Chromosomal translocations are one of the hallmarks of acute myeloid leukemia (AML), often leading to gene fusions and expression of an oncofusion protein. Over recent years it has become clear that most of the AML associated oncofusion proteins molecularly adopt distinct mechanisms for inducing leukemogenesis. Still these unique molecular properties of the chimeric proteins converge and give rise to a common pathogenic molecular mechanism. In the present study we compared genome-wide DNA binding and transcriptome data associated with AML1-ETO, CBFB-MYH11 and PML-RARA oncofusion protein expression to identify unique and common features. Our analyses revealed targeting of oncofusion binding sites to RUNX1 and ETS-factor occupied genomic regions. In addition, it revealed a highly comparable global histone acetylation pattern, similar expression of common target genes and related enrichment of several biological pathways critical for maintenance of AML, suggesting oncofusion proteins deregulate common gene programs despite their distinct binding signatures and mechanisms of action. AML is a progressive malignant disease and leads to deranged populations of red blood cells, platelets, and normal white blood cells in bone marrow. AML is the most common form of acute leukemia in adults, is more prevalent in ageing populations and is responsible for ~1% of cancer deaths worldwide . Howeverntiation .We and others have focused on unraveling molecular aspects of several oncofusion proteins associated with AML, such as AML1-ETO , 6, 7, 8Interestingly, genome-wide analysis revealed that molecularly AML1-ETO exhibits properties similar to PML-RARA. Both are believed to exert their functional influence by recruiting repressor complexes containing histone deacetylases to genomic target sites, thereby altering normal chromatin architecture and gene expression , 6. In cFrom the above it is clear that the advent of next generation sequencing has broadened the study of oncofusion proteins from single loci to a genome-wide scale. Together with large cohorts of gene expression data from AML patients that have become available , 21, 22,To identify the common binding and gene program of oncofusion proteins associated with AML we used previously identified target regions of PML-RARA in the t NB4 cell line , AML1-ETTo investigate whether the fusion proteins target a common transcription factor network we performed motif enrichment analysis on their binding sites, using weight matrices of AML1/RUNX1, C/EBPA, the ETS factor SPI1, GATA and TAL1, all transcription factors reported to be mutated in acute leukemias . This reProgression of leukemia is not a consequence of a single deregulated pathway but is believed to be a synergistic impact of a wide range of misregulated pathways including cell cycle, differentiation, signaling, apoptosis and self-renewal pathways , 25, 26.Interestingly, this common target gene set exhibits significant higher expression across all fusion protein expressing AML subtypes as compared to normal CD34+ cells Figure , but simDespite targeting a common gene program a subset of genes is controlled by only one of the three oncofusion proteins Figure , suggestAcetylation and deacetylation of histones catalyzed by histone acetyltransferases (HATs) and histone deacetylases (HDACs), respectively, are two of the several mechanisms controlling the complex process of transcription. HATs and HDACs play opposite roles in gene regulation by modulating chromatin structure and activity of certain transcription factors by differential acetylation .In cancerous cells acetylation levels are believed to be imbalanced and influencing expression levels of tumor suppressor genes and proto-oncogenes. Hyper-acetylated promoters of proto-oncogenes are suggested to increase expression of these genes and turn them into oncogenes. In contrast, hypo-acetylation of promoters of tumor suppressor genes invariably silences them . DeregulHere, we compared binding sites of the three oncofusion proteins for H3 acetylation (H3K9K14ac) enrichment and observed high levels of acetylation at CBFB-MYH11 binding sites as compared to AML1-ETO and PML-RARA Figure . This isMany breakpoints involved in specific chromosomal translocations have been cloned over the years. In most cases, however, the role of the chimeric oncofusion proteins in tumorigenesis has not been elucidated. In the case of AML, our analysis of PML-RARA, AML1-ETO, and CBFB-MYH11 were among the first to report on the genome-wide actions of oncofusion proteins. These genome-wide studies focused on revealing various structural and functional aspects of the oncofusion proteins like composition of the protein complexes formed by oncofusion proteins, the effects on the epigenetic landscape, the enriched pathways and deregulated gene networks. None of these studies carried out an integrative comprehensive analysis on the genome-wide maps of different oncofusion proteins and identify unique and mutually shared features between them.Despite the suggested differences between these oncofusion proteins, the data presented here reveals the binding sites of all three are demarcated by RUNX1 and ETS factors indicating the existence of a common functional RUNX1/ETS-factor module that is targeted in oncofusion mediated leukemogenesis. Similarly, the commonly targeted genes by all three oncofusion proteins show higher expression patterns in AML-subtypes as compared to CD34+ cells and enrich biological pathways crucial for leukemogenesis. Amongst these pathways are several signaling modules which likely stimulate cell proliferation, such as the TGFB signaling pathway . In addiAlthough utilizing distinct mechanisms to regulate the local epigenetic environment, with AML1-ETO and PML-RARA suggested to reduce acetylation (and expression of genes) , 10, 32 Together, the commonalities discussed in this study might present novel entry points for further treatment of AML patients with these translocations, for example by targeting the RUNX1/ETS-factor interaction, disruption of the associated complex, or use of specific compounds that target one of the common pathways altered by the fusion proteins, such as BCL2 inhibitors , 35 to bCell line were cross-linked with 1% formaldehyde for 20 min at room temperature, quenched with 0.125 M glycine and washed. Sonicated chromatin  was centrifuged at maximum speed for 10 min and then incubated with specific antibodies. Beads were washed sequentially with four different wash buffers and chromatin was eluted from the beads. Protein–DNA crosslinks were reversed, after which DNA was isolated and used for quantitative PCR or sequencing analysis.http://genome.ucsc.edu). ChIP-seq data can be downloaded from Gene Expression Omnibus accession numbers GSE46044, GSE23730, GSE30254, GSE18886 and GSE81992.End repair was performed using the precipitated DNA of ~6 million cells  using Klenow and T4 polynucleotide kinase (T4 PNK). A 3′ protruding A base was generated using Taq polymerase and adapters were ligated. The DNA was loaded on gel and a band corresponding to ~300 bp (ChIP fragment + adapters) was collected. The DNA was isolated, amplified by PCR and used for cluster generation on the Illumina HiSeq genome analyzer. The 35–45 bp tags were mapped to the reference human genome using the Burrows-Wheeler Alignment Tool (BWA) or eland program allowing one mismatch. For each base pair in the genome, the number of overlapping sequence reads was determined, averaged over a 10 bp window and visualized in the UCSC genome browser (http://cancergenome.nih.gov/) [Raw ChIP-seq data files for AML1-ETO, CBFB-MYH11 and PML-RARA oncofusion proteins, transcription factors RUNX1 and FLI1, and acetylation (H3K9K14ac) profiles in Kasumi-1, ME-1 and NB4  cells were downloaded and mapped to hg19 using bwa . Expressih.gov/) .https://genome.ucsc.edu/cgi-bin/hgLiftOver).Published DNA binding sites associated with AML1-ETO , CBFB-MYTags within a given region were counted and adjusted to represent the number of tags within 1Kb regions. Subsequently the percentage of these tags as a measure of the total number of tags of the sample was calculated and displayed as heatmaps and/or line graph.To determine genomic locations of binding sites, the peak files were analyzed using a script that annotates binding sites according to all RefSeq genes. With this script every binding site is annotated either as promoter (-500 bp to the Transcription Start Site), exon, intron or intergenic (everything else).For motif analysis of AML1-ETO, CBFB-MYH11 and PML-RARA binding sites, we used GimmeMotifs .http://software.broadinstitute.org/gsea/) [Pathway and gene ontology  enrichment analysis was performed using molecular signature database of Gene Set Enrichment Analysis  .The expression data associated with AML subtypes , 22 was


