Hoxd genes during limb development as a paradigm. These genes are first activated in proximal limb cells by enhancers located in one TAD, which is then silenced when the neighboring TAD activates its enhancers in distal limb cells. We transferred a distal limb enhancer into the proximal limb TAD and found that its new context suppresses its normal distal specificity, even though it is bound by HOX13 transcription factors, which are responsible for the distal activity. This activity can be rescued only when a large portion of the surrounding environment is removed. These results indicate that, at least in some cases, the functioning of enhancer elements is subordinated to the host chromatin context, which can exert a dominant control over its activity.The expression of some genes depends on large, adjacent regions of the genome that contain multiple enhancers. These regulatory landscapes frequently align with Topologically Associating Domains (TADs), where they integrate the function of multiple similar enhancers to produce a global, TAD-specific regulation. We asked if an individual enhancer could overcome the influence of one of these landscapes, to drive gene transcription. To test this, we transferred an enhancer from its native location, into a nearby TAD with a related yet different functional specificity. We used the biphasic regulation of  Here the authors show that a strong enhancer sequence can be controlled by the chromatin environment provided by a topologically associated domain (TAD) located nearby. An enhancer relocated by homologous recombination takes all the hallmarks of its new neighboring enhancers located in the recipient TAD. At highly pleiotropic loci, the collection of enhancers can be distributed across large genomic intervals, which are referred to as regulatory landscapes3. Such landscapes often correspond in linear size to topologically associating domains (TADs)6, defined as chromatin domains wherein the probability of DNA-to-DNA interactions is higher than with neighboring domains. These collections of enhancers likely reflect an evolutionary process whereby the emergence (or modification) of morphologies was accompanied by novel regulatory sequences with the ability to be bound by tissue-specific factors and hence alter the transcription of nearby target genes7.Genes with important functions during vertebrate development are frequently multifunctional, as illustrated by their pleiotropic loss-of-function phenotypes. This multi-functionality generally comes from the accumulation of tissue-specific enhancer sequences8, possibly as a way to maintain silencing or limit transcription to very low levels. These chromatin modifications disappear along with gene activation and are re-deposited once the gene and its enhancers are progressively decommissioned, thus preventing inappropriate transcription that may cause severe developmental problems (see ref. 9). This indicates that enhancer or promoter function can be affected by their local chromatin environments.This model, however, does not explicitly account for the potential influence of local genomic context to modulate interactions between promoters and nascent enhancers, thus adding another layer of complexity to our understanding of developmental gene regulation. For instance, many developmental gene loci are initially decorated with the chromatin mark H3K27me3 deposited by the Polycomb complex PRC2, before their activationtrans and its coordination would be ensured by the binding of multiple similar positive or negative factors. In the second scenario, a higher level of regulation is imposed by the general environment of the landscape, for example corresponding to a TAD-wide positive or negative regulation, which would tend to dominate individual enhancer-specific controls in order to minimize dangerous misexpression events.In the case of large regulatory landscapes containing many enhancers with similar tissue specificity, the question arises as to how their activation and decommissioning are coordinated and enforced over very large genomic intervals to overcome any contradictory inputs. Two scenarios can be considered in this context. The first possibility is that each enhancer sequence within the landscape independently responds to information delivered solely through the factors bound to it in a sequence-specific manner. Such a mechanism would act entirely in Hox genes in patterning and producing the main pieces of tetrapod appendages10. The emergence of the tetrapod limb structure was a major evolutionary change that facilitated animal migration onto land. A key step in this process was the acquisition of a fully developed distal part (hands and feet), articulating with the more ancestral proximal parts of the limbs . During development, the distal piece requires the specific function of several key genes, amongst which are Hoxa13 and Hoxd13. While the absence of either gene function induces a moderate phenotype in hands and feet12, the double loss-of-function condition leads to the agenesis of these structures12 thus suggesting a critical function for these two genes both during the development of distal limbs and its evolutionary emergence (see refs. 14). In this context, the regulatory mechanisms at work to control the expression of both genes in most distal limb cells were carefully analyzed either for Hoxa1316, or for Hoxd1317, and found to be quite distinct from one another.To address this issue, we used the development of the vertebrate limb as a paradigm and, in particular, the essential function of Hoxd genes, the evolutionary co-option of Hoxd13 into both the distal limbs and the external genitals was accompanied by the emergence of a novel TAD (C-DOM) containing multiple enhancers specific to either tissue or common to both20. This TAD is inactive until late in development when limb cells with a distal fate start to appear. In contrast, the expression of other Hoxd genes , which are all critical for the formation of more proximal parts of the limb21, are controlled by enhancers acting earlier and located in the adjacent TAD (T-DOM)22. The HoxD cluster lies in between these two TADs and contains several CTCF binding sites that create an interaction boundary between the two adjacent chromatin domains, insulating Hoxd13 and Hoxd12 from the rest of the gene cluster23. In the early stages of limb bud formation, Hoxd9, Hoxd10, and Hoxd11 are activated by the regulatory landscape located telomeric to the cluster (T-DOM) leading to the patterning and growth of long bones of the arms and legs. Subsequently, in a small portion of cells at the posterior and distal end of the early limb bud, enhancers located in the centromeric regulatory landscape (C-DOM) are switched on, driving the expression of 5′-located Hoxd genes in the nascent hands and feet22.In the case of Hoxd13 in distal cells, enhancers within T-DOM are decommissioned and a large part of this chromatin domain becomes decorated by H3K27me3 marks22. This switch in TAD implementation involves the HOX13 proteins themselves since their production in response to C-DOM enhancers participates in the repression of T-DOM enhancers, likely through direct binding25. In parallel, HOX13 proteins positively regulate C-DOM enhancers, thus re-enforcing the functional switch between the two TADs. In the absence of both HOXA13 and HOXD13, C-DOM is never activated whereas T-DOM enhancers continue to function due to the absence of both decommissioning and H3K27me3 coverage24. Therefore, in this context, HOXD13 shows the properties of both a transcriptional activator and a transcriptional repressor at different times and in different chromatin environments.The operations of these two TADs are mutually exclusive and as soon as C-DOM starts to upregulate the expression of Hoxd13 becomes activated by distal limb (digit) enhancers, it is critical that all proximal limb (forearm) enhancers are rapidly switched off, to prevent the latter may act on the former, a situation that was shown to be detrimental to limb morphology26. This bimodal regulatory situation thus provides a paradigm to assay for the presence of a ‘context-dependent’ repression of enhancer activity. Accordingly, we set out to introduce into the proximal limb-specific T-DOM domain, the strongest distal enhancer normally working within C-DOM to activate Hoxd13 in digit cells, asking whether this distal limb regulatory sequence would still be able to exert its function when relocated into a TAD where limb enhancers are being repressed in distal cells. We report that this enhancer element, which is functionally very penetrant when introduced at various random positions by non-targeted transgenesis, loses most of its distal limb activity when recombined within T-DOM, even though it continues to recruit the HOX13 factors, which are essential for its function in distal limb cells. We further show that part of the distal limb activity is restored when large portions of T-DOM are deleted in cis. We conclude that the function of this enhancer is inhibited by an in cis mechanism acting at the level of an entire chromatin domain, suggesting the existence, in this particular case, of a level of regulation higher than that of the enhancer sequences themselves.At the time when HoxD locus. We performed ATAC-seq in wild-type E12.5 distal limb cells and compared the signals with a previously reported H3K27ac ChIP-Seq dataset23. Because distal limb enhancers located at Hox loci were shown to colocalize with the binding of HOX13 proteins as assayed in ChIP-seq experiments25, we used a CUT&RUN approach for both HOXA13 and HOXD13 (together referred to as HOX13) to more precisely delineate enhancer elements controlled, at least in part, by these transcription factors.To evaluate how a tissue-specific enhancer would behave when relocated into a different chromatin and regulatory context, we searched for a candidate enhancer element that would display the strongest possible specificity for developing distal limb cells at the 27. This element was strongly bound by both transcription factors and accessible as judged by ATAC-Seq, suggesting that it is an active enhancer element in distal limb cells and that it controls the expression of the most posterior Hoxd genes there , and was never observed in the proximal limbs. This expression persisted until at least E13.5.We also generated a version of the transgene containing a Hoxd genes in the distal limbs and genitals18 and our ATAC-Seq and CUT&RUN experiments showed that element II1 is among the most strongly bound and accessible elements throughout the C-DOM  deletion. We collected embryos at E12.5, measured their levels of distal limb Hoxd mRNAs by RT-qPCR and looked at the transcript distribution by in situ hybridization. Using both approaches, we did not observe any significant change, neither in the level of Hoxd gene transcription in the distal limbs of embryos missing the II1 enhancer, nor in the spatial distribution  would presumably not directly impact the inserted element. Fertilized eggs were injected with both the targeting construct and various CRISPR components  showed a strong LacZ staining in the proximal limb  produced a strikingly different staining pattern  likely reflecting the LacZ staining in response to nearby proximal limb enhancers. In