In contrast, we describe a cell stress-responsive enhancers in late WIDs . lineage-specific DamID system by utilizing a specific However, GATC fragments with dynamic Pc tran- UAS-FLP(EXEL) that can be combined with any GAL4 sitions during development were also enriched for driver for maximum flexibility to permanently target CTCF and Su(Hw) insulator components, as well as DamID to different cell types and their descendants. for the histone demethylase Lsd1. Combined, these Genetic strategies based on individual GAL4 drivers can invoke earlier observations of insulator signatures be optimized and validated by G-trace analysis to reveal at dynamic Pc-targeted sites (Fig. 4a′) and imply that temporal and spatial patterns of the GAL4-targeted lin- Pc-binding dynamics at insulator elements, which are eage. Combined, the approach reported here opens the critical for organization of chromatin in the nucleus opportunity to track transitions of DNA binding sites in [48–52], are crucial to Pc function during differen- parent and daughter cell populations of a cell lineage over tiation. Intriguingly, a detailed analysis of our DamID time. profiles revealed that the Pc-bound GATC fragment Here we demonstrate that DamID sensitively detects sizes recovered from earlier developmental stages significant changes in Pc-binding between three differ - were larger than those recovered from late imaginal ent source samples. While Pc silencing is not globally 1 1 discs (Additional file 1: Fig. S5). Moreover, in scrib altered in a scrib mutant background, the transcriptional datasets, GATC fragment sizes occupied an interme- changes that correlated with altered Pc-binding at spe- diate distribution (Additional file 1: Fig. S5). The size cific loci allowed us to identify three novel Pc target genes range differences cannot be recapitulated by Dam pro- (Atf3, Ets21C, Ilp8), which are implicated in tissues stress files alone (data not shown). It may suggest that Pc- responses and tumour growth in many proliferating tis- binding to genome regions characterized by different sues [36–38, 43, 44]. We find that Atf3, AP-1 (Jra/Kay) GATC motif frequencies is developmentally regulated and Lola-binding sites are enriched at these genes that La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 12 of 17 are activated in scrib mutant discs, suggesting that these in a water bath. To analyse DamID profiles of young transcriptional regulators [38, 44, 54, 55] may oppose Pc WIDs, a heat shock was performed at 3 days after egg lay silencing to activate a PcG target gene network in tissue (AEL). To analyse DamID profiles of late WIDs, a heat repair and tumorigenic transformation. Curiously, tran- shock was performed at 5 days AEL. To account for the script levels of core PcG components are downregulated developmental delay characteristic of scrib homozygous by stress-induced JNK signalling  and two core PRC1 animals, scrib larvae were heat-shocked at 6 days AEL. transcripts are mildly reduced in scrib WIDs . This Afterwards, larvae were kept at 21 °C to maintain a low downregulation of PcG may sensitize Pc target genes, basal activity of the Hsp70 heat-shock promoter driving such as Atf3, Ets21C, Ilp8, upd3, SOCS36E and chinmo, expression of Dam and Dam-Pc transcripts. Wing imagi- for activation in stress-induced or tumorigenic contexts. nal discs were dissected 48 h after induction of hsflp . Our findings furthermore imply the high correlation Genomic excision of the STOP cassette from DamID between scrib and younger WID profiles indicates that constructs as a result of FLP activity was tested with a failure of scrib WID to undergo Pc-dependent fate dif- regular PCR protocols on gDNA extracted from WIDs ferentiation contribute to scrib phenotypes. Our analysis (see below) using the primers hhsp-int (actgcaactact- furthermore implies that such developmental transitions gaaatctgc) and Dam-r (cgctattgatatcggcaagg). mediated by Pc may be associated with insulator dynam- ics that could mediate global changes to accessibility of Tissue dissection and genomic DNA extraction Pc-regulated chromatin domains. How insulator dynam- Ten Drosophila larvae were dissected in cold Shields and ics may regulate dynamic Pc-binding during development Sang M3 medium, and WIDs were collected in 1.5-ml needs to be clarified in future studies. Similarly, while our tubes on ice. Discs were resuspended in a total volume analysis focused on Pc dynamics in different tissue states, of 400 µl lysis buffer (10 mM Tris–HCl pH 8.0; 10 mM a recent