Progression of malignancy to overt disease requires multiple genetic hits. Activation-induced deaminase (AID) can drive lymphomagenesis by generating off-target DNA breaks at loci that harbor highly active enhancers and display convergent transcription. The first active transcriptional profiles from acute lymphoblastic leukemia (ALL) patients acquired here reveal striking similarity at structural variation (SV) sites. Specific transcriptional features, namely convergent transcription and Pol2 stalling, were detected at breakpoints. The overlap was most prominent at SV with recognition motifs for the recombination activating genes (RAG). We present signal feature analysis to detect vulnerable regions and quantified from human cells how convergent transcription contributes to R-loop generation and RNA polymerase stalling. Wide stalling regions were characterized by high DNAse hypersensitivity and unusually broad H3K4me3 signal. Based on 1382 pre-B-ALL patients, the ETV6-RUNX1 fusion positive patients had over ten-fold elevation in RAG1 while high expression of AID marked pre-B-ALL lacking common cytogenetic changes.DOI:http://dx.doi.org/10.7554/eLife.13087.001 Some of the most common cancers found in children are called precursor leukemias, which may start to develop before birth. Cancerous cells often contain alterations to the genetic information in their DNA. In precursor leukemias, the most common genetic changes involve deleting, adding or rearranging segments of the DNA sequence.Several researchers have sequenced the entire DNA of childhood leukemia cells, with the result that almost all of the genetic alterations linked to these conditions have been catalogued. These efforts have shown that certain DNA regions are particularly affected by mutations, but no one knows why errors occur so frequently in these regions.Recent evidence also suggests that transcription – the process of producing useful molecules from a stretch of DNA – can play a role in generating genetic alterations. Heinäniemi et al. have now used a technique called global run-on sequencing to measure the extent of transcription in many different types of leukemia cells. This revealed that in the error-prone DNA regions, two processes – called convergent transcription and transcriptional stalling – interfere with transcription. Both processes temporarily leave the normally double-stranded DNA unzipped as two single strands and free of nucleosomes, which makes DNA more vulnerable to breaking. This would explain how pieces of DNA might be lost, added, or moved to cause the genetic errors that lead to leukemia.Further investigation revealed that two protein complexes called RAG and AID, which rearrange segments of DNA in immune cells, are likely to cause the errors in the vulnerable DNA regions. Different amounts of RAG and AID were present in different subtypes of leukemia cells, and these amounts also varied with the risk classification of the disease. Further studies are now needed to investigate the exact roles of these protein complexes. This could eventually help scientists devise strategies to protect the DNA of people with leukemia from these errors, which could reduce the risk of the cancer reoccurring.DOI:http://dx.doi.org/10.7554/eLife.13087.002 Cells iequences . The engequences . Howeverequences . Furtherequences , are relIn lymphomas, AID off-target effects localize to intragenic super-enhancer (SE) and promoter areas characterized by transcription from both strands, i.e. convergent transcription (convT) . NotablyCDKN2A locus, a significant SV site in childhood ALL  were retrieved based on HiC data from B-lymphoid lineage cells . TADs rePAX5 and ZCCHC7 genes are located within a TAD region with 20 breakpoints in the ETV6-RUNX1, 4 in HeH and 6 in MLL subtype (excluding the MLL-fusion itself)  . Frequen itself) . There w itself) . The nasPAX5, confirmed by the active enhancer chromatin marker H3K27ac, led to convT extending nearly 20 kb, with SV sites located between 3.7–9.7 kb downstream of the TSS (The prevailing notion is that active transcription start sites (TSS) in pre-B cells are susceptible to RAG off-targeting due to the H3K4me3 chromatin mark . HoweverZCCHC7 and RAG loci . The increased overlap was found for breakpoints with and without RSS motifs  . The senAs an additional experimental validation of R-loops, we used DNA-RNA-immunoprecipitation sequencing (DRIP-seq) results from ES cells  that correspond to detection of DNA-RNA hybrids . The 2.1TPI1 loci, representing an SV hotspot with the highest number of NR-breakp in ETV6-RUNX1 cases  were analysed for the genome-wide overlap with the transcriptional features. A 66% overlap was found with Pol2 stalling at intragenic regions  and a 44% genome-wide overlap with convT  that does not carry recurrent fusion genes or karyotypic changes  software (homerTools trim) to remove A-stretches originating from the library preparation. From the resulting sequences, those shorter than 25 bp were discarded.Cells were suspended in 10 ml of swelling buffer  and let swell for 5 min. The cells were pelleted for 10 min at 400 × g and resuspended in 500 µl of swelling buffer supplemented with 10% glycerol. Subsequently, 500 µl of swelling buffer supplemented with 10% glycerol and 1% Igepal was added drop by drop to the cells while being vortexed gently. Nuclei were washed twice with 10 ml of swelling buffer supplemented with 0.5% Igepal and 10% glycerol, and once with 1 ml of freezing buffer containing 50 mM Tris-HCl pH 8.3, 40% glycerol, 5 mM MgCl2 and 0.1 mM EDTA. Nuclei were counted and centrifuged at 900 × g for 6 min and suspended to a concentration of 5 million nuclei per 100 µl of freezing buffer, snap-frozen and stored -80°C until run-on reactions. The nuclear run-on reaction buffer  was pre-heated to 30°C. Then each ml of the NRO-RB was supplemented with 1.5 mM DTT, 750 μM ATP, 750 μM GTP, 4.5 μM CTP, 750 μM Br-UTP  and 33 μl of SUPERase Inhibitor . 50 μl of the supplemented NRO-RB was added to 100 μl of nuclei samples, thoroughly mixed and incubated for 5 min at 30°C. GRO-Seq libraries were subsequently prepared as previously described . BrieflyChIP-seq was performed using antibodies against the Ser2 and Ser5 phosphorylated forms of Pol2 and against the histone mark H3K4me3 in REH (N = 1) and Nalm6 cells (N = 2). Ser5 phosphorylation is present before the Pol2 is released to active elongation and it diminishes within the gene body and is greatly reduced downstream of the poly(A) site, where Ser2 phosphorylation is predominantly found . For ChIhttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and subsequently bases with poor quality scores were trimmed  using the FastX toolkit (http://hannonlab.cshl.edu/fastx_toolkit/). Samples sequenced on multiple lanes were pooled after quality control. Read stacks were collapsed from ChIP-seq files using fastx (collapse). The Bowtie software (bowtie-0.12.9v0.1.x)   was used9v0.1.x) .http://homer.salk.edu/homer) software was used in further processing of HiC-data. Paired-end reads were connected and read pairs with exact same ends were only considered once and read pairs were removed if they were separated by less than 1.5× the estimated sequencing insert length to remove likely continuous genomic fragments or re-ligation events. Paired-end reads originating from regions of unusually high tag density were left out by removing reads from 10 kb regions that contain more than five times the average number of reads. Background model for normalization of HiC-data was generated with 50 kb resolution. The topological domains were identified using the HOMER command’ findHiCDomains.pl’ using a resolution of 50 kb. This analysis is based on a statistic referred to as the ‘directionality index’, which describes the tendency of a given position to interact with either the chromatin upstream or downstream from its current position.Reads from paired-end sequencing were separately filtered and aligned to the genome using bowtie. The reads were checked for MboI restriction sites before doing the alignments and the sequences after GATC sites were trimmed out to improve mappability. The HOMER v4.3  was used to identify de novo transcripts from GRO-seq data using pooled sequencing reads per sample type. Deeply sequenced REH, Nalm6 and lymphoblastoid cells were used to define signal features in B-cell lineage and separate analysis was carried out for ES cells (see Combined tagDirectories from GRO-seq samples were made by pooling the sequencing data for each cell and sample type with fragment length set to 75. The findPeaks.pl program in the The HOMER v4.3 software (ells see . Gaps wePeaks were identified using findPeaks (-style histone –size 1000).7 mapped reads. The bigWig files were further converted to track hubs and visualized as strand-specific, overlaid MultiTracks as a custom Track Hub in the UCSC Genome browser.BedGraph and bigWig files were generated with reads in each sequencing experiment normalized to a total of 10The hg19 genome version from UCSC (available from iGenomes) was used to specify chromosome lengths in the analysis. The gene annotations from Refseq and UCSC known gene tables were retrieved using the UCSC Table Browser ). Unique transcript coordinates were used in analysis, that is, any transcripts sharing the same start and end coordinate were considered together. The TSS regions were defined as +/- 1 kb regions around the annotated start coordinate. Only transcripts mapping to canonical chromosomes were kept, also those on chrM were removed.Super-enhancer coordinates from CD19+, CD20+ and HSC cells were obtained  and mergTADs reflect the three dimensional structure of chromatin, forming natural boundaries that divide the chromosomes into sub-regions. To identify TADs with highest frequency of breakpoints, HiC-data analysis was performed using HOMER 4.3. As our goal is to generate a natural division of the genome into sub-regions that are relevant in context of transcriptional regulation, this approach is superior to arbitrarily assigning sub-regions based on fixed windows. The pre-B-ALL breakpoints and annotation data  were anaBroadChromHMM chromatin segmentations were obtained from GM12878 and H1 ES cells including the following segment types: 1_Active_Promoter, 2_Weak_Promoter, 3_Poised_Promoter, 4_Strong_Enhancer, 5_Strong_Enhancer, 6_Weak_Enhancer, 7_Weak_Enhancer, 8_Insulator, 9_Txn_Transition, 10_Txn_Elongation, 11_Weak_Txn, 12_Repressed", 13_Heterochrom/lo, 14_Repetitive/CNV, 15_Repetitive/CNV. The sizes of the segments of each type were used to calculate the total span from the genome. Each segment type was then overlapped with a combined bed file specifying convT and Pol2 stalling regions. Overlapping and non-overlapping pieces were returned and analyzed separately .Two types of breakpoints were distinguished based on RSS motif annotation to result in the following region assignment: regions containing a consensus RSS/heptamer sequence motif were used to categorize co-localized breakpoints as putative RSS-dependent breakpoints , while regions devoid of recognition sequence were used to classify RSS-independent lesions . Regions harboring unresolved breakpoints were left out from majority of analysis performed (285 regions harboring 295 breakpoints in the ETV6-RUNX1 subtype that were mainly isolated and non-recurrent). The RSS assignment for other breakpoints was obtained in the following way: the resolved breakpoints were extended to both sides by 10 bp, resulting in a 21 bp region. The MEME analysis in To evaluate recurrence, the breakpoint ends at 1 kb distance were stitched together to form regions  and local elevations in the signal , with detailed definitions given below. For genome-wide analysis of signal feature overlap with SV, feature tracks from several samples were combined. This approach was deemed most appropriate to address the dynamic nature of transcriptional activity and to avoid missing regions that due to high recurrence of SV may be deleted in subset of leukemic cells studied.ConvT regions were identified as transcripts that overlap on opposite strands by at least 100 bp as in . SubsequChange-point detection is the mathematical problem of finding abrupt changes in a signal, typically applied in context of time series . Both apThe HOMER command annotatePeaks.pl was used to create a transcriptional profile of active genes (RPKM > 0.5) in ES, lymphoblastoid and REH cells by scaling the histogram to each region (i.e 0–100%) using a bin size of 100. RLFS motif density was calculated across genes with the BEDtools coverage tool. A density plot representing RLFS frequency across gene regions