the distal forelimb samples (DFL), there was a further increase in signal over the HBB promoter, and the accessible region extended over the II1 enhancer portion of the transgene. This signal over the II1 enhancer was nevertheless weaker than that observed on the II1 element located in its native context  to ensure that all reads observed on the coverage tracks mapped uniquely to the native II1 element in C-DOM (II1 C-DOM) and the II1 transgene recombined into T-DOM (II1 T-DOM). In wild-type control samples, as expected, no ATAC signal was observed over the native II1 C-DOM, neither in proximal limb bud cells nor in forebrain cells Fig. , left. ILacZ transgene in distal limb bud cells, even though the promoter was clearly accessible.The increased accessibility over the enhancer portion of the relocated II1 transgene in T-DOM suggested that HOX13 transcription factor may still be able to bind. We assessed this by performing CUT&RUN experiments using HOXA13 and HOXD13 antibodies on distal limb cells from embryos homozygous for the II1 transgene in T-DOM and wild type controls. To discriminate between reads from II1 in C-DOM or in T-DOM, only those reads containing sequences uniquely mapping to sequences outside the II1 element itself were considered (see methods). As expected, HOX13 proteins bound strongly to the native II1 element in C-DOM, with a peak over the 3′ end of the enhancer in controls Fig. , left. A31 for our CUT&RUN samples and for a previously reported dataset using ChIP-Seq25. In both datasets, we found motifs for HOX13 factors within the II1 element at four different positions. Three of these sites are closely clustered at the 3′ end of the enhancer element , we performed a motif search analysisLacZ staining in some specific distal limb cells, even though this staining was distinct from the strong and general accumulation scored with the randomly integrated transgene  or three (Del 3 × 13) HOX13 binding sites  chromosome.In order to challenge this negative in-Mtx2 gene, up to, but not including, the II1 enhancer element in the T-DOM . This deleted region of T-DOM contains the CS39 and CS93 proximal limb enhancers32. In this deletion, we only scored a slight reduction in the extent of the LacZ staining in proximal limb cells . This portion of T-DOM contains the CS65 and PLE proximal limb enhancer elements26. In this deletion, we observed a severe loss of LacZ staining in proximal limb cells , not establish any contacts with the cluster at all, or would it adopt the interaction tropism of its new T-DOM neighborhood for the more ‘proximal’ limb genes ? We performed Capture Hi-C (CHi-C) on E12.5 proximal and distal forelimb cells micro-dissected either from wild-type embryos, or from embryos homozygous for the recombined II1 T-DOM transgene. The captured reads were mapped onto the mutant genome excluding all reads that would ambiguously map to both the II1 enhancer in C-DOM and in T-DOM, i.e., sequence reads that would not extend outside of the enhancer itself and hence could not be uniquely assigned to either one of the two sites.In distal limb bud cells at E12.5, strong chromatin contacts are detected between the II1 enhancer sequence (or a larger sequence including it) and the “posterior” part of the HoxD gene cluster, as revealed by the subtraction of the mutant contact signal from the wild type . Within these landscapes, gene activation potential can be distributed across multiple enhancers36 often leading to functional redundancy between them or to more complex interactions37. Alternatively, enhancer sequences can be grouped together in a more compact manner, either to ensure a coordinated function, as exemplified by the Globin genes40, or to maximize transcription in a given cellular context such as the compact regulatory structure referred to as super enhancers42.The importance and status of enhancer sequences have evolved considerably since their discovery6. As illustrated at the HoxD locus, the genomic dimensions of TADs sometimes correspond to the extents of regulatory landscapes22, with TADs providing boundaries to the interaction space of enhancers within the three-dimensional organization of the genome. Consequently, TADs have been considered to be permissive structures augmenting enhancer and promoter interactions, while simultaneously providing borders that prevent enhancers from interacting with elements outside the TAD, and hence to regulate genes in an inappropriate manner44. In this view, however, the enhancer sequence is considered as a regulatory element that can explore and act within the nuclear space somewhat freely unless TAD borders are present to frame its realm of action. Alternatively, there may be some loci where the function of an enhancer sequence can be subordinated to the global chromatin context of a given TAD thus introducing a level of regulatory control derived from a large chromatin domain rather than by individual DNA sequences45.Enhancers are often embedded within TADs, which are regions where certain DNA–DNA interactions are favored while adjacent regions are excluded from the interaction space30, which are responsible for its strong activation potential as demonstrated here by the deletion of these sites in the transgenic context, which leads to the loss of LacZ staining in digit cells. When recombined into T-DOM, the II1 enhancer is silenced yet it still recruits HOX13 factors and hence its silencing cannot be attributed to the absence of the necessary activating factors. In fact, HOX13 factors are also bound to T-DOM whenever this TAD becomes inactive in distal limb cells and covered by H3K27me3 marks24, suggesting that the same factors may act in both a positive and a negative manner in different chromatin contexts.The transfer of enhancer II1 into T-DOM may illustrate such a case, where a potent and highly penetrant enhancer sequence is inhibited precisely in those cells where it normally functions, by placing it within a global environment that is not operational in these cells. In its normal location among C-DOM enhancers, the II1 sequence is bound by HOX13 factors46, which may potentially drive the formation of phase-separated globules by co-condensing with transcriptional co-activators/co-repressors, as shown for these and other genes47. It is thus possible that, due to their high content in bound HOX13 proteins, both C-DOM and T-DOM are used to form large transcription-hub condensates38, leading to a positive transcriptional outcome for C-DOM in distal limb cells, and a negative outcome for T-DOM, within the same cells, due to the inclusion of additional co-factors that are specific to each domain48. This latter explanatory framework would also account for why the deletion of the enhancer II1 in vivo had essentially no detectable effect upon transcription of target Hoxd genes in distal limbs, much like what was reported for C-DOM enhancers used for external genitals18 as well as in other comparable instances37. In both cases, removing a single component of the aggregate would not matter too much, whereas removing several related enhancers would then have a measurable impact.One potential explanation for this observation is that HOX13 factors may function with more than one modality, depending on their context. On the one hand, these factors may bind to and activate an enhancer-reporter transgene in a sequence-specific manner, as many transcription factors do, leading to the pattern described herein and its absence in transgenes lacking the binding sites. On the other hand, both HOXD13 and HOXA13 proteins contain a large poly-alanine stretch50.). However, the former mechanism would not preclude the capacity for a single distal limb enhancer to trigger LacZ expression, as illustrated either by the weak staining detected when II1 was relocated into T-DOM, or when a large deletion brought the LacZ reporter close to Island 1, following the fusion between parts of C-DOM and T-DOM.This situation contrasts with those where a single enhancer is responsible for target gene activation, the deletion of which usually seriously impairs the structure  telomeric deletion did not substantially change the weak distal staining of the transgene, yet it severely reduced expression in the proximal domain, showing that the most potent proximal enhancers were located in the deleted interval. In contrast, the (Del Mtx2-II1-T-DOM) centromeric deletion consistently had the opposite effect, with only a slight reduction of the activity in the proximal limb domain while distal expression was re-activated, thus mapping the main T-DOM region carrying the repressive effect between the gene cluster and the integration site of the enhancer-reporter construct. However, it was not assessed whether this reactivated distal LacZ staining is due to the II1 enhancer itself or to the de-repression of other T-DOM enhancers, which would then act on the HBB promoter due to the in-cis proximity.The negative effect of T-DOM over the II1 enhancer construct inHoxD cluster, in both proximal (when T-DOM is active) and distal (when T-DOM is inactive) limb bud cells. In these two instances, however, the contacts were distributed differently. In proximal cells, contacts were established with the specific part of the cluster that is heavily transcribed, following the behavior of other T-DOM located enhancers22. In this case, these interactions were not directly driven by CTCF, for the integration site was selected at a distance from such sites. The enhancer was likely included in a global structure interacting with specific Hoxd promoters and possibly supported by CTCF sites within both the gene cluster and T-DOM51. In distal cells, the interactions were less specific and likely reflected contacts between large H3K27me3 decorated chromatin segments53, as suggested by the absence of contacts with those genes heavily transcribed in distal cells. There again, the interaction profile of the II1 enhancer followed its new neighboring proximal enhancers.Finally, the recombined II1 construct was able to specifically and rather strongly contact the 24. This mechanism is potent enough to prevent the expression of one of the strongest distal limb enhancers, after its recombination within T-DOM. This silencing is partly alleviated when a large piece of flanking chromatin is removed, suggesting the importance of neighboring sequences within the TAD to achieve this effect. When this distal enhancer was introduced into the proximal limb TAD, it followed the interaction tropism of its new neighbor proximal limb regulatory landscape, yet could not escape its repressive effects in the distal limb, thus illustrating the potential of chromatin domains, in some cases, to impose another level of coordinated regulation on top of enhancer sequence specificities.Altogether, we conclude that in distal limb bud cells, the necessary decommissioning of all previously acting proximal enhancers is partly achieved -or secured- by a TAD-wide silencing mechanism, as illustrated by the appearance of large domains of H3K27me3All experiments were approved and performed in compliance with the Swiss Law on Animal Protection (LPA) under license numbers GE 81/14 and VD2306.2 (to D.D.). All animals were kept in a continuous back cross with C57BL6 × CBA F1 hybrids. The sex of the embryos