study highlights large scale remodelling of HP1- EDTA pH 8.0; 100 mM NaCl; 0.5% SDS) with protein- dependent chromatin and of silent ‘black’ chromatin ase K (20 mg/ml, NEB) and incubated for 4 h at 55 °C. states in developmental transitions of neuron, which are Phenol–chloroform purification and RNase A (QIAGEN) also likely to play a role in imaginal disc development and digestion were followed up by a standard ethanol pre- tumorigenesis . cipitation to obtain pure DNA. Each sample was subse- quently run on 1% agarose gel to confirm DNA integrity Experimental procedures and to estimate DNA concentrations. DNA from control Fly stocks and experimental samples was isolated at the same time All stocks and experimental crosses were maintained on and processed in parallel. standard fly food at 18 °C or 25 °C unless otherwise spec- ified. The following transgenes and fly lines were used in DamID sample processing, PCR and NGS library this study: preparation Isolation of genomic DNA (gDNA) from WIDs is y,w ;Hsp70P( FRT.STOP#1)DamMyc( ZH51C-3xP3- described above. For each condition and stage, two inde- RFP); pendent biological samples were processed and analysed y,w;Hsp70P(FRT.STOP#1)DamMycPc(ZH51C-3xP3- as described in  with minor changes. Briefly, after RFP); gDNA extraction, 600 µg of gDNA was digested with U AS - Re dStinger,U AS -F L P. E xel3,Ubi -p63E( F RT. DpnI restriction enzyme (10 U, New England Biolabs) STOP)Stinger (G-trace); with CutSmart buffer (New England Biolabs) in a total ts ptcGAL4 and ptcGAL4, tubGAL80 /CyO; volume of 10 µl at 37 °C for 6 h. DpnI digestion was termi- ts rnGAL4; and rn[GAL4-DeltaS], tubGAL80 /TM6c nated with heat inactivation at 80 °C for 20 min. Digested ts en-GAL4, UAS-GFP; tub-GAL80 fragments were ligated to 12.5 pmol DamID adapters scrib ; with T4 ligase (Roche) with T4 ligase buffer in a total vol - hsflp ; ume of 20 µl for 16 h at 16 °C. Ligated gDNA fragments UAS-FLP( JD2); were subsequently digested with DpnII (10 U, New Eng- UAS-FLP(EXEL)(3) land Biolabs) in DpnII buffer (New England Biolabs) in a total volume of 50 µl for 1 h at 37 °C. Ten microlitres Organismal induction of DamID constructs of DpnII digested products was amplified by PCR using Development of embryos was synchronized by an 8-h MyTaq Red Mix (Bioline) with 50 µM Adr-PCR primers egg collection on standard fly food at 21 °C. FLP expres - in a total volume of 50 µl. PCR program: 10 min at 68 °C; sion, which was controlled by a heat-shock promoter 1 min at 94 °C, 5 min at 65 °C, 15 min at 68 °C; 1 min (hsflp ), was induced by a 1-h temperature shift to 37 °C at 94 °C, 1 min at 65 °C, 10 min at 68 °C—repeated 3X; La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 13 of 17 1 min at 94 °C, 1 min at 65 °C, 2 min at 68 °C—repeated heterogeneous HMM, which takes into account the dis- (17X). Twelve microlitres of PCR products was run on tance between adjacent bins . This algorithm was 1.5% agarose gel to examine the expected DNA smear. previously implemented in the Bioconductor package Primers and adaptors sequences are described in . snapCGH [67, 68]. We adapted the BioHMM algorithm PCR products were purified using QIAquick PCR puri - for identification of three Pc-binding states (‘enriched’, fication kit (QIAGEN) according to manufactures proto - ‘intermediate’ and ‘depleted’). The R code of adapted col. Samples were eluted in 50 µl of nuclease-free water. BioHMM algorithm is provided as Additional file 7: SF1. After purification, DNA concentration was determined The three-state HMM analysis outputs of each GATC with Qubit Fluorometric Quantitation (ThermoFisher) fragment were compared between ‘WT’ and ‘scrib’ data- and adjusted to 20 ng/µl for all samples prior to libraries sets, as well as between ‘early’ and ‘late’ development preparation for NGS. One microgram of DNA was trans- in WT, to assess the dynamics of Polycomb-binding ferred to a microTUBE AFA Fiber Screw-Cap 6 × 16 mm between two samples. To maintain the directionality of (Covaris) and sheared to an average size of around differences, the result of this comparison was reported as 350 bp, using a Covaris M220 focused-ultrasonicator either ‘gain’, ‘loss’ or ‘no change’ for each GATC fragment with the following settings: duty factor = 20%, peak inci- between ‘enriched’, ‘intermediate’ and ‘depleted’ HMM dent power = 50 W, cycles per burst = 200, time = 55 s, states. temperature = 6 °C. Illumina TruSeq PCR-free LT library preparation kit (Illumina) was used to obtain DamID-seq RNA‑seq and ChIP‑chip data analysis library