was then obtained as above.Data from replicate DRIP-seq experiments with two different restriction enzyme digestions (GSE45530) were used in the analysis. Log2 signal levels were quantified using HOMER at TSS regions. Statistical significance was estimated separately for the two different restriction enzyme digestions. RLFS motif search was performed using the software QmRLFS-finder that predicts R-loop forming sequences based on structural models of known sequences . The fasThe DNAse-seq peaks were first overlapped with the Pol2 stalling regions detected based on the GRO-seq signal. Only peaks with a score above 15 were considered. The width of the overlapping peaks was then compared to the width of peaks with no overlap using the Wilcoxon rank sum test. Next, top 5% widest peaks were obtained from the DNAse-seq and ChIP-seq data refer to . The oveGene expression data from pre-B-ALL studies was combined from microarray datasets retrieved from the NCBI GEO database as part of a data collection representing both healthy and malignant samples hybridized to hgu133Plus2 genome-wide microarrays (in preparation for submission). In total, 1382 pre-B-ALL samples were included. Probe-level quality control was performed to exclude samples with very high difference in data location or distribution as measured by median and interquartile range of raw probe intensities. Samples that passed this filtering were processed using the RMA probe summarization algorithm with probe mapping to Entrez Gene IDs , followed by bias correction using the R package ‘bias’. The Barnes-Hut-SNE algorithm  implementation from the R package ‘Rtsne’  was usedStatistical significance was estimated using several tests to ensure reliability, including tests that rely on assumptions about data distributions and empirical tests that rely on randomization of data points. The statistical tests used, exact values of N, definitions of center and dispersion and precision measures are indicated in Results, in the respective supplementary tables or figure legends.Test for independent random trials with binary (success/failure) outcome, with replacement. This test was used to assess the statistical significance of observing breakpoint events overlapping a transcriptional feature . Success in population was defined using 1 kb windows across the genome. The windows overlapping the studied feature was divided by the total number of 1 kb windows analyzed. E.g. in the Pol2 stalling analysis, the total number of windows overlapping Pol2 stalling regions divided by this number of 1 kb windows within gene coordinates , define probability of success.Test for independent random trials with binary (success/failure) outcome, without replacement. This test was used to assess the statistical significance of observing greater than or equal overlap frequency between breakpoints and an annotated set of genomic regions. E.g. to test for enrichment of breakpoints inside convT-positive enhancers, convT-positive enhancers with breakpoints define sample success; all enhancers with breakpoints population success; and sample taken is all convT-positive enhancers . The related Fisher’s test (implemented in BEDTools fisher) was used to obtain similar statistics with odds ratios.A nonparametric two-sided Wilcox test was performed to estimate whether two samples  come from the same population (R function wilcox.test). This test was applied to quantified signal levels compared between categories.This test can be used to obtain an empirical estimate of random overlap frequencies. The sampling was performed 1000-fold within the same genomic context as used in the analysis. To estimate the significance of overlap between stitched breakpoint regions with e.g. convT regions, the stitched regions were allocated random genomic coordinates, thus preserving the size distribution and breakpoint event frequencies within stitched regions. The observed random region overlap was used as the empirical p-value estimate. Further, the z-test was used to evaluate whether there was evidence to reject the null hypothesis that the observed feature overlap value would belong to the empirical distribution obtained. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jessica Tyler as the Senior Editor.Thank you for submitting your work entitled "Transcription-coupled genetic instability marks acute leukemia structural variation hotspots" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:A series of recent studies demonstrated that genetic diversification during adaptive immune responses of B cells comes with an increased risk of malignant transformation, i.e. when AID, RAG1 and RAG2 target non-immunoglobulin genes. Based on integrative analyses of transcriptional activity (GRO-seq) and genetic lesions (whole genome sequencing), Lohi and colleagues propose a novel scenario to explain how AID and RAG1/RAG2 can be aberrantly targeted to Non-Ig sites and thereby cause genetic lesions that drive malignant transformation.Essential revisions:1) The scenario in Figure 6 is potentially interesting but goes far beyond what is supported by the actual data presented in the study. The Discussion section dilutes the main findings by additional speculation. A more detailed discussion of their actual data  would be helpful. There is a lot of speculation on the Results section and in particular around findings that build up to this mechanism and it is not clear how the data/analysis supports these observations.2) The manuscript is of potential interest to a broader audience but mainly written for expert readers in computational biology. To make this work accessible to a wider group of scientists that are potentially interested in the topic, it would seem to be necessary to explain the rationale for the use of certain methods and techniques in some meaningful detail. For instance, the use of t-SNE blots (as for CyTOF) to correlate AID, RAG1 and RAG2 expression with cytogenetic subtypes is of interest, but not discussed what exactly can be seen in the diagrams and why this method was chosen.towards full transformation. In the fully established leukemia, secondary genetic events and consequences of oncogenic signaling may alter expression levels and obscure the role of AID and RAG1/RAG2 in these leukemia subsets. In addition, the significance of AID and RAG1/RAG2 expression only measured at the mRNA level is unclear. Western blot analyses should be performed if authors feel strongly about documenting different expression levels.3) Expression of AID and RAG1/RAG2 "markedly distinct between pre-B ALL subtypes": The measurements were performed in fully established ALL clones. The interpretation that different expression levels of AID and RAG1/RAG2 point to a different role of these enzymes in different cytogenetic subtypes is likely incorrect. As in B cell lymphoma, AID and RAG1/RAG2 act together in a multi-step process of clonal evolution 4) Do the proposed transcriptional features of instability at SV-sites coincide with cryptic RSS or minimal RAG1/RAG2 substrates? This would add to mechanistic plausibility of their scenario.5) For the TAD Analysis: Would TAD definition change if a lymphoid origin cell lines or tissue types were used? The authors divide TADS in quartiles on the basis of number of breakpoints within TADs and study how this correlates with% study of convT at these loci. The size range and density of the TADs can vary greatly (T2). The authors should ensure that TAD size does not confound this analysis.6) Correlation of 'transcriptional features' such as prevalence of POL2 stalling, DNA sequences that are susceptible to R-LOOP formation and also convergent transcription, with regions that are frequently rearranged in ALL, and in regions with high prevalence of RSS.7) Whilst there is a notable enrichment against other active promoters for example the 'width' of the 'stalling' region, although what this really represents, as defined from GRO-seq data, is the high density of active RNA polymerases, so is it possible that these represent regions of active transcription.8) With previous reports showing enrichment of genomic rearrangements at active promoters and enhancers it is not clear whether the observations here are a consequence of transcriptional activity in these regions or an enriched mechanism underpinning the regions that are frequently targeted. The authors should present a global analysis – see statistical review section.9) A major limitation of the analysis here is that there are not overlapping datasets from the same samples. As the authors have access to direct primary patient cells and cell lines representative of B-ALL their interpretations of the data would significantly benefit from performing RNAseq, POL2 chip, and potentially MNase-seq to support their observations .10) Overall there is a clear narrative issue, the dataset is complex and the analysis is not clear nor comprehensive – making it rather hard for the reviewer and in time a reader to comprehensively evaluate the analytical approaches used, as well as, the evaluate the interpretation of the findings, which often render conclusions as facts by citing other papers rather than being supported by the data in itself. It would be very helpful if the authors included a supplemental figure containing a flow chart describing what datasets where put together and which subsets of data were used for which analysis.11) Results should be interpreted on the basis of their analysis. Conclusions that account or include findings from the literature should be placed in the Discussion section.12) It would be useful if the authors performed a global analysis- accounting for chromatin segmentation in ALL and considering these features. It would be very interesting to show specifically how the parameters they consider  correlate with promoters/ active transcription first and then evaluate if these metrics show significant deviations (enrichment) in the areas most widely affected by genomic rearrangement in ALL and how do these differ between frequent breakpoints and rare breakpoints?13) Perhaps incorporating metrics that include expression of genes in those regions in B-ALL might strengthen this analysis and provide additional insights into the proposed mechanism. Whilst it may be really difficult to obtain such additional RNA seq data – the authors could consider using gene expression metrics from prior SNP array studies of distinct ALL subtypes. Essential revisions:1) The scenario in Figure 6 is potentially interesting but goes far beyond what is supported by the actual data presented in the study. The Discussion section dilutes the main findings by additional speculation. A more detailed discussion of their actual data  would be helpful. There is a lot of speculation on the Results section and in particular around findings that build up to this mechanism and it is not clear how the data/analysis supports these observations.We thank the reviewer for the suggestions on how to improve the presentation of the obtained results in context of previous work. We have reorganized the text to make a clear distinction between these. In addition, the data related to the mechanisms of how transcriptional features may contribute to genetic instability have been split into two figures (2) The manuscript is of potential interest to a broader audience but mainly written for expert readers in computational biology. To make this work accessible to a wider group of scientists that are potentially interested in the topic, it would seem to be necessary to explain the rationale for the use of certain methods and techniques in some meaningful detail. For instance, the use of t-SNE blots (as for CyTOF) to correlate AID, RAG1 and RAG2 expression with cytogenetic subtypes is of interest, but not discussed what exactly can be seen in the diagrams and why this method was chosen.We thank the reviewers for acknowledging the interest to a broad audience. We have expanded the rationale for method selection in the Results text and Materials and methods section, as suggested, and explain more in detail what is shown in the diagrams in the respective figure legends, to help the reader follow the analysis performed.3) Expression of AID and RAG1/RAG2 "markedly distinct between pre-B ALL subtypes": The measurements were performed in fully established ALL clones. The interpretation that different expression levels of AID and RAG1/RAG2 point to a different role of these enzymes in different cytogenetic subtypes is likely incorrect. As in B cell lymphoma, AID and RAG1/RAG2 act together in a multi-step process of clonal evolution towards full transformation. In the fully established leukemia, secondary genetic events and consequences of oncogenic signaling may alter expression levels and obscure the role of AID and RAG1/RAG2 in these leukemia subsets. In addition, the significance of AID and RAG1/RAG2 expression only measured at the mRNA level is unclear. Western blot analyses should be performed if authors feel strongly about documenting different expression levels.The reviewer raises an interesting point. The clinical relevance of comparing RNA expression in samples from fully established leukemia (i.e. diagnostic samples) between subtypes, has been addressed and supported with patient data in the earlier work by Swaminathan et. al 2015 Nat Immunol. Specifically, in their Results section Swaminathan et al. write “We correlated AICDA mRNA expression in patients at diagnosis with overall and relapse-free survival. […] Notably, higher-than-median AICDA expression was strongly indicative of poor overall patient survival.4) Do the proposed transcriptional features of instability at SV-sites coincide with cryptic RSS or minimal RAG1/RAG2 substrates? This would add to mechanistic plausibility of their scenario.The distinction between R-breakp and NR-breakp  was perhaps not explicit from our previous Methods description. We have modified the text with a more detailed explanation on how the RSS status was assigned. 