was not considered in this study. Mice were housed at the University of Geneva Sciences III animal colony with light cycles between 07:00 and 19:00 in the summer and 06:00 and 18:00 in winter, with ambient temperatures maintained between 22 and 23 °C and 45 and 55% humidity, the air was renewed 17 times per hour.Ta = 64 °C and then cycled 32× with Ta = 62 °C.When samples were to be used directly for experiments, a rapid protocol was implemented: Yolk sacs were collected and placed into 1.5 ml tubes containing Rapid Digestion Buffer (10 mM EDTA pH8.0 and 0.1 mM NaOH) then placed in a thermomixer at 95 °C for 10 min with shaking at 900 rpm. While the yolk sacs were incubating, the PCR master mix was prepared with Z-Taq (Takara R006B)  for 20 min each at room temperature on a rocker plate. After approximately 1 h of washing, the wash solution was removed and replaced with staining solution . The plate was wrapped in aluminum foil and placed on a rocker plate overnight at room temperature. The following morning the staining solution was removed and the embryos were washed three times in 1× PBS and then fixed in 4% PFA for long-term storage. Images of embryos were collected with an Olympus DP74 camera mounted on an Olympus MVX10 microscope using the Olympus cellSens Standard 2.1 software.Embryos were collected in ice-cold 1× PBS in a 12-well plate. They were then fixed for 5 min at room temperature in freshly prepared 4% PFA with gentle shaking on a rocker plate. After fixing they were washed three times in washing solution  at 1:1000 for 10 min. Hybridizations were performed at 69 °C and post-hybridization washes were performed at 65 °C. Staining was performed with BM-Purple (Roche 11442074001). All WISH were performed on at least three biological replicates. Images of embryos were collected with an Olympus DP74 camera mounted on an Olympus MVX10 microscope using the Olympus cellSens Standard 2.1 software.Embryos were collected at E12.5 and processed following a previously reported WISH proceduret tests were produced in DataGraph 4.6.1.Embryos were isolated from the uterus and placed into 1× DEPC-PBS on ice. The yolk sacs were collected for genotyping. The embryos were transferred into fresh 1× DEPC-PBS and the distal limb portion was excised, placed into RNALater (ThermoFisher AM7020), and stored at −80 °C until processing. Batches of samples were processed in parallel to collect RNA with Qiagen RNEasy extraction kits (Qiagen 74034). After isolating total RNA, first strand cDNA was produced with SuperScript III VILO (ThermoFischer 11754-050) using approximately 500 ng of total RNA input. cDNA was amplified with Promega GoTaq 2× SYBR Mix and quantified on a BioRad CFX96 Real Time System. Expression levels were determined by dCt (GOI–Tbp) and normalized to one for each condition by dividing each dCT by the mean dCT for each wild-type set. Supplementary Data 55 using a final concentration of 0.02% digitonin (Apollo APOBID3301). Cells were incubated with 0.5 μg/100 μl of anti-HOXD13 antibody (Abcam ab19866), 0.5 μg/100 μl of anti-HOXA13 (Abcam Ab106503) in Digitonin Wash Buffer at 4 °C. The pA-MNase was kindly provided by the Henikoff lab (Batch #6) and added at 0.5 μl/100 μl in Digitonin Wash Buffer. Cells were digested in Low Calcium Buffer and released for 30 min at 37 °C. Sequencing libraries were prepared with KAPA HyperPrep reagents (07962347001) with 2.5ul of adapters at 0.3uM and ligated for 1 h at 20 °C. The DNA was amplified for 14 cycles. Post-amplified DNA was cleaned and size selected using 1:1 ratio of DNA:Ampure SPRI beads (A63881) followed by an additional 1:1 wash and size selection with HXB. HXB is equal parts 40% PEG8000 (Fisher FIBBP233) and 5 M NaCl.Embryos were collected in ice-cold 1× PBS and yolk sacs were processed according to the rapid genotyping protocol described above. Embryos with the correct genotype were transferred to fresh PBS and dissected. The dissected tissue samples were transferred into 1× PBS containing 10% FCS and then digested with collagenase (see ATAC-Seq protocol below). For the HOXD13 and HOXA13 CUT&RUN, pools of cells from individual embryos were processed. All samples were processed according to the CUT&RUN protocol26, mapped either on mm10 or on the II1TDOM-542 mutant genome. The E11.5 whole-forelimb HOXA13 and HOXD13 ChIP-Seq datasets (SRR3498934 of GSM2151013 and SRR3498935 of GSM2151014) as well as E12.5 distal and proximal forelimb H3K27Ac and CTCF ChIP-Seq datasets  were processed similarly to what has been previously published56. Adapter sequences and bad quality bases were removed with Cutadapt57 version 1.16 with options -a GATCGGAAGAGCACACGTCTGAACTCCAGTCAC -A GATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT -q 30 -m 15 (−A being used only in PE data sets). Reads were mapped with bowtie58 2.4.1 with default parameters on mm10. Alignments with a mapping quality below 30, as well as discordant pairs for PE datasets, were discarded with samtools view version 1.860. Coverage and peak calling were computed by macs261 version 2.1.1.20160309 with options --bdg --call-summits --gsize ‘1870000000’, and -f BAMPE for PE. The HOX13 motifs where identified with findMotifsGenome.pl from the Homer tool suite31 using the narrowPeak of HOXA13 and HOXD13 ChIP as well as the third replicate of the HOXA13 and HOXD13 CUT and RUN with the option -size 50. The best motif of each of these four datasets was used to scan the sequence of the II1 enhancer. Four motifs were identified, for the three displayed in Fig. CUT and RUN libraries were sequenced paired-end on a HiSeq4000 sequencer and processed as ing for 5 min. The supernatant was removed and cells were gently resuspended in ice-cold 1× PBS. The cells in each sample were counted with a Countess (ThermoFisher) using Trypan Blue and checked for viability >90%. The volume needed to contain 50,000 cells was determined and that volume was transferred to a new tube and centrifuged at 4 °C at 500 × g for 5 min. The supernatant was removed and the cell pellet was gently resuspended in lysis buffer then immediately centrifuged at 500 × g for 10 min at 4 °C. The lysis buffer was removed and the cell pellet was gently resuspended in 50 μl tagmentation mix (Nextera FC-121-1030) and then incubated at 37° in a thermomixer at 300 rpm for 30 min. The samples were then mixed with Buffer QG from the Qiagen MinElute PCR Purification kit (28004) and processed according to that protocol, and then eluted from the column in 11ul EB. Samples were stored at −20 °C until ready for library preparation. For library preparations, the samples were amplified with Nextera Index Primers (FC-121-1011) using NEBNext High-Fidelity 2× PCR Master Mix (M0541) and cycled 11 times. After PCR the reactions were cleaned first with the Qiagen MinElute PCR Purification Kit and then with AMPure XP beads (A63881) at a ratio of 1.8:1.0 followed by elution with 15 μl EB.Samples used for ATAC-Seq were processed following the original protocol. Cells were collected in 1× PBS on ice and yolk sacs were collected for each sample. Embryos were rapidly genotyped (see above) and those with the correct genotype were transferred to fresh 1× PBS and dissected. Tissue samples were transferred into 300ul 1× PBS containing 10% FCS on ice until ready for processing. To each sample, 8ul of collagenase  was added and tubes were placed in a Thermomixer at 37 °C with shaking at 900 rpm for approximately 5 min or until the samples were completely disaggregated. The samples were then placed into a centrifuge at 4 °C and centrifuged at 500 × 57 version 1.16 with options -a CTGTCTCTTATACACATCTCCGAGCCCACGAGAC -A CTGTCTCTTATACACATCTGACGCTGCCGACGA -q 30 -m 15. Reads were mapped with bowtie62 2.4.1 with parameters -I 0 -X 1000 --fr --dovetail --very-sensitive on mm10 or on the II1TDOM-542 mutant genome. Alignments with a mapping quality below 30, discordant pairs, and reads mapping to the mitochondria, were discarded with bamtools version 2.4.1 [https://github.com/pezmaster31/bamtools]. PCR duplicates were removed with [http://broadinstitute.github.io/picard/index.html] version 2.18.2 before the BAM to BED conversion with bedtools63 version 2.30.0. Coverage and peak calling were computed by macs261 version 2.1.1.20160309 with options --format BED --gsize 1870000000 --call-summits --keep-dup all --bdg --nomodel --extsize 200 --shift -100.Adapter sequences and bad quality bases were removed with Cutadapt64. Sequenced DNA fragments were processed as previously reported but the mapping was performed on a mutant genome reconstructed from minion and Sanger sequencing (see below). A custom R (www.r-project.org) script based on the SeqinR package65 was used to construct a FASTA file for the mutant chromosome 2 from the wild-type sequence and the exact position and sequence of breakpoints.Samples used in the Capture Hi-C were identified by PCR screening embryos at E12.5 as described above. Collagenase-treated samples were cross-linked with 1% formaldehyde (ThermoFisher 28908) for 10 min at room temperature and stored at −80 °C until further processing. The SureSelectXT RNA probe design used for capturing DNA was done using the SureDesign online tool by Agilent. Probes cover the region mm9 chr2:72240000–76840000 producing 2× coverage, with moderately stringent masking and balanced boosting. Capture and Hi-C were performed as previously reported68 version 3.6. Heatmaps were plotted using version 3.7 of pyGenomeTracks69.Subtraction of matrices was performed with HiCExplorerXhoI site and LoxP sequence followed by sequence to the II1 enhancer. The 002 primer contains at its 5′ end a HindIII site. This PCR product was gel purified with Qiagen Gel Extraction Kit (28704). The PCR fragment and the pSKlacZ reporter construct were digested with XhoI and HindIII and ligated together with Promega 2× Rapid Ligation kit (C6711) to produce pSK-II1-LoxP-LacZ construct. This vector (15 μg) was cut with XbaI and XhoI to release the enhancer-reporter construct. The digest was separated on a 0.7% agarose gel for 90 min. The 4190 bp fragment was excised from the gel and purified with Qiagen Gel Extraction Kit (28704) and eluted in 30 μl EB followed by phenol-chloroform extraction and ethanol precipitation and then the pellet was dissolved in 30 μl TE . DNA was injected at 3 ng/μl into pronuclei. Five founder animals were identified carrying the transgene by PCR. Four male founders with the transgene were put into cross with wild-type females and embryos were collected at E12.5 to test for LacZ staining. All four male founder lines  produced distal limb staining but LacZ/40 was chosen for amplification of the breeding line due to high transmission of the transgene. For the assembly of the II1 Gfp TgN we used the same construction of the transgene as used for the II1 LacZ except that we cloned the GFP coding sequence from pCS2+:Gfp and inserted it using the NcoI and XbaI corresponding restriction sites between II1:HBB and the SV40 poly A signal.The II1 enhancer sequence (mm10 chr2:74075305-74075850) was amplified from the fosmid clone WI1-109P4 using primers 001 and 002 . The DNA was quantified by Qubit dsDNA and diluted to 5 ng/μl with IDTE (11-05-01-05). Wild-type fertilized eggs were injected with the construct and the supercoiled pX330 (Addgene #42230) expression vector