In homeotherms, the alpha-globin gene clusters are located within permanently open genome regions enriched in housekeeping genes. Terminal erythroid differentiation results in dramatic upregulation of alpha-globin genes making their expression comparable to the rRNA transcriptional output. Little is known about the influence of the erythroid-specific alpha-globin gene transcription outburst on adjacent, widely expressed genes and large-scale chromatin organization. Here, we have analyzed the total transcription output, the overall chromatin contact profile, and CTCF binding within the 2.7 Mb segment of chicken chromosome 14 harboring the alpha-globin gene cluster in cultured lymphoid cells and cultured erythroid cells before and after induction of terminal erythroid differentiation.We found that, similarly to mammalian genome, the chicken genomes is organized in TADs and compartments. Full activation of the alpha-globin gene transcription in differentiated erythroid cells is correlated with upregulation of several adjacent housekeeping genes and the emergence of abundant intergenic transcription. An extended chromosome region encompassing the alpha-globin cluster becomes significantly decompacted in differentiated erythroid cells, and depleted in CTCF binding and CTCF-anchored chromatin loops, while the sub-TAD harboring alpha-globin gene cluster and the upstream major regulatory element (MRE) becomes highly enriched with chromatin interactions as compared to lymphoid and proliferating erythroid cells. The alpha-globin gene domain and the neighboring loci reside within the A-like chromatin compartment in both lymphoid and erythroid cells and become further segregated from the upstream gene desert upon terminal erythroid differentiation.Our findings demonstrate that the effects of tissue-specific transcription activation are not restricted to the host genomic locus but affect the overall chromatin structure and transcriptional output of the encompassing topologically associating domain.The online version of this article (doi:10.1186/s13072-017-0142-4) contains supplementary material, which is available to authorized users. In mammals, tissue-specific genes are often located within permanently active chromosome regions, which are enriched with genes ubiquitously transcribed in various cell types. The current paradigm suggests that tissue-specific transcriptional regulation within complex genome environment depends on a specific spatial organization of the interphase chromatin –3. Non-rNPRL3 residing immediately upstream of the alpha-globin cluster. Although the AgGD is located in a permanently open (DNAse I-sensitive) highly acetylated chromatin in all cell types ). Gene ontology annotation reveals that the differentiation of HD3 cells is accompanied by a significant decrease in the expression of metabolism-related genes  [The studied genomic region harbors two gene-rich, highly transcribed zones  separated by a 1.6-Mb gene desert (zone II) containing a predicted homologue of the human gene ub, ACH) , 28, shoRHBDF1 promoter and the MPG promoter, and between MRPL28 and AXIN1 genes, i.e., within immediate neighborhoods of the AgGD. Within the alpha-globin gene cluster, intergenic transcription is detected exclusively in differentiated HD3 cells. In these cells, previous studies using RT-PCR and Northern blotting have demonstrated the presence of the so-called alpha-globin full-domain transcript, a long noncoding RNA presumably covering the region from its promoter within the NPRL3 gene to the 3′-enhancer of AgGD [NPRL3 promoter to the HBZ (π) gene, and between HBAD (αD) and HBAA (αA) genes , we observed a significant decrease in the interaction frequency with the gene desert exclusively for the AgGD  is partitioned into two TADs that are arbitrary designated as T1 and T2  and in proliferating HD3 cells .In lymphoid DT40 cells, the area containing the AgGD and several neighboring genes  are locenhancer  are accoIn the three cell types studied, the strong erythroid-specific enhancer of AgGD known as the major regulatory element (MRE)  is locatMPG gene  but not with the MRE [Alpha-globin gene domains of warm-blooded animals are located within areas enriched in widely expressed genes , 45. Hert TMEM8A , 46 whic the MRE   were incubated in the full growth media supplemented with 10 mM HEPES (pH 8.0) and 20 μM iso-H-7 (1-(5-isoquinolinylsulfonyl)-3-methylpiperazine dihydrochloride, Fluka, Seelze, Germany) at 42 °C in 100% air atmosphere.The avian erythroblastosis virus-transformed chicken erythroblast cell line HD3 (clone A6 of line LSCC) and the chicken lymphoid cell line DT40  were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 2% chicken serum and 8% fetal bovine serum at 37 °C in 5% CO3C libraries were prepared as described previously  with minFor the detection of the ligation products in DpnII-3C libraries, real-time PCR with TaqMan probes was employed. Primers and TaqMan probes for PCR analysis were designed using Primer Premier 5 software . The sequences of the primers and TaqMan probes are present in Additional file 5C primers covering region 9.8–12.5 Mb of chicken chromosome 14 were designed for HindIII restriction fragments using the alternating scheme in my5C.primers  with theThe universal T7-tail (5′-TAATACGACTCACTATAGCC-specific part-3′) and T3-tail (5′-TCCCTTTAGTGAGGGTTAATA-specific part-3′) were added to the forward and reverse 5C primers, respectively. 5′-ends of reverse 5C primers were phosphorylated. In total, 93 forward and 92 reverse primers were designed , which provides a set of dynamic programming algorithms to assess an ensemble of TAD segmentations derived from a TAD scoring function. We used its optimal segmentation finder, which is based on the Armatus algorithm [TADs were identified using the Lavaburst package  iterative mapping procedure  and Bowtie 2 version 2.2.1.Illumina HiSeq 2500 bcl2fastq v1.8.4 Conversion Software was used for base-calling. The reads were mapped independently to the region 11700000-12450000 of chromosome 14 using hiclib  (https:/Reads mapped in close proximity to DpnII restriction sites (5 bp) and possible PCR duplicates were eliminated. We also excluded reads mapped to extremely short (<100 bp) and long (>100 Kb) restriction fragments. The reads in pairs that mapped to the same restriction fragment or closer than 500 bp to each other in genomic coordinates were excluded as potential self-ligations and dangling end products. Uniquely mapped read pairs that passed filtering procedure were retained. Resulting pairs were binned into 5 Kb genomic windows of the region of interest. Bins corresponding to interactions within the same fragment were removed. For each bin, the mean C-TALE read count was calculated. To merge C-TALE replicates, we summed their matrices element by element. Then, the matrices were iteratively corrected . TAD annl-lysine coated coverslips placed in culture flasks. The cells were fixed in 4% paraformaldehyde for 10 min, permeabilized in 0.5% Triton X-100, washed in PBS, dehydrated in ethanol series, air-dried, stored at room temperature for two days, and then frozen at −80 °C. Probes were PCR-amplified from the CH261-75C12 BAC, primer sequences are presented in Table S4  was used for all calculations. Chimera [We used the integrative modeling platform (IMP)  to model Chimera  and PyMOhttps://genome.ucsc.edu/ENCODE/protocols/dataStandards/ENCODE_RNAseq_Standards_V1.0.pdf). The two biological replicates demonstrated a high correlation in all three cell types (Pearson’s r > 0.95).RNA-seq experiments were performed in two independent biological replicates, according to ENCODE recommendations  according to the manufacturer’s protocol. Total RNA was depleted with ribosomal RNA using the RiboZero Human/Mouse Kit (Illumina). For library preparation, a TruSeq Stranded RNA Sample Prep Kit (Illumina) was used following the manufacturer’s instructions. After preparation, the quality of the libraries was evaluated using quantitative PCR and Bioanalyzer.Gallus gallus reference genome  using the TopHat program [Illumina Casava 1.8.2 was used for base-calling. Reads were mapped to the  program  taking t program  was used program  was usedP-values were calculated per gene using quasi-likelihood negative binomial generalized log-linear model (glmQLFit function in edgeR). The P-values were adjusted with Benjamini–Hochberg correction for multiple testing. The threshold for false discovery rate was set to 1e-7 for detection of differentially regulated genes. For gene ontology analysis, we used DAVID as implemented in RDAVIDWebService R package. We assessed the enrichment of gene ontology terms of “Biological Process” category for the genes that changed expression level more than twofold with adjusted P-value <1e-04. The minimal number of genes present in the gene ontology category was set to 5. Resulting DAVID P-values of enriched terms were adjusted with Bonferroni correction.We used R package edgeR for gene expression analysis. Only genes with CPM > 1 in at least two out of six RNA-seq experiments were retained. We averaged CPM over replicates and calculated fold change of expression between experiments. To assess the intergenic transcription levels, we retrieved the regions between ENSEMBL genes and calculated read coverage with BedTools software . To compAdditional file 1: Figure S1. Analysis of the total rRNA-depleted RNA-seq data in the three studied cell types. (A) The Venn diagram showing the numbers of active genes shared between the studied cell types. (B) Cluster analysis of biological replicates based on total rRNA-depleted RNA-seq data. (C) Principal component analysis of the RNA-seq data. (D) Top-30 gene ontology terms for the genes downregulated  in differentiated HD3 cells compared to proliferating HD3 cells. The number of genes in each term is shown. (E) Scatter plots showing normalized level of transcription within intergenic regions genome-wide in the studied cell types.Additional file 2: Table S1. Transcription level of the genes from the studied region (CPM). Only genes with CPM > 1 in at least 2 replicates are presented. CPM values were averaged over replicates. Log2-fold change and FDR of difference between experiments are presented  Heatmaps demonstrating an increased interaction frequency between gene-rich zones I and III in both lymphoid and erythroid cells. A-like and B-like chromatin compartments are outlined using black opened rectangle and black dotted triangle, respectively. The first principal component is shown below the heatmaps. (B) The 4C profiles from [les from  revealinAdditional file 6: Figure S4.(A) Interaction heatmaps of the simulated polymer calculated with TADbit. Color bars in the heatmaps represent arbitrary partitioning of the studied region into several distinct fragments. (B) IMP-derived 3D models of the studied genome region. The central wireframe colored as the color bar in the map of the studied region represents the centroid model for simulations. The surface represents the Gaussian approximation of 1000 simulated models.Additional file 7: Table S3. Genomic coordinates of topologically associating domains within the studied region. TADs were identified using the optimal segmentation algorithm from the Lavaburst package with the with the Armatus scoring function.Additional file 8: Supplementary Methods. A detailed description of the C-TALE library preparation.Additional file 9: Table S5. C-TALE sequence statistics.Additional file 10: Table S4. Sequences of primers and TaqMan probes used for the 3C analysis. Sequences of primers used for FISH probe amplification.


