Complex molecular responses preserve gene expression accuracy and genome integrity in the face of environmental perturbations. Here we report that, in response to UV irradiation, RNA polymerase II (RNAPII) molecules are dynamically and synchronously released from promoter-proximal regions into elongation to promote uniform and accelerated surveillance of the whole transcribed genome. The maximised influx of de novo released RNAPII correlates with increased damage-sensing, as confirmed by RNAPII progressive accumulation at dipyrimidine sites and by the average slow-down of elongation rates in gene bodies. In turn, this transcription elongation ‘safe’ mode guarantees efficient DNA repair regardless of damage location, gene size and transcription level. Accordingly, we detect low and homogenous rates of mutational signatures associated with UV exposure or cigarette smoke across all active genes. Our study reveals a novel advantage for transcription regulation at the promoter-proximal level and provides unanticipated insights into how active transcription shapes the mutagenic landscape of cancer genomes. Precise orchestration of gene expression regulation upon DNA damage is essential for genome integrity. Here the authors identify a novel widespread stress-triggered defence mechanism that promotes rapid transcription-driven genomic surveillance thus limiting mutagenesis and shaping cancer genomes. Recent mapping of ultraviolet (UV) irradiation- and smoking-associated cancer genomes revealed an intriguing heterogeneity in the distribution of somatic mutations that implies a central role of transcription in maintaining DNA integrity16. While transcriptionally inactive regions solely rely on Global Genome-Nucleotide Excision Repair (GG-NER), the sensing activity of actively elongating RNA polymerase II (RNAPII) on interfering DNA lesions, caused by exposure to UV, tobacco smoke and other chemicals such as cisplatin, yields faster repair kinetics in the transcribed genome via the targeted action of transcription-coupled NER pathway (TC-NER)18. Therefore, ongoing transcription safeguards genome stability16 and can contribute to guaranteeing low mutation frequencies in the transcribed strand of active genes14. Nonetheless, transcription-dependent efficiency in DNA surveillance contrasts with prevailing views, which point to DNA damage-induced inhibition of transcription20.Environmental stress damages DNA and perturbs transcription and replication, thus impaired or overwhelmed DNA damage responses (DDR) will limit cell homeostasis recovery and promote mutagenesis, uncontrolled cell proliferation or cell death, leading to tissue dysfunctions, disease pathogenesis and ageing22 and occurs in defined sets of genes in response to various developmental24 or environmental cues27. Mechanistically, positive transcription elongation factor b (P-TEFb) complex is recruited at PPP sites and is licensed by the activation of the Cdk9 kinase module29. This switch enables phosphorylation of (i) the negative elongation factor (NELF), which is removed from PPP sites, (ii) the DRB sensitivity-inducing factor (DSIF), which is transformed into a positive elongation factor, and (iii) the serine 2 (Ser2) residues of the heptapeptide repeats of the C-terminal domain (CTD) of RNAPII main subunit rpb133. As a result, waves of transcription elongation sweep along the gene34. It has been reported that UV irradiation activates calmodulin–protein phosphatase 2B and 1A (PP2B and PP1A) signalling pathways and provokes the disruption of 7SK snRNP inactive complex, a major reservoir of poised P-TEFb in the nucleus, leading to a rapid increase in the pool of active P-TEFb35. Nevertheless, no further studies that address the transcriptional relevance of P-TEFb activation in response to UV have been reported.Productive transcription elongation is regulated by the accurate and coordinated release of RNAPII from promoter-proximal pause (PPP) sites located immediately (~60 bp) downstream of transcription start sites (TSS)36 further demonstrated that the identified transcription response has critical impact on the accelerated and unbiased removal of damaged DNA caused by environmental challenges. A striking consequence is the homogenous decrease of the mutation burden throughout the transcribed genome in UV- and smoke-associated cancers. Our findings therefore substantiate the importance for cells to pre-wire for prompt release of RNAPII into elongation. In addition, our data reveal unanticipated functional and molecular insights into an advanced transcription-dependent defence mechanism that transiently adjusts the regular transcription-firing program of active genes in human cells to accelerate damage-sensing and to actively preserve genetic integrity.Herein, we identified a novel metabolic function associated with active transcription elongation that is triggered immediately upon UV irradiation. Global de novo stress-induced RNAPII elongation waves are simultaneously released from promoter-proximal pausing sites, in virtually all expressed genes, regardless of gene size or expression level. Thorough comparisons of our functional genomics data with previously published data sets37 ) and ongoing transcription activity (nascent RNA-seq) in hTERT immortalised normal human foreskin fibroblasts (see Methods). We took advantage of previously established protocols to synchronise cells in G1 to limit cell-cycle heterogeneity and to achieve steady-state levels of RNAPII and nascent transcripts across the transcribed region (gene body). Briefly, serum-starvation for 72 h at confluency enriched for cells in G0/G1  of UV-C irradiation, caused an extensive reorganisation of transcription. Particularly in active genes, RNAPII isoforms displayed important changes in their occupancy profiles  before and after DNA damage induction for each gene and for all isoforms of RNAPII. We found a transient yet significant increase of EIs (ΔΕΙ > 1) at early time points during recovery (0.5 to 2 h post UV) at the vast majority (up to 90.6 %) of active genes for RNAPII-ser2P and for RNAPII-ser5P , thus matching the changes in levels estimated by RNAPII-hypo ChIP-seq  during the early recovery phase (+0.5 to +2 h) .Accordingly, we found that regions directly downstream of PPP do display substantial increases in nRNA levels between 0.5 and 2 h post UV  genes  affects the largest single group of co-regulated target genes for which RNAPII is released from PPP sites in response to a specific stimuli22 with 5,6-dichloro-1-β-d-ribofuranosylbenzimidazole (DRB), a widely used inhibitor than can reversibly prevent RNAPII PPP-release .To confirm that the release of RNAPII from PPP sites occurs de novo, we blocked the transition of RNAPII into productive elongationase Fig. . DRB trease Fig. , as confase Fig. . This trase Fig. . Notably45, UV irradiation did not abolish the recovery of nascent transcription after DRB removal , and we anticipate that DNA damages slow-down both de novo released and already transcribing RNAPII (see below). Importantly, this model is consistent with and explains the recent findings suggesting that transcription initiation and elongation still occurs in proximal areas upon UV-irradiation although progress into gene bodies is very slow23. Considering the increased probability that a progressing RNAPII will encounter a lesion in a travelling distance- and time-dependent manner, we anticipate that as more actively elongating RNAPII molecules are released into gene bodies, more frequent and faster transcription-dependent DNA repair will occur.Understanding the advantage of a combined widespread release of de novo elongating complexes gradually decelerating in gene bodies could answer an important open question: why cells need to prime such an extended proportion of active genes for potential prompt pause-release−2), which induces more UV lesions, produced an equally widespread release of RNAPII in virtually all active genes  are dictated by TT frequency47. We gained insights into the dynamics of DNA lesion recognition by RNAPII, as we sorted the TT sites in active genes according to their distance to TSS. We then clustered TT loci by their relative position to RNAPII wave fronts during recovery , Supplementary Fig. ery Fig. . Hence, ery Fig.  or by a To quantify the density of RNAPII around TT sites, we calculated the fold change (FC) in read density between UV-treated and NO UV conditions across the regions. We found that at 2 h post UV, up to 68.1 % of the analysed regions were highly enriched with RNAPII Fig. . To asse16. Together, these data (i) confirm that RNAPII de novo release into productive elongation promotes the accelerated and uniform recognition of DNA adducts across the whole active gene set and across each gene’s body and (ii) reveal the molecular causes underlying transcription elongation deceleration by UV lesions in vivo.We next assessed the importance of the de novo released elongation waves by quantifying the stalling of RNAPII at TT loci after irradiation in DRB-inhibited cells (+DRB + UV). The lack of elongation influx was sufficient to cancel the increase in ‘S - F’ scores in clusters located upstream of the wavefront positions  around intragenic TT loci, as described above for the RNAPII reads. In WT and in cells derived from Xeroderma pigmentosum C patients (XP-C), which can efficiently repair DNA lesions in active genes via TC-NER, we discovered that excision activity was enriched for TT loci located close to TSS, upstream of the wave front  -associated base substitutions, which implies that TC-NER limits mutation prevalence10. Skin melanoma and lung adenocarcinoma tumours are known to show strong probabilities for UV light (C > T) and smoke (G > T)-induced mutagenesis, respectively, especially in non-expressed genes. We selected C > T (or the reverse complement G > A) substitutions, which correspond to the most abundant UV-generated mutations11. Interestingly, although CPDs formation at CC, CT and TC dipyrimidines, is less frequent than at TT dimers46, they generate more frequently error-prone translesion DNA synthesis and this phenomenon results in C > T mutational events, which are the hallmark of UV-exposed genomes50. For smoking-related impairments, G > T (or the reverse complement C > A) substitutions were selected, because they correspond to the most abundant smoking adduct-generated mutations repaired by TC-NER12 (see Methods).One prediction from our findings is that mutation rates calculated for NER-associated signatures should remain low in all active genes independently of their steady-state expression level and of the density of pri-elongating RNAPII. To test this hypothesis, we took advantage of published clinical-relevant genome-wide mutation maps of genotoxin-exposed tissues that have developed cancer. We focused on two data sets12 (TS < NTS)  