During times of unpredictable stress, organisms must adapt their gene expression to maximize survival. Along with changes in transcription, one conserved means of gene regulation during conditions that quickly repress translation is the formation of cytoplasmic phase-separated mRNP granules such as P-bodies and stress granules. Previously, we identified that distinct steps in gene expression can be coupled during glucose starvation as promoter sequences in the nucleus are able to direct the subcellular localization and translatability of mRNAs in the cytosol. Here, we report that Rvb1 and Rvb2, conserved ATPase proteins implicated as protein assembly chaperones and chromatin remodelers, were enriched at the promoters and mRNAs of genes involved in alternative glucose metabolism pathways that we previously found to be transcriptionally upregulated but translationally downregulated during glucose starvation in yeast. Engineered Rvb1/Rvb2-binding on mRNAs was sufficient to sequester mRNAs into mRNP granules and repress their translation. Additionally, this Rvb tethering to the mRNA drove further transcriptional upregulation of the target genes. Further, we found that depletion of Rvb2 caused decreased alternative glucose metabolism gene mRNA induction, but upregulation of protein synthesis during glucose starvation. Overall, our results point to Rvb1/Rvb2 coupling transcription, mRNA granular localization, and translatability of mRNAs during glucose starvation. This Rvb-mediated rapid gene regulation could potentially serve as an efficient recovery plan for cells after stress removal. Cells do not always live in stable and optimal conditions, instead they are faced with various types of stresses, such as nutrient starvation, heat shock, toxins, pathogens, and osmotic stresses . In dynacis-acting RNA sequence elements in determining cytoplasmic mRNA fate  were directed to P-bodies and are poorly translated  are transcriptionally induced, actively translated, and remain diffuse in the cytoplasm; however, class II mRNAs are transcriptionally induced but become sequestered in both P-bodies and stress granules and are associated with inactive translation. Class II mRNAs are enriched for alternative glucose metabolic function such as glycogen metabolism . Surprisingly, instead of the mRNA sequence itself, the promoter sequence that sits in the nucleus directs the translation and cytoplasmic localization of the corresponding induced mRNAs. Specifically, Hsf1-target sequences were shown to direct mRNAs to be excluded from mRNP granules and well translated. However, the mechanism by which the promoter can couple steps of gene expression during glucose starvation is unclear. As the promoter exclusively resides in the nucleus, we hypothesize factors exist that interact with promoters and are co-transcriptionally loaded onto mRNA prior to nuclear export.Previously we found that during glucose starvation in yeast, promoter sequences play an important role in determining the cytoplasmic fate of mRNAs . mRNAs tanslated . MeanwhiGLC3) that are upregulated in transcription but downregulated in translation and have granular-localized mRNA transcripts. Rvb1/Rvb2  are two highly conserved AAA+ (ATPases Associated with various cellular Activities) proteins that are found in multiple nucleoprotein complexes. Structural studies have shown that in yeast they form an alternating heterohexameric ring or two stacked heterohexameric rings  in cells subject to 10 min of glucose deprivation. Those promoters had previously been shown to be sufficient to determine the cytoplasmic fate of the uniform ORF , a novel biochemical screening technique to identify co-transcriptionally loaded protein factors . Here, wform ORF .GLC3 promoter and on HSP30/HSP26 promoters . To further verify this enrichment, we compared our protein enrichment data against the CRAPome repository, a large database of contaminant proteins from various immunoprecipitation (IP) experiments, and we found that Rvb1 was significantly enriched on the GLC3 promoter-containing plasmid  was used to investigate Rvb1/Rvb2’s enrichment across the genome. Rvb1/Rvb2 were fused with a tandem affinity