Neuroectoderm is an important neural precursor. However, chromatin remodeling and its epigenetic regulatory roles during the differentiation of human neuroectodermal cells (hNECs) from human embryonic stem cells (hESCs) remain largely unexplored. Here, we obtained hNECs through directed differentiation from hESCs, and determined chromatin states in the two cell types. Upon differentiation, H2A.Z-mediated nucleosome depletion leads to an open chromatin structure in promoters and upregulates expression of neuroectodermal genes. Increase in H3K9ac signals and decrease in H3K27me3 signals in promoters result in an active chromatin state and activate neuroectodermal genes. Conversely, decrease in H3K9ac signals and increase in H3K27me3 signals in promoters repress pluripotency genes. Moreover, H3K9ac signals facilitate the pluripotency factor Sox2 binding to target sites unique to hNECs. Knockdown of the acetyltransferase Kat2b erases H3K9ac signals, disrupts Sox2 binding, and fails the differentiation. Our results demonstrate a hierarchy of epigenetic regulation of gene expression during the differentiation of hNECs from hESCs through chromatin remodeling. Lineage-specified cells have a more condensed chromatin structure compared to pluripotency stem cells.2 Nucleosomes are evicted upstream near transcription start sties (TSSs) or in distant enhancers to activate genes required for lineage commitment during differentiation of stem cells.5 Genome-wide comparisons of chromatin structure between mammalian pluripotent stem cells and differentiated cell types reveal that regions of difference in nucleosome occupancy are enriched in the loci associated with pluripotency and differentiation.6 Thus, dynamics of nucleosome positioning is important to cell fate commitment.Chromatin remodeling offers the epigenetic basis for transcriptional regulation and has a pivotal role in many biological processes such as cell differentiation, embryonic development, and so on. Nucleosome is the fundamental repeating structural unit of chromatin.7 Global changes in HMs during multilineages differentiation of human embryonic stem cells (ESCs) have been reported in previous studies.9 Comparisons between human pluripotent stem cells and donor fibroblasts identified lots of regions with differences in H3K4me3 and H3K27me3 signals.10 Profiling of HMs of mouse ESCs, neural progenitor cells (NPCs), and embryonic fibroblasts revealed that H3K4me3 and H3K27me3 effectively distinguish genes with different expression levels and reflect lineage potential.11 A recent HPLC-MS-based quantitative proteomics identified dozens of HM sites in mouse ESCs and NPCs and elaborated the effect of combinational HMs on the differentiation of mouse ESCs to NPCs.12 The above studies all mainly focused on profiling of HMs and the global changes from ESCs to NPCs. However, the role of HM changes in the differentiation largely remains unclear.Histone modifications (HMs) are another key impact factor for chromatin structure. HMs exert their epigenetic regulatory roles through changing chromatin state or recruiting non-histone proteins to chromatin.13 It was reported that the core pluripotency TF Oct4 binds to the target sites with primed epigenetic signatures during mouse somatic cell reprogramming.14 Motifs of distinct TFs are enriched in the regions with stage-specific transition during neural differentiation.15 However, it is unclear which HM(s) have the dominant role and how it regulates TFs' binding. It is also under debate at most time as whether HMs are causal or a consequence of TFs' binding.Transcription factors' (TFs) binding to the target sites is closely correlated with HMs for a precise regulation of gene expression. Some TFs contain a protein domain that recognizes specific type of HM. Consequently, the TFs may have an affinity for the specific HM. For example, bromodomain PHD finger TF (BPTF) has a higher affinity to H3K4me3 than H3K4me2 and discriminates against H3K4me1.16 Moreover, neuroectoderm has potentials to differentiate into neurons, astrocytes, and oligodendrocytes. Therefore, the differentiation of ESCs to neuroectoderm is of great significance for understanding of neural disease, for example, primitive neuroectodermal tumors. Epigenetic regulation has a critical role in the differentiation. For example, a subset of poised enhancers gained active chromatin signatures (H3K27ac) during differentiation of hESCs to neuroepithelium.17 However, to date, the epigenetic regulatory mechanisms underlying neuroectoderm specification remains elusive.Neuroectoderm specification is of major interest to developmental biology. Neuroectoderm (neuroepithelia) is a very important neural precursor. It develops into neural plate, providing the source of the central nervous system.19 This differentiation process resembles in vivo neural development and serves a model to study the mechanisms underlying human neuroectoderm specification.20 For example, Pax6 was identified as a transcriptional determinant of human neuroectoderm through this neural differentiation tool.21 In this study, we profiled transcriptome, nucleosome occupancy, core histone modifications, and genomic bindings of TF Sox2 for hESCs and hNECs. The integrative analyses of these data show that extensive nucleosome repositioning and histone modification changes occur upon differentiation. H2A.Z-mediated nucleosome disassembly in promoters leads to an open chromatin structure to activate transcription of neuroectodermal genes. Moreover, H3K9ac signals deposited by acetyltransferase Kat2b are essential to the differentiation through enhancing recruitment neuroectodermal factors Sox2 and Pax6 to their target sites.To unveil the patterns and roles of chromatin remodeling during the differentiation of human ESCs (hESCs) to human neuroectodermal cells (hNECs), we employed the differentiation method of hESCs to a synchronized population of hNECs.19 The synchronized population of hNECs have a characteristic of rosette formation that is morphologically distinct from hESCs egulated . The proegulated . Gene onegulated . Taken t2We scanned the genome with a 10-kb window and calculated nucleosome occupancy for each window. Nucleosome occupancy of the biological replicates is highly reproducible . There o25 Comparisons found that only 1% of nucleosomes are fixed without changing their positions upon differentiation, 6% are disassembled or reassembled, and the rest shift. Further analysis show that nucleosome loss is enriched in promoters , +1, +2, et al. nucleosomes around TSS in both cell types. The average nucleosome occupancy in promoters is lower in hESCs than hNECs. Surprisingly, there is a small peak in the NDR in hESCs indicating that NDRs of some genes are occupied by nucleosomes in hESCs but free of nucleosomes in hNECs  have a dominant role in control of gene activity in the differentiation of hNECs, we collected three sets of genes: pluripotency genes for hESCs, neuroectodermal genes for hNECs, and other lineage-specific genes for comparison, and examines HM dynamics in promoters and gene expression change. The results show that H3K4me3 levels largely decrease in pluripotency genes whereas H3K27me3 levels increase. H3K9ac levels change very little. Concomitantly, the gene expressions are downregulated . This su2(fold change of H3K9achNECs/hESCs)−log2(fold change of H3K27me3hNECs/hESCs) that is defined as Histone Modification Index (HMI). The distribution of HMI in genes is a normal distribution  in ESC growth medium , and 3.5 ml 14.3 M b-Mercaptoethanol ) supplemented with fresh 4 ng/ml recombinant human FGF basic . Then, the colonies were detached from MEFs and formed aggregates to initiate the neural differentiation procedure by withdrawing bFGF. After 4 days of suspension culture, the ESC growth medium was replaced with neural induction medium  to guide neuroectodermal specification. Then transferred the suspension cells to the 6-well plates without feeder on the sixth day. After another 4 days of adherent growth, we got hNECs on day 10.Human ESCs  were differentiated into hNECs as described previously.Cells were fixed by 4% paraformaldehyde at room temperature for 10 min, followed by 0.1% Triton X-100 permeabilization for 10 min and 1% BSA blocking for 1 h. They were then incubated with primary antibodies against Oct4 , Nanog , Sox2  or Pax6  at 4 °C overnight. The next day, cells were incubated with secondary antibodies against mouse or goat IgG  accordingly. Nuclei were counterstained with Hoechst (Invitrogen), and confocal images were taken on the Zeiss LSM 710 microscope .μg of total RNA was reversely transcribed into cDNA using SuperScript III (Invitrogen) and subjected to real-time PCR  using the Ssofast EvaGreen kit (Bio-Rad). The real-time PCR primers were listed in the Total RNA was isolated using the Trizol reagent (Invitrogen) and RNA concentration was determined by NanoDrop 2000 c . Overall, 1 7 cells were crosslinked with 1% formaldehyde for 10 min at room temperature, and crosslinking was terminated by addition of 125 mM glycine. Cells were harvested for lysis to isolate nuclei. Suspend nuclei in 500 μl of MNase digestion buffer (10 mM Tris-HCl (pH 7.5), 15 mM NaCl, 60 mM KCl, 1 mM CaCl2, 0.15 mM spermine, 0.5 mM spermidine, 1 × EDTA free protease inhibitor cocktail). Then digest nuclei with 2.5 μl of micrococcal nuclease  at 37 °C for 20 min. Then put the samples on ice and add EDTA to a final concentration of 10 mM to stop MNase digestion. Crosslinking was then reversed for at least 6 h at 65 °C along with proteinase K digestion. Nucleosomal DNA was extracted using phenol–chloroform and purified on 1.5% agarose gels.Overall, ~2 × 10μg of nucleosomal DNA was used for chromatin immunoprecipitation with 3–5 μg of HM antibodies as described in 400-μl ChIP buffer , 0.1% Triton X-100, 1XEDTA free protease inhibitor cocktail). Antibodies used are from Abcam : H3K4me3 (ab8580), H3K27me3 (ab6002), H3K36me3 (ab9050), H3K9ac (ab10812). The mixture was incubated overnight at 4 °C with rotation. Then, 30 μl of ChIP-Grade Protein G Magnetic Beads  were added in the mixture for another 2 h. After that, beads were sequentially washed three times with low salt buffer , 0.1% SDS, 1% Triton X-100, 150 mM NaCl), once with high salt buffer , 0.1% SDS, 1% Triton X-100, 500 mM NaCl), once with LiCl wash buffer ), and once with TE buffer (10 mM Tris-HCl (pH 8.1), 1 mM EDTA). Then beads were suspended in elution buffer (50 mM Tris-HCl (pH 8.1), 10 mM EDTA, 0.1–0.5% SDS) at 65 °C for 1 h.A total of 10–20 The purified mononucleosomal DNA by MNase digestion and ChIP'ed by HM antibodies were subjected to massively parallel DNA sequencing on Illumina HiSeq2000 platform  using 49 bp single end protocol.