Fig. , and forTS) Fig. . More inTS) Fig. . Given t51. By maximising the entry of RNAPII in all active genes, cells benefit from an accelerated and uniform DNA damage-sensing by RNAPII on the TS, and to a lower extent on the NTS, regardless of damage location, gene length and steady-state levels of transcription. This functional advantage is dependent on the release of elongation waves, as it is lost upon transcription inhibition (irradiation in the presence of DRB). In line with this, a boost in NER activity is observed at lesions located within the range of the newly released RNA polymerases. Our data also reveal clinically relevant consequences of this unanticipated defence mechanism. The somatic mutation landscapes of cancer genomes, such as melanoma and lung adenocarcinoma exposed to UV irradiation and cigarette smoke respectively, consistently display low and homogenous mutation rates in all expressed genes. Finally, our results substantiate the importance of the promoter-proximal transcription regulatory step and explain why cells can benefit from priming paused RNAPII in such an extended proportion of active genes.In this report, we characterise a novel metabolic function associated with active transcription. We propose that in response to genotoxic stress, threatened cells switch transiently to a ‘safe mode’ of RNAPII elongation Fig. . This deOur approach provides clear quantitative views on the distribution patterns and dynamic changes in the abundance of the three main isoforms of RNAPII (from pre-initiation complex formation to entry into productive elongation) and nascent RNAs in response to low doses of UV irradiation.52, UV irradiation promotes a rapid increase in the pool of active P-TEFb molecules in the nucleus35. We elucidate the functional consequences of this activation. Upon stress, the unleashed P-TEFb kinase activity (via cdk9) leads to extensive hyper-phosphorylation of RNAPII CTD (into RNAPII-ser2P)53 and accounts for the global and synchronous transition into productive elongation observed in virtually all active genes , and (ii) increased nascent RNA levels at the beginning of genes45. Challenged cells enable robust RNAPII-dependent accelerated ‘lesion scanning’ activity in all genes essential to their homeostasis. De novo released RNAPII molecules guarantee that lesions located very close to TSS or in less frequently expressed genes will also get promptly repaired. By transiently adjusting gene expression at the level of PPP-release, cells activate a pre-wired program of ‘safe’ mode elongation and limit potential biases associated with inherent stochasticity of the transcription initiation step55. In addition, sending waves of trailing RNAPII molecules throughout the transcribed genome could allow for the next lesions in the gene body to get efficiently detected and repaired even in the case of dissociation from chromatin of the initial damage-detecting and NER-triggering RNAPII molecules56. In turn, this pathway will ensure a smooth transcription restoration process in expressed genes after repair has been completed regardless of gene length and expression level.The identification of a UV-induced widespread release of elongating RNAPII also clarifies the findings of previous reports, which detected in response to UV irradiation, but interpreted differently: (i) increased levels of RNAPII in most active genes  demonstrate a striking parallel between the global wave-release of damage-sensing RNAPII molecules and the increased repair efficacy upon genotoxic stress in all active genes. As shown in Fig. vs distal difference in excision-reads observed in both WT and XP-C cells 1 h post UV is due to de novo wave propagation of RNAPII and to the substantial increase in the probability of transcription-dependent repair. As previously reported36, we note that XP-C cells show an increased XR-seq rate of UV lesions in comparison to WT cells. This might be partially due to the lack of repair in intergenic regions and non-template strands in XP-C cells. Thus, coverage of XR-seq reads is limited to a smaller part of the genome and a substantial overestimation of the enrichment in read density in genic region is expected. Additionally, in these cells, the absence of GG-NER pathway might affect the probability of the available core NER factors to be recruited at transcription-blocking lesions.Functional genomic cross-comparisons of our RNAPII ChIP-seq data with NER-specific excised DNA data . Indeed, these tumours originate from skin and lung tissues that may have been exposed to environmental stress such as UV-irradiation and tobacco smoke, which trigger NER activity. By extracting the substitutions in a tissue-, strand- and expression-specific manner we found that, the probability of mutagenesis remains low throughout the TS of active genes regardless of these genes’ level of expression. To some extent a reduced mutation rate is also observed in the NTS of active genes, thus confirming better efficacy of both NER subpathways.Maps of somatic mutations of genotoxins-exposed cancer genomes such as melanoma and lung adenocarcinoma8 for review).In nature, differential exposures to UV light may also provoke overcoming a signalling threshold at which the response we present here can be activated. It can be speculated that cells can determine when dangerous levels of exposure have been reached to justify a rapid response. In addition, the levels of exposure requiring the activation of such ‘safe’ mode might be lower for cells that replicate more often, as these will need to benefit from accelerated repair before they can divide safely, thus preventing potential cancer stem-cell formation . In that sense, we believe that inhibiting the ability of tumour cells to boost transitioning of RNAPII into productive transcription elongation, while promoting genotoxic stress, may also improve cancer therapeutics.Our results demonstrate that the global release of elongating RNAPII waves promotes NER efficacy and can in turn preserve genetic accuracy. We thus anticipate that general deficiencies in transcription elongation and/or TC-NER will limit such genome-protective effects. In line with this, during the past few years the biomedical field has concentrated on the investigation of novel markers ruling DNA damage response pathways. This significant effort fuels the discovery of new potentially ‘druggable’ targets and could facilitate patients’ access to safe and precise cancer therapies62. Cells negative for mycoplasma (Jena Bioscience) were propagated under standard culture conditions in Dulbecco’s Modified Eagle’s Medium , supplemented with 10% v/v fetal bovine serum , 1% v/v penicillin–streptomycin (Thermo Scientific) and in G418  for selection, in a humidified incubator at 37 °C and 5% CO2. When applicable, after one week in culture confluent cells were synchronised by 72 h of serum-deprivation (starving in DMEM + 0.5% FBS) followed by release for indicated times in complete medium (released in 10% FBS) before treatment to rule out cell cycling heterogeneities, to increase the proportion of cells in G1 at time of UV irradiation, and to boost steady-state levels of transcription. Cells were UV-C irradiated  at indicated doses. Transcription Inhibitor 5,6-dichloro-1-β-d-ribofuranosyl-benzimidazole  or other chemicals were added directly in the growth media, unless otherwise indicated, at concentrations and times specified in the figures.Cell line used in this study was VH10 normal human foreskin fibroblasts (VH10) hTERT immortalised as previously described55 with minor modifications. Antibodies used are as follows: Pan-RNAPII (Rpb1 N-terminus) , RNAPII CTD (phospho-S2)  from Abcam; RNAPII CTD (phospho-S5)  and RNAPII CTD (hypophosphorylated)  from Millipore (see Supplementary Methods). Final ChIP DNA was quantified on a Qubit 2.0 Fluorometer  and ChIP specificity was checked by qPCR analyses performed with 10–100 pg of ChIP and Input DNA in duplicate reactions with qPCRBIO SyGreen mix (PCR Biosystems) on Roche Light Cycler 96 instrument. We validated at least two independent ChIP replicates by ChIP–qPCRs. If individual ChIPs showed enrichment in expected genomic regions  DNA were combined to reach ~1–10 ng of material. ChIP and Input DNA were then subjected to library preparation for high-throughput sequencing (see below).Crosslinked ChIP was carried out from at least two independent cultures of cells per condition, as previously describedNascent RNA (nRNA) was isolated using the Click-iT Nascent RNA Capture Kit (Life Technologies), according to the manufacturer’s protocol. In brief, two independent cultures of cells were treated, as indicated in the legends, with 5-ethynyl uridine  for 10 min, at specified time points, before total RNA was isolated with Trizol as described in the previous section. 5 μg (quantified by Nanodrop) of DNAse-treated  total RNA was biotinylated with 0.5 mM biotin azide and bound to streptavidin beads. Nascent RNA (nRNA: EU-containing RNA captured on the beads) was fragmented and used for first strand cDNA synthesis before being subjected to second strand cDNA synthesis as described in the previous section. Eluted DNA was size-selected and purified with Ampure XP beads before library preparation.65. Nascent RNA was labelled with EU for at least 10 min before RNA extraction at indicated times of recovery with Trizol reagent as described above. For t = 0 total RNA was isolated immediately after EU labelling. For the other time points, at time 0, DRB was washed-out by PBS and mock (NO UV) or UV-irradiated (20 J m−2). PBS was replaced with fresh complete medium, to allow for normal recovery, before RNA was isolated at the indicated time. For DRB inhibition (DRB-ChIP-seq), two independent cultures of cells were incubated with DRB (100 μM) 10 min before UV irradiation (20 J m−2). After irradiation, cells were left to recover for indicated times in the presence of DRB before crosslinking, chromatin isolation and ChIPing for RNAPII-ser2P (as above).For preDRB-nRNA-seq, two independent cultures of cells were pre-incubated with DRB (100 μM) for 3 h to drain elongating polymerases from gene bodies based on previously described protocols55 with minor modifications. DNA-modifying enzymes were purchased from NEB. dsDNA was end repaired, 3′-end adenylated and ligated to barcoded (for multiplexing) Truseq (synthesised by IDT) or NEBNext adapters according to manufacturer’s guidelines. Samples were PCR amplified by insuring reduced amplification bias  and size-selected (200–500 bp) through double selection by Ampure XP Beads as previously described66. Libraries were assayed on a BioAnalyzer  and next-generation sequencing was performed at Genecore-EMBL, using the Illumina HiSeq 2000 for single end 50 bp reads.Library preparation was performed essentially according to standard Illumina protocols and as previously