purification (TAP)-tag at the C-terminus and purified by rabbit IgG beads. The TAP-tagged strains grow at a normal rate (~90 min doubling time), which suggests TAP-tagging does not generally disrupt the endogenous protein function of these essential proteins. Here, we performed ChIP-seq on Rvb1, Rvb2, and the negative control Pgk1 in 10 min of glucose starvation . We took advantage of the specific interaction between a phage-origin PP7 loop RNA sequence and the PP7 coat protein (HSP30). Therefore, we specifically engineered the interaction between Rvb1 or Rvb2 and two types of mRNAs driven by the class I heat shock promoters (HSP30/HSP26). Strikingly, binding of both Rvb1 and Rvb2 alters the cytoplasmic fates of these class I heat shock mRNAs to be similar to the class II alternative glucose metabolism mRNAs  and one class I gene (HSP30) and then quantified their protein induction after 30 min glucose starvation. While both class II genes had robust mRNA induction upon glucose starvation in the control samples , one laboratory strain that is closely related to S288C. In yeast cloning for the C-terminal fusion on the endogenous proteins , we used plasmids of Pringle pFA6a and pKT system , gifts f660 of cells reached 0.4, half of the culture was harvested as the pre-starved sample. The other half of the culture was transferred to the prewarmed synthetic complete medium lacking glucose (SC-G medium) by centrifugation method. Cells are centrifuged at 3000 × g, washed once by SC medium, and resuspended in the same volume as the pre-starvation medium of SC medium. Glucose starvation was performed at the same 200 r.p.m. shaking speed and 30°C. The length of the glucose starvation time varies from 10 min to 30 min depending on the experiments.The background yeast strain w303 (EY0690) was used for all experiments. For cells cultured in the functional experiments, cells were streaked out on the yeast extract peptone dextrose (YPD) agarose plate (BD) from the frozen stocks and grew at 30°C for 2 days. Single colony was selected to start the overnight culture for each biological replicate. Cells were grown at 30°C in batch culture with shaking at 200 r.p.m. in synthetic complete glucose medium . When the ODURA3 gene with BstBI digestion and transformation into yeast. The CoTrIP plasmid was constructed by modifying the ZP66 pUC-TALO8 plasmid that contains eight copies of the Lac operator. The EcoRI sites in ZP66 were mutated to NotI using QuikChange II Site-directed mutagenesis kit (Stratagene). Promoter-specific reporters driving CFP were inserted into pUC-TALO8-NotI by digesting with NheI followed by Gibson Assembly. The plasmid backbone was then digested with NotI, gel purified, ligated, and transformed into yeast using standard lithium acetate transformation. Then, 1 L of yeast were grown overnight in SCD medium-Trp to maintain selection on the CoTrIP plasmid, until an OD660 0.3–0.4. Cells were filtered, washed with SC-G -Trp media, and resuspended in 1 L of prewarmed media and grown at 30°C for 5 min. Cells were then refiltered, resuspended in 4 mL of PBS in a glass Petri dish, and crosslinked using UV from a Stratalinker 1800 . Crosslinked cells were pelleted and resuspended in 2 mL of Buffer H 150  plus protease inhibitor (P8215 MilliporeSigma) and then dripped into liquid N2 to be cryogenically ball milled using a Retsch PM100. Ground lysate was clarified by spinning at 3500 × g for 5 min at 4°C, isolating the supernatant and spinning at 12K for 5 min at 4°C. Supernatant was aliquoted and frozen at –80°C. Then, 10 µL of unpacked Anti-FLAG M2 Magnetic Beads (M8823 MilliporeSigma) per sample were pre-washed with Buffer H 150. Then, 300 µL of extract was added to magnetic beads and incubated at 4°C for 3 hr with rotation. Beads were then washed three times with Buffer H 150, three times with Buffer H 300 (300 mM KCl), and once with Buffer H 150. Then, 500 µg/mL 3xFLAG peptide (F4799 MilliporeSigma) was diluted in Buffer H 150 and CoTrIP plasmids were eluted with 100 µL elution buffer with FLAG peptide. Elutions were taken forward for DNA, RNA, and mass spectrometry. Mass spectrometry was performed through the Yeast Resource Center