5) For the TAD Analysis: Would TAD definition change if a lymphoid origin cell lines or tissue types were used? The authors divide TADS in quartiles on the basis of number of breakpoints within TADs and study how this correlates with% study of convT at these loci. The size range and density of the TADs can vary greatly (T2). The authors should ensure that TAD size does not confound this analysis.There appears to be a misunderstanding regarding the division of TADs into quartiles. This division was done based on the number of breakp per bp, i.e. considering the TAD size as indicated in the To have better consistency across data sources, we repeated the TAD analysis using HiC data from deeply sequenced B-lymphoblastoid cells that recently became available and substituted these results to 6) Correlation of 'transcriptional features' such as prevalence of POL2 stalling, DNA sequences that are susceptible to R-LOOP formation and also convergent transcription, with regions that are frequently rearranged in ALL, and in regions with high prevalence of RSS.The referee suggests a valid approach that is very similar to what we have carried out based on enrichment metrics. As the data is essentially discrete (presence/absence of feature or breakpoint counts), we carried out statistical analysis using binomial and hypergeometric tests. In parallel, empirical estimation of random overlap frequency served as an additional confirmation.Analysis that stratifies breakpoint sites based on the recurrence (frequent rearrangements in ALL) was presented in 7) Whilst there is a notable enrichment against other active promoters for example the 'width' of the 'stalling' region, although what this really represents, as defined from GRO-seq data, is the high density of active RNA polymerases, so is it possible that these represent regions of active transcription.We thank the referee for raising this important question. A transcript that would have uniform signal in GRO-seq from its TSS to TTS would correspond to a genomic region where the Pol2 elongation rate is uniform. However, this is rarely observed . A Pol2 stalling region corresponds to elevated signal within an actively transcribed region. As described in the review by Jonkers and Lis, Pol2 has been documented to slow down e.g. at exons (to facilitate splicing), R-loops  and is actively paused at TSS regions . Each of these events are visible as a signal increase in GRO-seq. Our changepoint analysis assigns a relative increase in the signal (cut-off at highest 90%) within a transcribed region as a Pol2 stalling site, which corresponds to local slowing of Pol2. Notice that the signal data is scaled and analyzed separately for different gene regions.One important control that we are observing real stalling events is presented in the new As additional experimental data that could assist in interpreting the GRO-seq signal profile, we performed Pol2 ChIP-seq using antibodies against the Ser2 and Ser5 phosphorylated forms. Ser5 phosphorylation is present before the Pol2 is released to active elongation . Using a similar analysis of the signal profile to detect Pol2 stalling from the ChIP-seq profile, we found highest overlap of RSS-motif containing SV sites with the Ser5 form, typically considered as the paused/stalled Pol2. However, also locally elevated Ser2 phosphorylated Pol2 has been found to be relevant for genomic instability : in this case the stalling typically occurs at gene ends. We have included new references to literature in the Introduction, Methods and Discussion to assist in interpretation of the signal profiles presented.The width of Pol2 stalling was analyzed since displaced nucleosomes in vicinity of Pol2 could enhance access to RSS sites and thereby RAG cleavage events. To support this conclusions, we performed additional analysis see new  to charaAs an additional control, we correlated the width of Pol2 stalling at the TSS region and the expression level (rpkm within the gene body). There was weak negative correlation, indicating that the vulnerability of TSS with wide stalling is not simply due to higher expression level.8) With previous reports showing enrichment of genomic rearrangements at active promoters and enhancers it is not clear whether the observations here are a consequence of transcriptional activity in these regions or an enriched mechanism underpinning the regions that are frequently targeted. The authors should present a global analysis – see statistical review section.It appears that a similar concern is raised here as in (7): whether high transcription activity alone explains the overlap. We addressed this question in two ways:In the TAD analysis, we performed an additional control where we used the ENCODE chromatin segmentation for defining active promoters and enhancers. Subsequently, we overlapped these regions with convT/Pol2 stalling to obtain a measure for the base pair span i) with convT/Pol2 stalling overlap and ii) without overlap. Next, the% of TADs spanned by each feature type was compared between breakpoint frequency quartiles. There was a consistent increasing trend see new  for the We analyzed breakpoint hotspot regions that localize to annotated gene regions. The genes were binned by the transcriptional level to control for the effect of transcriptional activity. Subsequently, the significance of overlap with the transcriptional features was evaluated for the different bins. The result was consistent across a wide range of expression levels and this new data has been included to The new analysis results support the conclusion that convT/Pol2 stalling are enriched at regions that are frequently targeted.9) A major limitation of the analysis here is that there are not overlapping datasets from the same samples. As the authors have access to direct primary patient cells and cell lines representative of B-ALL their interpretations of the data would significantly benefit from performing RNAseq, POL2 chip, and potentially MNase-seq to support their observations .We have carefully considered this comment and added new experimental validation and supporting data from pre-B-ALL and B-lineage cells. However, we would like to stress that the Pol2 transcription process itself should not fundamentally differ between leukemic and non-leukemic cells. Characteristic and reproducible transcriptional features at specific genomic regions can be identified by comparing samples of different origin. Here, we have identified where Pol2 stalling and convT occurs from pre-B-ALL patient and cell line material that represent several different subtypes, from normal B-lymphoid cells and ES cells. We then related the features detected from B-lineage cells to SV event frequency in pre-B-ALL patients on a genome-wide level –4, whileFollowing the reviewer’s suggestion, we performed additional Pol2 ChIP-seq data to support the detection of Pol2 stalling events. We also considered MNase-seq but obtaining high enough sequence depth was deemed unfeasible. As an alternative, we tested a ChIP protocol that involves MNase digestion. The signal profile obtained therefore reflects Pol2 enrichment on the background of nucleosome positions. We focused here on the overall signal profile across gene regions  but we make the data available for further analysis that also models the nucleosome positioning. RNA-seq was not included as the signal does not reflect active ongoing transcription (mainly mature RNAs are measured) and is additionally influenced by RNA stability, which would complicate the analysis.We have also extended the results evaluating the properties of regions with Pol2 stalling with more data from pre-B-ALL and B-lineage cells (included to 10) Overall there is a clear narrative issue, the dataset is complex and the analysis is not clear nor comprehensive – making it rather hard for the reviewer and in time a reader to comprehensively evaluate the analytical approaches used, as well as, the evaluate the interpretation of the findings, which often render conclusions as facts by citing other papers rather than being supported by the data in itself. It would be very helpful if the authors included a supplemental figure containing a flow chart describing what datasets where put together and which subsets of data were used for which analysis.We thank the reviewer for the suggestions how to improve the manuscript. We have reorganized the manuscript and modified 11) Results should be interpreted on the basis of their analysis. Conclusions that account or include findings from the literature should be placed in the Discussion section.We have re-organized the Results and Discussion text to address this.12) It would be useful if the authors performed a global analysis- accounting for chromatin segmentation in ALL and considering these features. It would be very interesting to show specifically how the parameters they consider  correlate with promoters/ active transcription first and then evaluate if these metrics show significant deviations (enrichment) in the areas most widely affected by genomic rearrangement in ALL and how do these differ between frequent breakpoints and rare breakpoints?We thank the reviewer for the suggested use of chromatin segmentation data and inclusion of additional results to further support the relevance of transcriptional activity. We added a new figure  that comNext, we utilized the chromatin segmentation generated by the ENCODE consortium that is based on a large collection of ChIP-seq data from B-lymphoblastoid cells (unfortunately a similar segmentation is not available directly from ALL cells). Similar as in Subsequently, we analyzed whether the convT/Pol2 stalling had relevance as a specific feature within active promoters and enhancers defined by the chromatin segmentation. These results are presented as an additional figure supplement to We deemed these two approaches appropriate to address first the relevance of transcription and then to distinguish the specific contribution of convT/Pol2 stalling. Correlating convT/Pol2 stalling with active transcription is not meaningful as, by definition, these transcriptional features occur within areas of active transcription. We correlated the width of Pol2 stalling at the TSS region with the expression level of the transcript. Comparison of frequent and rare breakpoints is presented in 13) Perhaps incorporating metrics that include expression of genes in those regions in B-ALL might strengthen this analysis and provide additional insights into the proposed mechanism. Whilst it may be really difficult to obtain such additional RNA seq data – the authors could consider using gene expression metrics from prior SNP array studies of distinct ALL subtypes.Adding expression metrics from pre-B-ALL is an excellent suggestion. By “those regions” we understand the reviewer refers to TADs and chromatin segmentation data. We analyzed expression within TADs by calculating the median and maximum expression level in pre-B-ALL cells for the co-localized genes. To this end, we utilized the 1382 patient transcriptomes available in our integrated dataset across pre-B-ALL studies (these expression metrics are included in The new analysis results support the conclusion that convT/Pol2 stalling are enriched properties of regions that are frequently targeted.