containing the sgRNA sequence (r4g9: mm10 chr2:75269597–75269616). Animals were genotyped  were attached . The cloning vector was linearized with 29. Yolk sacs were isolated from embryos containing the II1 T-DOM transgene and digested with Tail Digestion Buffer (see above) and Proteinase K overnight at 55 °C with no shaking. The following day the samples were incubated at 95 °C for 10 min to inactivate the Proteinase K followed by ethanol precipitation and eluted in 200 μl of 10 mM Tris pH7.5. CRISPR guides . Two master mixes of guide RNAs and Cas9 protein (1081059) were prepared  following the nCATS protocol with minor changes71 version 2.15 with parameter -ax map-ont. Only primary alignments were kept with samtools view version 1.1060 and reads mapping to II1 (mm10:chr2:74073413-74076528) or to the insertion region (mm10:chr2:75262998–75286118) were further analyzed. Read sequences were compared to the wild-type genome, the expected mutant genome as well as the sequence of the cloning vector using a Perl script as in ref. 72 with the following modification: 20 bp of the MinION reads were tested against the reference for 5 bp-sliding windows and only 20-mers completely identical to unique 20-mers in the reference were kept. The output was then processed in R (www.r-project.org) to display dot plots. The in-depth analysis of reads for allele 320 allowed to propose a configuration that would match all reads containing in total four times the II1-LacZ construct.Bases were called from the fast5 files using Guppy base-caller (Oxford Nanopore Technologies) for CPU version 5.0.16 + b9fcd7b. Reads were mapped on mm10 with minimap2BglII and AgeI restriction sites at the 5′ and 3′ ends respectively. The mutant enhancer sequences (Del 2 × 13 and Del 3 × 13) were restriction digested along with pSKlacZ and ligated together with Promega 2X Rapid Ligation kit to produce pSK-II1Del2X13lacZ and pSK-II1Del3X13lacZ. The enhancer-reporter fragments were released from the vector with BglII and XhoI and purified as above. Pro-nuclear injections were performed by the transgenic platform of the University of Geneva, medical school (CMU). Embryos were collected at approximately E12.5 and stained for LacZ.The enhancer constructs with mutations Fig.  in the tHBB:LacZ transgene was PCR amplified and Sanger sequenced to identify transgenic embryos containing the mutagenized enhancer element. Embryos that were mosaic for the mutation were not included in this analysis. The embryos were LacZ stained (see above) at 37 °C for 16 h, washed in PBS and post-fixed, then stored in 70% ethanol for photographing.Guide sequences were selected from the UCSC mm10 genome browser track CRISPR/Cas9 -NGG Targets. The crRNA Alt-R guides were synthesized by IDT. Males homozygous for the II1 T-DOM 542 allele were crossed with super-ovulated wild type females (BL6XCBA-F1) and fertilized eggs were collected. The embryos were electroporated with CRISPR guides (12 μg of each guide) and TrueCut Cas9 v2 protein (Thermo Fisher A36497) with a NEPA21  and then reimplanted into surrogate females. Embryos were collected at E12.5. Yolk sacs were digested and the II1:The same methodology was used for this experiment as in the Del II1 T-DOM TFBS experiment above. The sequences for CRISPR guides used in this experiment are listed in Supplementary Data 74. The guide sequence  morula-stage embryos and let developed until blastula prior to transfer into CD1 foster females by the transgenic mouse platform at the University of Geneva Medical School76.The two targeted insertions of control transgenes into T-DOM, genetic editing, and cellular culture were performed as previously reported (Supplementary Fig. Hoxa13 and Hoxd13 were performed on three wild-type embryos for each probe. The II1 Gfp embryos in Supplementary Fig. n = 13, n = 16 for g2 + g6 embryos, and n = 6 embryos containing the large deletion Del C-T g2 + g6. See images in Supplementary Fig. n = 6, n = 5, n = 5, and n = 6 for each confirmed genotype. Capture Hi-C experiments in Fig. ATAC-Seq and HOXA13 and HOXD13 CUT&RUN experiments in Fig. Further information on research design is available in the Supplementary InformationPeer Review FileDescription of additional Supplementary FileSupplementary Dataset 1Supplementary Dataset 2Reporting Summary


Gene expression profiling has identified numerous processes altered in aging, but how these changes arise is largely unknown. Here we combined nascent RNA sequencing and RNA polymerase II chromatin immunoprecipitation followed by sequencing to elucidate the underlying mechanisms triggering gene expression changes in wild-type aged mice. We found that in 2-year-old liver, 40% of elongating RNA polymerases are stalled, lowering productive transcription and skewing transcriptional output in a gene-length-dependent fashion. We demonstrate that this transcriptional stress is caused by endogenous DNA damage and explains the majority of gene expression changes in aging in most mainly postmitotic organs, specifically affecting aging hallmark pathways such as nutrient sensing, autophagy, proteostasis, energy metabolism, immune function and cellular stress resilience. Age-related transcriptional stress is evolutionary conserved from nematodes to humans. Thus, accumulation of stochastic endogenous DNA damage during aging deteriorates basal transcription, which establishes the age-related transcriptome and causes dysfunction of key aging hallmark pathways, disclosing how DNA damage functionally underlies major aspects of normal aging. In aging mouse livers, 40% of elongating RNA polymerases are stalled, biasing transcriptional output dependent on gene length. This transcriptional stress appears to be caused by endogenous DNA damage. Aged rat and fruit fly brains produce fewer messenger RNAs3 and cell-to-cell variation in transcription is increased in several tissues6, while gene-to-gene transcriptional coordination is decreased in aging7. However, transcription in aging is mainly studied in relation to gene expression changes. Transcriptomics significantly contributed to the identification of numerous cellular pathways and processes affected in aging10. Although part of age-related, organ-specific gene expression changes can be explained by transcription factors, microRNAs12, altered cell type composition13 and epigenetic changes15, a recent transcriptomics meta-analysis indicated that most gene expression similarities between aged mouse organs could not be attributed to these known regulatory mechanisms8.Aging is characterized by progressive molecular, cellular and physiological decline resulting in reduced vitality, age-related diseases and increased mortality. Because many processes decline or are altered with age17 and the aforementioned transcriptional phenotypes19, mainly based on similarities to cells exposed to DNA-damaging agents or premature aging DNA repair disorders such as Cockayne syndrome and trichothiodystrophy. These conditions have defects in transcription-coupled repair (TCR), which leads to stalled RNA polymerases on DNA lesions20, suggesting that transcription-blocking DNA damage could also be involved in normal aging. Although endogenous transcription-blocking DNA lesions accumulate in normal aging25, it is currently not clear whether they elicit significant transcriptional responses. In this study, we analyzed the basal transcription underlying gene expression changes in normal wild-type (WT) aged mice using an in vivo nascent RNA sequencing method combined with RNA polymerase II (RNAPII) chromatin immunoprecipitation followed by sequencing (ChIP–seq) and confocal imaging. We reveal a strong age-related transcriptional decline and skewing of transcriptional output by accumulating DNA damage as a general aging phenotype, causing age-related transcription changes in general, particularly affecting life span-determining aging hallmark pathways.DNA damage accumulation has been postulated as an underlying cause of normal agingn = 3 per group) received a single intraperitoneal injection with ethynyl-uridine (EU), a uridine analog that is incorporated into newly synthesized RNA in vivo26. Five hours after injection, fluorescence staining of EU revealed a 1.5-fold-reduced EU signal in old livers  and aged (2 years) WT male mice , which resulted in a significant higher proportion of intronic reads compared to total RNA sequencing Fig. . Controln = 3,970) that represent >90% of RNAPII-dependent nascent RNA production were split into 3 equal bins from its TSS to the transcription termination site (TTS) and corresponding reads from nascent RNA and of RNAPII ChIP–seq mapped in each bin were compared between old and adult liver. Using clustering analysis, we identified genes that were transcriptionally upregulated or downregulated in aging over all bins both in nascent RNA and RNAPII ChIP–seq  or downregulated (n = 394) genes are biologically relevant for aging, we used the Enrichr tool for gene set enrichment analysis (GSEA)28 to compare these gene signatures with the published aging perturbation database containing 34 mouse liver and 15 rat liver mRNA expression profiles. The transcriptionally upregulated and downregulated gene signatures closely resembled the published rodent liver aging profiles  were indeed on average significantly longer compared to all expressed genes or transcriptionally upregulated or downregulated genes  in nucleotide content across gene bodies, transcriptional error rate, alternative splicing, chromatin accessibility, histone modifications associated with euchromatin or DNA methylation patterns, which would point to epigenetic changes being responsible  light, which induces known quantities of transcription-blocking DNA lesions37. EU staining and EU-seq demonstrated a dose-dependent transcriptional decline, in which 2 J m−2 UVC, which corresponds to approximately 1.6 transcription-blocking lesions per 100 kb DNA37, induced transcription levels 24 h after UV exposure similar to the livers of WT 2-year-old mice  from gen Fig. . MDFs cuice Fig. . These d38. First, we confirmed that UV-induced DNA damage leads to a coding strand bias in our ChIP–seq protocol, which disappeared after time for repair  gene, a >265-kb long gene frequently downregulated in aged livers across numerous independent studies40, in Xpg−/− and Ercc1Δ/− mutant mice34 and in cell cultures exposed to UV light41. Ghr demonstrates a clear GLPT and increased RNAPII abundance across the gene body. We also noticed a 20% shift in reads toward the coding strand in aged livers  strand. This should lead to a strand amplification bias in favor of the coding strand that can be visualized by strand-specific ChIP–seq as shown for UV-induced transcription-blocking lesionsers Fig. , indicaters Fig. , therebyIgf1 gene , representing genes with high transcriptional stress levels (TShigh), for functional examination. Notably, we found a highly significant overlap with the overall profiles of six independent studies representing downregulated mRNAs after UVC-induced DNA damage  in the aging perturbation library using the Enrichr tool for GSEA. For comparison, transcriptionally upregulated and downregulated gene sets were also included. We found a significant presence of TShigh genes in 65% of all aging datasets -corrected aggregated set Fig. . In fact38, we