μl of sonication buffer , and sonicated with 6 rounds of 30-s on and 30-s off on ice using XL-2000 Misonix sonicator with power output of 7 Watts. It is critical that the average length of the sheared chromatin is about 250 bp, with length ranging from 150–500 bp. The fragmented DNA was immunoprecipitated with 3~5 μg of specified antibody  as described in the above ChIP-seq for HMs. The ChIP'ed DNA was subjected to massively parallel DNA sequencing on Illumina HiSeq2000 platform  using 49 bp single end protocol.Genomic DNA was crosslinked and extracted as described above MNase-seq. The purified genomic DNA was suspended in 500 ChIP-qPCR was conducted according to user manual of ChampionChIP PCR Array from SABiosciences . Briefly, DNA fragments were prepared as above MNase-seq or ChIP-seq. Relative signal abundance in regions of interest in sample DNA was measured by qPCR using Power SYBR. Fragmented genomic DNA or IgG-immunoprecipitated DNA was used as control sample. Relative signal enrichment was calculated using ΔΔCt method by normalizing Ct values against control sample. The ChIP-qPCR primers were were listed in the 7 cells were mixed with 5 μg of each TALEN targeting AAVS1 locus.32 And 40 μg of AAVS1-SA-PURO-PA-U6 promoter-shRNA-PA donor after trypsin/EDTA solution treatment and then electroporated . Transfected cells were plated on MEF with ROCK-inhibitor for the first 24 h. Then we used 1 μg/ml puromycin  to select cells cultured with MEF-conditioned medium. Individual colonies were picked up after about 15 days and identified by PCR using primers for homologous recombination forward: 5′-CTTCCGCATTGGAGTCGCTTTA-3′ and reverse: 5′-ACAGGAGGTGGGGGTTAGAC-3′ or wild type forward: 5′-CAGCCGGTCCTGGACTTTGTC-3′ and reverse: 5′-AGCCGGGAACCGCTCAACTC-3′.We knocked down Kat2b in hESCs by TALEN. CAG-GFP fragment was replaced by U6 promoter and shRNA sequence from AAV-CAGGS-EGFP . The shRNA targeting sequences were: shKat2b#1 (5′-CGAACTCTAATCCTCACTCAT-3′), shKat2b#2 (5′-CCAGCCAGCTAGGCATCCAAA-3′), shKat2b#3 (5′-GGAAGCTGGATTAATTGACAA-3′). Before electroporation, hESCs were cultured in ROCK-inhibitor Y-27632  for 3 h. About 1 × 1033 The data contained 3 replicates. The.CEL files were analyzed using the affy package within R/Bioconductor.34 Robust multichip averaging (RMA) was used to correct for background, normalize and generate expression data.35 The Limma (linear models for microarray data) package was then used to identify differentially expressed genes with the adjusted P-value<0.05 and expression fold change ⩾2.36Gene expression microarray data of the two cell types were downloaded from ArrayExpress database .37 The tool Gene Set Enrichment Analysis (GSEA v2.1.0)24 was used to identify any priori defined set of genes that share common biological function or regulation and show statistically significantly, concordantly different expression levels between two samples. GSEA was performed using the gene sets of gene ontology (C5) and user-defined HESC SPECIFIC MARKERS gene set that was collected from published paper.38 Metric for ranking genes was set to Signal2Noise. Gene ontology gene sets were curated by MSigDB database v4.0. Other parameters used default values.GO analysis for gene sets was performed using the tool PANTHER.Homo sapiens reference genome (hg19) using Bowtie-1.0.0  were further analyzed, although patterns were identical when all nucleosomes were analyzed. Nucleosome fuzziness was calculated as the standard deviation of the coordinates of all reads defining the same nucleosome as described previously.40 It measures how spread out a nucleosome position is. Each nucleosome was assigned to promoter, genic or intergenic regions depending on the location of the nucleosome midpoint.Sequencing reads were aligned to 0.0 ref.  with up Two nucleosomes from the two cell types are fixed if their midpoints locate at the same position. The nucleosome is gain or loss if the distance between two midpoints is ⩾107 bp. That is, the two nucleosomes overlap less than 20 bp. The rest of nucleosomes shift upon differentiation.The original composite distribution of nucleosome around TSS was calculated by aggregating nucleosomal read count at each distance relative to TSS as follows: each read represents a nucleosome by extending toward 3′ end to a length of 147 bp. The midpoint of extended read defines the nucleosome position. We summed total read counts at each site within ±2 kb of TSS. The nucleosomal read count was further normalized by the uniquely mapped total reads. We further binned the nucleosome occupancy by a 10- bp interval of nucleosome distance to TSS, and smoothed it with 5-bin moving average and 1-bin step size.41 without reads extension. Nucleosome positions predicted by GeneTrack was used as input with nucleosome width shrunk to 127 bp for better visualization. Heat map for nucleosomes with H3K36me3 modifications was drawn in the same manner. Heat map for RNA Pol II was drawn in the similar way except that its peaks predicted by MACS-1.4.2ref.  with default settings were used.Nucleosome organization in the regions −0.3 kb to +1 kb of TSS was plotted as heat map by the tool seqMINERHomo sapiens reference genome (hg19) as above nucleosomal reads. Similarly, only the uniquely mapped reads were retained for prediction of nucleosome with HM by GeneTrack using the same parameters as above nucleosome prediction. Occupancy of nucleosomes with HM were normalized as Reads Per Kilobase per Million mapped reads (RPKM). Nucleosomes with HM that had occupancy as RPKM>1 were retained for following analysis. Promoters (±500 bp of TSS) were categorized to eight classes  depending on the composition of nucleosomes with modifications located in promoters. For example, H3K4me3+ promoters contained only nucleosomes with H3K4me3, H3K4me3+/H3K27me3+ contained only both nucleosomes with H3K4me3 and nucleosomes with H3K27me3, and so on.HM ChIP-seq reads were aligned to Occupancy of a HM was measured as its read counts in promoters that were normalized as reads per kilobase per million mapped reads (RPKM). HM occupancy ratio of hNECs to hESCs was calculated, transformed to log2, and represented as heat map.We further used ROC curve to identify which HM(s) were the best classifier for the significantly DEGs. In first case, upregulated DEGs were treated as actual positives, the rest were actual negatives. The fold changes of HM occupancy in promoters were calculated as above and sorted descendingly. Each fold change value was used as a threshold. For each threshold, the genes, whose fold changes of HM occupancy in promoters were greater than the threshold, were predictive positives. Otherwise, the genes are predictive negatives. Then, the true positive rate  and false positive rate  were calculated for each threshold. The ROC curve was thus TPR as a function of FPR. The ROC curve for downregulated DEGs was plotted in the same manner except that downregulated DEGs were treated as actual positives, the rest were actual negatives.ref.  with a P-value cutoff of 10−5 using hNECs as treatment sample and hESCs as background. The regions with significant decrease in H3K27me3 upon differentiation were detected similarly using as hESCs treatment sample and hNECs as background. We retained the regions with H3K9ac or H3K27me3 occupancy >1 RPKM. We combined these regions as chromatin remodeling regions. De novo motif discovery in these regions was done by the tool MEME43 as follows: the DNA sequences of CRRs were retrieved as input to MEME for motif finding with E-value cutoff of 0.05 and motif width of 6–16 bp. The other parameters used default values. To compare the predicted motifs to the known ones, the TOMTOM motif comparison tool (Version 4.11.3) from the MEME suite was used to search a database of vertebrates (in vivo and in silico) using the default parameters .The regions with significant increase in H3K9ac upon differentiation were detected by the tool MACS-1.4.2Homo sapiens reference genome were retained for peak calling by the tool MACS-1.4.2 (ref. P-value cutoff of 10−4 and fold change of 10. The de novo motif finding in the Sox2 peak sequences was done by Cistrome Analysis Pipeline27 as follows: we chose the SeqPos motif tool in the section Integrative Analysis from the Cistrome toolbox. The coordinates of Sox2 peaks in BED format was the input file. The ‘de novo motif search' motif database from the ‘curated cistrome motif database' was used. The species ‘Homo sapien or Mus musculus' was used. The rest of parameters were default. The tool HOMER was applied to find enriched motifs of co-factors.44 Only the motifs with the highest alignments to known transcription factors, nonredundant matrixes and non-repetitive sequences were retained. Sox2 occupancy in peaks was normalized as RPKM. Browser tracks for Sox2 occupancy were generated using the tool IGV 2.3.45As above HM sequencing data analysis, only Sox2 reads uniquely mapped to 4.2 ref.  with a P46The accession numbers for the MNase-seq data and the ChIP-seq data reported in this paper are GEO: GSE76084. The H2A.Z ChIP-seq data was downloaded from GSM1003579. Pax6 binding peaks in hNECs was kindly provided by the author from the published work.