describedFor all next-generation sequencing analyses, we developed in-house scripts and pipelines to automate and analyse the data consistently (see below for details). Codes are available upon request. Sequenced data and generated wig profiles are available on Gene Expression Omnibus (GEO) (Accession ID: GSE83763). Briefly, reads were quality trimmed, Illumina adaptors were removed and filtered reads were aligned uniquely to the GRCh37/hg19 reference genome with specific parameters for each experiment (see Supplementary Methods for details). Sequenced, mapped and normalised read stats are depicted in Supplementary Data 64; (ii) neighbouring isoforms with inter-TSS distance <1 kb were excluded from the analysis to avoid potential artefacts of analysing reads from nearby gene promoters and of merging distinct genes with overlapping promoter-proximal regions; (iii) only protein coding and lncRNA genes were kept for analysis according to the NCBI RefSeq biotype specification. Consequently, the analysis resulted in n = 19,775 curated genes.The 54,669 isoforms of release 72 of RefSeq collection were filtered as follows: (i) neighbouring TSSs within distance of less than 500 bp were collapsed and the longest isoform was kept for each gene as described previouslyn = 164,896 merged exons out of n = 205,798 individual exons.For constructing our custom exon annotation, we merged (bedtools merge -d 1) all the overlapping genes for each strand separately. Genes with overlapping exons encoded in opposed strands were not considered in order to avoid multiple strand references. Consequently, the analysis resulted in 67 we called ChIP-seq peaks for RNAPII-hypo, -ser5p and -ser2P ChIP-seq in steady-state (NO UV) conditions, using only non-redundant reads68 comparing to Input and ‘-no-model’ option. The list of significant ChIP-seq peaks (fdr < 0.05 and fold change > 1) is presented in Supplementary Data 69 and were considered active (n = 8954) ). Poised genes were defined as the union of RNAPII-ser5P and –hypo genes that did not overlap with -ser2P peaks (n = 953). The rest of the genes were considered as inactive (n = 9868). All genes annotation and expression status information is presented in Supplementary Data 2 RPKM kernel density distribution plot70.Constitutive gene expression status in the steady state was determined before UV irradiation as follows: Briefly, using MACS2ks n = 95. The res71, by taking the average coverage in rpm in the gene body  divided by the average coverage on the promoter-proximal region (Dp), as defined in the previous section. EI changes (ΔEI) between conditions were calculated by dividing EIs as indicated in legends, and were used to assess differential RNAPII Promoter Escape upon treatments. The proportion of genes with increased escape after treatment are shown in percentages and Chi-square test (χ2) determines if observed number of genes with ΔEI > 1 differs from expected value purely by chance. PCC were calculated in Excel or R statistical package accordingly.Promoter escape indexes (EI) were calculated for curated genes, as previously defined with minor adjustments72 or DeepTools (http://deeptools.ie-freiburg.mpg.de/) were used to extract read densities at genomic regions (metagenes or TT loci) of interest as indicated in the figures. Heatmaps were generated directly in the software from matrixes of binned read densities (bin size is indicated in the figures) for all considered individual (n) items (metagenes). Matrices were also exported to excel for (i) plotting average density profiles (smoothing achieved by a moving window of the bin size as indicated), (ii) for determination of wave front and backend positions and (iii) for calculation of elongation rates (see following section). When applicable the matrix of read densities was sub-divided in categories  and the average density profiles of each cluster were considered separately. For determination of fold changes profiles, the number of reads per bin for a given sample were divided by the number of reads in the indicated control sample and expressed as Log2 FC.ChIP-seq and nRNA-seq data were subjected to read density analysis after normalisation of all samples per experiment (see above). For heatmaps and average density profiling and differential enrichment profiling, SeqMINER 1.3.3n considered metagenes crossed an arbitrary threshold representing the transition point indicated in the figures. These positions were calculated relatively to the TSS and expressed in kb. Average elongation rates (kb min−1) were calculated from the composite average plots as previously described65. Importantly, this method accounts for variability across metagenes (n) given the large number of measurements and the distance covered between two time points.The front (or leading edge) and the backend (lagging edge) position of the RNAPII or nRNA wave at each time point was computed as the position at which the average densities of 6 bp) on the non-coding strand and further filtered for TT loci with the distance to next/previous item (non-overlapping) greater than 70 bp. These corresponded to n = 29,612 active genic TTs .Exon start loci positions were extracted from our custom exon annotation, we filtered a representative subset of regions by selecting exons id number equal to 236. Raw data files were downloaded from Gene Expression Omnibus . Meta-analysis involved that read counts were normalised. We computed heatmaps matrices and average reads density plots as described in the section ‘Read densities heatmaps and average plots’. Around TT loci, we analysed both strands together, as defined in figure legends, and as described in the previous section. We also computed average reads density plots, for both strands separately for analysis over gene bodies as explained forChIP-seq above.The strand-specific genome-wide maps of nucleotide excision repair of the UV-induced DNA damage (CPDs), available for three different cell lines (i) wild-type (WT) NHF1 skin fibroblasts, (ii) XP-C mutants, lacking the global genome nucleotide excision repair mechanism  and (iii) CS-B mutants lacking transcription-coupled repair (TC-NER-deficient), were obtained from Hu et al.2 FC in Rd between irradiated (+UV) and steady state (NO UV) were calculated within the observed regions  for each cluster. For multiple comparisons, normal distribution was assumed as supported by the log2 FC values. One-way analysis of variance (one-way ANOVA) was performed to test for significant differences between examined conditions, while pairwise t-tests with Benjamini–Hochberg (BH) adjusted P-values were also performed as post hoc tests, to identify the pairs that differed.To estimate the general enrichment of reads in a region between conditions, we relied on LogTo estimate more specifically the stalling of RNAPII (RNAPII-ser2P ChIP-seq reads) or the excision activity (XR-seq reads) we calculated the difference (‘S - F’ score) in Rd between the summit (S: specific accumulation at the centre of the regions) and the flanks (F: background expected from the wave passing-by) for TT loci and for exon start loci.P-values were also performed as post hoc tests, to identify the pairs that differed.For multiple comparisons, normality was not assumed, and the non-parametric Kluskal–Wallis one-way analysis of variance was performed to test for significant differences between examined data sets. Pairwise Wilcoxon rank-sum tests with BH adjusted exon start + 3 × SD[S - F]exon start. Chi-square (χ2) test were calculated for pairwise comparisons (TT vs exon start) of numbers of FC and ‘S - F’ score above threshold in matching clusters. P-values reflect if observed values differ from expected value purely by chance.The percentage of regions in a cluster displaying FC or ‘S - F’ scores larger than an arbitrary threshold were calculated in TT loci and in exon start loci references. The FC threshold was set to be FC = 2, while the threshold for ‘S - F’ score was estimated from the exon start data and set to ‘S - F’ = average[S - F]7 were downloaded from ftp://ftp.sanger.ac.uk/pub/ cancer/AlexandrovEtAl. We considered all the mutations listed in the files name_clean_somatic_mutations_for_signature_analysis.txt where name = ‘Melanoma’ or ‘Lung_Adeno’. We analysed separately WGS and WES data separately when possible. We filtered the substitutions according to the scheme presented in Supplementary Fig. 11. Noteworthy, CC, CT and TC CPDs, although less frequent than TT dimers46, generate more frequently error-prone translesion DNA synthesis, which results in more C > T mutational events50. For smoking-related impairments, G > T (or the reverse complement C > A) substitutions were selected because they correspond to the most abundant smoking adducts-generated mutations repaired by TC-NER12. In order to extract the trinucleotide context of these selected mutations, we retrieved from hg19 fasta file the flanking bases around each selected mutation. We then filtered all the hits corresponding to the most frequently characterised trinucleotide events for UV (T(C)C > T(T)C (and G(G)A > G(A)A)) and smoking (T(G)G > T(T)G (and C(C)A > C(A)A)) in Melanoma and Lung Adeno data sets, respectively and generated BED files. As mutational events derive from unrepaired cytidine adducts (UV50) or unrepaired benzo[a]pyrene Guanine adducts (smoking), we were able to determine whether the mutations were on the template strand (TS) or on the non-template strand (NTS) . We filtered out the genes that were not present in our custom annotation and we ranked genes by decreasing expression values. Quantiles of expression were determined arbitrarily. Non-expressed (NE) genes corresponded to the genes with RPKM values below the threshold established in Kernel distributions as outlined above (see ‘Gene transcripts annotation and gene expression status definition’ section). The remaining expressed (E) genes were separated in three categories of the same size where Hi, Med and Lo denote high, medium and low expression levels, respectively.Gene expression levels RPKM were extracted from nascent RNA-seq data generated for normal (non-stressed) skin and lung human fibroblasts . Mutation frequencies were calculated by averaging the number of mutations detected in all analysed samples, for WES or WGS data sets, over the considered gene regions and by normalising the value to what it would be in a region of 1 Mb of DNA . Given the non-linearity of WES data, which is due to the non-homogenous density of exons along gene bodies, we corrected mutation prevalence scores in each considered window as a function of the relative exon density measured in the considered regions (see Exons map in Supplementary Fig. The data reported in this manuscript have been deposited with the Gene Expression Omnibus under accession code GSE83763. Data are available from authors upon reasonable request.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3