estimate that an initial RNAPII stalled on a lesion will block approximately three subsequent RNAPII complexes causing queuing. Underlying mechanisms responsible for age-related gene expression changes have been largely elusive and often thought to result from active regulatory mechanisms such as promoter regulation8, also in DNA repair mutant premature aging mouse models17. However, we suggest that passive transcriptional stress by DNA damage in combination with gene architecture, that is, gene length, accounts for a substantial fraction of these changes.This study provides evidence that transcription-blocking DNA damage during normal aging causes frequent genome-wide elongating RNAPII stalling, which leads to reduced, gene-length-dependent transcriptional output resulting in dysregulation of many pathways known to affect aging Fig. . Based o53, of which most are quickly repaired by dedicated repair processes. DNA damage as cause of aging is to a large extent based on premature aging syndromes with underlying genome instability, such as TCR-defective Cockayne syndrome and trichothiodystrophy, which exhibit short life span and many premature aging features predominantly in postmitotic tissues. Corresponding mouse models54 display mRNA transcriptomes that significantly overlap with aged WT mice19. We now show that passive transcriptional stress, instead of active gene regulation, is responsible in shaping the aging transcriptome. Although we did not rule out all putative reasons explaining the observed transcriptional stress phenotype, we identified a bias toward the nontranscribed strand in RNAPII ChIP–seq data from WT aged livers, indicating transcription-blocking DNA damage in template strands as the most likely cause for RNAPII stalling. Although DNA damage accumulates during aging, it was hitherto unclear whether these levels are sufficient to elicit aging responses in WT organisms. Candidate endogenous transcription-blocking lesions, aldehydes21, advanced glycation end products22 and cyclopurines25, can accumulate in aged organs to sufficient levels to explain the observed transcriptional stress. Thus, spontaneous, endogenous DNA damage accumulation, similar to progeroid Cockayne syndrome and trichothiodystrophy, causes transcriptional stress in normal aging.Each cell may suffer up to 100,000 DNA lesions per day6. Transcriptional stress could further impact cellular functioning by promoting loss of protein complex stoichiometry, a phenotype seen in aged killifish55 and C. elegans56. Also, imbalanced expression of large and small genes due to transcription-blocking lesions in cell cultures can induce cell death and has been proposed to be a premature aging signal in Cockayne syndrome57. Finally, RNAPII stalling itself is also a direct cue for aging. Genetic dissection of TCR and corresponding hereditary syndromes indicates that molecular consequences of TCR mutations correlate with the severity of premature aging20. TCR defects that permanently stall RNAPII on DNA lesions lead to more severe forms of accelerated aging than repair defects that still permit accessibility of the lesion for other repair pathways20. This was further proven in mutant mice with a point mutation in RNAPII that abolishes the DNA damage-induced ubiquitination required to remove stalled RNAPII from a DNA lesion, which exhibited reduced life span and premature aging38. RNAPII stalled on a DNA lesion leads to R-loop formation and activation of DNA damage checkpoint ATM42, which can induce cell death or senescence58, thereby providing a mechanism for how RNAPII stalling leads to aging. R-loops increase in the eyes of aged fruit flies, predominantly in long genes59, providing further proof for this scenario. We show that approximately 40% of all elongating RNAPII complexes are stalled by DNA damage in WT aged livers; thus, the identical aging signal that causes progeroid syndromes also occurs in normal aging. Interestingly, brains from patients with Alzheimer’s disease also displayed reduced expression of long genes compared to age-matched controls60, suggesting that the magnitude of transcriptional stress is involved in age-related disease etiology. Conversely, longevity-promoting intervention dietary restriction restores the loss of long gene expression29, indicating that longevity interventions can alleviate transcriptional stress. In conclusion, we propose that endogenous DNA lesion accumulation with age triggers transcriptional stress that shapes age-related gene expression profiles in many organs and tissues, is present over wide evolutionary distances and can explain how accumulating DNA damage causes functional decline, thereby strengthening the primary role for DNA damage in the aging process17.Our data indicate how DNA damage causes aging via transcriptional stress. Transcriptional stress largely determines aging expression profiles in multiple organs impacting organ function and particularly causes the dysfunction of many aging hallmark pathways. Additionally, the stochastic nature of DNA lesions may explain transcriptional noise, which increases in agingMus musculus) in F1 C57BL6J/FVB (1:1) hybrid background, were euthanized at 15 weeks and 104 weeks of age. WT C57BL6J and FVB strains are frequently obtained from the Jackson Laboratories to maintain a standard genetic background. DNA repair-deficient premature aging mouse models that were generated in house and WT littermates in F1 C57BL6J/FVB (1:1) hybrid background were euthanized at 4 and 10 weeks for Ercc1Δ/− mutants35; and 7 and 14 weeks for Xpg−/− mutants34. All animals were bred and maintained on AIN93G synthetic pellets . Animals were maintained in a controlled environment  and were individually housed in individual ventilated cages under specific pathogen-free conditions at the Animal Resource Center . No statistical methods were used to predetermine sample sizes but our sample sizes are similar to those reported in previous publications41. Data collection and analysis were not performed blind to the conditions of the experiments and randomization of animals to experimental groups was not applicable. No animals were excluded from the experimental groups in any analysis.Mouse housing and experiments were performed according to the Animal Welfare Act of the Dutch government, following the Guide for the Care and Use of Laboratory Animals and with the guidelines approved by the Dutch Ethical Committee in full accordance with European legislation. The institutional ethical committee for animal care and usage approved all animal protocols. WT male mice  MDFs were isolated from the tails of the corresponding mouse models and cultured in DMEM supplemented with 10% FCS and 1% PS at 5% CO2 and 3% O2.To assess endogenous DNA damage-induced de novo RNA synthesis, Mice were injected intraperitoneally with 5-EU (AXXORA) 0.088 mg per gram of body weight. Five hours after intraperitoneal injection, mice were euthanized. Tissue samples were formalin-fixed for fluorescence staining or snap-frozen for the RNA isolation and ChIP experiments.Slices measuring 3–5 µm were cut from paraffin-embedded, formalin-fixed liver pieces. Slices were mounted on microscope slides . For RNAPII staining, samples were deparaffinized with xylene, rehydrated with an alcohol gradient and washed with Milli-Q water before antigen retrieval . The antibodies used were: Alexa Fluor 594-RPB1 antibody (in 1:250 dilution), recognizing all forms of RNAPII independently of the phosphorylation status of their CTD ; RNAPII-ser2p ; RNAPII-ser5p ; phospho-ATM ; and phospho-histone H2A.X  all in 1:500 dilution. To reduce the background fluorescence level, a mouse-on-mouse detection kit was used . Sections were counterstained using DAPI or Hoechst 33342.61. Statistical significance was calculated from normalized fluorescence intensity values using an unpaired Student’s t-test in Prism version 7.04 (GraphPad Software). For EU-labeled nascent RNA staining in vitro, cells were grown on coverslips. In confluent growth dishes, medium was replaced with fresh medium supplemented with 1% FCS and (when indicated) moved to 20% O2 for the indicated time. Medium was renewed twice a week. To measure de novo RNA synthesis after UVC treatment, Ercc1∆/− and WT MDFs (both C57BL6J/FVB F1 hybrid background) were cultured to confluency and maintained as described above followed by UVC irradiation  using a 254-nm germicidal lamp (Philips). The assays were performed 24 h later to allow the MDFs to recover from the immediate transcriptional effects in trans. To assess their transcriptional level, 1 mM EU was added to the medium for 1 h before cell collection for total RNA extraction or fixed for fluorescence staining. Cells were washed with ice-cold tris-buffered saline (TBS) and fixed for 20 min on ice in 4% formalin. Subsequently, cells were washed in 3% bovine serum albumin (BSA) in TBS and permeabilized using 0.5% Triton X-100 in TBS for 20 min at room temperature. The coverslips were then washed twice with 3% BSA in TBS and incubated with Click-iT reaction mix (Invitrogen) for 30 min. After the Click-iT reaction, cells were washed once with 3% BSA in TBS and once with TBS before being incubated in TBS containing 1:1,000 Hoechst 33342 for 30 min. Samples were mounted using Prolong Diamond (Invitrogen). Images were obtained with a LSM700 ZEISS Microscope and EU staining intensity in nuclei was quantified with Fiji (Image J 1.53q).After the xylene-based paraffin removal and rehydration steps  we used the Click-iT RNA Alexa Fluor 488 Imaging Kit  according to the standard immunofluorescence protocol. Images were taken by a ZEISS LSM 700 system. Nascent RNA staining intensity was quantified by calculating the integrated density values for each nuclear staining using the Fiji software62. To selectively isolate EU-labeled nascent RNA, we used the Click-iT nascent RNA Capture Kit : biotin azide was attached to the ethylene groups of the EU-labeled RNA using Click-iT chemistry. The EU-labeled nascent RNA was purified using MyOne Streptavidin T1 magnetic beads. Captured EU-RNA attached on streptavidin beads was immediately subjected to on-bead sequencing library generation using the TruSeq mRNA Sample Preparation Kit v2 (Illumina) according to the manufacturer’s protocols with modifications. The first steps of the protocol were skipped; directly on-bead complementary DNA (cDNA) was synthesized by reverse transcriptase (Super-Script II) using random hexamer primers. The cDNA fragments were then blunt-ended through an end-repair reaction, followed by dA-tailing. Subsequently, specific double-stranded barcoded adapters were ligated and library amplification for 15 cycles was performed. PCR libraries were cleaned up, measured on an Agilent Bioanalyzer using the DNA1000 assay, pooled at equal concentrations and sequenced per three in one lane on a HiSeq 2500.Total RNA was isolated from snap-frozen liver and kidney slices or scraped cells using the miRNeasy kit (QIAGEN) including the on-column DNase step . RNA quality and quantity were estimated with the Bioanalyzer (Agilent Technologies) and only high-quality RNA (RNA integrity number >8) was used for further analyses. Total RNA sequencing was performed as described elsewhereSnap-frozen liver was minced in ice-cold PBS, homogenized in a Dounce homogenizer