Head and neck cancer (HNC) is the seventh most common malignancy in the world and its prevailing form, the head and neck squamous cell carcinoma (HNSCC), is characterized as aggressive and invasive cancer type. The transcription factor II A (TFIIA), initially described as general regulator of RNA polymerase II-dependent transcription, is part of complex transcriptional networks also controlling mammalian head morphogenesis. Posttranslational cleavage of the TFIIA precursor by the oncologically relevant protease Taspase1 is crucial in this process. In contrast, the relevance of Taspase1-mediated TFIIA cleavage during oncogenesis of HNSCC is not characterized yet. Here, we performed genome-wide expression profiling of HNSCC which revealed significant downregulation of the TFIIA downstream target CDKN2A. To identify potential regulatory mechanisms of TFIIA on cellular level, we characterized nuclear-cytoplasmic transport and Taspase1-mediated cleavage of TFIIA variants. Unexpectedly, we identified an evolutionary conserved nuclear export signal (NES) counteracting nuclear localization and thus, transcriptional activity of TFIIA. Notably, proteolytic processing of TFIIA by Taspase1 was found to mask the NES, thereby promoting nuclear localization and transcriptional activation of TFIIA target genes, such as CDKN2A. Collectively, we here describe a hitherto unknown mechanism how cellular localization and Taspase1 cleavage fine-tunes transcriptional activity of TFIIA in HNSCC. HNC is typically diagnosed at advanced stages with metastases resulting in a 5-year survival rate of less than 50%2. The prevailing form of HNC, head and neck squamous cell carcinoma (HNSCC), is characterized as a very aggressive and invasive cancer type affecting multiple sites of the upper aerodigestive tract like the nasal cavity, mouth, salivary glands, larynx and pharynx3. Major risk factors associated with the development of HNSCC are tobacco use, alcohol consumption and high-risk human papilloma virus infections (HPV)4. Due to the late disease presentation of the patient, lack of suitable biomarkers, and corresponding drugs for individually targeted therapy approaches, survival rates for HNSCC have not improved significantly within the last years7. Currently, the main prognostic parameters of HNSCC are the size and location of the tumour, the presence of distant metastasis and cervical lymph node metastases, which is not sufficient to evaluate the disease outcome9. Despite advances in therapy, the treatment of HNSCC still often comes along with functional impairment and cosmetic deformity of vital functions of the aerodigestive tract, such as breathing, swallowing, speech, hearing and smell3. Although the use of kinase inhibitors or antibodies has gained increasing clinical relevance, there is still urgent need for effective therapies and novel drug targets.Head and neck cancers (HNC) are among the most common malignant neoplasms in humans11. In addition, Taspase1 is overexpressed in a variety of solid tumours, including HNSCC12. The human Taspase1 gene encodes a protein of 420 amino acids (aa) resembling the Taspase1 proenzyme. Based on structural similarities Taspase1 was classified as a type 2 asparaginase exhibiting several specific characteristics13. In contrast to the exclusively cis-active type 2 asparaginases, only Taspase1 is able to cleave distinct substrates in trans by hydrolyzing its target proteins at conserved (Q3[FILV]2D1↓G1′x2′D3′D4′) sites15. During mammalian development, Taspase1 plays an important role in the regulation of correct segmental identities, head morphogenesis and spermatogenesis19. However, the molecular mechanisms how Taspase1 may affect substrate functions through site-specific proteolysis still remain to be determined. Importantly, no specific small molecule or genetic inhibitors are available worldwide, hampering not only to further dissect Taspase1’s disease mechanisms, but also precluding the full assessment of its clinical impact22.The protease Threonine Aspartase1 or Taspase1 has been identified as a promising new anti-cancer target which is critically involved in the development of aggressive infant leukaemias and HER2-associated breast cancer via its substrate MLL23. TFIIA has been initially characterized as part of the preinitiation complex initiating RNA polymerase II transcription24. TFIIA is composed of three subunits, α, β, and γ encoded by two separated genes, TFIIAαβ and TFIIAγ. The γ-subunit is conserved among different species, whereas sequence similarity in TFIIAαβ is limited mostly to the N-terminal region of the α-subunit and the C terminus covering most of the β-subunit25. TFIIAαβ is posttranslationally processed by Taspase1 at an evolutionary conserved cleavage site QVDG (aa 272 to 275)23. Interestingly, both uncleaved αβ and the cleaved α- and β-subunits can be found in association with the TFIIAγ subunit in vivo27. Additionally, both complexes interact with the TATA-binding protein (TBP) on DNA and support transcription to similar extents in vitro and in reporter assays29. Therefore, uncleaved and cleaved forms of TFIIA may have distinct gene regulatory functions in differentiation. The observation that cleavage is the prerequisite for proteasome mediated degradation of TFIIA28 indicates that cleavage regulates TFIIA stability and thus, transcriptional activity. This hypothesis was supported by a study showing that Taspase1-mediated cleavage of TFIIA ensures proper head formation during mouse development18. It has been suggested that TFIIAαβ cleavage by Taspase1 results in suppression of CDKN2A expression and finally, in proper head formation18. The CDKN2A gene locus encodes for the cell cycle regulators p16INK4a, p19ARF and p21CIP, blocking cell cycle progress in G1 and S phase30. Especially the tumour suppressor p16INK4a is in focus of interest as putative biomarker for HNSCC.Besides MLL other essential proteins, such as the precursor of the transcription factor IIA (TFIIA) have been identified as native Taspase1 targets31. Nucleocytoplasmic transport takes place through nuclear pores and is tightly regulated by specific signals and transport receptors31. In general, active nuclear import is mediated by short stretches of basic amino acids, termed nuclear localization signals (NLS), which interact with specific import receptors12. In contrast, signal mediated nuclear export pathways are less understood32. A well-characterized class of nuclear export signals (NES) consist of short leucine-rich stretches interacting with the export receptor Crm133. Leucine-rich NES have been identified in an increasing number of cellular and viral proteins executing heterogeneous biological functions. Especially for transcription factors, executing their biological function within the cell nucleus, such regulated subcellular localization provides an attractive way to control their activity34. This indeed has been demonstrated for several key players of signal transduction cascades36.Controlled distribution of macromolecules within different cellular compartments is an elaborated way of controlling protein activity. In eukaryotic cells, spatial and functional division is ensured by the nuclear envelope separating the nucleus from the cytoplasm37, we identified a significant fraction of TFIIA accumulating to the cytoplasm due to a highly efficient nuclear export signal. TFIIA´s ability to also reside in the cytoplasm not only counteracts its nuclear localization but also affects its transcriptional activity together with the proteolytic cleavage by Taspase1. Collectively, by regulating the nuclear availability of TFIIA, the interplay of nuclear export and Taspase1-mediated cleavage fine-tunes transcriptional regulation of TFIIA target genes, such as CDKN2A.Besides its relevance during normal head development, the molecular mechanisms how Taspase1 may affect TFIIA functions in malignant tissue of HNSCCs still remain to be determined. In order to close this knowledge gap, we combined gene expression analysis of HNSCC tumours with cell-based analysis of TFIIA localization and proteolytic cleavage. Although TFIIA was initially described as a nuclear transcriptional regulatorversus lymph node metastasis (M) and the corresponding non-malignant tissue (N), tissues were obtained from 20 patients undergoing surgical resection. In contrast to other studies, in which genetic and epigenetic variations of individual patients require the analysis of large patient cohorts to increase the relevance of the obtained data sets, our study design focused on a patient cohort from which N, PT as well as M could be surgically recovered from the same patient. All cases were diagnosed histopathologically and staged according to the TNM classification recommended by the UICC  ram Fig. . Non-malram Fig. , whereasram Fig. .Figure 118. In order to investigate the relevance of Taspase1-TFIIA-CDKN2A signalling also in malignant tissue of the head, gene expression was compared in PT versus N as well as M versus N . Similar results were obtained for M versus N. Here, 11 out of 15 patients showed downregulation of CDKN2A  proteins which regulate a variety of biological processes by translocating between cytoplasm and the nucleus42. Remarkably, TFIIA was initially described as a nuclear protein by the UniProtKB/Swiss-Prot database [P52655-TF2AA_HUMAN], but was also assigned to the cytoplasmic compartment by gene ontology [GO:0005737]. Here, we aim at the resolution of this inconsistency and the detailed characterization of TFIIA’s cellular localization in epithelial tumour cells.Although we found a significant downregulation of TFIIA-regulated CDKN2A locus in HNSCC patients, expression of TFIIA itself seems to be not altered. Besides transcriptional regulation, protein function could be also controlled by cellular localization and intracellular transportTFIIAαβ open reading frame (ORF) from total RNA isolated from head and neck tumour tissue and confirmed its consistency with the ORF provided by official databases (UniProtKB - P52655). Since binding of the conserved TFIIAγ subunit does not differ dramatically between cleaved and uncleaved TFIIA forms, we focused on the analyses only of the TFIIAαβ precursor protein which will be termed TFIIA for convenience in the following text. In contrast to the reported nuclear localization, transient expression of TFIIA-GFP fusion protein revealed a predominant cytoplasmic localization in living interphase A431 cells after 24 h . Additionally, this motif is highly conserved in known TFIIA homologs  in which two essential hydrophobic amino acid residues in the putative export signal were changed into Ala  induces nuclear accumulation comparable to the single NES mutation  prevented nuclear export for at least 6 h. Collectively, these experiments unambiguously identified a leucine-rich NES in the α-subunit of TFIIA.To finally prove that the predicted sequence indeed functions as a NESmut-GFP, TFIIACSmutNESmut-GFP, a change of their prior nuclear localization to a cytoplasmic localization was detected, indicating deficiency in active nuclear access. In the absence of nuclear export, only active import signals could result in a nuclear accumulation of the diffusion-deficient GST fusion constructs. In summary, our results suggest that the dynamic cellular localization of TFIIA is actively regulated by its NES, but not by active nuclear import.Besides active nuclear export, nuclear accumulation of TFIIA