Transcription start-site (TSS) selection and alternative promoter (AP) usage contribute to gene expression complexity but little is known about their impact on translation. Here we performed TSS mapping of the translatome following energy stress. Assessing the contribution of cap-proximal TSS nucleotides, we found dramatic effect on translation only upon stress. As eIF4E levels were reduced, we determined its binding to capped-RNAs with different initiating nucleotides and found the lowest affinity to 5'cytidine in correlation with the translational stress-response. In addition, the number of differentially translated APs was elevated following stress. These include novel glucose starvation-induced downstream transcripts for the translation regulators eIF4A and Pabp, which are also translationally-induced despite general translational inhibition. The resultant eIF4A protein is N-terminally truncated and acts as eIF4A inhibitor. The induced Pabp isoform has shorter 5'UTR removing an auto-inhibitory element. Our findings uncovered several levels of coordination of transcription and translation responses to energy stress.DOI:http://dx.doi.org/10.7554/eLife.21907.001 The production of new proteins is a complex process that occurs in two steps known as transcription and translation. During transcription, the cell copies a section of DNA to make molecules of messenger ribonucleic acid (or mRNA for short) in the nucleus of the cell. The mRNA then leaves the nucleus and enters another cell compartment called the cytoplasm, where it serves as a template to make proteins during translation. A mRNA molecule contains a sequence of building blocks known as nucleotides. There are four different types of nucleotides in mRNA and the order they appear in the sequence determines how the protein is built.Both transcription and translation consume a lot of energy so they are highly regulated and sensitive to environmental changes. However, since transcription and translation happen in different cell compartments, it is not known if and how they are coordinated under stress.Tamarkin-Ben-Harush et al. studied transcription and translation in mouse cells that were starved of glucose. The experiments show that the identity of the very first nucleotide in the mRNA – which is dictated during transcription – has a dramatic influence on the translation of the mRNA, especially when the cells are starved of glucose. This first nucleotide affects the ability of a protein called eIF4E, which recruits the machinery needed for translation, to bind to the mRNA. The experiments also show that there is a dramatic increase in the number of distinct mRNAs that are transcribed from the same section of DNA but translated in a different way during glucose starvation.The findings of Tamarkin-Ben-Harush et al. show that transcription and translation are highly coordinated when cells are starved of glucose, allowing the cells to cope with the stress. The next step is to further analyze the data to find out more about how transcription and translation are linked.DOI:http://dx.doi.org/10.7554/eLife.21907.002 Transcription start site (TSS) selection and alternative promoter (AP) usage increase transcriptome diversity and its regulation. For example the level of transcription initiation can vary between different transcription start sites (TSS) under different growth conditions, in response to a specific signal or in different cell types and tissues. In addition, mRNA isoforms with different 5’ leaders can vary in their translation efficiency or their half-lives. Likewise, AP usage can lead to the generation of protein isoforms that differ in their N-termini and as a result have different or even opposite biological functions. Recent large-scale promoter analysis in hundreds of human and mouse primary cell types shed light on the prevalence of AP usage in mammals . SeveralThe process of protein synthesis is highly energy consuming and tightly regulated by the availability of nutrients, oxygen and growth factors. Downregulation of the translation machinery is a major mechanism that allows cells to preserve energy and cope with environmental deficiencies. Under these conditions translation of many mRNAs is inhibited but that of others is unchanged or even enhanced in order to survive the stress. The translation inhibition response is mediated by several mechanisms, in particular by the impairment of key initiation factors eIF2 and eIF4E . StresseIn the present study we aimed to obtain a global view of the effects of TSS selection on translation following metabolic energy stress. To this end, we combined polysomal profiling with quantitative assessment of the 5’ ends of mRNAs. By comparing transcript isoforms that differ in their 5’ end we identified hundreds of genes with APs that are differentially translated, in particular following energy stress, suggesting that a major determinant of the differential response is associated with transcription-induced APs. We also determined the contribution of TSS nucleotides to translation. Strikingly, while the cap-proximal nucleotides have no significant effect on translation under optimal growth conditions, they display dramatic effects on the translational response to stress. We demonstrate that eIF4E levels drop following the stress and that the binding affinity of eIF4E towards capped mRNA with different first nucleotide varies significantly, with a relatively lower affinity to 5’ polypyrimidine as in TOP mRNAs. We next characterized two genes encoding translation regulatory factors with differentially translated APs. The first is eIF4A, the helicase subunit of the cap complex eIF4F, in which we identified a novel glucose-starvation-induced intronic promoter. The induced isoform, which has distinct 5’UTR and initiating AUG, is efficiently translated in energy deficient cells in spite of the global translation inhibition. The resultant protein is N-terminally truncated and acts as an eIF4A inhibitor, most likely to facilitate the stress response. The second is poly-A binding protein  in which repression of the major TOP-containing isoform following stress is coupled with the induction of a downstream TSS that generates an mRNA isoform with a much shorter 5’UTR that is highly translated. Interestingly, the induced isoform lacks the well-characterized Pabp auto-inhibitory element . Our finTo obtain a global and quantitate view of the impact of transcription start site (TSS) selection on translation efficiency following cellular stress we combined polysomal profiling with quantitative assessment of the 5’ ends of mRNAs  note th and indeWhen the results of two independent experiments were combined and normalized using internal controls, 9286 promoters that corresponded to 6861 genes had at least 500 reads per promoter and were considered in further analysis . Under tWe next analyzed the translational response of individual promoters following the GS stress. For this we determined the ribosome occupancy  of transcripts emanating from each promoter and calculated the effect of GS on the RO (referred to as the RO effect) for each experiment. A promoter was considered as translationally affected if its RO was affected by at least two-fold in each of the two biological repeats . With thOur approach enabled us to compare the translation efficiency of transcript isoforms emanating from APs of the same gene as schematically shown in Among APs corresponding to the same ORF, those resulting in shorter 5’UTRs had higher RO under basal conditions . On the We also compared the mRNA levels of the transcript isoforms following GS stress. We calculated the change in overall mRNA levels of each promoter following GS, and compared the effects on the APs of each gene . 140 genThe CapSeq data can provide insights into the impact of the exact nucleotide context of the TSS on translation efficiency. To determine the effect of the initiating nucleotides on translation efficiency we first analyzed all the non-redundant TSS positions in our data. We found that in MEFs, under basal and GS conditions, ~34% of TSSs initiated with adenosine (A), ~23% with cytidine (C), ~30% with guanosine (G) and ~13% with thymidine (T) , similarRepresentative examples of TSSs that are separated by just a few bases within the same promoter and display differential translational reaction to GS are shown in Another example is the p1 promoter of the Sars (Seryl-tRNA synthetase) gene that has multiple TSSs  binding protein (Pabp), a central translation initiation regulatory factor, has several annotated promoters. The major promoter under basal conditions is p1, which generates a TOP mRNA . This mRRenilla luciferase (RL) gene and transfected into MEFs , to act as a promoter. The upstream sequences of p2 were placed upstream to a promoter-less nto MEFs . The resArhgef2 (Rho/Rac guanine nucleotide exchange factor 2) is another interesting example of a gene with APs that are differentially regulated by GS  that can bypass the inhibitory effect of uAUGs. Alternatively, translation can be activated by uORF-mediated mechanisms in which phosphorylation of the subunit of eIF2α favors translation of genes containing multiple uAUGs .In summary, by measuring isoform-specific translation in basal and energy stress conditions we uncovered previously unknown regulatory mechanisms that broaden our understanding of how stress alters cellular translatome and transcriptome and how these processes are coordinated. We anticipate that further analysis of the data and focusing on specific examples will provide additional novel insights of transcription-translation links.Untreated and 8 hr glucose starved MEFs were incubated with 100 μg/ml Cycloheximide (Sigma) for 5 min and then washed twice with cold buffer containing 20 mM Tris pH 8, 140 mM KCl, 5 mM MgCl2 and 100 μg/ml Cycloheximide. The cells were collected and lyzed with 500 μl of same buffer that also contains 0.5% Triton, 0.5% DOC, 1.5 mM DTT, 150 units RNAse inhibitor (Eurx) and 5 μl of protease inhibitor (Sigma). The lyzed samples were centrifuged at 12,000g at 4°C for 5 min. The cleared lysates were loaded onto 10–50% sucrose gradient and centrifuged at 41,000 RPM in a SW41 rotor for 90 min at 4°C. Gradients were fractionated and the optical density at 254 nm was continuously recorded using ISCO absorbance detector UA-6. The collected samples were then merged to create three fractions: Polysome-free (F), Light (L) and Heavy (H). The use of three pools is sensitive to large changes in translation but relatively insensitive to smaller changes. RNA was isolated for each fraction using Trizol and Direc-Zol RNA mini-prep kits (Zymo Research). RNA spikes (0.25 ng GFP and 1pg Luciferase) that were transcribed  and capped  in-vitro using T7 RNA Polymerase were added into each fraction. Equivalent RNA volume was taken from each fraction for the library preparation for TSSs sequencing using the CapSeq method (with modifications) as previously described . SpecifiReads were mapped to the mouse genome (mm9 assembly) using STAR aligner and to the GFP and luciferase sequences using Bowtie2. The BAM file was altered to contain just the first base of the alignment using a custom Java script  and covemRNA differential effect = ftp://ftp-igor.weizmann.ac.il/pub/hubANA.txtThe data can be accessed in Gene Expression Omnibus (GEO) accession no. GSE93981 or tracked using UCSC genome browser at: MEFs from a WT mouse  and HEK293T (from ATCC) were maintained in DMEM supplemented with 10% fetal calf serum, 100 units/ml penicillin, 100 mg/ml streptomycin and 1 mM Sodium pyruvate (for MEFs only). Cells were tested negative for mycoplasma by PCR. Cells were harvested after the indicated period of glucose starvation [glucose free media  with 10% dialyzed FBS] or 24 hr after transfection and lysates were subjected to SDS-polyacrylamide gel electrophoresis followed by western blot. The levels of proteins were determined using the following antibodies: anti-GFP , anti-eIF4A , anti-eIF4E , anti-HA , anti-Pabp  and anti-Tubulin (Sigma).The pEGFP-N1 (Clontech), pEGFP-N1 with 5’UTR bearing secondary structure and the HA-eIF4A plasmids were previously described . To consFor knocking-down eIF4E, HEK293T cells were transfected with Dharmacon siGENOME SMART pool siRNA  using DharmaFECT1 transfection reagent. The Dharmacon ON-TARGETplus Nontargeting siRNA #3 was used as a negative control. 