and filtered through a cell strainer . After adding formaldehyde , the homogenate was shaken on ice for 10 min and quenched with glycine. Pelleted homogenate was washed with ice-cold PBS, resuspended in cell lysis buffer  and incubated for 10 min on ice. Samples were centrifuged and resuspended in nuclei lysis buffer . Samples were further homogenized by Dounce homogenizer and incubated on ice for 10 min. The nuclear fraction was resuspended in sonication buffer . The chromatin was sonicated with a Bioruptor (Diagenode) sonicator into 100–500 bp fragments and centrifuged to remove any remaining cell debris. From the supernatant, 15 µg chromatin was used for one round of immunoprecipitation. For ChIP–seq, five samples were pooled. Dynabeads M-280 Sheep Anti-Rabbit IgG beads were used for the immunoprecipitation step. Chromatin samples were precleared with beads at 4 °C, for 2 h. The precleared chromatin samples were rotated overnight at 4 °C with the RNAPII antibodies: RNAPII-ser2p, RNAPII-ser5p or RNAPII RPB1-NTD-specific antibody . Dynabeads were added for 2 h to the samples to pull down protein–DNA complexes. After immunoprecipitation, samples were washed twice with the following buffers: sonication buffer, twice with buffer A , twice with buffer B  and finally twice with Tris-EDTA buffer . The bound fraction of the chromatin was isolated using the IPURE DNA recovery for ChIP Kit (Diagenode). Sequencing libraries were generated using the Illumina TruSeq ChIP Library Preparation Kit. Samples were sequenced on the HiSeq 4000 platform.63) using the parameters: SLIDINGWINDOW:4:15 LEADING:3 TRAILING:3 ILLUMINACLIP:adapter.fa:2:30:10 LEADING:3 TRAILING:3 MINLEN:36. The remaining reads were successively aligned to the mouse ribosomal DNA (BK000964.3), mitochondrial sequences  and mouse reference genome (GRCm38/mm10) using Tophat2 v.2.0.9 (ref. 64) with default settings except for the -g 1 option. ChIP–seq reads were aligned to the mm10 mouse reference genome using Bowtie65 v.2.1.0. The public total RNA-seq dataset applied the same mapping algorithm with EU-seq using the corresponding reference genome (hg19 and ce10) to study the nascent RNA dynamics in aging among species. The same mapping algorithm as used with ChIP–seq was applied to the other public data.EU-seq reads were preprocessed with the quality control software FastQC v.0.11.9, FastQScreen v.0.14.0 and Trimmomatic v.0.35  were generated in the same manner. To investigate the productive elongation process per gene, genomic regions around the TSS and TTS of genes were divided into k proportional bins ). Genes with length smaller than 10 kb were removed from the study to avoid too many reads mapping to short genes overlapping bins in the gene proportional elongation analysis.All RefSeq (release: 95) genes, exons and introns were extracted from the UCSC Genome Browserk) and the number of all expressed genes (n) were selected for each EU-seq and public total RNA dataset collection. Therefore, the ‘all expressed genes’ set in WT aging contain: n = 3,970 genes and were divided into k = 20 bins . Ercc1Δ/− mice ; Xpg−/− mice ; UV-treated Ercc1Δ/− MDFs ; WT aging kidney ; human tendon ; C. elegans . To match WT aging EU-seq data with the corresponding RNAPII ChIP–seq data (generated from the same liver), the corresponding genes from the ‘all expressed genes’ gene set were also selected in the RNAPII ChIP–seq datasets. The intra-sample-specific background was determined by calculating the reads in the intergenic regions and proportionally removed. The overall background signal was subtracted using the DNA input samples. To biologically define the ‘all expressed genes’  in WT aging, we performed a k-mean clustering analysis combined with EU-seq and total ChIP–seq reads between adult and old samples. Under the criterion describes in Extended Data Fig. k-mean cluster analysis as four biological groups: promoter-upregulated genes, n = 778 (EU-seq and RNAPII ChIP–seq level increased across three bins); promoter-downregulated genes, n = 394 (EU-seq and RNAPII ChIP–seq level decreased across three bins); GLPThigh genes, n = 914 ; remainder genes, n = 1,884 . To study the relationship between gene length and transcriptional stress phenotype, the expressed genes  in WT mice were divided into six groups according to their length, each containing a similar number of genes: 10–22 kb ; 22–30 kb ; 30–50 kb ; 50–70 kb ; 70–110 kb  and >110 kb . In figures measuring gene class behavior, we first calculated the per gene the average signal from n = 3 mice followed by averaging the signal for all genes in the gene class.A Python pipeline (K_bining.py) was created that takes aligned RNA/EU/ChIP–seq reads in BAM/BW format as input to quantify reads in the transcription elongation region of genes and HTSeq was performed for read quantification in the aforementioned genomic regions. Reads per million (RPM) was applied to normalize different sequencing libraries to exclude technical variation  in further studies. The ‘all genes’ gene set comprises all genes with at least one read mapping in the first kilobase. A gene set with genes that have at least 1 RPM in each of the 20 bins was constructed and termed ‘all expressed genes’. To study read distribution across gene bodies and due to sequence depth and data quality, different number of bins , Reactome Pathway Databases, Aging Perturbations from GEO and the down datasets from Enrichr68. Datasets in the Aging Perturbations from the GEO and the down datasets from Enrichr were only included if young/adult >8 weeks, old is >14 months and age difference between young/adult and aged organs is >6 months ; human old is >56 years with at least an age difference of >12 years. We adopted a threshold FDR < 0.05. Aggregated P values for the main identified biological processes were calculated by combining the P values of the corresponding detected subpathways using Fisher’s exact test.Gene ontology and functional clustering analyses of TSk proportional bins, where mean read counts from the EU-seq and RNAPII ChIP–seq dataset was calculated. Counts were subsequently normalized to the first bin and plotted. The percentage density changes per bin were calculated by: old(readcount) / adult(readcount) × 100%. Since the first and last bin includes the signal at the TSS in which RNAPII promoter proximal pausing is present, and the TTS in which RNAPII accumulates, we defined the middle 18 bins as the transcription elongation phase. The 3-bin heatmap is derived from the 18-bin data by aggregating 6 subsequent bins and calculating log2 fold changes of every gene between old and adult samples for both EU-seq and total RNAPII ChIP–seq reads. To determine the percentage increase RNAPII stalling or unproductive RNAPII in aged livers . Subsequently, the observed total RNAPII ChIP–seq read count per sample was determined in each aged liver sample. Then, we subtracted the expected total RNAPII ChIP–seq read count from the observed total RNAPII ChIP–seq read count and divided this by the expected read count in aged liver: RNAPII stalling in aging (%) = (observed RNAPII read count − expected RNAPII read count) / expected RNAPII read count × 100%. To analyze nucleotide composition, the top 50 genes from GLPThigh and bottom 50 from the remainder genes were selected within the length range of 70–110 kb and were divided into 35 bins from TSS to TSS + 70 kb (2 kb per bin). The nucleotide composition percentage  was determined by Qualimap v.2.21 (ref. 69). The transcription error ratio in the EU-seq datasets was calculated using BioConductor seqTools (R v.1.2.0. and IRanges packages)70. Total error rates were calculated as the percentage of total reads with a mismatched base at each read position during the alignment step. Analysis of EU-seq read abundance at splicing donor and acceptor sites was carried out using a custom-written script: Splicingdonor&acceptorfinder.py in which the expression values from ±49 bp around the splicing donor and acceptor site for all selected genes were captured by the HTSeq v.0.6.0. Alternative splicing events were detected by Astalavista v.4.0 (ref. 71) with default settings. DNA methylation status was detected by Qualimap v.2.21 and deepTools v.2.0 (ref. 72). Average RNAPII profiles at promoters (±750 bp around the TSS) and average histone modification profiles  at the TSS and gene bodies were plotted using HOMER v.4.11 software (annotatePeak.pl command)73.The RNAPII travel ratio is the ratio between RNAPII on the TSS and RNAPII in the first 1-kb gene body in old and adult. The total RNAPII density value around the TSS (±300 bp) was divided by the total RNAPII density value on the first 1-kb gene body measured from 300 bp downstream of TSS for each gene. To compare nascent RNA synthesis from the first 1-kb region of the first introns or from the TSS to 1 kb downstream of all genes between adult and old mouse livers, all datasets were only normalized based on sequencing read depth, not correcting for the approximately 1.5-fold reduced number of intronic sequences in aged liver. To measure productive transcription elongation, all expressed genes were divided into 38, which is based on the observation that PCR amplification of RNAPII ChIP–seq libraries is biased toward the coding strand if there is a transcription-blocking DNA lesion in the template strand on which RNAPII is stalled, with some modifications. We first monitored whether our specific ChIP–seq protocol could detect strand bias and optimized the analysis by using our previously published RNAPII ChIP–seq data after UVB treatment74. In short, forward and reverse reads from RNAPII ChIP–seq were separated and processed by SAMtools v.1.9 (ref. 75) and counted by BEDtools v.2.27.1 (ref. 76). For each gene in the selected gene set, we first corrected for the orientation of the template strand (forward/reverse strand) because genes are located on both the forward and reverse strand. Then, we calculated for each gene in the dataset the fraction bias toward the coding strand and subsequently the strand bias was calculated across all expressed genes in the gene set.To calculate the number of RNAPII stalled on a lesion compared to queuing behind the initially stalled RNAPII, we first estimated the number of DNA lesions in our expressed genes dataset, which has a total length of 280,010,046 bp. With a lesion density of 1.6 per 100,000 bp in a diploid genome, we expect approximately 8,960 DNA lesions. Since DNA lesions are equally occurring on both the coding and template strands and the latter is only important for RNAPII stalling, there are 4,480 DNA lesions in the template strand of the selected gene set. If we assume that all DNA lesions are obstructing an RNAPII complex and we have an estimated 18,000 stalled RNAPII complexes per cell, we estimate that for every RNAPII stalled on a DNA lesion three RNAPII complexes are queuing. The strand bias analysis in the RNAPII ChIP–seq data was done as described elsewhereIn vitro experiments are based on triplicates of independent experiments and the plots are presented as the means, unless otherwise indicated. Details of the statistical tests and quantifications used in this study are described in the corresponding parts of the main text, figure legends or X(t) is a Poisson process then the probability