by James Moresco of the Yates lab and was funded through a P41GM103533 Biomedical Technology Resource Center grant. Data-dependent acquisition of MS/MS spectra was performed with an LTQ-Orbitrap. Tandem mass spectra were extracted from raw files using RawExtract 1.9.9 (http://www.yeastgenome.org) using ProLuCID , respectively, with gentle shaking at room temperature for 15 min. Crosslink was quenched by introducing 0.5 M of glycine for 5 min at room temperature. Cells were harvested by centrifugation at 3000 × g at 4°C, washed twice in the ice-cold TBS buffer . Cells were resuspended in 400 µL of ChIP lysis buffer , and lysed by bead-beating (Biospec Products) for 1 min five times. Lysis was verified under microscope. Lysates were sonicated by Covaris Sonicator to ~500 bp fragments . Lysates were centrifuged at 15,000 × g at 4°C to remove the cell debris and diluted to 1 mL. Save 10% of the clear lysate to verify the sonication by protein digestion using Pronase, reverse crosslinking, RNA digestion using RNase A, and running the samples on 1% agarose gel. Then, 50 µL of IgG-Dynabeads per sample was used. The protocol of making the IgG-Dynabeads from Dynabeads M-270 Epoxy (Thermo Fisher) was taken from The protocol was developed based on Saccharomyces cerevisiae genome using Bowtie2 , and harvested. Cells were resuspended in 400 µL of ice-cold RIP lysis buffer , and lysed by bead-beating (Biospec Products) for 1 min five times. Bright-field microscopy was used to verify that more than 90% of cells were lysed. The lysates were centrifuged softly at 1000 × g at 4°C for 10 min to remove cell debris and diluted to 500 µL. Clear lysates were treated with 5U RQ1 DNase and 5U RNase inhibitor (Promega) at 37°C for 15 min. Then 1% of the lysate was saved as the input and for Western blotting, respectively. Also, 50 µL of IgG-Dynabeads per sample was used. The protocol of preparing the IgG-Dynabeads from Dynabeads M-270 Epoxy (Thermo Fisher) was taken from The RIP protocol was developed based on the protocol from 660 to ~0.4 in SCD medium at 30°C and glucose-starved in SC-G medium for 15 and 30 min. Then, 100 µL of cell culture was loaded onto a 96-well glass-bottom microplate (Cellvis). Cells were imaged using an Eclipse Ti-E microscope (Nikon) with an oil-immersion ×63 objective. Imaging was controlled using NIS-Elements software (Nikon). Imaging analysis was performed on Fiji software.Cells were grown to an OD660 to ~0.4 in SCD medium at 30°C and glucose-starved in SC-G medium for 30 min. Then, 90 µL of cell culture was loaded onto a Cellstar non-transparent white 96-well flat-bottom plate (Sigma-Aldrich). OD660 of cells was taken for each sample. For cells treated with cycloheximide (CHX), CHX was added to a final concentration of 100 µg/mL to stop the translation for 5 min. To measure the nanoluciferase signal, 11 µL of substrate mix  was added and mixed with the samples by pipetting. Measurements were taken immediately after addition of substrate mix by Tecan Infinite Lumi plate reader. To analyze the data, the luciferase level of samples was firstly divided by the OD660 level of the samples. Then the normalized luciferase level of non-CHX-treated sample was further normalized by subtracting the luciferase level of CHX-treated sample. For glucose readdition experiments, cells were starved for 30 min and then 2% glucose was added back to the cultures and then the luciferase production was followed over a 10 min period.The nanoluciferase (nLuc) assay was adapted from methods previously described by RVB2 gRNA forward and reverse complement oligos were annealed together and then further extended using NM637 and NM636. This PCR product was inserted into the TetO gRNA vector pNTI661 by digesting this vector with BamHI/HindIII as described previously . Cells wThe Western blotting protocol was adapted from ACT1 abundance, and the fold change between samples was calculated by a standard ∆∆Ct analysis. All data were included except for one sample that had high technical variation, and another that had very high ACT1 CT values. Both samples were flagged as analysis began.The RT-qPCR protocol was adapted from https://jupyter.org/). To