The pathway of sulfate assimilation, which provides plants with the essential nutrient sulfur, is tightly regulated and coordinated with the demand for reduced sulfur. The responses of metabolite concentrations, enzyme activities and mRNA levels to various signals and environmental conditions have been well described for the pathway. However, only little is known about the molecular mechanisms of this regulation. To date, nine transcription factors have been described to control transcription of genes of sulfate uptake and assimilation. In addition, other levels of regulation contribute to the control of sulfur metabolism. Post-transcriptional regulation has been shown for sulfate transporters, adenosine 5′phosphosulfate reductase, and cysteine synthase. Several genes of the pathway are targets of microRNA miR395. In addition, protein–protein interaction is increasingly found in the center of various regulatory circuits. On top of the mechanisms of regulation of single genes, we are starting to learn more about mechanisms of adaptation, due to analyses of natural variation. In this article, the summary of different mechanisms of regulation will be accompanied by identification of the major gaps in knowledge and proposition of possible ways of filling them. Figure 1). PAPS is the donor of activated sulfate for sulfation of peptides and small metabolites primarily in secondary metabolism serine for the SAT experiments the flux of sulfur was not assessed in these plants , the enzyme synthesizing the cysteine precursor OAS, showed a clear correlation between the level of the transcript for this gene and size of the plants . The same plants . On the e plants . The atpselenate . Interescessions . The flurily GSH , 2011. Irily GSH , 2010.SULTR1;2 and APR3 isoform of APS reductase were used as tools to dissect the regulation of the pathway in several genetic approaches  transcription factor has been identified . SLIM1 bch topic .BIG gene, which encodes a protein necessary for the polar transport of auxin  genes  genes . The genU) genes . The UPE element . A new t element . Howeverhy5 mutant was not entirely surprising . The disrupted transcriptional regulation is reflected in altered flux through the pathway and also sulfate uptake. Importantly, HY5 is the only transcription factor for which evidence of direct binding on the corresponding promoters has been obtained and a direct transcriptional regulation can be unequivocally confirmed lyase take part in another example of protein–protein interactions affecting sulfur metabolism. The cytosolic isoform OASTL-A interacts with a STAS domain of SULTR1;2 transporter and reduces the sulfate uptake rate in yeast heterologous system . The OAScyc20-3 mutants. The increase in SAT activity causes elevated thiol content to combat the oxidative stress , is feedback regulated by GSH . The plaArabidopsis APK1 isoform . Loss of PFT1 results in altered transcriptional regulation of APR by light, in an isoform specific pattern . While A pathway . However pathway . As part pathway . Mediato pathway . The conArabidopsis with ABA led to a strong decrease of APS reductase enzyme activity without affecting transcript levels of its three isoforms  sulfate or levels of downstream product(s) of sulfate assimilation. The dissociation of cysteine synthase complex described above might be part of the latter response . On the e supply . These rsultr1;2 . Under nsultr1;2 . When cosultr1;2 . Interesly SLC13 . It seemApart of sulfate, other small molecules seem to be integral components of sulfate assimilation regulatory networks. Many metabolites affect individual components of the pathway, such as pathway intermediates OAS, cysteine, glutathione, sugars, or the phytohormones jasmonate, salicylate, ABA, ethylene, nitric oxide, and cytokinins. All these metabolites and many others can potentially be signals in the regulation of the pathway, but on the other hand, their effects may be only indirect and pleiotrophic. Still, several of these metabolites can be considered true signals. The role of cytokinins in repressing the expression of sulfate assimilation genes at sufficient sulfur availability seems to be well established  and is sAPR3). The independent verification of the gene cluster by three methods/datasets establishes OAS as a signal and a direct component of the regulatory network, but the mechanism of its action is still left open (However, the one compound that immediately comes to mind, when signals are mentioned, is OAS. OAS has been discussed as signal for decades but this role has often been met with controversy. OAS induces transcript levels and activity of sulfate transporters and APS reductase . Incubateft open . The rol2S, cysteine, and GSH. Since these metabolites are highly interconnected, feeding of one compound results in increased levels of others, it is not easy to identify the real signal. H2S is a specific case, since it was recognized as a gaseous signal in human and animal world (2S being a signal in plants as well, protecting against a large range of stresses and even promoting growth (2S and its signaling function is complicated and controversial and is discussed in several recent reviews (Another signal is necessary to transmit the information of sufficient or elevated concentration of reduced sulfur compounds. There are three candidates, Hal world  there arg growth . The sto reviews . Both cy reviews . This se reviews , althougArabidopsis (It is obvious that our knowledge of molecular mechanisms of regulation of sulfate assimilation has been improved. But in many aspects this knowledge is still patchy. We know that SLIM1 is a central regulator of sulfate deficiency response, but do not know the DNA sequence and the complement of promoters it binds. We know that there must be at least one other factor controlling the induction of APR by sulfate limitation, but not the nature of this factor. We know that sulfate assimilation is preferentially localized in bundle sheath cells surrounding the veins in bidopsis , but we bidopsis , but apaAPR2 and ATPS1 have been found to affect the levels of foliar sulfate. For both genes, substantial variation in amino acid sequence has been found, including those that strongly diminished the enzyme activities (APR2 were found among Arabidopsis accessions, associated with high sulfate and total sulfur content (Arabidopsis, similar approaches have been made directly with crops and similar haplotypes have been identified (Many reports applying quantitative genetics and exploiting natural variation to dissect a control of complex traits identified metabolic genes underlying the variation . In QTL tivities . Two mor content . Genome  content . These n content , such li content . It is, entified . Thus, mThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mycobacterium tuberculosis as a pathogen is the ability to sense and adapt metabolically to the diverse range of conditions encountered in vivo, such as oxygen tension, environmental pH and nutrient availability. Although nitrogen is an essential nutrient for every organism, little is known about the genes and pathways responsible for nitrogen assimilation in M. tuberculosis. In this study we have used transcriptomics and chromatin immunoprecipitation and high‐throughput sequencing to address this. In response to nitrogen starvation, a total of 185 genes were significantly differentially expressed  highlighting several significant areas of metabolic change during nitrogen limitation such as nitrate/nitrite metabolism, aspartate metabolism and changes in cell wall biosynthesis. We identify GlnR as a regulator involved in the nitrogen response, controlling the expression of at least 33 genes in response to nitrogen limitation. We identify a consensus GlnR binding site and relate its location to known transcriptional start sites. We also show that the GlnR response regulator plays a very different role in M. tuberculosis to that in non‐pathogenic mycobacteria, controlling genes involved in nitric oxide detoxification and intracellular survival instead of genes involved in nitrogen scavenging.A key component to the success of  Escherichia coli  may be the preferred nitrogen source in et al., Corynebacterium glutamicum is performed by the TetR‐type response‐regulator AmtR, which controls transcription of over 33 genes  with 61% identity to that from S. coelicolor , and transcriptomics over nitrogen run‐out. We identified key changes in the metabolic network in response to nitrogen limitation, showing that the main nitrogen metabolism‐related response is the production of ammonium both through the reduction of nitrite and from aspartate; however, M. tuberculosis does not appear to scavenge ammonium or other nitrogen sources (e.g. urea) from the environment. We found changes in general metabolism such as an increased methylcitrate–isocitrate lysase cycle and changes in the cell wall phthiocerol dimycoceroserate (PDIM) and peptidoglycan make up. We show that GlnR is a global regulator in mycobacteria, controlling the expression of at least 67 genes in response to nitrogen stress, although intriguingly only the minority of GlnR controlled genes are directly involved in nitrogen metabolism. We identified 35 GlnR‐binding sites, 22 of which controlled the differential gene expression of at least 19 genes in nitrogen limitation. This includes divergently transcribed genes, and both up‐ and down‐regulated genes, showing GlnR functions as an activator and repressor of transcription. Using the 25 GlnR binding sites found in intergenic regions, we identified a consensus DNA‐binding motif. Overall, this work re‐illustrates the complexity of metabolism in mycobacteria and how M. tuberculosis has adapted its nitrogen metabolism for life inside a vertebrate host.The aim of this study was to extend our global nitrogen stress analyses to the obligate human pathogen Mycobacterium tuberculosis was grown in nitrogen‐free Sauton's medium containing either 1 mM ammonium chloride (nitrogen limiting) or 30 mM ammonium chloride (nitrogen excess)   during nitrogen limitation (days 8 and 9) compared with nitrogen replete (day 7). Genes were considered to be significantly differentially expressed if their expression changed greater than twofold compared with their expression at day 7 with a false discovery rate (FDR) corrected P‐value of < 0.1. A complete list of significantly DE genes identified by these criteria can be viewed in File S2. At nitrogen run‐out, day 8, 185 genes were DE (96 up and 89 down) and at day 9, 214 genes were DE (137 up and 77 down) greater than twofold  were taken at three time points  and applied to an et al., M. smegmatis  to indicate their position in the network. The reactions were grouped into 19 modules according to the AMBIENT analysis   and western analysis using a GlnR specific antibody . The M. tuberculosis glnR KO mutant did not grow with nitrate as the sole nitrogen source, or show any phenotype under nitrogen limitation, and did not up‐regulate nirB expression upon nitrogen limitation (data not shown). There was also no difference in the response of the mutant to environmental stresses including pH 3, hydrogen peroxide or a nitric oxide donor (data not shown). The expression profiles of M. tuberculosis wild type and glnR deletion mutant grown in nitrogen limiting conditions were then obtained. Cells were harvested 1 day after nitrogen run‐out, total RNA was extracted and cDNA hybridised to the M. tuberculosis microarray as described. Data were normalized and genes were considered significantly differentially expressed if they exhibited a difference in gene expression of greater than twofold with an FDR corrected P‐value of < 0.1. Fully annotated microarray data have been deposited in BμG@Sbase and ArrayExpress. A total of 52 genes were significantly up‐regulated and 15 significantly down regulated (File S3). This indicates