according to manufactures protocol. Next-gen- RNA-seq datasets were obtained from . Genes were eration sequencing was run on Illumina GenomeAna- selected for further analysis according to the statisti- lyzer IIx cBot machine. fastq file analysis was performed cal significance (adjusted p val< 0.05) and subsequently according to methods described in . divided in upregulated and downregulated expression according to the change in transcript levels. Differential Bioinformatic tools—general information gene expression was provided as log of the fold change 1 XL26 Bioinformatic analysis was performed using R (v. 3.4.0) between WT, scrib and Psc/Su(z)2 datasets. ChIP- (https ://www.r-proje ct.org/) and bedtools (v. 2.26.0) soft- chip datasets were downloaded from the modENCODE ware (http://bedto ols.readt hedoc s.io/en/lates t/#). Analy- repository (http://www.moden code.org/): Pc in S2 cells sis for enriched regulatory elements was performed using (ID 3791), Pc in DmBG3 cells (ID 325), Pc in embryo (ID i-cisTarget (https ://gbiom ed.kuleu ven.be/apps/lcb/i-cisTa 3957). Sequence overlap of microarray probe sequences rget/index .php) . in ChIP-chip datasets and Dam-normalized GATC frag- ments in DamID-Seq datasets was analysed using bedtool Identification and characterization of Pc‑bound target sites intersect function. Pearson’s correlation between DamID- DamID-seq fastq files were processed as described pre - seq and ChIP-chip data was calculated by correlating the viously  with the following two modifications. The intensity of Dam-normalized Pc-binding at each GATC mapping of reads onto GATC fragments by the software fragment in either WT or scrib datasets to the intensity ‘HTSeq-count’ was performed with a higher stringency of Pc-binding at the corresponding microarray probe criterion (by using the ‘intersection_strict’ instead of for the respective Pc ChIP-chip analysis from S2 cells, ‘union’ overlap resolution mode). GATC fragments show- DmBG3 cells or embryo. ing highly discordant values between replicates were excluded from the analysis as described . Transcription‑associated GATC fragments (taGATCf ) Pc-binding sites (‘bound’ targets) were identified based Regulatory regions associated with genes differentially on Dam-normalized log2-transformed DamID-seq expressed in scrib were defined as genomic regions profiles by fitting a three-state hidden Markov model spanning 2.5 kb upstream to 1.5 kb downstream of the (HMM) to define ‘enriched’, ‘ intermediate’ and ‘depleted’ transcriptional start sites (TSS) of the selected genes. Pc-binding states for each GATC fragment, as described Briefly, the coordinates of the regulatory regions were previously (Additional files 2: SF2, 3: SF3, 6: SF4) [26, calculated from the TSS coordinates and the strand 32–35]. We chose a three-state model to avoid random on which the TSS mapped on. This information was assignment of intermediate binding to either ‘enriched’ acquired from Flybase (Batch Download, http://flyba or ‘depleted’ states . Thus, while ‘intermediate’ states se.org) (genome annotation dm6) using the FB.ID of all could arise for any biological, genetic or technical rea- differentially expressed genes. Subsequently, genome sons, we could distinguish them in our analysis. coordinates of GATC fragments were converted into As the lengths of the genomic GATC fragments (bins) the appropriate genome annotation (dm3 → dm6, Lift- are not of equal size, we used the BioHMM algorithm, a Over tool—UCSC, https ://genom e.ucsc.edu/cgi-bin/ La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 14 of 17 hgLif tOver ) and mapped to the regulatory regions transitioning in and out of ‘enriched’ HMM states, and using the intersect function in bedtools2 (no limitations no change in Pc-binding in scrib by staying ‘enriched’ were considered on the amount of overlap between the (excluding ‘depleted’ and ‘intermediate’ HMM states). two coordinates’ sets). Only GATC fragments that over- Figure 5d: one analysis was performed on GATC lapped with selected regulatory regions were defined as fragments that were defined as ‘enriched’ in WT Early transcription-associated GATC fragments (taGATCf) profiles after the three-state HMM analysis (Fig. 5d). and used for the comparative analysis of DamID- Another analysis was performed on pools of GATC seq and RNA-seq data in wild-type and scrib WIDs fragments with gain or loss of Pc-binding in ‘WT Late’ (Fig. 3a). Subsequently, regulatory regions mapping to discs by in and out of ‘enriched’ HMM states. upregulated or