accurately describe the dynamics of induced mRNA transcription, we used an ordinary differential equation as follows:The mathematical modeling method was adapted from where X is the mRNA concentration, α is the degradation constant, and β is the transcription rate.We assumed that transcription and degradation rates play essential roles in shaping the overall curve of mRNA increase, and these parameters stay constant over the course of induced expression. We then hypothesized that Rvb1/Rvb2 binding to mRNAs could either increase β or decrease α, leading to greater mRNA abundance than the PP7 control. To observe the effects of varied transcription or degradation rates on the mRNA abundance, we solved the differential equations with different parameters using ODEINT algorithm, and generated time profiles of the mRNA fold in log2 scale. Solution to the differential equation was expressed as the following function of change in X with respect to time:0 is the mRNA level at t = 0, the initial time of mRNA induction. Since the degradation rate was proportional to the mRNA concentration, we expected the curves to have a steady increase, followed by a gradual leveling off where the mRNA concentrations stay constant over time. Closer look at the differential equation showed that at steady state (dX/dt = 0) the mRNA concentration is determined by the ratio of β to α:where Xwhereas for the time it takes for the curve to transition into steady state, inversely proportional to the degradation constant, is given byThus, we showed that for a log-log plot, the expected shape of the curves can be altered by varying α and β. At constant α, increasing β would only shift the curve up, while at constant β, increasing α would cause the mRNA abundance to enter steady state more rapidly. Through the comparison between mathematical modeling and experimental data, we could infer the actual effects of Rvb1/Rvb2 binding to mRNA on the transcription and decay rates.660 between 0.3 and 0.4. Then, cells were collected by filtration, resuspended in SC-G medium. After 15 min, 2% glucose was added back. CHX was added to a final concentration of 0.1 mg/mL for 1 min, and cells were then harvested. Cells were pulverized in a PM 100 ball mill (Retsch), and extracts were digested with RNase I followed by the isolation of ribosome-protected fragments by purifying RNA from the monosome fraction of a sucrose gradient. Isolated 28-base sequences were polyadenylated, and reverse transcription was performed using OTi9pA. OTi9pA allowed samples to be multiplexed at subsequent steps. RNA-seq was performed on RNA depleted of rRNA using a yeast Ribo-Zero kit (Epicentre). Samples were multiplexed and sequenced on a HiSeq analyzer (Illumina).The ribosome profiling protocol was adapted from S. cerevisiae rRNA sequences using Bowtie sequence aligner , the ribosome occupancy for each mRNA was calculated by taking the total number of ribosome reads  in the downstream region (120 bp from the AUG to the end of the ORF) and dividing this by the number of mRNA reads (RPM) in the same region. The ribosome occupancy along the mRNA was calculated by dividing the ribosome read counts at each base pair along the gene by the average number of mRNA reads per base pair for each gene.To analyze the ribosomal profiling and RNA-seq sequences, reads were trimmed of the 39 run of poly(A)s and then aligned against  aligner . Reads t This study provides convincing evidence that the Rvb1 and Rvb2 proteins preferentially associate with the promoters of a class of genes that exhibit increased mRNA expression but translational repression and association with mRNA granules in response to acute glucose starvation. They show that Rvb1/Rvb2 associate with the target mRNAs in addition to their promoters, in a manner requiring only the gene promoter. Tethering Rvb1 or Rvb2 to a reporter mRNA is sufficient to repress its translation, stimulate its synthesis, and induce its sequestration in cytoplasmic granules; whereas Rvb2 knockdown eliminates the translational repression of several reporter mRNAs. These compelling findings support the important demonstration that Rvb1/Rvb2 are loaded on transcripts co-transcriptionally and accompany them into the cytoplasm where they repress translation in a manner associated with the accumulation of the repressed mRNAs in granules. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Review Commons.][Editors' note: this paper was reviewed by  We thank the reviewers for their expertise and thorough comments on our manuscript. We believe their comments and our revisions have strongly improved the manuscript. Point by point replies to comments are given below.Reviewer #1 ):This is a very interesting paper with novel observations. The authors find that, in yeast, Rvb1/2 AAA+ ATPases couple transcription, mRNA granular localization, and mRNAs translatability during glucose starvation. Rvb1 and Rvb2 were found to be enriched at the promoters and mRNAs of genes involved in alternative glucose metabolism pathways that are transcriptionally upregulated but translationally downregulated during glucose starvation.The following are some commentsIntroduction1. "Structural studies have shown that they form a dodecamer comprised of a stacked Rvb1 hexametric ring and a Rvb2 hexametric ring."– Rvb1 and Rvb2 form a heterohexameric ring with alternating arrangement (not homohexamers that stack on top of each other as suggested by this sentence)– In yeast, they oligomerize mostly as single hexametric rings, with dodecamers reported being less than 10% in frequency in vivo We have changed the text to more appropriately reflect this. “Structural studies have shown that in yeast they form an alternating heterohexameric ring or two stacked heterohexameric rings .”ResultsSection: Rvb1/Rvb2 are identified as potential co-transcriptionally loaded protein factors on the alternative glucose metabolism genes1. "These two proteins are generally thought to act on DNA but have been found to be core components of mammalian and yeast cytoplasmic stress granules"– These two papers extensively show Rvb1/Rvb2 localization to granules/condensates under stress/nutrient starvation conditions and should be cited. The Rvb1/2 foci were named Rbits:i. Rizzolo et al. 2017 https://doi.org/10.1016/j.celrep.2017.08.074ii. Kakihara et al. 2014 https://doi.org/10.1186/s13059-014-0404-4We have added citations to these two relevant papers.2. "a portion of them becomes localized to cytoplasmic granules that are not P-bodies in both 15-minute and 30-minute glucose starvation conditions "– Supplement figure 2 only includes results under 30-min glucose starvation, no 15-min data was shownWe have corrected the text and only reference 30-minute glucose starvation.3. Figure 1C, unclear whether p-value here is for FC of GLC3 over HSP or FC of GLC3 over CRAPome. In addition, both FC datasets should have p-values.We have clarified the p-value comes from our GLC3 vs HSP comparison in the figure legend. Unfortunately, we did not have the absolute spectral counts from the FC versus the CRAPome and could not calculate significance for the CRAPome comparison.Section: Rvb1/Rvb2 are enriched at the promoters of endogenous alternative glucose metabolism genes1. "Here, we performed ChIP-seq on Rvb1, Rvb2, and the negative control Pgk1 in 10 minutes of glucose starvation "- Unclear what figure is being referred to, panel A or panel B?We revised to the following. “Here, we performed ChIP-seq on Rvb1, Rvb2, and the negative control Pgk1 in 10 minutes of glucose starvation ”2. "Structural studies have shown that Rvb1/Rvb2 can form a dodecamer complex. Their overlapped enrichment also indicates that Rvb1 and Rvb2 may function together."- They function together regardless of forming a dodecamer or not, as they assemble as heterohexamersWe have revised to the following. “Structural studies have shown that Rvb1/Rvb2 assemble as heterohexamers . Their overlapped CHIP enrichment further supports that Rvb1 and Rvb2 function together along DNA.”Section: Engineered Rvb1/Rvb2 tethering to mRNAs directs the cytoplasmic localization and repressed translation1. Does binding of any protein to PP7 loop in this construct alter cytoplasmic fate? A control such as GFP-CP or any other protein attached to CP should be used.As suggested, we have expressed GFP-PP7-CP plasmid in the