that GlnR mediates (directly or indirectly) the expression of at least 67 genes.In order to determine the genes regulated by GlnR, we constructed a GlnR null mutant. Substitution of the nirB) promoter region to confirm the enrichment of known GlnR binding regions in nitrogen limited cells compared with nitrogen replete cells; a gene thought not to be GlnR regulated (Rv1360) was included as a negative control . The immunoprecipitated DNA was then prepared for next generation sequencing using the Illumina ChIP‐seq library kit. Sequencing of the DNA libraries generated approximately 144 million reads per sample which were mapped to the M. tuberculosis genome. All ChIP‐seq data files have been deposited into ArrayExpress (accession number E‐MTAB‐2492). GlnR binding regions were identified using the peak‐calling algorithm SISSRs  as a GlnR binding site corroborated our ChIP‐seq data , further validation of four novel GlnR binding sites identified in this study by electromobility shift assays (EMSA) were performed. Two hundred base pair DNA regions, representing the binding regions from peaks 13, 17, 18 and 20, were incubated with increasing concentrations of recombinant GlnR protein. Fig. Fig. S4). In addition, DNA enrichment of another four novel GlnR binding sites identified in this study  was verified by rate‐limiting qPCR. Enrichment of these four genomic regions by GlnR IP was observed in nitrogen limitation and not in nitrogen excess .In order to identify directly regulated GlnR genes, we used ChIP‐seq to identify the location of GlnR binding sites in the genome during nitrogen limitation. Cells were grown in 1 mM (limiting) or 30 mM (excess) ammonium chloride, and DNA–protein complexes were cross‐linked 1 day after ammonium depletion; nitrogen excess samples were cross‐linked at the same time point, cells were then lysed and the DNA sheared by sonication. GlnR‐bound DNA fragments were immunoprecipitated using affinity‐purified anti‐GlnR polyclonal antibody. Prior to Illumina library construction, we performed quantitative PCR on the nitrite reductase  differential expression of 10 genes, 9 genes up regulated and 1 down regulated (Table File S4).In order to identify the genes directly controlled by GlnR, and thus determine the GlnR regulon, we compared the 35 GlnR binding sites with the profile of transcripts regulated by GlnR during nitrogen limitation Table . Twenty‐ed Table , indicatInitially, all 35 GlnR binding site sequences (200 bp) identified in this study were submitted to MEME to determine if there was a motif that was responsible for GlnR genomic binding. A consensus motif was initially identified, but this motif was only present in 18 of the 35 binding sites (data not shown). Therefore, the MEME search was restricted to just GlnR binding sites in intergenic regions. With the additional constraint, a significant consensus motif was identified which was present in all GlnR regulon binding sites  showed increased abundance in the GlnR deletion mutant, while narG, which has an additional promoter‐associated GlnR site, had decreased abundance. Localisation of the potential promoter‐associated GlnR sites in the context of a whole genome map of transcription start sites (TSSs) in M. tuberculosis  was strongly down‐regulated in the GlnR knockout (Table ileS)  and the remaining 8 were within coding regions  modify its function rather than increased levels of GlnR. A survey of five published studies, and unpublished data from the TBdb resource, identifies two studies where there is no differential expression of glnR under a variety of conditions , surfactants/lipids)  observed differential expression of glnR on exposure to DETA/NO, H2O2, oleic acid and palmitate.The identification of M. smegmatis  and nitrite (nirBD) reductase operons activated by GlnR. The nirBD operon transcription is strongly activated by the intergenic GlnR‐binding site, whereas the narGHJI operon contains two GlnR sites, one promoter‐associated site upstream of narG and one within the gene  also have GlnR‐binding sites. It may be that production of nitrite by NarGHJI is limited by substrate availability, and not enzyme levels (as reflected by only modest changes in gene expression), but that as ammonium levels fall and metabolism of nitrate increases, a much larger increase in NirBD activity is needed to remove potentially toxic nitrite. GlnR does not regulate narGHJI in M. smegmatis, so the up‐regulation in response to nitrogen stress must be controlled by another mechanism, which is more in keeping with its ability to metabolise a much wider range of nitrogen sources than are evident here. The importance of nitrate–nitrite metabolism to the survival of M. tuberculosis inside human macrophages has recently been reported  in metabolism are linked. For example, Rv2928 (fatty acyl ACP hydrolase) and Rv2930 (fatty acyl CoA ligase) both act on phthiocerol and are down‐ and up‐regulated respectively. Aspartate metabolism, which has been proposed to be an important source of nitrogen Several AMBIENT nodes are marked as ‘none’ in Fig. M. tuberculosis is much more restricted in terms of the number of GlnR‐binding sites, with only 26 intergenic ones  and −30 (glnA2) would be consistent with GlnR regulation of transcription from the downstream promoter in each case. The resulting change in 5′ untranslated region may affect the efficiency of translation. A similar switch from distal to proximal TSS occurs in the starvation response of thiosulphate sulphuryltransferase sseB, with an overall increase in transcript abundance in the GlnR knockout. In spite of GlnR binding at −23 with respect to the TSS, there was no change in the level of csoR transcript in the knockout. In this case, the GlnR binding site overlaps that of CsoR itself, which inhibits its own expression under conditions of low copper concentrations  and M. tuberculosis H37Rv glnR KO (this study) were used in this study. M. tuberculosis was grown aerobically in Middlebrook 7H9 liquid broth  at 37°C, 100 rpm. Optimised nitrogen limiting conditions have been described  KH2PO4, 0.05% (w/v) MgSO4, 0.2% (w/v) citric acid, 0.005% (w/v) ferric citrate, 0.2% (v/v) glycerol, 0.0001% (v/v) ZnSO4, 0.015% (v/v) tyloxapol) and inoculated into Sauton's nitrogen‐free medium, supplemented with 1 mM (nitrogen limiting) or 30 mM (nitrogen excess) ammonium chloride  to a starting OD60nm of 0.04. Growth was monitored by OD600nm. Ammonium ions in the culture supernatant were quantified using an AquaQuant Ammonium detection kit (Merck).M. tuberculosis glnR KO mutant was constructed by a recombineering approach as previously described  . Bands were visualised using a LAS‐3000 Fuji imager.To analyse GlnR binding to gene promoter regions, DNA fragments were PCR amplified from Table S1. Reaction mixtures consisted of GlnR‐immunoprecipitated DNA (0.3 ng), 1 × BioMix (Bioline), 1 μM of each primer and 5% (v/v) dimethyl sulfoxide (Sigma). PCR was carried out in a thermocycler T3000 (Biometra); 95°C for 5 min, 23 cycles of 95°C 30 s, 55°C 30 s, 72°C 1 min, with final extension 72°C for 8 min. DNA was visualised on a 2% agarose gel.To identify enrichment in GlnR‐immunoprecipitated DNA, a rate‐limiting PCR was performed. DNA was immunoprecipitated and purified as described under ChIP. DNA sequences were amplified using primers listed in Mycobacterium tuberculosis strains were grown in triplicate in nitrogen limiting conditions until external nitrogen was depleted. Total RNA was extracted from exponentially growing cells using the GTC/Trizol method . qRT‐PCR reactions were carried out in a final volume of 10 μl . Amplification was performed on an Applied Biosystems 7500 Real‐Time System . Linear amplification and amplification efficiencies for each TaqMan primer/probe were determined. Real‐time analysis was performed on RNA from three independent cultures and quantification of Invitrogen). Labelled cDNA was purified by Qiagen MinElute column, combined with 10 × CGH blocking agent and 2× Hi‐RPM hybridisation buffer (Agilent) and heated (95°C for 5 min) prior to loading onto microarray slides. Slides were incubated overnight in an Agilent rotating oven at 65°C, 20 rpm. After hybridization, slides were washed (5 min at room temperature) with CGH Wash Buffer 1 (Agilent) and 1 min at 37°C with CGH Wash buffer 2 (Agilent). Slides were scanned immediately, using an Agilent High Resolution Microarray Scanner , at 2 μm resolution, 100% PMT. Scanned images were quantified using Feature Extraction software v 10.7.3.1. RNA for tiling microarray hybridization was directly labelled using the Kreatech Universal Linkage System , which is based on the stable binding of platinum to the N7 position of guanine, according to the manufacturer's instructions.Labelled cDNA was prepared from 1 μg of total RNA using Cy3‐dCTP  and SuperScript II reverse transcriptase with random hexamer primers . Multiple optimal hybridisation 60‐mer oligonucleotide sequences were designed (Oxford Gene Technologies), from which a minimal non‐redundant subset of oligonucleotides were selected with target coverage of three 60‐mers per gene. Arrays were manufactured on the Inkjet in situ synthesized platform (Agilent) using the 8 × 60k format. Analysis of gene expression over nitrogen run‐out was performed using a 180k Tiling array, designed as described . Data generated from the Agilent Feature Extraction software for each sample were imported into R. Replicate probes were mean summarised and quantile normalised using the pre‐process Core R package. The limma R package   and also ArrayExpress (Accession No. A‐BUGS‐47). All sequencing data have been deposited in ArrayExpress (Accession No. E‐MTAB‐2492). The other data sets supporting the results of this article are included within the article and its additional files.The Tiling array design is available in BμG@Sbase (Accession No. A‐BUGS‐47; Supporting informationClick here for additional data file.


H-Ras and K-Ras and overexpression of SAF-1 increases H-Ras and K-Ras gene expression. Chromatin immunoprecipitation (ChIP) analyses demonstrated in vivo interaction of SAF-1 at highly purine-rich sequences present at the proximal promoter region, upstream of the transcription start site, in H-Ras and K-Ras genes. Previous studies have shown that these sequences are nuclease hypersensitive and capable of forming G4 quadruplex structure. Together, our results show the presence of a novel transactivating loop, in which, Ras and SAF-1 are interconnected. These findings will help defining molecular mechanisms of abnormal overexpression of Ras in breast tumors, which seldom show genetic Ras mutations.In the majority of breast cancers, overexpression and hyperactivation of Ras in the tumor microenvironment play significant role in promoting cancer cell growth, angiogenesis, and metastasis. We have previously shown that vascular endothelial growth factor (VEGF) expression in triple negative breast cancer cells is regulated, at least in part, by SAF-1 (serum amyloid A activating factor 1) transcription factor. In this study we show that transformation of normal MCF-10A breast epithelial cells by constitutively active, oncogenic Ras, induces the DNA-binding activity and transcription function of SAF-1. Furthermore, we show that inhibition of MEK/MAPK-signaling pathway prevents Ras-mediated activation of SAF-1. Interestingly, silencing of SAF-1 expression in breast cancer cells by SAF-1-specific short hairpin RNAs (shRNAs) significantly reduced H-Ras and K-Ras mRNA level. We show that SAF-1 is a direct transcriptional regulator of  Ras plasmid DNA which has been shown to directly promote VEGF expression and in vitro angiogenesis α in the induction of VEGF expression 2-His2 type zinc finger, often bind to this promoter region Tumor microenvironment (TME) is a dynamic entity which determines tumor growth, invasion, and metastasis. A number of cellular processes