downregulated genes were further sub- divided according to transitions in Pc-binding at each icisT ‑ arget analysis on taGATCf fragments mapping of their associated taGATCf (‘gain’, ‘loss’ or ‘no change’ to the presumptive regulatory region of Pc‑targeted genes: for each GATC fragment between ‘enriched’, ‘intermedi- Sequences of all regulatory regions established for the ate’ and ‘depleted’ HMM states). The entire regulatory analysis of taGATCf were first converted to a genome region was subsequently classified as gain in Pc-bind- annotation suitable for icis-Target analysis (dm6 ing, if one or more taGATCf within this region ‘gained’ → dm3) and then subdivided into their respective Pc-binding and other taGATC fragments displayed ‘no group (group I, group II, group III and group IV). The change’. Conversely, a regulatory region was classified icis-Target analysis was performed on groups II and III as loss in Pc-binding, if one or more taGATCf within independently (Fig. 4c–c″). this region ‘lost’ Pc-binding and other taGATC frag- ments displayed ‘no change’. Finally, regulatory regions which contained a mix of taGATCf with both gain Immunohistochemistry and loss HHM states were classified as mixed (m1- low To detect the Myc-tagged Dam proteins, expression mRNA levels, m2-high mRNA levels, Additional file 1: of Dam and Dam-Pc constructs was boosted by a heat Fig. S3.D) and not considered in subsequent analysis. shock for 1 h at 37 °C 6 h prior to dissection to strongly As a result, the described method subdivides regu- induce the Hsp70 promoter. This heat shock induces latory regions into the following four groups: group abnormally high Dam and Dam-Pc expression levels I (RNA—up regulated; Pc-binding—gain); group II that can be detected by immunohistochemistry but are (RNA—down regulated; Pc-binding—gain); group III unsuitable for genomic DamID profiling and reduce (RNA—up regulated; Pc-binding—loss); and group IV cell viability. Larvae were dissected and cuticles were (RNA—down regulated; Pc-binding—loss). fixed for 15 min at room temperature in 4% paraform - aldehyde (PFA). Washing steps were performed in 0.1% Triton X-100/PBS (PBT). The following antibodies were Analysis for enriched regulatory elements using i‑cisTarget incubated overnight at 4 °C: rabbit α-Dcp-1 (1:500, Cell We performed our i-cisTarget analysis adhering to Signalling), mouse α-H3S10p (1:2000, Abcam), mouse an enrichment score threshold = 2 and rank thresh- α-Myc (1:50, DSHB). Secondary antibodies (Molecu- old = 10,000. We defined significantly enriched motifs lar Probes), DAPI and phalloidin-TRITC (Sigma) were by setting the normalized enrichment scores (NES) > 3. incubated at room temperature for 2 h. Experimen- For factors with multiple enriched motifs, we selected tal and control samples were processed together and only the one with the highest NES. The following fea - imaged on the same microscope (Leica TCS SP-5). tures (Databases 3.0 of i-cisTarget) were selected during the analysis: PWMs, TF binding sites, non-TF binding sites, histone modifications. These parameters were Adult wing imaging common to all icis-Target analysis. Adult flies were collected 12 h after eclosion and stored in 2-propanol. Wings were dissected and mounted in Euparal (Sigma) on regular slides for microscopy. Imag- icisT ‑ arget analysis on GATC fragments with assigned HMM ing was done using a stereoscopic zoom microscope transitions: (Nikon, SMZ745). Figure 4a–a″: this analysis was performed on pools of GATC fragments with the following defined HMM transition states: gain or loss of Pc-binding in scrib by La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 15 of 17 Genome Analysis, Gene Center Munich, Ludwig‑Maximilians‑University Additional files Munich, Feodor‑Lynen‑Str. 25, 81377 Munich, Germany. Division Gene Regu‑ lation, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amster‑ dam, The Netherlands. Additional file 1. Supplemental figures S1–S5. Additional file 2: SF2. WIG file containing the results of the three ‑state Acknowledgements HMM analysis of wild type wing imaginal discs in late third instar stage for We thank the reviewers for critical comments on the manuscript. We thank all GATC fragment mapped on dm6 genome annotation. The following B. Bunker, D. Bilder, T. Straub, A. Ivankin for technical help and advice with values were attributed to the three states: enriched = 1, intermediate = 0, bioinformatics and data analysis. We thank the Bloomington Stock Centre and depleted = −1. DSHB for providing fly stocks