48 hr after the initial transfection, cells were transfected with the indicated GFP reporter plasmids. Cells were harvested 24 hr after the second transfection.eIF4e was purified from E.coli BL21(DE3) bacteria transformed with eIF4E-pET-30a construct . Bacteria were grown with Kanamycin at 37°C up to OD(600) = 0.8. Then, IPTG (0.5 mM) was added and bacteria were harvested after overnight incubation at 18°C. The samples were lysed using sonication in TPA buffer , 100 mM KCl) added with 2.5 mM PMSF, 10 mM β-mercaptoethanol and protease inhibitor cocktail (Sigma). All the buffers were prepared using Diethylpyrocarbonate (DEPC)-treated double distilled water. The lysate was subjected to centrifugation and subsequent ultracentrifugation in order to remove membrane associated RNAses. The soluble fraction was purified on gravity column using Ni-NTA His-Bind Resin , eluted with the TPA buffer containing 250 mM imidazole and subsequently dialyzed. Fractions containing eIF4E protein underwent gel filtration chromatography .Capped RNA oligos were chemically synthesized as previously described . For theTotal RNA extracted from control and 8 hr glucose starved cells was used as a template to create cDNA with superscript II (Invitrogen) according to the manufacturer’s instructions using gene-specific primers. The cDNA was purified using a PCR purification kit , followed by addition of polyG to the 5’-end using TdT enzyme (Promega) for 1 hr at 37°C. The reaction was terminated by heat inactivation for 15 min at 65°C, and the products were purified with the PCR purification kit (Qiagen). The modified cDNA was used as a template for PCR with Phusion polymerase (NEB) using nested reverse primers and forward PolyC primer. PCR products were run on 6% polyacrylamide gel. 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 favorably evaluated by James Manley (Senior Editor) and three reviewers, one of whom, Timothy W Nilsen (Reviewer #1), is a member of our Board of Reviewing Editors. The following individual involved in review of your submission has agreed to reveal their identity: Orna Elroy-Stein (Reviewer #3).Thank you for submitting your article "TSS nucleotides via differential eIF4E binding and alternative promoter usage mediate translational response to stress" 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.As you will see all are positive about the work but some essential changes must be made via revision. In that regard please address the major comments of reviewer 2 as thoroughly as possible. Also explain CapSeq in the text and add the supplementary material requested during revision by reviewer 3.Reviewer #1:In this interesting manuscript authors provide new insight into translational dynamics that underlie cellular responses to energy stress.Major findings include the demonstration that under limiting eIF4E levels, different cap proximal nucleotides have different affinities for the factor with purine containing TSS's preferentially recognized.Additionally, the authors show that under stress a new isoform of eIF4A is synthesized that acts as an inhibitor of eIF4A and that PABP is upregulated via synthesis of isoforms with shorter 5'UTRs.eLife.Overall, the work is well performed and carefully presented and is viewed to be appropriate with minor revision for Major point the use of the term translational efficiency throughout should be changed to ribosome occupancy.Reviewer #2:In this manuscript, Dikstein and colleagues investigate the relationship between transcription start site usage and translational efficiency during energy stress. The authors perform a global analysis of transcription start sites (using CapSeq) across polysome fractions in the presence and absence of energy stress, with the goal of identifying changes in translational efficiency that correlate with changes in promoter usage. These data reveal a striking relationship between the identity of the cap-proximal nucleotides and the translational response to stress, with mRNAs beginning with purines being relatively refractory to stress. Biochemical analyses indicate that the cap-binding protein eIF4E has an intrinsic preference for purine-initiated capped mRNAs, which – together with the finding that eIF4E levels are reduced during stress – may explain the translational efficiency data. The authors further present data that stress-induced changes in start site usage for the eIF4A and PABP mRNAs also contribute to the stress response. Together the authors present a model in which changes in TSS selection induced by stress are a critical component of the translational response to stress, with the initiating nucleotides playing a particularly important but previously unappreciated mechanistic role.eLife after addressing all of the major concerns listed below.Overall, the manuscript reports an exciting finding that would be of interest to those interested in gene regulation. However, the current version of the manuscript lacks sufficient validation of the genome-wide datasets on which the entire paper is based. In addition, there are a number of caveats to both the experimental approaches and interpretations that should be addressed. For these reasons, I would only recommend publication in 1) The authors' measure of "translational efficiency" used for all analyses is poorly chosen and can be more accurately described as a measure of "ribosome occupancy". Rather than collecting many fractions from across a sucrose gradient in order to infer the number of ribosomes per mRNA , the authors only collected the "free", light and heavy polysomes. Even with these three pieces of data, the authors combined the light and heavy polysome data for unknown reasons and calculated "translational efficiency" as simply (light+heavy)/(light+heavy+free). The result is a metric that is extremely insensitive to changes in translation rate, since only if an mRNA shifts >2-fold in/out of the "free" fraction containing 0-1 ribosomes will an mRNA be labeled as down/up-regulated. This is problematic for the authors' conclusion that >70% of transcripts are unchanged at the translational level in response to stress  and impl2) There is an underlying assumption of the authors' analyses that is never explicitly stated: That transcript isoforms found to differ in the location of the 5' cap are identical in all other respects. Alternatively, transcripts generated by different promoters might also be spliced differently or use alternative polyadenylation sites, any of which could contribute to the measured translational efficiencies. By attributing any differences in translation to transcription start site, the authors are implying causation from what is only a correlation. In addition to raising this possibility in the text, the authors should undertake some validation experiments for some of their example genes to show that differences in transcription start site alone are sufficient to impart different polysome sedimentation to the mRNAs.3) Based on the examples of CapSeq reads depicted e.g., , the num4) From Reviewer #3:only in TOP-mRNAs, was never realized before. Thus, the current study points out for the first time at the importance of intrinsic properties of the translation machinery to TOP mRNA translation regulation by illuminating the critical importance of TSS/cap proximal nucleotides. Second important discovery is the stress-induced differential transcription of eIF4A and PABP genes which drives the (i) formation of inhibitory eIF4A isoform and (ii) high levels of PABP via translational regulation mediated by the alternative 5'UTR. Since both genes encode translation regulatory factors, this finding provides a demonstration of the tight linkage between transcription and translation in response to stress signals. Additional insights of the study relate to interesting examples many of which are relevant to gene expression during differentiation and cellular transformation.This elegant study address the usage of AP and TSS in response to energy stress and provides global and unbiased information about the translation status of each and every transcript. By doing so, an additional layer of complexity in gene expression is uncovered which refers to the contribution of transcription-translation linkage to proteome shaping in response to stress. This by itself is an important take-home message, which opens up the door to future similar studies concerning the transcription-translation linkage angle in response to other stressors and physiological signals. Moreover, there are additional novel findings, holding important implications. First, is the discovery that the affinity of eIF4E to capped mRNA with a proximal C nucleotide is significantly lower compared to its affinity to capped- proximal purines. The experiment was performed using cutting-edge label-free technology. This is a major contribution to the long-lasting question related to TOP-mRNAs translation regulation under conditions of limiting eIF4E. The purpose of proximal C nucleotide, Comments:The strengths of this study are several fold: (I) generation of an important high quality dataset which is useful for scientists interested in further analyses; (II) important insightful information gathered by the analysis described in the manuscript; (III) experimental