regulate different elements of the TME and determine the status of the tumor ranging from dormancy to aggressive growth. Angiogenesis is one of these distinct elements that trigger tumor growth by promoting vascular endothelial growth factor (VEGF) expression and subsequent vascular growth 2-His2 type zinc finger and binds to the VEGF proximal promoter region that overlaps the Sp1-binding site. SAF-1 and its human homolog, myc-associated zinc finger protein (MAZ) MAZ gene has been closely linked to the malignant phenotype of this disease ras increases DNA-binding and transcriptional activities of SAF-1/MAZ via phosphorylation through the MAP-kinase pathway. In this context, SAF-1/MAZ could be a target for Ras/MAPK-directed therapeutics.Recently, we reported a novel mechanism of VEGF induction in aggressive triple negative breast cancer cells by transcription factor serum amyloid A activating factor 1 (SAF-1) Ras family contains three genes , which encode four highly homologous Ras proteins of ∽21 kDa size. H-Ras and K-Ras genes were first identified as cellular homologs of Harvey and Kirsten rat sarcoma virus oncogenes while N-Ras was identified in human Neuroblastoma cells Ras family of proto-oncogenes are very common; being found in 20–30% of many human cancers, including bladder, pancreas, colon, thyroid, and lung Ras gene mutations ras genes confer gain-of-function by providing constitutive activation that drives uncontrolled cell proliferation and malignant transformation. Besides mutation, overexpression of Ras that facilitates Ras-mediated signaling has been reported in human cancer Ras occurs are less understood. During our studies on Ras-mediated SAF-1 induction, we have observed that once activated, SAF-1 acts as a transcriptional inducer of H-Ras and K-Ras and we reveal the presence of a transactivation loop in which oncogenic Ras increases DNA-binding and transcriptional activities of SAF-1 which in turn, increases transcription of Ras forming a feed-forward regulatory pathway.Human MCF-10A and MDA-MB-468 cells were obtained from American Type Culture Collection (ATCC), cultured and stored following ATCC protocol of authentication by short terminal repeat analysis. The cells were maintained in Dulbecco's modified Eagle's medium containing high glucose (4.5 g/L) and supplemented with 7% fetal bovine serum.ras cells, MCF-10A cells were transfected with pCMVrasV12 plasmid, expressing constitutively active ras oncogene . As a control, MCF-10A cells were transfected with the same vector containing only the neo gene. The transformed cells were selected by culturing in medium containing 400 μg/mL geneticin (G418). In some transfection assays, SAF-1/MAZ short hairpin RNAs (shRNAs), and control shRNAs  were used. For overexpression of SAF-1, cells were transfected with either pcD-SAF-1 expression plasmid or a mutant SAF-1 plasmid construct. For transfection analysis, the cells were transfected with different reporter plasmid DNA (0.5 μg) as described before To generate MCF-10A-VEGF promoter sequences from nucleotide position −1179 to +21, was prepared by ligating this DNA into pBLCAT3 vector. A tandem copy of two SAF-1-binding sites is located within nucleotide position −130 and −30 of this VEGF promoter DNA VEGF promoter, was prepared as described earlier VEGF promoter DNA into pBLCAT3 vector. The 0.6H-Ras-CAT reporter was prepared by ligating, into pBLCAT3 vector, a promoter region of human Ha-rasrasH-Ras and K-Ras promoter DNA fragments were prepared by PCR amplification and verified by DNA sequencing. SAF-1 expression plasmid, pcD-SAF-1, was prepared by inserting full-length SAF-1 cDNA with a FLAG tag into pcDNA3 vector . A mutant SAF-1, mut pcD-SAF-1(V71), containing a mutation at the MAP kinase phosphorylation site by replacement of threonine at amino acid position 71 of SAF-1 with valine was used. Both the wild-type pcD-SAF-1 and mutant pcD-SAF-1(V71) were described earlier Wild-type and mutant SAF-CAT reporter plasmids, designated as wt SAF-CAT and mt SAF-CAT, respectively, were constructed by ligating into the pBLCAT2 vector, three tandem copies of the wild-type SAF-binding element of SAA gene promoter from nucleotide position −254 to −226 containing the following sequences: wild-type: 5′-CCCTTCCTCTCCACCCACAGCCCCCATGG-3′ and mutant: 5′-CAATGAGTCGAGACCGTCGACATCCATGG-3′ as described earlier 32P-labeled probes, sequences from −135 to +29 of VEGF promoter that contains SAF-1-binding elements SAA promoter as described before Nuclear extract preparation and DNA-binding assays were performed as described before μg of total RNA using TaqMan reverse transcription reagents and analyzed for SAF-1, Ha-Ras, K-Ras, and glyceraldehyde-3-phosphate (GAPDH) according to the manufacturer's protocol . Transcript levels were normalized against GAPDH. All experiments were done using biological triplicates and experimental duplicates. The primers were: forward, 5′-GCTCAGGACTTAGCAAGAAG-3′ and reverse, 5′-GTATTTACATAATTACACACTTTG-3′ for human K-Ras; forward, 5′-GGGGCAGTCGCGCCTGTGAA-3′ and reverse, 5′-CCGGCGCCCACCACCACCAG-3′ for human H-Ras; forward, 5′-CTCCAGTCCCGCTTCT-3′ and reverse, 5′-GGGAGCAAGTCCACCT-3′ for human SAF-1/MAZ; forward, 5′-CCCTTCATTGACCTCAACTACATG-3′ and reverse, 5′-TGGGATTTCCATTGATGACAAGC-3′ for human GAPDH.Total RNA was isolated using a RNA isolation kit . Relative expression levels of SAF-1, H-Ras, and K-Ras was determined by real-time RT-PCR using gene-specific primers. The cDNAs were prepared by reverse transcription from 0.5 K-ras and human H-ras gene promoters. Primers used for amplification of human K-Ras promoter were 5′-TCCGGGTCAGAATTGGCG-3′ and 5′-GTTCCGCGCTCGATTCTTCT-3′, which yields an amplicon of 313-bp. Primers used for amplification of human H-Ras promoter were 5′-GTCTCCAGCCAAGCCCAAC-3′ and 5′-ACTCACCGTTCACAGGCG-3′, which yields an amplicon of 363-bp.Chromatin immunoprecipitation (ChIP) assays were performed following a method as described t test was used. Values of P < 0.05 were considered to represent a significant difference.To compare multiple sets of data, a one-way analysis of variance (ANOVA) with post hoc Fisher's least significant difference test was used. For paired data sets, a two-tailed rasV12 expression vector or a control empty vector DNA. MCF-10A is a spontaneously immortalized cell line originated from nonmalignant breast epithelial cells and these cells have many characteristics of normal breast epithelium, including inability to form colonies in soft agar and tumors in nude mice Ras oncogene, MCF-10A cells undergo transformation and these transformed cells exhibit hall marks of transformation and epithelial-mesenchymal transition (EMT), including anchorage-independent growth, loss of requirement of hormones, invasiveness, and tumorigenicity in nude mice rasV12 transfected MCF-10A cells, the DNA-binding activity became markedly higher  phosphorylate and activate MEK1/2 dual specificity kinases, which then phosphorylate and activate the ERK1 and ERK2 MAPKs. Activated ERKs translocate to the nucleus where they phosphorylate and regulate various transcription factors. To determine if Ras-mediated activation of SAF-1 proceeds through Raf-MEK-ERK-MAPK cascade, MCF-10A-ras cells were grown with or without PD98059, which is a specific inhibitor of mammalian MEK-1/2 and ERK (P42/44 MAP kinase). PD98059 markedly inhibited DNA-binding activity of SAF-1/MAZ in MCF-10A-ras cells  plasmid DNAs  and oncogenic pCMVrasV12 plasmid DNAs , that contains a defective MAP-kinase phosphorylation site DNAs Fig.. There wDNAs Fig.. The levRas genes WAF1/CIP1, hTERT, PPARg1, Prox-1 and VEGF (data not shown), and surprisingly, it showed downregulation of Ras mRNA expression. Quantitative RT-PCR analysis revealed downregulation of both H-Ras and K-Ras mRNAs by about 50% in SAF-1 knockdown MDA-MB-468 cells  or its inverted complement, which have been shown to be essential for the promoter transcriptional activity Ras promoters appeared to bear considerable similarity with the consensus SAF-1/MAZ -binding element, RGGGRAGGRR, in which R is a purine ras and K-ras , located in the promoter region of the human insulin gene K-Ras promoter VEGF promoter Ras gene promoter as a site of interaction of SAF-1/MAZ and involvement of such interaction in ras expression could have a significant impact in understanding the role of SAF-1/MAZ in breast cancer and perhaps in other cancer as well due to ubiquitous presence of Ras in many cancers and its direct link to SAF-1/MAZ which is revealed in the current study.We showed that silencing of SAF-1 reduces K-Ras and H-Ras mRNA level Fig. and SAF-enes Fig. and B. Tters Fig. and D. LA common feature of transcription networks is the presence of feedback regulatory loops used for amplification of an initial signal through positive reinforcement or through a negative regulation. Our investigation elucidated an important aspect of cancer cell signaling that cancer thrives on multiple intermolecular conjunctions to sustain oncogenicity by depicting (Ras→SAF-1/MAZ→VEGF) signaling network axis Fig.. Downreg


OCT4, SOX2, KLF4 and c-MYC, and one noncoding ribonucleic acid (RNA) gene, the microRNA (miRNA) miR302/367 cluster. We designed over 300 ZF-TFs whose targets lie within 1 kb of the transcriptional start sites (TSSs), screened them for increased messenger RNA or miRNA levels in transfected cells, and identified potent ZF-TF activators for each gene. Furthermore, we demonstrate that selected ZF-TFs function with alternative activation domains and in multiple cell lines. For OCT4, we expanded the target range to −2.5 kb and +500 bp relative to the TSS and identified additional active ZF-TFs, including three highly active ZF-TFs targeting distal enhancer, proximal enhancer and downstream from the proximal promoter. Chromatin immunoprecipitation (FLAG-ChIP) results indicate that several inactive ZF-TFs targeting within the same regulatory region bind as well as the most active ZF-TFs, suggesting that efficient binding within one of these regulatory regions may be necessary but not sufficient for activation. These results further our understanding of ZF-TF design principles and corroborate the use of ZF-TFs targeting enhancers and downstream from the TSS for transcriptional activation.Artificial transcription factors are powerful tools for regulating gene expression. Here we report results with engineered zinc-finger transcription factors (ZF-TFs) targeting four protein-coding genes,  These) system ,11. More) system .OCT4 expression in embryonic stem (ES) cells , SOX2, KLF4, c-MYC (MYC) and the miR302/367 transcription unit were designed using the Sigma-Aldrich CompoZr® ZFN algorithm. ZF-TFs with the NF-ĸB p65 activation domain were constructed by CompoZr ZFN Operations in pcDNA3.1 (Invitrogen). The three most active OCT4-, SOX2-, KLF4- and MYC-p65 constructs are available from Sigma-Aldrich as CZFA17184, CZFA2136, CZFA1504 and CZFA1041, respectively. ZF-2xp65, ZF-VP16 and ZF-VP64 constructs were generated by ligating ZF fragments into Acc65I-BamH1-digested vectors containing the respective activation domain with all other elements identical to the p65 constructs .Five- or six-fingered ZF proteins targeting human The human cell lines HEK293, K562 and BJ fibroblasts were obtained from the American Type Culture Collection. HEK293 and BJ cells were grown in Dulbecco's modified Eagle's medium, supplemented with 10% FBS, 2-mM l-glutamine, 1-mM sodium pyruvate and 0.1-mM nonessential amino acids; K562 cells were grown in Iscove's Modified Dulbecco's Medium, supplemented with 10% FBS  and 2-mM L-glutamine. Cultures were split 1 day before transfection and were at ∼70% confluency for HEK293 and BJ cells, and ∼0.5 million cells per milliliter for K562 at the time of transfection. For the initial screen of ZF-TFs targeting −800 to +200 on OCT4, SOX2, KLF4 and MYC, HEK293 cells were nucleofected with Amaxa® Cell Line 96-well