and antibodies. We thank the IMPRS‑LS and LSM Additional file 3: SF3. WIG file containing the results of the three ‑state Munich graduate schools for supporting our students. HMM analysis of scrib wing imaginal discs for all GATC fragment mapped on dm6 genome annotation. The following values were attributed to the Competing interests three states: enriched = 1, intermediate = 0, depleted = −1. The authors declare that they have no competing interests. Additional file 4: Table S1. Genes in Group I‑IV; List of genes with Availability of data and materials number of GATC fragments within the presumptive regulatory region (2.5 The datasets generated during the current study will be made available in kb upstream to 1.5 kb downstream of the transcriptional start sites ( TSS)) the GEO repository. They are submitted for manuscript review as additional displaying transitions between Pc‑binding states (gain, loss or no ‑ change files (Additional file 8: DamID_HMM_Dm3.txt, Additional file 9: DamID_Raw_ transition between enriched, intermediate and depleted HMM states) in Counts_Dm3.txt) mapped on Dm3 Drosophila genome annotation. scrib if compared to WT profiles, and changes in gene expression levels of the respective gene in scrib to whose TSS the taGATC fragments had Consent for publication been mapped to. Group I (RNA – up regulated; Pc binding – gain); group II Not applicable. (RNA – down regulated; Pc binding – gain); group III (RNA – up regulated; Pc binding – loss); group IV (RNA – down regulated; Pc binding – loss); Ethics approval and consent to participate m1 (RNA – down regulated; Pc binding – loss and gain); m2 (RNA – up Not applicable. regulated; Pc binding – loss and gain). Additional file 5: Table S2 i‑ cisTarget Analysis of group II and III genes; Funding List of regulatory elements identified by i‑ cisTarget that either represent Funding for this work was provided by the DFG (CL490‑1 to AKC) and Russian transcription and chromatin‑binding factors or specific histone modifica‑ Fundamental Scientific Research Program (0310‑2018‑0009 to AP) and the tions enriched within the presumptive regulatory region (2.5 kb upstream Russian Science Foundation (16‑14‑10288 to AVP). to 1.5 kb downstream of the transcriptional start sites ( TSS)) of genes belonging to group II (RNA – down regulated; Pc binding – gain); group III Publisher’s Note (RNA – up regulated; Pc binding – loss). Springer Nature remains neutral with regard to jurisdictional claims in pub‑ Additional file 6: SF4 WIG file containing the results of the three ‑state lished maps and institutional affiliations. HMM analysis of wild type wing imaginal discs in early third instar stage for all GATC fragment mapped on dm6 genome annotation. The Received: 20 January 2018 Accepted: 21 May 2018 following values were attributed to the three states: enriched = 1, inter‑ mediate = 0, depleted = −1. Additional file 7: SF1 Script developed by A. Ivankin used to perform the three‑state HMM analysis based on the previously published BioHMM algorithm (Marioni et al. ). References 1. Deal RB, Henikoff S. The INTACT method for cell type ‑specific gene Additional file 8. Pc binding intensities (log2), normalized to Dam‑bind‑ expression and chromatin profiling in Arabidopsis thaliana. Nat Protoc. ing and averaged between two replicates, and the corresponding results 2011;6(1):56–68. from the three‑state HMM analysis for all three biological samples (early 2. Steiner FA, Talbert PB, Kasinathan S, Deal RB, Henikoff S. Cell‑type ‑specific and lat wild type wing imaginal discs, and scrib wing imaginal discs) nuclei purification from whole animals for genome ‑ wide expression and mapped on dm3 genome annotation. chromatin profiling. Genome Res. 2012;22(4):766–77. Additional file 9. Not normalised read counts per GATC fragment 3. Bonn S, Zinzen RP, Perez‑ Gonzalez A, Riddell A, Gavin AC, Furlong EE. Cell for all sequenced samples mapped on dm3 genome annotation. type‑specific chromatin immunoprecipitation from multicellular complex samples using BiTS‑ ChIP. Nat Protoc. 2012;7(5):978–94. 4. Schauer T, Schwalie PC, Handley A, Margulies CE, Flicek P, Ladurner AG. CAST‑ ChIP maps cell‑type ‑specific chromatin states in the Drosophila Authors’ contributions central nervous system. Cell reports. 2013;5(1):271–82. MLF, GG and AKC designed the experiments. MLF, GG, AC, LB, SK and HB per‑ 5. Pindyurin AV, Pagie L, Kozhevnikova EN, van Arensbergen J, van Steensel formed the experiments. MLF, GG, AP, BS and AKC analysed the data. MLF and B. Inducible DamID systems for genomic mapping of chromatin proteins AKC wrote the paper. All authors read and approved the final manuscript. in Drosophila. Nucleic Acids Res. 2016;44(12):5646–57. 