validation of several high impact examples. The weakness of the manuscript in its present form is the absence of Supplementary excel file Tables containing the information related to the performed analysis (item II). Such tables, containing list of genes grouped and itemized according to specific features described and discussed in this study should be added.The explanatory text to Legend to Polysomes association may reflect attenuation at the elongation level. See for example Shenton et al. JBC 2006 (showing translation attenuation at the elongation level in response to oxidative stress). This notion should be mentioned in the text. In addition, please add to the legend to Informative details on the genomic locus at the lower part of Subsection “The TSS nucleotides influence the cap-binding affinity of eIF4E”, last paragraph: should add after 'RNA oligos' "whose first three nucleotides are CCU, ACU or GCU".Discussion, second paragraph: The work of Tcherkezian et al. 2014 is incorrectly interpreted. In this paper LARP1 was identified in a proteomic screen for proteins that associate with the mRNA 5′ cap, but was shown to be associated with PABP, not with eIF4E or eIF4G (whereas it does co-immunoprecipitated with eIF4A).Subsection “Differential translation of transcript isoforms is part of the stress response”, second paragraph – add a reference to the statement that the truncated eIF4A retains its interaction with eIF4G1.Discussion, end of fourth paragraph: IRES is not the only possibility. The uAUG-burdened long 5'UTR of eIF4A might confer either IRES-independent or 5'cap-dependnet translation regulation .Discussion, last paragraph: To be specific, must change from stressful conditions to energy stress. Indeed it may imply that a similar mechanism is operative in response to other stressful conditions, but this should be added as a possibility. As you will see all are positive about the work but some essential changes must be made via revision. In that regard please address the major comments of reviewer 2 as thoroughly as possible. Also explain CapSeq in the text and add the supplementary material requested during revision by reviewer 3.Reviewer #1:[…] Major point the use of the term translational efficiency throughout should be changed to ribosome occupancy.We have changed the term ‘translation efficiency’ with ‘ribosome occupancy’ throughout.Reviewer #2:[…] Overall, the manuscript reports an exciting finding that would be of interest to those interested in gene regulation. However, the current version of the manuscript lacks sufficient validation of the genome-wide datasets on which the entire paper is based. In addition, there are a number of caveats to both the experimental approaches and interpretations that should be addressed. For these reasons, I would only recommend publication in eLife after addressing all of the major concerns listed below.1) The authors' measure of "translational efficiency" used for all analyses is poorly chosen and can be more accurately described as a measure of "ribosome occupancy". Rather than collecting many fractions from across a sucrose gradient in order to infer the number of ribosomes per mRNA , the authors only collected the "free", light and heavy polysomes. Even with these three pieces of data, the authors combined the light and heavy polysome data for unknown reasons and calculated "translational efficiency" as simply (light+heavy)/(light+heavy+free). The result is a metric that is extremely insensitive to changes in translation rate, since only if an mRNA shifts >2-fold in/out of the "free" fraction containing 0-1 ribosomes will an mRNA be labeled as down/up-regulated. This is problematic for the authors' conclusion that >70% of transcripts are unchanged at the translational level in response to stress  and implAs suggested we have changed the term ‘translation efficiency’ with ‘ribosome occupancy’ throughout. We accept the suggestion and added an explanation in the text that “the use of three pools  is sensitive to large changes in translation but relatively insensitive to smaller changes.”2) There is an underlying assumption of the authors' analyses that is never explicitly stated: That transcript isoforms found to differ in the location of the 5' cap are identical in all other respects. Alternatively, transcripts generated by different promoters might also be spliced differently or use alternative polyadenylation sites, any of which could contribute to the measured translational efficiencies. By attributing any differences in translation to transcription start site, the authors are implying causation from what is only a correlation. In addition to raising this possibility in the text, the authors should undertake some validation experiments for some of their example genes to show that differences in transcription start site alone are sufficient to impart different polysome sedimentation to the mRNAs.We have now added two examples that demonstrate that the TSS and not other aspects of the transcript, confers sensitivity to eIF4E availability. We used a GFP reporter either under a CMV promoter that initiates at an A nucleotide, or Rpl18 promoter that initiates at a C nucleotide. Upon eIF4E depletion by siRNA or its inactivation by the 4EGI-1 drug, we found a clear differential sensitivity of the same GFP transcripts see . In addi3) Based on the examples of CapSeq reads depicted e.g., , the numAs stated in our response to point 2 above, we now added validation experiments for differential translation using 5’RACE (without ligation) see  and 6E. 4) From The aim of the specific examples is to demonstrate that the global analysis is valid to specific genes and has potential biological consequences. A subset of those examples such as eIF4A, PABP and the newly added Rpl18 is also followed by experimental validation , 5 and 6Reviewer #3:[…] Comments:The strengths of this study are several fold: (I) generation of an important high quality dataset which is useful for scientists interested in further analyses; (II) important insightful information gathered by the analysis described in the manuscript; (III) experimental validation of several high impact examples. The weakness of the manuscript in its present form is the absence of Supplementary excel file Tables containing the information related to the performed analysis (item II). Such tables, containing list of genes grouped and itemized according to specific features described and discussed in this study should be added.We now provide an excel file containing several sheets with all the described gene lists .We now added explanatory sentences for determination of the 5’ends of transcripts in the polysomal profiling and additional details of the size selection procedure in the Methods.The explanatory text to We revised the text for better clarity. Thanks for noting the error with the numbers which was corrected.Legend to Thanks for this note. Indeed, the last sentence was moved to Polysomes association may reflect attenuation at the elongation level. See for example Shenton et al. JBC 2006 (showing translation attenuation at the elongation level in response to oxidative stress). This notion should be mentioned in the text. In addition, please add to the legend to We clarified that the inhibition of translation in response to energy stress is at the levels of initiation and elongation (see Introduction).Informative details on the genomic locus at the lower part of As suggested we added chromosomal location and genomic structure for each browser display presentation.eLife style is to place the supplementary figures adjacent to the main figure. As The Subsection “The TSS nucleotides influence the cap-binding affinity of eIF4E”, last paragraph: should add after 'RNA oligos' "whose first three nucleotides are CCU, ACU or GCU".Thanks for the suggestion, the phrase was added.Discussion, second paragraph: The work of Tcherkezian et al. 2014 is incorrectly interpreted. In this paper LARP1 was identified in a proteomic screen for proteins that associate with the mRNA 5′ cap, but was shown to be associated with PABP, not with eIF4E or eIF4G (whereas it does co-immunoprecipitated with eIF4A).Thanks, this was corrected.Subsection “Differential translation of transcript isoforms is part of the stress response”, second paragraph – add a reference to the statement that the truncated eIF4A retains its interaction with eIF4G1.A reference was added.Discussion, end of fourth paragraph: IRES is not the only possibility. The uAUG-burdened long 5'UTR of eIF4A might confer either IRES-independent or 5'cap-dependnet translation regulation .Thanks for raising this point, we added it to the Discussion.Discussion, last paragraph: To be specific, must change from stressful conditions to energy stress. Indeed it may imply that a similar mechanism is operative in response to other stressful conditions, but this should be added as a possibility.Corrected.


E. coli Dam methylase in vivo, without the requirement for cell-sorting (CATaDa). We have profiled chromatin accessibility in individual cell-types of Drosophila neural and midgut lineages. Functional cell-type-specific enhancers were identified, as well as novel motifs enriched at different stages of development. Finally, we show global changes in the accessibility of chromatin between stem-cells and their differentiated progeny. Our results demonstrate the dynamic nature of chromatin accessibility in somatic tissues during stem cell differentiation and provide a novel approach to understanding gene regulatory mechanisms underlying development.During development eukaryotic gene expression is coordinated by dynamic changes in chromatin structure. Measurements of accessible chromatin are used extensively to identify genomic regulatory elements. Whilst chromatin landscapes of pluripotent stem cells are well characterised, chromatin accessibility changes in the development of somatic lineages are not well defined. Here we show that cell-specific chromatin accessibility data can be produced via ectopic expression of  For an embryo to successfully develop into an adult animal, specific genes must act in different types of cells. Though all the cells have the same genes encoded within their DNA, looking at the way that the DNA is packaged can indicate which parts of the DNA are important for that particular cell type. If regions of DNA are “open” one can infer that those regions are actively involved in gene regulation, whereas “closed” regions are considered less important.It is currently difficult to determine which parts of the DNA are open within an individual cell type in a complex organ, such as the brain. Existing methods require the cells to be physically isolated from the tissue, which is technically challenging.To overcome this issue, Aughey et al. have now developed a method that does not require