Nucleofector® Kit SF (Lonza) and program CM-130 or Nucleofector Solution V (Lonza) and program Q-001. For subsequent experiments, HEK293 cells were transfected with Lipofectamine 2000 (Life Technologies) or TransIT-LT1 (MirusBio) in 12-well cultures according to the manufacturer's instructions. BJ cells were transfected with TransIT-2020 (Mirus Bio) and K562 cells were nucleofected with Nucleofector Solution V and program T-016 following the manufacturer's protocols.PPIA (cyclophilin A) endogenous control mRNA were measured by real-time quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) using Quantitative RT-PCR ReadyMix (QR0100), as recommended by the manufacturer. Human OCT4, SOX2 and KLF4and c-MYC Taqman gene expression assays  and Human PPIA Endogenous Control Taqman assay (4326316E) were from Life Technologies. All results were normalized to PPIA using the delta tC/qC method.For messenger RNA (mRNA) results shown in Figures miR-302a and Human SNORD48  Taqman miRNA assays were from Life Technologies. miR-302a and SNORD48 endogenous control RNA levels were measured by reverse transcription and qPCR with M-MLV (Moloney Murine Leukemia Virus) Reverse Transcriptase and JumpStart Taq ReadyMix for Quantitative PCR from Sigma-Aldrich Biotechnology following Life Technologies’ thermocycling conditions for miRNA RT and Sigma's for qPCR. miR-302a levels were normalized to SNORD48 using the delta qC method.For microRNA (miRNA) results shown in Figure Total cell lysates were prepared from cells at 48 h  or 72 h  was performed using Sigma's Imprint® Chromatin Immunoprecipitation Kit (CHP1) as instructed in the User Guide, with the following modifications. Preliminary experiments showed that Protein A Magnetic Sepharose Xtra (GE28-9670-56) with anti-mouse IgG (Immunoglobulin G) bridging antibody (M7023) gave higher signal and lower background than the strip wells provided with CHP1, so the magnetic Sepharose was used for results presented here. Preliminary experiments also showed no difference in FLAG-ChIP results from ZF-TF-transfected cells with formaldehyde cross-linking for 5 versus 10 min, nor with shearing for 2, 3 or 4 × 10 min, 30 s on/30 s off at maximum energy on a Diagenode Biorupter. Analysis on an Agilent Bioanalyzer High Sensitivity chip showed DNA was sheared to generate fragments between ∼350 and 50 bp in all cases, with peaks between 200 and 120 bp. Therefore, 8 min of formaldehyde cross-linking and 3 × 10 min shearing were used for the results presented. Each sheared chromatin preparation was split into three equal amounts, and ChIP was performed with anti-FLAG (F1804), anti-H3K27me3 (SAB4800015) or mouse IgG (I5381). qPCR primers were designed with PrimerBLAST (Supplementary Table S6). qPCR was performed with KiCqstart PCR mix (KCQS01) and primers at 0.4 μM on a Stratagene MX3000P using the following cycling parameters: denature at 94○C, 3 min followed by 40 cycles of 94○C, 15 s and 60○C, 1 min.Eleven ZF-TF constructs were subcloned from their original CompoZr vector, which contained a single C-terminal FLAG tag, into pVAX-NELD-GFP (CompoZr GFP-ZFN expression vector) to generate N-terminal triple FLAG-tagged constructs. HEK293 cells were seeded at 10OCT4, SOX2, KLF4 and c-MYC. Designs spanned a region from −800 bp to +200 bp relative to the major transcriptional start site (TSS). In an effort to conduct an unbiased screen that would identify potent transcriptional activators, we designed ZF-TFs predicted to bind throughout the 1-kb promoter region, as evenly spaced as possible. Note that CompoZr ZF design rules preclude design of ZFs targeting repetitive regions or other sequences sufficiently similar to allow off-target binding. As a consequence, target sites could not be evenly distributed. Most of the ZF domains consisted of six ZF subunits with a few five-finger constructs, resulting in ZF-TFs with either an 18- or 15-bp DNA-recognition site (Supplementary Tables S1–S5). Each six or five-finger ZF coding sequence assembly was cloned between an N-terminal nuclear localization signal and a C-terminal NF-ĸB p65 activation domain to generate a series of ZF-TF expression constructs for each gene. Each series included 59–120 ZF-TFs that target either the forward or the reverse DNA strand.To identify artificial TFs that upregulate targeted gene expression, we initially designed ZF-TFs to an ∼1-kb region of the promoter for OCT4 −800 to +200 promoter region resulted in 2-fold or higher OCT4 transcript levels compared to control cells . Results with representative ZF-TFs that increased target gene transcript levels greater than 2-fold are shown in Figure OCT4, SOX2, KLF4 and c-MYC transcripts are low in HEK293 cells without ZF-TFs, all four transcripts were detected within the range of their Taqman assays (qC = 30–32). Furthermore, all positive results were verified by performing three biological replicate experiments (i.e. seeding and transfecting from three separate starter cultures on three different days).Engineered ZF-TFs were screened in HEK293 cells for the ability to upregulate target gene expression. Plasmid DNA encoding each ZF-TF was transfected into cells and mRNA levels from each target gene were measured using qRT-PCR ∼48 h after transfection. Potent ZF-TF activators were identified for each target gene. Five ZF-TFs targeting the cis-acting sequences that can be located at great genetic distances form the core promoter. The OCT4 proximal and distal enhancers have been previously defined as key regions important in regulating OCT4 expression in different cell types -p65, which targets the OCT4 proximal enhancer, and ZF(OCT4−r2342)-p65, which targets the OCT4 distal enhancer, increased OCT4 transcript levels more than 16-fold in HEK293 cells. Therefore, both enhancers are good targets for artificial transcription activators.Regulation of gene expression is often controlled by ll types ,25,40. Zpression ,22. HoweOCT4 TSS. All six ZF-TFs increased OCT4 mRNA levels more than 2-fold in HEK293 cells, and three ZF-TFs, ZF (OCT4+r253)-p65, ZF(OCT4+414)-p65 and ZF(OCT4+r420)-p65 activated OCT4 transcript levels more than 16-fold -p65. Three days post-transfection, cells were harvested and OCT4 protein levels were determined by western analysis. OCT4 was readily detected in total cell lysates when cells were transfected with the ZF-TF, compared to undetectable levels of OCT4 in control cells  as for one ZF-TF (1.6 μg). Therefore, multiple ZF-TFs can work in concert, as shown by others -p65) appears mostly degraded. After 48 h, cells were cross-linked, and chromatin was prepared and sheared. Each batch of sheared chromatin was split into three equal portions, and ChIP was performed with anti-FLAG, anti-H3K27me3 (positive control) or mouse IgG (negative control). qPCR was performed on ChIP DNA with primers targeting ≤65 bp from each ZF-TF target site, as well as primers to a region of H3K27-methylated, closed chromatin in the distal promoter of MYT (Myelin transcription factor) (MYT (i.e. at levels greater than non-target regions on OCT4) from all 12 chromatin preps , demonstrating that all were ChIP-suitable. IgG pulled down no or barely detected amounts of DNA (≤0.003% of input) in all cases. FLAG-ChIP for cells transfected with vector alone also gave no or barely detected amounts of DNA (≤0.003% of input) for all primers. On the other hand, anti-FLAG ChIP with chromatin from cells expressing each of the 11 ZF-TFs pulled down more DNA from OCT4 upstream regions than from non-target MYT -p65) gave FLAG-ChIP yields similar to the three most active ZF-TFs -p65). Two of these three inactive/weakly active ZF-TFs bind within known regulatory regions (ZF(OCT4−r2352)-p65, distal enhancer, inactive; ZF(OCT4+286)-p65, proximal promoter, weakly active), while the third binds in the region between the proximal and distal enhancers (ZF(OCT4–1777)-p65, inactive). The entire ChIP experiment was repeated with a second set of cultured and transfected HEK293 cells, and similar results were obtained. Therefore, as expected, not all ZF-TFs that bind OCT4 can upregulate OCT4, but unexpectedly, not all ZF-TFs that appear to bind efficiently in known enhancer or promoter regions can upregulate OCT4.To determine what role the ability of ZF-TFs to bind their targets plays in T Figure , althougTo test whether ZF-TFs could also be used to activate noncoding genes, as recently shown for TALE-TFs , we desiOCT4, SOX2, KLF4 and c-MYC were tested with VP16, VP64 or 2Xp65 (two tandem copies of the p65 domain) in addition to p65. For KLF4, the original ZF-TF containing the p65 activation domain induced the greatest increase in transcript levels  gave the strongest western bands, but the p65 construct gave the best activation of KLF4.The activation domain linked to a ZF-TF can have a large influence on the level of transcriptional activation achieved by such artificial TFs ,23,30. Us Figure . On the n Figure , and for5 Figure . These rs Figure . WesternOCT4 that were tested  increased OCT4 mRNA levels in both K562 and BJ fibroblasts, although the relative increase in OCT4 transcript levels in BJ fibroblasts -p65 induced OCT4 mRNA levels several 100-fold in K562 cells. SOX2-ZF-TFs were also tested in K562 and BJ cells -VP64 and ZF(SOX2–88)-VP64 induced an increase in SOX2 mRNA levels relative to the control that was substantially greater than that observed in HEK293 cells , non-induced SOX2 mRNA levels were not. The basal level of SOX2 mRNA was considerably higher in HEK293 (Cq ∼29), and too low for reliable detection in BJ and K562 (Cq ∼38). Therefore, the fold-expression values for SOX2 in BJ and K562 are approximations only. Regardless of these differences, the results demonstrate that active ZF-TFs are capable of upregulating targeted genes in multiple cell types and have access to the ZF recognition sequence in at least the three cell types tested.All of the ZF-TFs described here were identified based on their ability to activate targeted gene transcription in HEK293 cells. As different cell types can express different TFs, and the chromatin state at promoters can vary depending on the cell type, we were interested in whether active ZF-TFs identified in HEK293 cells would also activate target gene expression in other cell types. We tested selected ZF-TFs in K562 human chronic myelogenous leukemia cells and BJ human foreskin fibroblast cells. BJ fibroblasts were of particular interest, as these cells are often used for cellular reprogramming by overexpressing the same genes targeted by our ZF-TFs . As shows Figure  was muchs Figure  and K562s Figure  cells. Ss Figure . It shouOCT4, SOX2, KLF4 and c-MYC. Our success rate was lower than reported in other studies, as expected since we did not confine our ZF-TF target sites to known regulatory elements. The active ZF-TFs identified upregulated their target genes from 2-fold to over 500-fold, depending on the cell type, activation domain fused to the ZF-TF and the gene targeted. The levels of activation achieved with several ZF-TFs were similar to or higher than those previously reported for ZF-TFs targeting OCT4 . Likewise, a target region 1 kb downstream from the TSS was previously reported for active engineered ZF-TFs .To further explore locations within a gene that might be good sites for ZF-TFs to bind and upregulate transcription, we designed ZF-TFs targeting the upstream enhancer as well as the region downstream from the TSS for KLF4 TSS ,33. SincOCT4 were located in the upstream enhancer regions of the gene, confirming the results of others  themselves -p65, which is expected to bind in this region, upregulates OCT4 mRNA and protein, consistent with the pioneer TF model.Perhaps not surprisingly, two of the three best activators for f others ,20,22 thf others , as has emselves . Bindingn Figure  33). In. InOCT4 miR302−r270)-p65 and ZF(miR302–218)-p65, have predicted DNA-binding sites that overlap these SOX2 and