might be enabled by an active nuclear localization signal (NLS) mediating importin-dependent nuclear import. In contrast to the results for the NES, bioinformatic analyses  employing regions of clustered basic amino acid residues as consensus sequence for NLS, did not indicate the presence of an active NLS in TFIIA (data not shown). In order to finally prove the absence of active nuclear import processes, we cloned diffusion-deficient TFIIA variants adding an N-terminal GST-tag to our constructs Fig. . The resT234V-mCherry  did not alter TFIIA’s predominant cytoplasmic localization. As a control, co-expression of Taspase1 or its inactive mutant did not affect the cytoplasmic localization of the cleavage-deficient mutant TFIIACSmut-GFP. Interestingly, the double mutant TFIIACSmutNESmut also did not change its localization neither by co-expression of active nor inactive Taspase1  resulted in a significant increase in reporter gene activation. Taken together, we could show that not only Taspase1-mediated cleavage of TFIIA, but also the integrity of its NES is able to regulate transcriptional activity of CDKN2A in epithelial cancer cells.It has been reported that Taspase1-mediated cleavage of TFIIA reduces expression of the CDK inhibitors p16uct Fig. . Therefo19. Interestingly, our data support the hypothesis that Taspase1 fine-tunes the transcriptional activity of TFIIA via its subcellular localization and proteolytic cleavage. Our study revealed that the TFIIAαβ precursor contains a biologically active nuclear export signal , but lacking a nuclear localization signal (NLS). The fact that transiently transfected, unprocessed TFIIA localized predominantly to the cytoplasm which was counteracted by treatment with the nuclear export inhibitor LMB, but unaffected by genetic inactivation of the Taspase1 cleavage site suggests accessibility of the NES in the uncleaved, full-length protein. On the other hand, nuclear import of TFIIA might be mediated via interaction with other proteins capable of nuclear import. For TFIIAγ, no NLS and thus no active nuclear import has been described so far. But there is evidence for other proteins to be shuttled inside different cellular compartments albeit lacking active transport signals. We discovered a comparable shuttling mechanism for the nuclear export of Taspase1 itself, making use of the NES of its interaction partner NPM145.Focusing on disease-relevant signalling-pathways in head and neck cancer, the Taspase1-TFIIA-CDKN2A axis is an interesting and potentially druggable target for HNSCC. In the past it could be demonstrated that Taspase1 uses different strategies to regulate biological processes like proliferation, cell cycle, differentiation and apoptosisWith respect to proteolytic processing, overexpression of Taspase1 but not of catalytically inactive variants enhanced TFIIA’s direct nuclear accumulation, and uncleaved TFIIA mutants revealed an enhanced and elongated cytoplasmic localization. This leads us to the conclusion that processing of TFIIA by Taspase1 might mask the NES, albeit lacking support by the incomplete structural data available so far (pdb 1NVP). Possible scenarios include conformational changes in the α-subunit, which would shift the relative position of the subunit assembly and in turn lead to a complete masking of the NES. This could also be the result of interactions between TFIIA and other transcriptional factors, such as TBP.10, Taspase1 controls the expression of CDK inhibitors encoded by CDKN2A locus (p16INK4a and p19ARF)46. Reporter gene studies revealed a transcriptional activation of p16INK4a by TFIIA which was most prominent in TFIIA variants with genetically enforced nuclear localization in combination with impaired proteolytic cleavage. p16INK4a is an important tumour suppressor gene, involved in the p16/cyclin-dependent kinase/retinoblastoma gene pathway of cell cycle control47. The gene encodes an inhibitor of CDK4 and CDK6, which regulate the phosphorylation of retinoblastoma gene and the G1 to S phase transition of the cell cycle30. p16INK4a expression has been characterized in several tumour types ranging from its loss or downregulation to significant overexpression48. In head and neck cancer, p16INK4a overexpression has been suggested to have a major impact on treatment response and survival in patients with HPV-positive tumours49. HPV-related cancers presenting p16INK4a overexpression are very sensitive to radiotherapy, and have a better prognosis than HPV-negative tumours50. On the other hand, loss of CDKN2A expression by deletion, mutation, or hypermethylation is common in HNSCC and has been suggested as druggable target51. Although loss of p16INK4a could not be validated as prognostic factor for HNSCC patients, it was suggested that it may be used to predict overall patient survival in early-stage head and neck tumours30. While genetic inactivation of p16INK4a has been one of the most prominent genetic changes identified in human cancers to date, it has been reported that p16INK4a function can be regulated via other complex cellular events, such as oncogene activation52. In our present study containing only HPV-negative HNSCC tumours, we found a moderate but highly significant downregulation of p16INK4a suggesting rather expressional downregulation than complete loss or silencing of the CDKN2A gene. It has been reported that Taspase1-mediated cleavage of TFIIA affects the conformation of TFIID/TFIIA promoter complexes and thus, enables assembly with TFIID and other tissue-specific transcription factors53. Thereby, Taspase1-mediated cleavage of TFIIA regulates tissue-specific expression profiles, e.g. during spermatogenesis16 and mammalian head morphogenesis18, and is moreover essential for subsequent proteasomal degradation. The regulation of TFIIA protein levels via Taspase1-mediated cleavage is another regulatory key in this network allowing differential gene expression. In summary, this supports the hypothesis that in contrast to uncleaved TFIIAαβ/γ inducing bulk transcription, Taspase1 cleavage is regulating the switch between general and very specific transcriptional patterns and allows developmental fine-tuning. In undifferentiated cancer cells, Taspase1 activity might be regained and thus, originally developmental regulation processes might be reactivated. In summary, we propose a hitherto unknown mechanism how CDKN2A expression could be fine-tuned via the Taspase1-TFIIA signalling-pathway in head and neck tumours.Besides its crucial role for regulating the MLL-CyclinE axis in HER2/neu-positive breast cancer54.Ab used: α-TFIIA ; α-GAPDH ; α-GFP ; α-Taspase1 . Appropriate HRP-, Cy3- or FITC-conjugated secondary antibodies  were used. Reagents were from Sigma Aldrich  unless stated otherwise. Cells were treated with the export inhibitor Leptomycin B (LMB) (10 nM) as describedTissue samples were obtained from patients undergoing surgical resection at the departments of otolaryngology of the Universities of Frankfurt and Mainz. Investigation has been conducted in accordance with the ethical standards according to the Declaration of Helsinki and according to national and international guidelines. The study protocol has been approved by the ethics committees “Ethik-Kommission des Fachbereichs Medizin, Universitätsklinikum der Goethe-Universität” (#83756604) and “Ethik-Kommission der Landesärztekammer Rheinland-Pfalz” (#83748515) after obtaining the patients’ informed consent to participate in the study and was processed anonymously. All cases were diagnosed histopathologically as HNSCC and staged according to the TNM classification of malignant tumours recommended by the ‘Union International Contre le Cancer’ UICC. In this study, tumour specimens, corresponding non-malignant tissue, and lymph node metastasis were analysed. Specimens included oropharyngeal and laryngopharyngeal carcinoma of different tumour size (T1-T4), lymph node status (N0-2), no distant metastasis (M0) and grading G1-G3. Upon resection samples were immediately placed on ice and snap-frozen in liquid nitrogen within 30 min. Histological analyses were performed to ensure that each specimen contained >70% tumour tissue and <10% necrotic debris. Samples not meeting these criteria were rejected.55.Frozen tissue samples (30–50 mg) were collected in 1ml Trizol  and dispersed using an Ultra-Turrax T25 tissue homogenizer . Total RNA was extracted according to the recommendations given by the manufacturer’s Trizol protocol and further purified on RNeasy Mini spin columns . Integrity and purity of total RNA were assessed on a Bioanalyzer 2100  using a RNA 6000 Nano LabChip Kit (Agilent) according to the manufacturer’s instructionsTotal RNA (5 µg) was used to prepare biotinylated cRNAs for hybridization, following the guidelines given in the Affymetrix GeneChip Expression Analysis Technical Manual. cRNA clean-up was performed on RNeasy Mini filters (Qiagen). In all, 10 µg of fragmented, labelled cRNA were hybridized to Affymetrix HG-U133A arrays  using standard conditions . Arrays were washed and stained in a Fluidics Station 400 (Affymetrix) and scanned on a Gene Array Scanner 2500 (Agilent), as recommended by Affymetrix. Raw fluorescence intensities from all hybridizations were normalized applying variance stabilization with additional scaling. Additionally, MAS5 as well as gcRMA expression values were calculated.Data and cluster analyses were performed using Affymetrix Microarray Suite 5.0 (MAS5) and GeneSpring GX software . All samples were normalized to the median of control samples. Each measurement for each gene in those specific samples was divided by the median of that gene’s measurements in the corresponding control samples. The gene list was subjected to a student’s t-test . The resulting list was further filtered for confidence using a Benjamini-Hochberg false discovery rate correction. A 2.0-fold cut-off filter was then applied to identify genes that were preferentially up- or downregulated. Heat-plots are used for visualizing gene expression: yellow indicates no change in expression, red enhanced expression (upregulated), and blue suppressed expression (downregulated).vs.N). Data was filtered to meet the criteria p < 0.05 in our comparison analysis.The Ingenuity Pathway Analysis (IPA) tool (Ingenuity Systems) was used to identify pathways related to differentially expressed genes in primary tumours  normalization for the.CEL data, followed by a quantile normalization to compare expression results across specimens. For the comparison of primary tumours to normal mucosa (PTvs.N), and metastasis to normal mucosa (Mvs.N) of differentially expressed genes, we also utilized the limma package. For multiple testing a Bonferroni correction was performed.Analysis of the data was performed using R 2.15.2 with the 56. Twelve-bit black and white images were captured using a digital Axiocam CCD camera . Quantitation, image analysis and presentation were performed as described57. All assays were performed in triplicates. Purification and microinjection of recombinant GST-GFP transport substrates were performed as described in detail33. Automated analysis of the molecular translocation assay was performed using the Cellomics ArrayScan® VTI Imaging Platform  as described58. Scans were performed sequentially with settings to give sub-saturating fluorescence intensity, and a minimum of 400 valid objects per well was recorded.Cell lines used in the study were maintained and transfected