6. Southall TD, Gold KS, Egger B, Davidson CM, Caygill EE, Marshall OJ, Brand Author details AH. Cell‑type ‑specific profiling of gene expression and chromatin bind‑ Faculty of Biology, Ludwig‑Maximilians‑University Munich, Grosshaderner ing without cell isolation: assaying RNA Pol II occupancy in neural stem Strasse 2‑4, 82152 Planegg, Martinsried, Germany. Department of Envi‑ cells. Dev Cell. 2013;26(1):101–12. ronmental Systems Science, ETH Zurich, Universitätstrasse 16, 8092 Zurich, 7. Pindyurin AV. Genome‑ wide cell type‑specific mapping of in vivo Switzerland. Department of Health Sciences and Technology, ETH Zurich, chromatin protein binding using an FLP‑Inducible DamID system in Dros‑ Schorenstrasse 16, 8603 Schwerzenbach, Switzerland. Center for Biological ophila. Methods Mol Biol. 2017;1654:99–124. Systems Analysis, Albert‑Ludwigs‑University Freiburg, Habsburgerstrasse 49, 8. Marshall OJ, Southall TD, Cheetham SW, Brand AH. Cell‑type ‑specific 79104 Freiburg, Germany. Institute of Molecular and Cellular Biology, Siberian profiling of protein‑DNA interactions without cell isolation using targeted Branch of Russian Academy of Sciences, Acad. Lavrentiev Ave. 8/2, Novosi‑ DamID with next‑ generation sequencing. Nat Protoc. 2016;11(9):1586–98. birsk 630090, Russia. Max‑Delbrück ‑ Center for Molecular Medicine (MDC), 9. Kind J, Pagie L, Ortabozkoyun H, Boyle S, de Vries SS, Janssen H, Amen‑ Robert‑Rössle‑Str. 10, 13092 Berlin, Germany. Laboratory for Functional dola M, Nolen LD, Bickmore WA, van Steensel B. Single‑ cell dynamics of genome‑nuclear lamina interactions. Cell. 2013;153(1):178–92. La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 16 of 17 10. Beira JV, Paro R. The legacy of Drosophila imaginal discs. Chromosoma. 33. Meuleman W, Peric‑Hupkes D, Kind J, Beaudry JB, Pagie L, Kellis M, Rein‑ 2016;125(4):573–92. ders M, Wessels L, van Steensel B. Constitutive nuclear lamina‑ genome 11. Steffen PA, Ringrose L. What are memories made of? How Polycomb and interactions are highly conserved and associated with A/T‑rich sequence. Trithorax proteins mediate epigenetic memory. Nat Rev Mol Cell Biol. Genome Res. 2013;23(2):270–80. 2014;15(5):340–56. 34. Babu MM. Computational approaches to study transcriptional regulation. 12. Chittock EC, Latwiel S, Miller TC, Muller CW. Molecular architecture of Biochem Soc Trans. 2008;36(Pt 4):758–65. polycomb repressive complexes. Biochem Soc Trans. 2017;45(1):193–205. 35. Ali M, Rincon‑Arano H, Zhao W, Rothbart SB, Tong Q, Parkhurst SM, 13. Muller J, Verrijzer P. Biochemical mechanisms of gene regulation by poly‑ Strahl BD, Deng LW, Groudine M, Kutateladze TG. Molecular basis for comb group protein complexes. Curr Opin Genet Dev. 2009;19(2):150–8. chromatin binding and regulation of MLL5. Proc Natl Acad Sci USA. 14. Geisler SJ, Paro R. Trithorax and Polycomb group‑ dependent regulation: a 2013;110(28):11296–301. tale of opposing activities. Development. 2015;142(17):2876–87. 36. Toggweiler J, Willecke M, Basler K. The transcription factor Ets21C drives 15. Sparmann A, van Lohuizen M. Polycomb silencers control cell fate, devel‑ tumor growth by cooperating with AP‑1. Sci Rep. 2016;6:34725. opment and cancer. Nat Rev Cancer. 2006;6(11):846–56. 37. Kulshammer E, Mundorf J, Kilinc M, Frommolt P, Wagle P, Uhlirova 16. Classen AK, Bunker BD, Harvey KF, Vaccari T, Bilder D. A tumor suppressor M. Interplay among Drosophila transcription factors Ets21c, Fos and activity of Drosophila Polycomb genes mediated by JAK‑STAT signaling. Ftz‑F1 drives JNK ‑mediated tumor malignancy. Dis Model Mech. Nat Genet. 2009;41(10):1150–5. 2015;8(10):1279–93. 17. Bunker BD, Nellimoottil TT, Boileau RM. Classen AK. Bilder D: The transcrip‑ 38. Atkins M, Potier D, Romanelli L, Jacobs J, Mach J, Hamaratoglu F, Aerts S, tional response to tumorigenic polarity loss in Drosophila. eLife; 2015. p. Halder G. An ectopic network of transcription factors regulated by hippo 4. signaling drives growth and invasion of a malignant tumor model. Curr 18. Schwartz YB, Kahn TG, Nix DA, Li XY, Bourgon R, Biggin M, Pirrotta V. Biol. 2016;26(16):2101–13. Genome‑ wide analysis of Polycomb targets in Drosophila melanogaster. 39. Colombani J, Andersen DS, Leopold P. Secreted peptide Dilp8 coordi‑ Nat Genet. 2006;38(6):700–5. nates Drosophila tissue growth with developmental timing. Science. 