NANOG-binding sites. In addition, two of the best ZF-TF activators for SOX2 target the region adjacent to a NANOG-binding site. These results suggest that selecting ZF-TFs that bind near or overlap with sites used by naturally occurring TFs, especially ‘pioneer TF’-binding sites, may be a successful strategy for designing active ZF-TFs.The promoter region of the miR302/367 cluster also contains binding sites for OCT4 and NANOG, as well as SOX2 Figure , which hSOX2, KLF4 and c-MYC, the same regions were shown to be DNase hypersensitive in several somatic cell lines as well . However, for OCT4 they were not . Although HEK293 cells, which were used for most of the results presented here, were not among the cells evaluated, HEK293T and K562 cells were. None of our active ZF-TF target sites for OCT4 are within DNase-hypersensitive regions reported for HEK293T or K562. These results support those of Perez-Pinera et al. -p65, which binds within the distal enhancer and ZF(OCT4 +286)-p65, which targets downstream from the proximal promoter. Weakly active ZF-TF, ZF(OCT4+286)-p65, targets a site 33 bp further from the proximal promoter than does the active ZF-TF, ZF(OCT4+r253)-p65. However, ZF-TFs ZF(OCT4+r414 and +440)-p65, which were not included in the ChIP analysis, target sites even further downstream and both activate OCT4 expression ∼20-fold. On the other hand, inactive ZF-TF, ZF(OCT4−r2352)-p65, targets a site 10 bases further into the distal enhancer than does the active ZF-TF, ZF(OCT4 –r2342)-p65. Since the most active ZF-TFs for OCT4 target sites slightly downstream from known regulatory elements, the distal enhancer, proximal enhancer and proximal promoter   . Others  (ERBB2)  or have  (ERBB2) .SOX2 in HEK293 cells was observed using a ZF-TF containing a VP64 activation domain. A ZF-TF containing a 2xp65 activation domain was most efficient at upregulating OCT4 in HEK293 cells, but the same ZF-TF with a VP16 or VP64 domain failed to activate OCT4 transcription efficiently. These results indicate that the cellular machinery is present in HEK293 cells to activate transcription from VP16/64, but suggest that different factors are required at different promoters or positions within the promoter for optimal transcriptional activation. All ZF-TFs that activated transcription in one cell type also led to increased mRNA levels in the other cell types tested; however, differences in relative activation were observed in some cases. As previously noted, differences in the epigenetic state of a promoter or levels of endogenous accessory factors in different cell types likely contribute to the ability of a ZF-TF to activate transcription in different cell types.In addition to the target location for the activating ZF-TF, the attached functional domain and the cell type into which the ZF-TF is delivered can influence targeted gene activation levels Figure . The greOCT4 (Figure Several artificial TFs have been designed to activate genes involved in reprogramming, but there is yet to be a report of using such tools alone to reprogram cells. However, with the additional ZF-TFs reported here, the number of activators targeting such genes has substantially increased. Having a collection of these tools may allow for further analysis of the transcriptional requirements for cellular reprogramming. Other groups have reported that combinations of TALE-TFs can act synergistically to upregulate targeted genes, including miR302/367 ,26. Alth4 Figure . It willSupplementary Data are available at NAR Online.


NGATHA genes (NGA) form a small subfamily within the large family of B3-domain transcription factors of Arabidopsis thaliana. NGA genes act redundantly to direct the development of the apical tissues of the gynoecium, the style, and the stigma. Previous studies indicate that NGA genes could exert this function at least partially by directing the synthesis of auxin at the distal end of the developing gynoecium through the upregulation of two different YUCCA genes, which encode flavin monooxygenases involved in auxin biosynthesis. We have compared three developing pistil transcriptome data sets from wildtype, nga quadruple mutants, and a 35S::NGA3 line. The differentially expressed genes showed a significant enrichment for auxin-related genes, supporting the idea of NGA genes as major regulators of auxin accumulation and distribution within the developing gynoecium. We have introduced reporter lines for several of these differentially expressed genes involved in synthesis, transport and response to auxin in NGA gain- and loss-of-function backgrounds. We present here a detailed map of the response of these reporters to NGA misregulation that could help to clarify the role of NGA in auxin-mediated gynoecium morphogenesis. Our data point to a very reduced auxin synthesis in the developing apical gynoecium of nga mutants, likely responsible for the lack of DR5rev::GFP reporter activity observed in these mutants. In addition, NGA altered activity affects the expression of protein kinases that regulate the cellular localization of auxin efflux regulators, and thus likely impact auxin transport. Finally, protein accumulation in pistils of several ARFs was differentially affected by nga mutations or NGA overexpression, suggesting that these accumulation patterns depend not only on auxin distribution but could be also regulated by transcriptional networks involving NGA factors.The four  The carpel is the female reproductive organ of the angiosperm flower and its most distinctive feature. Carpels typically occur at the center of the flower forming the gynoecium, most commonly fused into a single pistil (a syncarpic gynoecium) or less frequently as individual organs that collectively form an apocarpic gynoecium composed of several pistils. The gynoecium confers major advantages to flowering plants: provides protection for the ovules; enables pollen capture and pollen tube guidance and supports self- and inter-specific incompatibility; finally, after fertilization of the ovules, the gynoecium develops into a fruit, which protects the developing seeds and facilitates seed dispersal  mutants, affected in the AUXIN RESPONSE FACTOR 3 gene; the phenotypes of mutants defective in auxin transport such as pinoid (pid) or pin-formed1 (pin1); and on the effects of inhibiting polar auxin transport (PAT) in gynoecium morphology. According to this model, auxin would be produced in the apical end of the pistil and transported basipetally, creating a gradient along the apical-basal axis that would be translated into the differentiation of the different functional modules: high apical auxin levels would direct the differentiation of style and stigma, intermediate levels would specify the ovary, and low basal levels, the gynophore. The Nemhauser model has been very useful to frame the role of different players in Arabidopsis carpel development, but conclusive proof of the proposed auxin gradient has never been obtained. Actually, detailed descriptions of auxin accumulation throughout gynoecium development using a DR5rev::GFP reporter have shown that auxin maxima are formed in the apical domain, first as isolated foci and later as a continuous apical ring, while the proposed gradient cannot be observed , a gene encoding a flavin monooxygenase-like enzyme involved in auxin synthesis , nga2-2 (line SM.20993) nga3-3 (AMAZE En-1 line 6AAi79), and nga4-3 (AMAZE En-1 line 6AAB133i) alleles were used to generate nga quadruple mutants. Genotyping was performed as previously described . Libraries for sequencing were prepared from 2–4 μg total RNA using Illumina TruSeq RNA kits and sequenced with Illumina HiSeq2000. Quality control on the raw sequence data was done using FastQC (Babraham Bioinformatics). Reads were aligned to whole genome sequences from the TAIR10 A. thaliana database (www.arabidopsis.org) and analyzed using the CLC Genomics workbench (www.clcbio.com). RPKM (reads per kilobase per million) was considered as expression values. Two biological replicates for wildtype and three for nga and 35S::NGA3 were used for sequencing. After normalization, Bagerley's test and a FDR correction were used for statistical analysis of samples. Genes with a corrected FDR p-value < 0.05 and with a fold change >1.4 or <-1.4 were selected for gene ontology analysis with the agriGO toolkit . GFP was excited using a 488 nm line of an argon ion laser. GFP emission spectra were collected between 500–550 nm and plastid autofluorescence was collected between 601 and 790 nm.For the micro-application experiment, 80 mg of indolacetic acid (IAA)  were dissolved in 2 mL of ethanol. IAA, or ethanol for mock treatment, was added to 10 gr of lanolin containing 2.5% liquid paraffin. The lanolin paste was applied to the apical end of stage 8–10 gynoecia using plastic pipette tips under a dissecting microscope, resulting in apical parts completely covered by lanolin paste. The gynoecia were observed after 2 weeks and photographed under a dissecting scope.nga mutants and plants overexpressing NGA3  encodes an enzyme that catalyzes the conversion of IAM to IAA and it has been proposed to contribute to auxin synthesis through an alternative pathway to the TAA1/YUC route . A limited partial rescue of the nga phenotypes was observed, with restored apical closure of the gynoecium, but no development of style or stigma typical cells, suggesting that the lack of auxin accumulation in this domain was probably not the only factor causing the nga phenotypes  and at the valve margins  do not show phenotypic defects  are reduced, but not absent, from nga mutant apical gynoecia. Interestingly, NGA3 constitutive expression appears to induce the expanded expression at valve margins of ARF1, ARF11, and ARF18, similarly to what was observed for TAA1 expression, suggesting that they might be responsive to local auxin synthesis putatively mediated by TAA1. It has been described that 35S::NGA3 fruits have enlarged dehiscence zones and thus, this expanded expression domains could be also due to an indirect effect of NGA3 overexpression on fruit morphology. Because no function has been assigned yet to ARF1, ARF11, or ARF18 in gynoecium development, it remains to be studied whether ARF regulation may mediate NGA functions in this process.Finally, protein accumulation patterns for several ARFs expressed through gynoecium development have been described in wildtype, YUC family, as well as TAA1 and AMI1 were not expressed in this domain in nga quadruple mutants, but only showed moderately increased expression in 35S::NGA3 lines. Accordingly, DR5rev::GFP showed no activity in nga mutants but no significant differences in 35S::NGA3 pistils when compared to wildtype. It is thus tempting to speculate that NGA could only direct auxin synthesis in the presence of other factors, for which SHI/STY family members are strong candidates. In addition, NGA altered activity affected the expression of PID and WAG2, regulators of PIN subcellular localization, and thus likely had an impact on auxin transport in parallel to the effect on auxin synthesis. Finally, protein accumulation in pistils of several ARFs was differentially affected by nga mutations or NGA overexpression, suggesting that these accumulation patterns depend not only on auxin distribution but could be also regulated by transcriptional networks involving NGA factors. Again, NGA3 constitutive expression did not result in wide activation of ARF expression in the gynoecium, reinforcing the idea of NGA requiring additional factors to exert their regulatory functions.In summary, our work shows that NGA factors impact on auxin signaling pathways at multiple levels throughout pistil development. First, and more importantly, NGA factors appear to be essential, but not sufficient for auxin synthesis in the apical developing gynoecium, since several members of the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.