isolation of the cells. The new technique involves using genetic engineering to introduce an enzyme called Dam into specific cell types in living fruit flies. This enzyme adds a chemical label on regions of open DNA, which can then be detected. Aughey et al. tested this technique on various cells of the developing brain and gut, and were able to see differences in the openness of DNA that corresponded to the action of genes that are important in each cell type. The data also contain trends that help to understand the role of open DNA in development. For example, mature cells were shown to overall have less open DNA than the stem cells that divide to generate them.Aughey et al. hope their new technique will be of use to other researchers working with either fruit flies or mammalian tissues. The knowledge that scientists will gain from identifying how open DNA contributes to gene regulation, in both healthy and diseased tissues, will further our understanding of human development and the biology of diseases such as cancer. During the development of a multicellular organism, gene expression is tightly regulated in response to spatially and temporally restricted signals. Changes to gene expression are accompanied by concomitant changes to chromatin structure and composition. Therefore chromatin states vary widely across developmental stages and cell types. Functional regions of a genome, including promoters and enhancers, can be identified by their relative lack of nucleosomes. These regions of ‘open chromatin’ can be assayed by their accessibility to extrinsic factors. Consequently, chromatin accessibility profiling techniques are commonly used to investigate chromatin states (reviewed in ). Chromain vivo models. Whilst recently developed methods such as ATAC-seq have become popular and address many of the limitations inherent to earlier techniques such as DNAse-seq (i.e. requires fewer cells and increased assay speed), these techniques still require the physical separation of cells and isolation of genomic DNA before chromatin accessibility is assayed  results in specific methylation of open chromatin regions whilst nucleosome bound DNA is protected . HoweverChromatin Accessibility profiling using Targeted DamID (CATaDa) yields comparable results to both FAIRE and ATAC-seq methods, indicating that it is a reliable and reproducible method for investigating chromatin states. By assaying multiple cell types within a tissue, we show that chromatin accessibility is dynamic throughout the development of Drosophila central nervous system (CNS) and midgut lineages. These data have also enabled us to identify enriched motifs from regulatory elements that dynamically change their accessibility during differentiation, as well as to identify functional cell-type-specific enhancers. Finally, we show that compared to their differentiated progeny, somatic stem cell Dam-methylation signals are more widely distributed across the genome, indicating a greater level of global chromatin accessibility.Transgenic expression of fusions of Dam to DNA-binding proteins is a well-established method used to assess transcription factor occupancy (DNA adenine methyltransferase identification - DamID) . RecentlE. coli Dam, using tissue-specific GAL4 drivers in Drosophila, would specifically methylate regions of accessible chromatin exclusively in a cell-type of interest. Detection of these methylated sequences could yield chromatin accessibility profiles for defined cell populations in vivo  (Chromatin accessibility profiles produced with CATaDa in the eye disc were highly reproducible between replicates (r= 0.947) . CATaDa = 0.947) . We also= 0.947) .−5) and peak heights at shared ATAC and CATaDa peaks show significant correlation   . We foun= 0.138) . Given t= 0.138) . We also= 0.138) . To furt= 0.138) . The lac= 0.138) .It was previously shown that ATAC-seq and FAIRE-seq data demonstrated high chromatin accessibility at experimentally validated eye-antennal enhancers . CATaDa Drosophila, neurons are derived from asymmetrically dividing neural stem cells (NSCs). NSC divisions produce one self-renewing daughter NSC and a ganglion mother cell (GMC), which divides once more to produce neurons or glia (worniu- GAL4), GMCs and newly born neurons (R71C09-GAL4 ). In brief, a false discovery rate (FDR) was calculated for peaks (formed of two or more consecutive GATC fragments) for the individual replicates. Then, each potential peak in the data was assigned a FDR. Any peaks with less than a 1% FDR were classified as significant. Significant peaks present in both replicates were used to form a final peak file. Any gene (genome release 6.11) within 5 kb of a peak (with no other genes in between) was identified as a potentially regulated gene.Peaks were called and mapped to genes using a custom Perl program  different (between respective cell types) regions. These regions were identified by running the peak calling program on comparison gff files . The other ~3% are GATC fragments that show >20 rpm difference in isolation, therefore are not identified by the peak calling program. Hierarchical clustering was performed using Morpheus . SequencPotentially regulated genes, with a peak height of at least 10 rpm were submitted to GOToolBox  for GO aGATC fragments were identified with at least a 10 rpm difference in all replicates between at least two cell types. Any peak >2 kb from a transcriptional start site that did not overlap with coding sequence was designated an enhancer. Enhancers satisfying these criteria, which were covered by an available Vienna VT  or Janelhttps://github.com/tonysouthall/Monte_Carlo_simulation [https://github.com/elifesciences-publications/Monte_Carlo_simulation]). Comparisons of areas under curve in For comparing Dam data with ATAC and FAIRE, Monte Carlo experiments were performed using a custom Perl script  and three reviewers, one of whom is a member of our Board of Reviewing Editors. The following individual involved in review of your submission has agreed to reveal their identity: Gabriel E. Zentner (Reviewer #3).Thank you for submitting your article "CATaDa reveals global remodelling of chromatin accessibility during stem cell differentiation in vivo" for consideration by eLife paper. Thus I'd like to propose that we publish your paper, after revisions as discussed here and in the reviews, as a "Tools and Resources" article. Please consider that publishing in this category will in no way diminish the import of your work. On the contrary, since CATaDa is a unique and generally applicable tool, we expect it to be highly appreciated by our readership and widely cited. As to revisions, one thing all the reviewers agreed upon is the paper should include a more thorough, genome-wide comparison of the CATaDa, ATAC, and FAIRE methods for chromatin profiling, and discussion of the pros and cons of CATaDa in relative terms. This is important as ATAC and FAIRE are already widely used. A number of other suggested revisions can be found in the reviews, below. We hope you will consider publishing this interesting work as a Tools and Resources report.The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission. From the reviews, and discussion between the reviews, there was a consensus that the paper has greatest value in the category of "Tools and Resources", which is a specific type of short-format Reviewer #1:The technique detailed in the manuscript is an interesting alternative to currently used methods for chromatin analysis such as ATAC- and FAIRE-seq. In particular the possibility to forego the requirement to isolate the cell type of interest is very attractive, as it would speed up the process, thus limiting the time required for tissue manipulation prior to chromatin extraction. However, we feel that the following points could be improved:• A more complete, genome-wide comparison between CATaDa and ATACseq and FAIREseq is necessary to make this a useful into to this new method.• As different driver lines are used, it would be beneficial to test the expression levels of the Dam in the various cell types here discussed.• The technique is stated to be quite reproducible and • In the third paragraph of the subsection “CATaDa profiling shows dynamic changes in chromatin accessibility during differentiation of the nervous system”, it is shown that "GMC/ newly born neuron profiles frequently show intermediate signal" at several loci where gene expression is activated/inactivated during development. In how many of the loci where such behavior is experimentally known to occur can this be observed? Can similar patterns be identified with ATAC- and FAIRE-seq just as well?• In the fifth paragraph of the subsection “CATaDa profiling shows dynamic changes in chromatin accessibility during differentiation of the nervous system”, is discussed the ability to identify sequence motifs in regions of the NSC genome classified as open. However, only one example of identified motif (i.e. E-box) is reported. What other known motifs have been identified? Also, in regards of the motifs for which no known transcription factor could be identified, how likely are they to be true binding motifs rather than random sequences?• In regards to the Gene Ontology analysis described in the last paragraph of the subsection “CATaDa profiling shows dynamic changes in chromatin accessibility during differentiation of the nervous system”, it would be good if a supplementary table could be added, reporting the top categories and their relative significance of enrichment, rather than just list a few terms that were expected.• The comparison described in the last paragraph of the subsection “Chromatin accessibility in adult midgut cell types”, between brain and midgut cells feels superfluous. Although it's good that brain and midgut cells cluster in two different groups, it is fairly obvious given the very different function of the two organs. This paragraph could be rewritten by giving more space to the direct comparison of NSC and ISC, highlighting similarities in the chromatin in the vicinity of genes (e.g.) related to growth and proliferation.• Subsection “Enhancer prediction from Dam Accessibility data”, last paragraph, it would be helpful to know how many reporter lines were tested and how many matched the prediction of enhancer activity. Also, this would be a good opportunity for further comparison with ATAC- and FAIRE-seq.