19. Ringrose L. Polycomb comes of age: genome‑ wide profiling of target 2012;336(6081):582–5. sites. Curr Opin Cell Biol. 2007;19(3):290–7. 40. Garelli A, Gontijo AM, Miguela V, Caparros E, Dominguez M. Imaginal discs 20. Schwartz YB, Pirrotta V. Polycomb silencing mechanisms and the man‑ secrete insulin‑like peptide 8 to mediate plasticity of growth and matura‑ agement of genomic programmes. Nat Rev Genet. 2007;8(1):9–22. tion. Science. 2012;336(6081):579–82. 21. Duffy JB, Harrison DA, Perrimon N. Identifying loci required for follicular 41. Amoyel M, Anderson AM, Bach EA. JAK/STAT pathway dysregulation in patterning using directed mosaics. Development. 1998;125(12):2263–71. tumors: a Drosophila perspective. Semin Cell Dev Biol. 2014;28:96–103. 22. Thibault ST, Singer MA, Miyazaki WY, Milash B, Dompe NA, Singh CM, 42. Loubiere V, Delest A, Thomas A, Bonev B, Schuettengruber B, Sati S, Buchholz R, Demsky M, Fawcett R, Francis‑Lang HL, Ryner L, Cheung LM, Martinez AM, Cavalli G. Coordinate redeployment of PRC1 proteins sup‑ Chong A, Erickson C, Fisher WW, Greer K, Hartouni SR, Howie E, Jakkula presses tumor formation during Drosophila development. Nat Genet. L, Joo D, Killpack K, Laufer A, Mazzotta J, Smith RD, Stevens LM, Stuber C, 2016;48(11):1436–42. Tan LR, Ventura R, Woo A, Zakrajsek I, Zhao L, Chen F, Swimmer C, Kopc‑ 43. Flaherty MS, Salis P, Evans CJ, Ekas LA, Marouf A, Zavadil J, Banerjee U, zynski C, Duyk G, Winberg ML, Margolis J. A complementary transposon Bach EA. chinmo is a functional effector of the JAK/STAT pathway that tool kit for Drosophila melanogaster using P and piggyBac. Nat. Genet. regulates eye development, tumor formation, and stem cell self‑renewal 2004;36(3):283–7. in Drosophila. Dev Cell. 2010;18(4):556–68. 23. Evans CJ, Olson JM, Ngo KT, Kim E, Lee NE, Kuoy E, Patananan AN, Sitz 44. Davie K, Jacobs J, Atkins M, Potier D, Christiaens V, Halder G, Aerts S. D, Tran P, Do MT, et al. G‑ TRACE: rapid Gal4‑based cell lineage analysis in Discovery of transcription factors and regulatory regions driving in vivo Drosophila. Nat Methods. 2009;6(8):603–5. tumor development by ATAC‑seq and FAIRE‑seq open chromatin profil‑ 24. van Steensel B, Delrow J, Henikoff S. Chromatin profiling using targeted ing. PLoS Genet. 2015;11(2):e1004994. DNA adenine methyltransferase. Nat Genet. 2001;27(3):304–8. 45. Santabarbara‑Ruiz P, Lopez‑Santillan M, Martinez‑Rodriguez I, Binagui‑ 25. Hariharan IK, Bilder D. Regulation of imaginal disc growth by tumor‑ Casas A, Perez L, Milan M, Corominas M, Serras F. ROS‑induced JNK suppressor genes in Drosophila. Annu Rev Genet. 2006;40:335–61. and p38 signaling is required for unpaired cytokine activation during 26. van Bemmel JG, Filion GJ, Rosado A, Talhout W, de Haas M, van Welsem T, drosophila regeneration. PLoS Genet. 2015;11(10):e1005595. van Leeuwen F, van Steensel B. A network model of the molecular organi‑ 46. Roy S, Ernst J, Kharchenko PV, Kheradpour P, Negre N, Eaton ML, zation of chromatin in Drosophila. Mol Cell. 2013;49(4):759–71. Landolin JM, Bristow CA, Ma L, Lin MF, et al. Identification of functional 27. Negre N, Brown CD, Ma L, Bristow CA, Miller SW, Wagner U, Kheradpour elements and regulatory circuits by Drosophila modENCODE. Science. P, Eaton ML, Loriaux P, Sealfon R, et al. A cis‑regulatory map of the Dros‑ 2010;330(6012):1787–97. ophila genome. Nature. 2011;471(7339):527–31. 47. Verfaillie A, Imrichova H, Janky R, Aerts S. iRegulon and i‑ cisTarget: recon‑ 28. Kharchenko PV, Alekseyenko AA, Schwartz YB, Minoda A, Riddle NC, structing regulatory networks using motif and track enrichment. Curr Ernst J, Sabo PJ, Larschan E, Gorchakov AA, Gu T, et al. Comprehensive Protoc Bioinformatics. 2015;52(2):1611–39. analysis of the chromatin landscape in Drosophila melanogaster. Nature. 48. Moshkovich N, Nisha P, Boyle PJ, Thompson BA, Dale RK, Lei EP. RNAi‑ 2011;471(7339):480–5. independent role for Argonaute2 in CTCF/CP190 chromatin insulator 29. Shimbo T, Du Y, Grimm SA, Dhasarathy A, Mav D, Shah RR, Shi H, Wade PA. function. Genes Dev. 2011;25(16):1686–701. MBD3 localizes at promoters, gene bodies and enhancers of active genes. 49. Philip P, Boija A, Vaid R, Churcher AM, Meyers DJ, Cole PA, Mannervik M, PLoS Genet. 2013;9(12):e1004028. Stenberg P. CBP binding outside of promoters and enhancers in Dros‑ 30. Negre N, Hennetin J, Sun LV, Lavrov S, Bellis M, White KP, Cavalli G. Chro‑ ophila melanogaster. Epigenetics Chromatin. 2015;8:48. mosomal distribution of PcG proteins during Drosophila development. 