that there is no event in a time interval  reads exp(-λt) where λ is the intensity of the Poisson process. Equally, the probability that there is at least one event in the time interval  is thus 1 − exp(-λt). For each RNA species in our specified RNA length classes identified in the EU-seq datasets, the probability that at least one EU has been incorporated was subsequently computed using the formula above. Clearly, since x < y, Poisson processes with higher intensity will necessarily exhibit a larger probability that at least one EU has been incorporated than Poisson processes of lower intensity. Three groups of RNA species were examined: (1) ≤300 nucleotides ; (2) between 1,000 and 3,000 nucleotides ; and (3) between 2,000 and 4,000 nucleotides . The number of RNA species reflect the total number present in the Mus musculus genome database (Ensembl). The latter two classes, although still representing short RNA species, are incorporated as a positive control in which a difference, if there is 1.5-fold less EU available, is not expected. In all cases, the probability vectors were not Gaussian as calculated by Kolmogorov–Smirnov test; thus, for each fixed intensity of the underlying Poisson process, the median and interquartile range (IQR) for the probability that at least one EU is incorporated are calculated. Significance between 1.5-fold-apart intensities was calculated by the Mann–Whitney U-test.A statistical and probabilistic framework was generated for EU incorporation for a range of distances between EU molecules, and in case of a 1.5-fold reduction for a range of EU incorporation distance differences. The probability that at least one EU is incorporated into nascent RNA was modeled in the situation where there is a 1.5-fold reduction in EU incorporation in old mouse livers due to lower or slower uptake or processing. The assumptions were: (1) as there is at least a >400-fold surplus of biotin for every incorporated EU in nascent RNA in the Click-iT reaction, the reaction is saturated or follows the same asymptote; (2) only one EU incorporation per RNA molecule is sufficient to isolate that specific molecule; (3) EU incorporation is a stochastic process in which the concentration of available EU in the total nucleotide pool linearly correlates with the distance between EU molecules in the nascent RNAs. If there is an EU availability difference between adult and old mice, it is expected that in short RNA species (≤300 nucleotides) the probability of at least one EU incorporation is significantly lower and thus we would empirically observe a lower percentage sequence read mapping to such small RNA species in aged liver. The process of EU incorporation was modeled into nascent RNA species by means of a Poisson process. Specifically, one can think of the number of EU incorporations into nascent RNA as a Poisson process not in time, as it is generally used, but in length as measured in nucleotides. Mathematically, if Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-022-01279-6.Reporting SummaryPeer Review FileSupplementary Tableshigh genes and six independent transcriptomics studies with genes downregulated by UV damage. Table related to Fig. 5. Supplementary Table 2. Cellular pathways and processes that are significantly overrepresented in the TShigh category. Table related to Fig. 5.Supplementary Table 1. Overlap between TSSource Data Fig. 1Statistical source data.Source Data Fig. 2Statistical source data.Source Data Fig. 3Statistical source data.Source Data Fig. 4Statistical source data.Source Data Fig. 5Statistical source data.Source Data Fig. 6Statistical source data.Source Data Extended Data Fig. 1Statistical source data.Source Data Extended Data Fig. 2Statistical source data.Source Data Extended Data Fig. 3Statistical source data.Source Data Extended Data Fig. 4Statistical source data.Source Data Extended Data Fig. 5Statistical source data.Source Data Extended Data Fig. 6Statistical source data.Source Data Extended Data Fig. 7Statistical source data.


Surprisingly, each homozygous allele produced fewer RNAs than the corresponding hemizygous allele, suggesting the possibility of allelic competition in homozygotes. However, the competition was not observed when the enhancer-promoter interaction was weakened by placing the reporter construct in a different chromosome location or by moving the enhancer further away from the promoter. Moreover, the reporter gene showed reduced transcriptional activity when a partial transcription unit (either an enhancer or reporter gene only) was in the homologous position. We propose that the transcriptional machinery that binds both the enhancer and promoter regions, such as RNA Pol II or preinitiation complexes, may be responsible for the allelic competition. We showed that the degree of allelic interference increased over developmental time as more Pol II was needed to activate zygotic genes. Such allelic competition was observed for an endogenous gene as well. Our study provides new insights into the role of 3D interallelic interactions in gene regulation.The mechanism by which transcriptional machinery is recruited to enhancers and promoters to regulate gene expression is one of the most challenging and extensively studied questions in modern biology. We explored the possibility that interallelic interactions between two homologous alleles might affect gene regulation. Using an MS2- and PP7-based, allele-specific live imaging assay, we visualized  Drosophila embryos demonstrated that some enhancers work additively with each other, while others work synergistically or competitively, such that multiple enhancers can drive higher or lower transcriptional activity than a single enhancer  at transcription loci facilitate the binding of Bicoid (Bcd) and Dorsal (Dl) activators to the target DNA , Mediators, pre-initiation complexes (PICs), and transcription factors (TFs) form clusters at active transcription loci . It has rget DNA . This idin trans . AltogetDrosophila embryos, we measured the transcriptional activity of a reporter gene in one allele from homozygous and hemizygous embryos. Surprisingly, we found that one homozygous allele produced fewer RNAs than its hemizygous counterpart. This decrease was manifested mainly as a change in transcriptional amplitude, implying that the number of RNA Pol II loaded to each allele was reduced in homozygotes. Interestingly, this allelic competition at the homologous locus was not observed in the absence of strong enhancer-promoter interactions.In this study, we provide evidence that two homologous alleles may compete and affect the level of RNA production. Using allele-specific MS2- and PP7- based live imaging methods in early snail alleles also interfere with each other. Our study provides new insights into a mechanism of transcriptional regulation in 3D environments.To examine which transcriptional machinery might affect interallelic interactions, we measured the transcriptional activity of a reporter gene when the homologous allele contains only an enhancer or a promoter-reporter gene. Unexpectedly, both the “Enhancer Only” and the “Promoter Only” allele on the homologous position were sufficient to decrease the transcriptional activity. This implies that the transcriptional machinery binding to both the enhancer and promoter plays a role in the allelic competition. Based on these results, we propose that homologous alleles may share the same local transcription hub and that each allele produces a reduced number of RNAs when the number of Pol II in the hub is limiting—especially upon strong enhancer-promoter interactions. Indeed, we showed that the competition was observed only in the nuclear cycle 14 (NC14) when massive zygotic genome activation occurs. Lastly, we demonstrated that endogenous Detailed Materials and Methods are in the snail shadow enhancer (snaSE) between hemizygous and homozygous embryos to test the possibility that homologous alleles may interact with each other  or the PP7 coat protein (PCP), fused with GFP or mCherry, respectively . The other 50% have one copy of the yellow reporter gene marked with MS2 stem-loops (hemizygotes)  . To noteMS2-yellow allele in homozygous embryos produced about 25% fewer RNAs than the hemizygous MS2-yellow allele  the timing of transcription initiation, (ii) the duration of active transcription, and (iii) the average amplitude of transcription in each transcriptionally active nucleus. We found that transcription was initiated about 6 min after the onset of NC14 in both hemizygous and homozygous embryos . The durs allele . Indeed, of NC14 .sna expression domain uniformly exhibited lower transcriptional amplitude, or if the boundary nuclei where the concentration of the Dorsal activator is lower showed a greater reduction in amplitude. Similar to what we observed in the single-cell analysis, the level of RNA production decreased, but the overall width and pattern of the sna boundary remained unchanged  and TAFII is limiting  or the promoter-reporter gene cassette without the cis-linked enhancer (“Promoter Only”)  . If the MS2-yellow allele from both the “Enhancer Only” and the “Promoter Only” constructs behaved like the MS2-yellow allele from homozygous constructs, exhibiting reduced transcriptional activity compared to the hemizygotes (>MS2-yellow in the 3rd chromosome (control). All four constructs exhibited similar kinetics of transcriptional activation with a slight delay in activation in the “Promoter Only” construct  and TAFII is limiting in osophila . For exapression . In our Furthermore, the allelic competition observed in the “Enhancer Only” and “Promoter Only” constructs indicates that a common factor that binds to both the enhancer and the promoter may be responsible for the observed allelic interference . Many pasnail alleles interfere with each other (trans may not be affecting the reduced transcriptional activity in homologous alleles, we believe that different allelic displacement between the homozygous and hemizygous alleles could have affected their transcriptional efficiency.We acknowledge that thousands of genes are being transcribed in early embryos, and it is not intuitive to think that one additional transgene can affect the overall balance of TFs, Pol II, and PICs in each nucleus. Yet, others have reported similar phenomena of allelic competition, and we have also demonstrated that endogenous ch other  (Waymackch other , 2021. Wch other , it was ch other . We did MS2-yellow reporter gene, there may exist just a single hub at that specific 3D location that is accessible by the reporter gene.We suggest that the localized clustering of Pol II and TFs in each nucleus allows allelic competition. Many recent papers have reported the presence of “transcription hubs” where TFs, Mediators, Pol II, and PICs form a cluster and the genes within each hub share the transcriptional machinery . AccordiTaken together, we suggest that localized clusters of transcription hubs can limit the number of available molecules that bind to the enhancer and promoter regions, inducing allelic competition for strongly expressed genes. Our study provides additional insight into how the distribution of Pol II clusters in the nucleus and subsequent interallelic competitions can affect enhancer-mediated transcriptional regulation.