• In • Subsection “Distribution of sequencing reads reveals changes in global chromatin accessibility during lineage development”, second paragraph, it is stated that • When comparing GATC fragments as described in the "Materials and methods" section ("Motif identification" and "Cell type specific enhancer prediction" sections), it is not stated whether the difference in RPM values were selected via statistical tests or nor. In the latter case, it would be best to use the replicates in a statistical test to evaluate "real" differences between cell types.Reviewer #2:E. coli Dam methylase in Drosophila followed by mapping of GATC methylation sites. The main advantage of the method over previous approaches is that it requires fewer cells and does not require isolation of cells of interest. Using this method the authors profile chromatin accessibility of specific cell types in the Drosophila neuronal and midgut lineages and find changes in chromatin accessibility at different developmental stages.Various chromatin accessibility methods have been used to map potential regulatory elements, beginning with DNase I hypersensitive sites to more recent FARE and ATAC-seq methods. These methods can be useful when combined with other strategies that more directly report on the function of regulatory elements. In this study, the authors develop a method (CATaDa) to map open chromatin regions based on tissue-specific expression of the Drosophila community (based on requests for reagents). As acknowledged by the authors, the resolution of the method is limited by the occurrence of GATC sites (1/~250 bp). My main concern is that the profiling the authors report provides limited functional insight into the roles of specific enhancers or other regulatory elements in regulation of cell type-specific gene expression. The significance of the global changes in chromatin accessibility described by the authors also remains unclear.The method the authors have developed clearly has some advantages over ATAC-seq, probably the most popular current method for assays of genome-wide chromatin accessibility. However, unlike ATAC-seq, this method requires the generation of transgenic animals that express Dam in specific cell types. This may limit its general use but the authors report great interest from the Reviewer #3:Drosophila, finding that chromatin accessibility changes during differentiation and is ultimately reduced in terminally differentiated cells. CATaDa also facilitated the detection of cell type-specific enhancer elements. I think the paper is a nice demonstration that Dam works as a reagent for tissue-specific profiling of chromatin accessibility in vivo (the stepwise changes in GO terms enriched in NSCs to neurons are particularly nice). The fact that CATaDa does not require cell isolation prior to chromatin profiling as FAIRE or ATAC-seq does makes it a strong choice for this purpose. I think the method will be useful, though I would like to see more extensive comparison to FAIRE and ATAC-seq.Chromatin accessibility is a hallmark of active regulatory regions and is dynamic during development. While many chromatin profiling approaches have been implemented in cultured cells, mapping chromatin accessibility in tissues has been more difficult due to the need to physically dissociate target cells from the organism of interest. Here, the authors present CATaDa, a variation on their earlier TaDa technique for tissue-specific in vivo profiling of protein-DNA interactions. CATaDa makes use of untethered Dam's propensity for methylating open regions of chromatin as a means to assay chromatin accessibility. The authors test CATaDa in neural and midgut stem cells from My read of the manuscript is that CATaDa was a hammer in search of a nail, which turned out to be stem cell differentiation. My feeling is that the biological aspects of the story are case studies for the method  rather than a story in their own right. I am therefore inclined to think that the paper would be better suited to being a Tools and Resources article, as both the method and the data generated would be useful for the community. Reviewer #1:The technique detailed in the manuscript is an interesting alternative to currently used methods for chromatin analysis such as ATAC- and FAIRE-seq. In particular the possibility to forego the requirement to isolate the cell type of interest is very attractive, as it would speed up the process, thus limiting the time required for tissue manipulation prior to chromatin extraction. However, we feel that the following points could be improved:• A more complete, genome-wide comparison between CATaDa and ATACseq and FAIREseq is necessary to make this a useful into to this new method.We recognise the need for further comparisons between these methods. To address this, we have included the following new analyses in our revised manuscript.- New analysis of genome wide comparison of intersecting CATaDa peaks with ATAC and FAIRE – including highlighting differences between peaks at promoters vs. other genomic loci .- Demonstrated increased frequency of CATaDa signal in proximity to ATAC/FAIRE peaks .- We investigate the prevalence of peaks appearing only in CATaDa data using different stringencies for peak calling .- We account for much of the difference between methods by analysing GATC site abundance at different genomic loci .- We show correlation between ATAC and CATaDa peaks .- Further discussion of differences between techniques.• As different driver lines are used, it would be beneficial to test the expression levels of the Dam in the various cell types here discussed.The ribosome re-initiation technique results in levels of Dam that are so low that it is undetectable by any of the means at our disposal . Furthermore, we have no data to suggest that the rate of Dam translation relates to transgene expression in a linear manner. Therefore, even if we were able to compare the expression levels of the GAL4 drivers between individual cell types in the tissues in question, this would not necessarily be informative in regards to the levels of Dam protein present in these cell types. If high expression of GAL4 drivers were causing Dam to be expressed at a level that is too high, this would be apparent as we would not see the dynamic range of methylation signal observed in all cell types, as Dam methylation would be saturating.• The technique is stated to be quite reproducible and We have updated and expanded • In the third paragraph of the subsection “CATaDa profiling shows dynamic changes in chromatin accessibility during differentiation of the nervous system”, it is shown that "GMC/ newly born neuron profiles frequently show intermediate signal" at several loci where gene expression is activated/inactivated during development. In how many of the loci where such behavior is experimentally known to occur can this be observed? Can similar patterns be identified with ATAC- and FAIRE-seq just as well?To our knowledge, there are no available RNA-seq datasets for these cell types (i.e. GMCs), which would be necessary for us to be able to define such loci. Therefore it is difficult to make these kinds of conclusions on a larger scale than the examples which we have highlighted. Likewise, there are no chromatin accessibility data available (again to our knowledge), therefore it is not possible to make this comparison. For us to perform these experiments would be beyond the remit of this study.• In the fifth paragraph of the subsection “CATaDa profiling shows dynamic changes in chromatin accessibility during differentiation of the nervous system”, is discussed the ability to identify sequence motifs in regions of the NSC genome classified as open. However, only one example of identified motif (i.e. E-box) is reported. What other known motifs have been identified? Also, in regards of the motifs for which no known transcription factor could be identified, how likely are they to be true binding motifs rather than random sequences?We have included a new supplementary figure  related to growth and proliferation.We have rephrased parts of this paragraph as suggested, and added a new supplementary figure  highligh• Subsection “Enhancer prediction from Dam Accessibility data”, last paragraph, it would be helpful to know how many reporter lines were tested and how many matched the prediction of enhancer activity. Also, this would be a good opportunity for further comparison with ATAC- and FAIRE-seq.We have amended the manuscript to address this point.• In Based on the size and morphology of these cells they are probably enteroblasts (EBs). This is supported by the fact that they seem to appear in proximity to the ISCs. The figure legend has been updated to reflect this.• Subsection “Distribution of sequencing reads reveals changes in global chromatin accessibility during lineage development”, second paragraph, it is stated that We believe that the way this sentence was written in our previous submission may have been misleading. We initially observed this trend with a limited number of datasets, which we included as a supplementary figure, however we were unable to provide a meaningful statistical comparison until we added more replicates to the analysis as explained in the subsequent paragraph. The statistical in question that supports our observation is included in We have re-written this part of the manuscript for improved clarity.• When comparing GATC fragments as described in the "Materials and methods" section ("Motif identification" and "Cell type specific enhancer prediction" sections), it is not stated whether the difference in RPM values were selected via statistical tests or nor. In the latter case, it would be best to use the replicates in a statistical test to evaluate "real" differences between cell types.~97% of the fragments with > 20 RPM difference lie within statistically significant (FDR < 1%) different regions. The other ~3% are GATC fragments that show >20 rpm difference in isolation, therefore are not identified by our peak calling program and do not have an FDR assigned. We have clarified this point in the Materials and methods section.Reviewer #2:[…] The method the authors have developed clearly has some advantages over ATAC-seq, probably the most popular current method for assays of genome-wide chromatin accessibility. However, unlike ATAC-seq, this method requires the generation of transgenic animals that express Dam in specific cell types. This may limit its general use but the authors report great interest from the Drosophila community (based on requests for reagents). As acknowledged by the authors, the resolution of the method is limited by the occurrence of GATC sites (1/~250 bp). My main concern is that the profiling the authors report provides limited functional insight into the roles of specific enhancers or other regulatory elements in regulation of cell type-specific gene expression. The significance of the global changes in chromatin accessibility described by the authors also remains unclear.We have demonstrated that cell specific chromatin accessibility profiling with CATaDa allows the identification of cell specific enhancers see , despiteRegarding the significance of the global changes in chromatin accessibility, we have included some further discussion in our revised manuscript.