50. Cubenas‑Potts C, Corces VG. Architectural proteins, transcription, and the PLoS Biol. 2006;4(6):e170. three‑ dimensional organization of the genome. FEBS Lett. 2015;589(20 Pt 31. Tolhuis B, de Wit E, Muijrers I, Teunissen H, Talhout W, van Steensel B, van A):2923–30. Lohuizen M. Genome‑ wide profiling of PRC1 and PRC2 Polycomb chro ‑ 51. Li HB, Muller M, Bahechar IA, Kyrchanova O, Ohno K, Georgiev P, Pirrotta V. matin binding in Drosophila melanogaster. Nat Genet. 2006;38(6):694–9. Insulators, not Polycomb response elements, are required for long‑range 32. Filion GJ, van Bemmel JG, Braunschweig U, Talhout W, Kind J, Ward LD, interactions between Polycomb targets in Drosophila melanogaster. Mol Brugman W, de Castro IJ, Kerkhoven RM, Bussemaker HJ, et al. System‑ Cell Biol. 2011;31(4):616–25. atic protein location mapping reveals five principal chromatin types in 52. Li HB, Ohno K, Gui H, Pirrotta V. Insulators target active genes to transcrip‑ Drosophila cells. Cell. 2010;143(2):212–24. tion factories and polycomb‑repressed genes to polycomb bodies. PLoS Genet. 2013;9(4):e1003436. La Fortezza et al. Epigenetics & Chromatin (2018) 11:27 Page 17 of 17 53. Rosenfeld JA, Wang Z, Schones DE, Zhao K, DeSalle R, Zhang MQ. Deter‑ 61. Papp B, Muller J. Histone trimethylation and the maintenance of mination of enriched histone modifications in non‑ genic portions of the transcriptional ON and OFF states by trxG and PcG proteins. Genes Dev. human genome. BMC Genom. 2009;10:143. 2006;20(15):2041–54. 54. Ferres‑Marco D, Gutierrez‑ Garcia I, Vallejo DM, Bolivar J, Gutierrez‑Avino 62. Lee N, Maurange C, Ringrose L, Paro R. Suppression of Polycomb group FJ, Dominguez M. Epigenetic silencers and Notch collaborate to promote proteins by JNK signalling induces transdetermination in Drosophila malignant tumours by Rb silencing. Nature. 2006;439(7075):430–6. imaginal discs. Nature. 2005;438(7065):234–7. 55. Blanco E, Ruiz‑Romero M, Beltran S, Bosch M, Punset A, Serras F, 63. Marshall OJ, Brand AH. Chromatin state changes during neural develop‑ Corominas M. Gene expression following induction of regeneration in ment revealed by in vivo cell‑type specific profiling. Nat Commun. Drosophila wing imaginal discs. Expression profile of regenerating wing 2017;8(1):2271. discs. BMC Dev Biol. 2010;10:94. 64. Vogel MJ, Peric‑Hupkes D, van Steensel B. Detection of in vivo protein‑ 56. Doggett K, Turkel N, Willoughby LF, Ellul J, Murray MJ, Richardson HE, DNA interactions using DamID in mammalian cells. Nat Protoc. Brumby AM. BTB‑zinc finger oncogenes are required for ras and notch‑ 2007;2(6):1467–78. driven tumorigenesis in Drosophila. PLoS ONE. 2015;10(7):e0132987. 65. Maksimov DA, Laktionov PP, Belyakin SN. Data analysis algorithm for 57. Brock AR, Seto M, Smith‑Bolton RK. Cap ‑n‑ collar promotes tissue DamID‑seq profiling of chromatin proteins in Drosophila melanogaster. regeneration by regulating ROS and JNK signaling in the Drosophila Chromosome Res. 2016;24(4):481–94. melanogaster wing imaginal Disc. Genetics. 2017;206(3):1505–20. 66. Marioni JC, Thorne NP, Tavare S. BioHMM: a heterogeneous hid‑ 58. Orsi GA, Kasinathan S, Hughes KT, Saminadin‑Peter S, Henikoff S, Ahmad den Markov model for segmenting array CGH data. Bioinformatics. K. High‑resolution mapping defines the cooperative architecture of 2006;22(9):1144–6. Polycomb response elements. Genome Res. 2014;24(5):809–20. 67. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis 59. Sekyrova P, Bohmann D, Jindra M, Uhlirova M. Interaction between Dros‑ B, Gautier L, Ge Y, Gentry J, et al. Bioconductor: open software devel‑ ophila bZIP proteins Atf3 and Jun prevents replacement of epithelial cells opment for computational biology and bioinformatics. Genome Biol. during metamorphosis. Development. 2010;137(1):141–50. 2004;5(10):R80. 60. Harris RE, Setiawan L, Saul J, Hariharan IK. Localized epigenetic silenc‑ 68. Smith ML, Marioni JC, Hardcastle TJ, Thorne NP. snapCGH: Segmentation. ing of a damageac ‑ tivated WNT enhancer limits regeneration in mature Bioconductor: Normalization and processing of aCGH data users’ guide; Drosophila imaginal discs. Elife. 2016;. https ://doi.org/10.7554/eLife .11588 . 2006. Ready to submit your research ? Choose BMC and benefit from: fast, convenient online submission thorough peer review by experienced researchers in your ﬁeld rapid publication on acceptance support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year At BMC, research is always in progress. Learn more biomedcentral.com/submissions
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