Arabidopsis thaliana, GLN1;2 and GLT1 are the GS and GOGAT isoenzymes induced in response to ammonium supply and playing key roles in ammonium utilization. Although recent studies suggest gene regulatory networks involved in transcriptional regulation of ammonium-responsive genes, direct regulatory mechanisms for ammonium-induced expression of GS/GOGAT remain unclear. In this study, we revealed that the expression of GLN1;2 and GLT1 in Arabidopsis is not directly induced by ammonium but is regulated by glutamine or post-glutamine metabolites produced by ammonium assimilation. Previously, we identified a promoter region required for ammonium-responsive expression of GLN1;2. In this study, we further dissected the ammonium-responsive region of the GLN1;2 promoter and also performed a deletion analysis of the GLT1 promoter, which led to the identification of a conserved ammonium-responsive region. Yeast one-hybrid screening using the ammonium-responsive region of the GLN1;2 promoter as a decoy sequence revealed a trihelix family transcription factor DF1 that binds to this region. A putative DF1 binding site was also found in the ammonium-responsive region of the GLT1 promoter.Ammonium absorbed by roots is assimilated into amino acids. The glutamine synthetase/glutamate synthase (glutamine 2-oxoglutarate aminotransferase) (GS/GOGAT) cycle is essential to this biological process. In  The nitGenome sequencing has revealed various isoenzymes of GS/GOGAT in plants. Among them are the isoenzymes expressed in plant roots in response to ammonium supply, such as GS1;2 and NADH-GOGAT1 from rice  and GLN1GS gene expression  as an inhibitor of GS. When MSX was given simultaneously with ammonium, rice NADH-GOGAT1 gene expression was not induced. However, when glutamine was given concurrently with MSX, rice NADH-GOGAT1 gene expression increased. These results suggest that rice NADH-GOGAT1 expression is not directly induced by ammonium, but by glutamine or its post-glutamine metabolites  accession was used for all experiments. Plants were cultured in a growth chamber controlled at 22°C with 60% relative humidity under 12 hours light and 8 hours dark cycle as described previously (-2 s-1. Plants grown for two weeks under sterile conditions on MGRL (Molecular Genetics Research Laboratory) medium containing 7 mM nitrate as the nitrogen source , GLT1 (At5g53460) and GLU2 (At2g41220) promoters were generated as follows. The 5’-intergenic regions upstream of coding sequences of GLN1;2, GLT1 and GLU2 were amplified by polymerase chain reaction (PCR) from genomic DNA of Arabidopsis Col-0 accession and cloned as promoter fragments to generate the GFP reporter fusion constructs. PCR was carried out using KOD plus DNA polymerase  and pairs of forward and reverse oligonucleotide  at the 5’-end. The rest of the forward primers designed for the amplification of GLT1 and GLU2 promoter regions have an overhang of a BamHI site (GGATCC) at the 5’-end. The reverse primers were designed to have the complementary sequences with the 5’-untranslated regions immediately upstream of the translation initiation sites of GLN1;2, GLT1 and GLU2. These reverse primers have an overhang of an NcoI site (CCATGG) at the 5’-end. The ATG in the NcoI site is the translation initiation site for GFP. The amplified PCR products were subcloned into pCR-Blunt II-TOPO , and fully sequenced to confirm the identity. These promoter fragments were then cut out as a HindIII-NcoI fragment  or BamHI-NcoI fragments (for GLT1 and GLU2) and cloned into respective restriction sites of pTH-10KI, replacing the cauliflower mosaic virus (CaMV) 35S promoter, to obtain the promoter:GFP:teminator cassettes. pTH-10KI is the modified version of CaMV35S-synthetic GFP  vector or BamHI-EcoRI fragments (for GLT1 and GLU2) and cloned into pBI101 (Takara Bio Inc.). These binary vector plasmids were introduced into Agrobacterium tumefaciens GV3101 (pMP90) by freeze-thaw method as previously described (The fusion gene constructs with various lengths of cleotide Table 1.T) vector and has escribed . Arabidoescribed . Transgeescribed  containi2.3GFP visualizations in whole plants transformed with the promoter:GFP gene constructs were performed as described previously . Express2.4UBQ2 (At2g36170) as a constitutive internal control.Extraction of total RNA, reverse transcription, and real-time PCR were performed as described previously . Total R2.5GLN1;2 promoter was used as a bait fragment. The bait fragment was cloned into the pAbAi vector (Takara Bio Inc.). The vector was linearized with BbsI and subsequently transferred to Saccharomyces cerevisiae strain Y1H Gold with a LiAc method. Auto-activation was checked by the growth of the bait strain on the medium containing various concentration of Aureobasidin A (AbA). The prey library used in this study was the complete Arabidopsis transcription factor collection  screening was performed using the Matchmaker Gold Yeast One-Hybrid Library Screening System (Takara Bio Inc.). The 41 bp ammonium-responsive region  found in the llection . Approxi33.1GLT1 and two ferredoxin (Fd)-dependent GOGAT coded by GLU1 and GLU2 in its genome. The qPCR analysis showed that GLT1 was the major GOGAT expressed in Arabidopsis roots in addition stems, flowers, and siliques when the plants were grown hydroponically  and one plastidial GS (GS2: GLN2) in Arabidopsis  has been shown to induce  in rice , we usedeviously  and in 3.4GLT1, we created a 5’-deletion series of GLT1 promoter:GFP fusion constructs and introduced them into Arabidopsis by Agrobacterium infection. D1, D2, D3, D4, D5, D6 and D7 are the fusion constructs generated to have the GLT1 promoter regions from positions –2,814, –2,200, –2,100, –2,000, –1,930, –1,730 and –1,050 bp, respectively, cloned in front of GFP  in the 5’-intergenic region upstream of the translation initiation site of ervation .3.5GLN1;2 promoter between the positions –3,604 and –3,564 bp upstream of the translation initiation site  as a decoy sequence. Approximately 3.7 × 104 yeast colonies were screened, and 24 positive clones were obtained, of which 22 gave the insert sequence information (GLN1;2 promoter (GLT1 promoter (In our previous study, we found the ARR in the ion site . To explion site  was screormation Table 3.GLN1;2 starting from the position –3,583 bp and lacking the DF1-binding site (GLN1;2 promoter region with or without the ARR starting from –3,604 and –3,564 bp, respectively, were used in parallel to study the ammonium responsiveness of GFP reporter expression (GLN1;2 promoter:GFP lines, only the T1 lines showed a significant increase in GFP mRNA accumulation on ammonium relative to no nitrogen control (GLN1;2 promoter:GFP lines, the endogenous GLN1;2 mRNA levels increased upon ammonium treatment by 3.0 to 4.4-fold relative to no nitrogen control (GLN1;2 promoter (GLN1;2 promoter and between –2,100 and –2,001 bp in the GLT1 promoter, respectively.To gain further insights into this ARR, we generated promoter:GFP lines designated T2 with a construct which has the 5’-promoter region of ing site 5A. T1 apression . Among t4GLT1 encoding the NADH-dependent GOGAT and GLN1;2 encoding the cytosolic GS, the two key enzymes for ammonium assimilation in Arabidopsis roots (Plants show different adaptations to ammonium environments even within a species, and their responses to ammonium environments are genetically diverse . Arabidois roots 2.GLT1 is the most highly expressed GOGAT in Arabidopsis roots (GLU2 is lowly expressed but GLU1 transcript was hardly detected in roots (GLT1 is the ammonium-inducible form of GOGAT (GLN1;2, GLT1 expression is not directly induced by ammonium, but is rather dependent on glutamine or post-glutamine metabolites (OsGS1;2, OsNADH-GOGAT1, and OsAS1 genes in rice (GLT1, GLU2 does not respond to ammonium in roots (GLT1 is suggested as the isoenzyme playing a central role in supplying substrates to GS when ammonium is supplied as the nitrogen source (GLN1;2 and GLT1 in the epidermis and cortex in response to ammonium supply, and these cell types where the promoters of the two genes were active overlap significantly (GLT1 mRNA levels on ammonium supply is promoter-dependent and likely due to increased transcriptional activity (The tissue-specific expression patterns of Arabidopsis genes encoding GOGAT isoenzymes indicate that of GOGAT S1B; how in rice . In contn source . The resficantly . The incficantly . We founactivity , and as activity  and protactivity  accumulaGLN1;2 promoter is important for this GS isoenzyme to respond to ammonium (GLN1;2 promoter is particularly important. A GLN1;2 promoter lacking this region was unable to express GFP in the root surface cell layers in response to ammonium (GLN1;2 as a decoy sequence. Our findings implicate that ARRs of GS/GOGAT involved in assimilation of ammonium in roots contain a conserved sequence signature to which DF1 binds (GLN1;2 and GLT1, which has not been reported previously (GLN1;2 and GLT1 used by We have previously shown that a 41-bp sequence in the ammonium . In thisammonium Figure 5ammonium  and ArabF1 binds  and contF1 binds . The mucF1 binds . The expF1 binds . ShibataF1 binds . DF1 is F1 binds . The netF1 binds . We predeviously . The proGLN1;2. This is because AHLs are predicted to bind TTTAATT, which is located within a non-essential region downstream of the ARR of GLN1;2. ZFHD2 is also predicted to bind TTTAATT. ZFHD2, like AHL, is not expected to be a transcription factor directly involved in ammonium response. TTG1 and LBD22 are known to bind bHLH. The ammonium-responsive sequence of GLN1;2 used a decoy in our yeast one-hybrid screening contains a bHLH binding sequence CAACTC (GLN1;2. Future analysis of mutants and overexpressors of these transcription factors will help better understand how they interact with GLN1;2 and GLT1 promoter regions to modulate expression of these key enzymes involved in ammonium utilization.In our yeast one-hybrid screening, various homologs of the AHL family transcription factor were repeatedly isolated. However, we consider it unlikely that AHLs are directly involved in modulating the ammonium responsiveness of e CAACTC . It is pThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.KI, HT, and SK contributed to conception and design of the study. KI and EI performed the qPCR analysis. KI, EI, and SK performed microscopic analysis. KI, EI, and HT prepared for whole mount GFP image. CY, HM, and SK performed yeast one hybrid screening. KI and SK performed promoter analysis. KI and SK wrote the first draft of the manuscript. HT edited the manuscript. All authors contributed to the article and approved the submitted version.