as describedT234V as untagged or fusions with autofluorescent proteins were described12. Plasmids encoding TFIIA variants were amplified from full length TFIIA cDNA and cloned into pc3-GFP or pGex-GFP using BamHI/NheI-restriction sites. Plasmid TFIIACSmut-, TFIIANESmut-, and pGex_TFIIANESmut-GFP were generated by splice overlap extension polymerase chain reaction as reported59. pF143 were described60. Plasmids were verified by sequence analysis57.Expression constructs encoding TFIIA, Taspase1 and Taspase161. Immunofluorescence was performed as reported in detail36. Western blot data shown are representative for at least three independent experiments (n = 3).Preparation of whole cell lysates was carried out as described using a physiological lysis buffer . Equal loading of lysates was controlled by reprobing blots for GAPDH as described4 293T cells seeded in 24-well plates 24 h before transfection. Cells were transfected at 80% confluence using PEI (2.7 µl PEI per µg DNA) as described1. Specifically, 350 ng of either the empty pGL3 basic or the pGL3 basic reporter vector containing the wild-type p16INK4a 5’-UTR were used along with 50 ng of the control pRLSV40 plasmid introduced to normalize transfection efficiency and 200 ng of the respective TFIIA variants or pc3-GFP as negative control. Cells were harvested 24 h after transfection, and luciferase assays were carried out using the dual-luciferase reagent . Briefly, relative light units (RLU) were measured for both luciferases, firefly and renilla, normalized for transfection efficiency and plotted as relative luciferase activity for each TFIIA variant. Assays were performed in triplicate, and data shown are representative for at least three independent experiments (n = 3).For luciferase reporter gene assays, 5 × 10http://research.nki.nl/fornerodlab/NES-Finder.htm)54. Alignments and clustalW analyses were performed using BLAST (http://www.blast.ncbi.nlm.nih.gov/) and the BioEdit sequence alignment editor (http://www.mbio.ncsu.edu/BioEdit) with the human TFIIA amino acid sequence on NCBI databases (homo sapiens [GI: 433500]; bos taurus [GI: 297479971]; canis lupus familaris [GI: 73964364]; danio rerio [182891266]; gallus gallus [GI: 121308873]; mus musculus [GI: 12313735]; pan troglodytes [GI: 114654237]; rattus norvegicus [GI: 11559984]; xenopus laevis [GI: 148236361. Unless stated otherwise, data were obtained from three independent experiments done in triplicate.TFIIA proteins were analysed using NES-Finder (The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.Supplementary Information


DREB1D expression in stomatal guard cells under water deficit is mediated differentially by promoter haplotypes from sensitive and tolerant coffee genotypes.Fine-tuning of  DREB1D gene  in plant responses to water deficit and cold stress, studies analysing its regulation by transgenic approaches are lacking. In the current work, a functional study of three CcDREB1D promoter haplotypes  isolated from drought-tolerant and drought-sensitive clones of Coffea canephora was carried out in plants of C. arabica stably transformed by Agrobacterium tumefaciens by analysing their ability to regulate the expression of the uidA reporter gene in response to water deficit mimicked by polyethylene glycol (−2.0 MPa) and low relative humidity treatments. A deletion analysis of their corresponding 5′-upstream regions revealed increased specificity of β-glucuronidase activity in the polyethylene glycol and low relative humidity treatments, with high expression in leaf mesophyll and guard cells in full-length constructs. RT-qPCR assays also revealed that the HP16 haplotype (specific to clone tolerant to water deficit) had stronger and earlier activity compared with the HP15 and HP17 haplotypes. As most of the cis-regulatory elements involved in ABA-dependent and -independent networks, tissue specificity and light regulation are common to these haplotypes, we propose that their organization, as well as the nucleic acid polymorphisms present outside these boxes, may play a role in modulating activities of DREB1D promoters in guard cells.Despite the importance of the  This spC. canephora clones (such as DT clone 14 and DS clone 22) were used to identify candidate genes whose expression was induced by drought stress (CcDREB1D (encoding dehydration responsive element binding transcription factor 1D) was of outstanding interest since its expression was up-regulated under conditions of water deficit in leaves of DT clone 14 but not in those of DS clone 22  plants, particularly due to laborious genetic transformation protocols often limiting plant regeneration.The DREB1/CBF (cold-binding factor) gene expression is mainly induced by cold, it was also reported to be involved in the control of plant development and response to other stress stimuli  is rapidly induced by water deficit but not by cold in Arabidopsis  activities in drought in leaves of transgenic tobacco. However, functional characterization of the DREB1D promoter has not been reported. In an attempt to better understand the tissular and environmental regulation of CcDREB1D promoter activity, as well as the regulation of allele-specific expression depending on the haplotype sequence, our study set out to (i) isolate and compare the promoter haplotypes of CcDREB1D genes from DT clone 14 and DS clone 22 of C. canephora, (ii) evaluate their ability to regulate expression of the uidA reporter gene by monitoring GUS histochemical activity in transgenic plants of C. arabica subjected to different abiotic stresses, and (iii) analyse the expression of uidA and endogenous CaDREB1D genes in leaves of these coffee plants.Despite the importance of OsDREB1B , GmDREB3T clone 14 and DS clone 22 of C. canephora Conilon have been described previously  at 26 °C and 80% relative humidity (RH).Deviously . For AgrT clone 14 and DS clone 22 of C. canephora Conilon were used to extract genomic DNA and isolate CcDREB1D promoters. Genomic DNA was extracted from 20 mg of ground leaves as previously described by CcDREB1D promoter haplotypes were amplified independently by PCR with high fidelity Taq Platinum® DNA polymerase according to the supplier’s instructions (Invitrogen)  using 100 ng of genomic DNA and 0.2 µM of the DREB-F1/DREB-R1 primers  from C. canephora (http://coffee-genome.org/), was analysed using the PlantPAN  supposed to be essential for their regulation by abiotic stress. The same web interfaces were used to predict the presence of CREs in the HP15, HP16 and HP17 haplotypes of the CcDREB1D promoters , medium (M: +182/−966), and long (L: +182/−1.308) fragments of the CcDREB1D promoter haplotypes, and the reverse primer R-DREB carrying the BglII restriction site . The resulting PCR products were subcloned into the pGEM-T Easy vector , digested with HindIII and BglII and cloned in the pBI121 vector  double-digested by the same enzymes. Following ligation and transformation in E. coli, the recombinant constructs were named according to their haplotype (HP) of the CcDREB1D promoter and length , namely as pHP16S and pHP17S , pHP16M (+182/−966), and pHP15L, pHP16L and pHP17L (+182/−1308) .Binary vectors were generated by PCR using high fidelity ion site . The pos2/−1308) . Each reA. tumefaciens strain LBA1119 harboring the recombinant vectors was grown at 28 °C for 24 h in yeast extract mannitol agar (YMB) selective medium with rifampicin (25 mg l−1) and kanamycin (50 mg l−1). Transgenic lines of C. arabica were generated by A. tumefaciens-mediated transformation using 12-month-old embryogenic calli competent for transformation as previously described (CaMV35S:uidA) and pBI101 (uidA-promoterless) vectors used as positive and negative controls, respectively. Each kanamycin-resistant callus regenerated cotyledonary embryos giving plantlets after 4–5 months that were cultivated on MS medium with active charcoal (1 g l−1) under sterile conditions prior to testing for fast dehydration experiments. Once regenerated, DNA was extracted from leaf explants and successively tested by (i) PCR using the nptII- (kanamycin) and uidA-specific primers to confirm T-DNA integration in genomic DNA of C. arabica-transformed plantlets, and (ii) RT-qPCR to select transgenic lines harboring a single insertion of T-DNA (data not shown) representing around 50% of T1 transgenic coffee lines (CcDREB1D promoter haplotypes was studied in primary transformants (T1) of C. arabica grown in vitro and subjected to the abiotic treatments described below. For each construct, 64 plantlets from eight independent transformation events were arranged in eight batches of eight plants. In each assay, each plant batch was subjected independently to water deficit for 0, 3, 6, 12, or 24 h, with 0 h  corresponding to ‘no stress conditions’.Regulation of the in vitro plantlets with two to four pairs of leaves were placed in a laminar flow cabinet under a 0.49 m s−1 air flow for 1 h until reaching a visible dehydrated state. Each pHP construct was composed of five plantlets corresponding to independent transformation events.Genetically transformed D) was induced by 9% RH at 27 °C under controlled conditions. A theoretical D was calculated applying the formula D=[1−(RH/100)]×PWS (kPa), where PWS (saturated vapor pressure)=3779 Pa at 28 °C. To create a 9% RH, 400 ml of KOH supersaturate solution was poured into the lower compartment of a 5 l bioreactor  which remained sealed throughout the experiment. Plantlets were placed in the upper compartment of the bioreactor in 55 mm Petri dishes having their upper part exposed to the outside environment and radicles immersed in MS medium   to the MS medium, at 25 °C, using the empirical equations defined by −1 5-bromo-4-chloro-3-indolyl-D-glucuronic acid (Sigma-Aldrich), and 2.5 mM potassium ferrocyanidine). After infiltrating (vacuum for 2 × 10 min) the staining solution in plant tissues, the samples were incubated at 37 °C for 24 h, and then rinsed with water before image acquisition with a Retiga 2000R camera .Histochemical GUS assays were performed with different tissues  derived from the same plant as those immersed in GUS staining solution  at 4 °C for 24 h, rinsed with water and then dehydrated . After observation with a Nikon binocular SMZ 1500 loupe, samples were embedded in 6% agarose for subsequent sections in a Microm HM650V vibrating blade microtome. For bright field microscopy observation, 50 µm-thick leaf sections were examined using a DM600 Leica microscope  and photographed. GUS expression patterns appeared highly conserved for the eight lines transformed with the same promoter construct.CcDREB1D promoter activity. For each pHP construct, the same three independent transgenic lines were studied along the entire kinetics. The proportion of GUS-stained guard cells (p) was obtained by p=x/n, where x is the number of stained guard cells and n the total number of guard cells (=150) per leaf zone. These values were assessed in 24 × 36 mm areas distributed in six pre-delimited leaf zones. For each pHP construct, three leaves from plants of three independent transformation events were sampled for each time  in the PEG and low RH assays, and the respective proportion means (from 450 guard cells) and standard deviations for each time point were calculated.For bright field microscopy, GUS-stained leaves were fixed (50% methanol and 10% acetic acid) at 4 °C for 24 h, rinsed with water and incubated for at least 3 days in clearing solution  to remove all leaf pigments. Prior to observation, tissues were rinsed with 70% ethanol and assembled on microscope slides for photography as described before. The proportion of GUS-stained guard cells on the abaxial epidermis of coffee leaves was calculated to estimate Total RNA was extracted from leaves frozen in liquid nitrogen that were further ground and treated as described previously (uidA (GUS-F/R) and CcDREB1D (DREBA09-F/R). Data were analysed using SDS 2.1 software (Applied Biosystems) to determine cycle threshold (Ct) values. The specificity of the PCR products generated for each set of primers was verified by analysing the Tm (dissociation) of amplified products. PCR efficiency (E) was estimated using absolute fluorescence data captured during the exponential phase of amplification of each reaction with the equation (1+E)=10(−1/slope) (E)Ct−ΔΔ where ΔCt,target=Ct,targetgene−CCaGAPDHt, and ΔΔCt=ΔCt,target−ΔCt,referencesample, with the T0 samples being used as references for each construct. Expression levels were normalized with the expression of the CaGAPDH gene (GB accession number GW445811 using primer pair GAPDH-F/R) as the endogenous control (PCR experiments were performed as previously described by CcDREB1D promoter regions of C. canephora drought-tolerant (DT) clone 14 and drought-sensitive (DS) clone 22 revealed the existence of three haplotypes named HP15, HP16 and HP17, diverging from each other by several SNPs and INDELs (JXB online). The HP15 haplotype was common (without any SNPs) to both clones, while HP16 was specific to DT clone 14 and HP17 specific to DS clone 22  concentrating most of the nucleotide variability  and DOF (−1017/−1013) DNA boxes present in HP15 and HP16 were missing in HP17  and further analysed to investigate GUS coloration in abaxial leaf surfaces. As controls, no staining was detected in plants transformed with the pBI101 vector  whereas CcDREB1D promoter influenced the regulation of these sequences, the activity of these promoters was evaluated by calculating the proportion of GUS-stained guard cells on leaf abaxial regions in a time course response to water deficit assays simulated by PEG  and to low RH assays  of the H assays . For theH assays . HoweveruidA gene expression in leaves of coffee plants transformed by pHP15L, pHP16L, and pHP17L and subjected to PEG treatment showed low variability for each construct at each time of the water deficit bioassay. Similar expression profiles were obtained for all the constructs, showing a gradual increase in uidA gene expression with PEG treatment, which reached maximum expression after 6 h (pHP16L and pHP17L) or after 12 h (pHP15L) and declined thereafter  promoters of C. canephora to PEG and low RH treatments by analysing their ability to regulate the expression of the uidA reporter gene in stably transformed C. arabica plants.In our study, we investigated the responses of different haplotypes of the CcDREB1D promoters harbored several abiotic stress-responsive CREs in common, including those involved in tissue-specific and light regulation. They also contained ABRE and DRE elements, thereby suggesting possible regulation of these promoters by both ABA-dependent and -independent networks  and eight MYC-related binding sites (CANNTG), which are both motifs known to play a key role in the regulation of DREB1/CBF genes by cold-stress. Regarding MYC-related DNA motifs, two of them contained the preferable target sequence CAAATG  of the positive regulator inducer of CBF expression 1 (ICE1).The results presented here indicate that the HP15, HP16 and HP17 haplotypes of networks . Interesnetworks . This isCcDREB1D haplotypes also contained several MYB-binding sites that can act as either an enhancer or repressor of promoter activity. One of them (MYB1AT: AAACCA) is a putative DNA binding site of the MYB15 trans-acting factor known to regulate negatively the expression of DREB1D/CBF genes  of the CcDREB1D promoters  of these promoters necessary for enhancing gene expression by several abiotic stresses , fine-tuned regulation of the uidA reporter gene was observed in coffee plants transformed by the full-length constructs , with GUS activities restricted to leaf mesophyll and guard cells under stressed conditions. In that case, the degree of CcDREB1D haplotype activity in guard cells also appeared to increase with the number of DOF-binding sites, from the short (six sites) to the full-length (11/12 sites) promoter regions. One possible explanation could be an increase in the numbers of regulatory sites recognized by trans-acting factors known in the longest constructs of CcDREB1D haplotypes. For example, of the five MYB DNA boxes found within the −608/−1308 region, three MYBCORE and one MYB2AT are positive regulators and one (MYB1AT) is a putative binding site of MYB15 trans-acting factor known to repress DREB1/CBF genes  and two in the distal (−966/−1308) regions of the CcDREB1D promoters) closely located to DOF-binding elements. Therefore, GT-1 DNA binding sites could participate together with DOF elements in forming such clusters necessary for targeting guard cell-specific gene expression.Previous studies showed that CcDREB1D haplotypes was up-regulated under low air relative humidity (RH 9%). In that case, a slight increase in GUS-stained guard cells was observed in pHP15L- and pHP17L-transformed plants, while this proportion increased slightly but regularly in pHP16L-transformed plants in the RH treatment. The different time courses of GUS-stained guard cells observed between the HP15, HP16, and HP17 haplotypes during the PEG and RH treatments clearly revealed differences in the fine-tuning of the regulation of these CcDREB1D promoters, which were undoubtedly related to the nucleic acid polymorphism identified between these sequences directly affecting CREs and/or altering their cluster organization (see discussion below). Our results also indicated that the PEG and RH treatments affected the time course of activity of the HP16 and HP17 haplotypes of CcDREB1D promoters differently. For example, GUS-stained guard cells were detected earlier in pHP16L- and pHP17L-transformed plants in response to the PEG treatment compared with the RH treatment. PEG treatment is known to mediate low Ψw by reducing the osmotic potential of the nutrient solution, thereby mimicking low soil water potential that leads to a water deficit in the whole plant  is expected to delay the response of the CcDREB1D promoter to water deficit. To simulate such conditions, our transgenic coffee plants were subjected to a water deficit of −2.0 MPa mimicked by the low RH treatment, which is known to increase the leaf-to-air water vapor pressure deficit (VPD), to generate evaporative demand and, consequently, to elevate the transpiration rate  of plant promoters in regulating gene expression differentially  in HP16, with this INDEL being localized close to GT1 (−1037/−1027) and DOF (−1017/−1013) DNA binding sites. Several other polymorphisms, localized close to essential CREs, were also identified mainly in the polymorphic domains −627/−806 and −932/−1121. Within the −627/−806 region, the presence of (i) MYB- and MYC-like regulatory elements, respectively known as activator and repressors of transcription by drought, and (ii) a tandem (×3) repeat of the GAAWTT unidentified motif (−722/−739) only present in the HP16 haplotype is worth noting. Compared with the HP16 sequence, a G to A transition (in position −1013) in the HP17 haplotype, leading to the loss of one DOF domain, is also worth noting. Since DOF transcription factors are involved in the regulation of various plant processes  they had most of the essential CREs in common, and (ii) HP15 and HP16 were more closely related than HP17 regarding INDEL-type polymorphisms. By comparing CREs between HP16 and HP15, very few differences were observed regarding the boxes known to recognize key transcription factors involved in abiotic stress. The fact that these haplotypes of rocesses , such asuidA reporter gene. For all the haplotypes, uidA-specific transcripts were accumulated after 3–12 h of PEG treatment (maximum after 6–12h) and declined thereafter. Although similar in terms of profiles, differences in promoter strength were found, with the HP16L promoter being stronger and occurring sooner (significant expression after 3 h) than the other two haplotypes. It is worth noting that the accumulation of uidA transcripts followed quite well the expression profile of the endogenous CaDREB1D gene, which was also up-regulated in the PEG treatment. In that case, the quantity of CaDREB1D transcripts detected after 24 h of treatment was very weak and did not differ from that before water deficit treatment. CaDREB1D transcripts were almost barely detectable after 6 h. Our results strongly suggest that the transient expression of the CaDREB1D gene under water deficit directly reflected the transitory activity of DREB1D promoters.The increase in GUS activities observed in the water deficit mimicking treatments in leaves of transgenic coffee plants transformed by the pHP15L, pHP16L, and pHP17L constructs was confirmed by monitoring expression of the DREB1/CBF genes has been commonly reported to be mainly induced by cold, our results clearly indicated that the expression of the DREB1D genes of C. arabica was rapidly induced by water deficit mimicking treatments and that the up-regulated expression of these genes was controlled at transcriptional level. These results also showed that the different promoter haplotypes of the CcDREB1D gene of the diploid C. canephora were correctly recognized by the transcriptional machinery of the allotetraploid species C. arabica.Even though the expression of T clone 14 of C. canephora) had greater strength, as well as longer activity, under low RH conditions compared with HP15 (found in both DT clone 14 and DS clone 22) and HP17 (isolated from DS clone 22) haplotypes. These characteristics might explain the higher stomatal conductance (gs), and consequently the more efficient mechanisms in controlling the transpiration rate observed under water deficit in DT clone 14 compared with DS clone 22  on the regulation and activity of the CcDREB1D haplotypes.Summarizing, the variations in intensities and staining patterns between the three haplotypes tested during this study suggested that HP16  with the CcDREB1D consensus sequence  of GUS activity in dehydrated control plants of Fig. S3. Confocal laser scanning microscopy (CLSM) micrographs from leaf cross-sections with immunolocalization of GUS protein.CcDREB1D coffee promoters.Table S1. List of nucleic acid polymorphisms found in the HP15, HP16, and HP17 haplotypes of CcDREB1D promoters can be found in the GenBank database under accession numbers KM281308, KM281309, and KM281311, respectively.Sequence data for the HP15, HP16, and HP17 haplotypes of CcDREB1D promoter haplotypes, made the constructs and analysed these sequences. GSCA, LFT, HE, and ED carried out the genetic transformation of C. arabica, applied abiotic stress, and carried out GUS staining and microscopy analyses with the help of FG. LFT extracted RNA samples used by J-CB to perform qPCR experiments and analysed the results together with TJ. HE, BB, ACA and PM designed the study, drew up the experimental design and implemented it. GSCA, ACA, TJ, PM, and HE wrote the article, which was approved by all the authors.GSCA cloned and sequenced the Supplementary_Table_S1_Figures_S1_S3Click here for additional data file.