HSP30prUTR-nLuc-PP7-MS2 yeast strain and found that this tethering has no significant effect on protein synthesis induction, mRNA induction, and translatability .2. No statistical analysis was done for Figure 4E quantificationWe have added statistical analysis for Figure 4E .3. "Results showed that after replenishing the glucose to the starved cells, the translation of those genes is quickly induced, with an ~8-fold increase in ribosome occupancy 5 minutes after glucose re-addition for Class II mRNAs "– Would be important to see this recovery (increase in translation after glucose replenishment) in one of the reporter constructs used in the paper, such as GL3 promoter driven CFP.Unfortunately, tagging of the endogenous GLC3 gene with nLuc disrupted normal regulation of GLC3 and it became constitutively expressed. We have instead tagged other Class II mRNAs (GSY1 and HXK1) as well as Class I mRNAs (HSP30 and HSP26) and found that the Class II reporters show more robust induction upon glucose re-addition than Class I mRNAs further supporting our ribosome profiling data .Section: Engineered Rvb1/Rvb2 binding to mRNAs increases the transcription of corresponding genes1. How many biological replicates is in Figure 5B? There does not seem to be any error bars/gray sections indicating sample variation. P-value was also not calculated.We have added the number of biological replicates to the figure legend (n=4) and statistical analysis to Figure 5B .Reviewer #1 (Significance (Required)):This is a very interesting manuscript that ascribes yet another function of the highly conserved RVB1/2 AAA+ ATPases.Referee Cross-commentingAll reviewers agree that this an interesting paper. However, the reviewers do suggest specific experiments to verify some of the results. Carrying out these experiments will definitely improve the paper.Reviewer #2 ):In their manuscript entitled "Rvb1/Rvb2 proteins couple transcription and translation during glucose starvation", Chen and co-authors use genetics and microscopy to demonstrate how budding yeast regulate cytoplasmic translation by their promoter sequences by two conserved ATPases Rvb1 and Rvb2 during nutrient stress. The authors show that these two ATPases repress translation of target mRNAs and then propose that these two proteins also recruit mRNAs to P bodies. The authors show that Rvb1/2 preferentially binds in the presence of Class II promoters using CoTrIP, that Rvb1/2 binds specifically at Class II promoters using ChIP-seq, that Rvb1/2 are bound to transcripts with Class II promoters using RIP-Seq, that tethering of Rvb1/2 to a transcript decreases its translatability and that Rvb1/2 binding to a transcript increases its transcript levels by increasing transcription and not slowing mRNA decay.The CoTrIP experiment is clever and for the most part well executed. The key conclusions are largely convincing but some clarifications are nevertheless needed (see below). Overall, this paper is well written with well executed experiments that largely support the authors' model. No major additional experiments are needed to support the claims of the paper. There are a few minor concerns that should be addressed before this manuscript gets published. These are:Minor comments:1) Are Rvb1/2 components (enriched in) of P bodies? The model proposed by the authors suggests this but no data is show.We find minimal overlap between P-bodies and Rvb1/2 by microscopy – Figure 1—figure supplement 2. We describe this in the text, as well have added reference to previous results also not seeing overlap between cytoplasmic Rvb1/2 granules and P-bodies. “Microscopy revealed that Rvb1/Rvb2 are predominately present in the nucleus when cells are not stressed but a portion of them becomes localized to cytoplasmic granules that are distinct from P-bodies after 30-minute glucose starvation conditions. Similar results were previously seen with 2-deoxyglucose driven glucose starvation, where Rvb1 formed cytoplasmic foci independent of P-bodies and stress granules .”2) Figure 1A: The model proposed by the authors indicates that Rvb1/2 and other proteins are recruited to the mRNAs in a promoter-dependent manner and not mRNA sequence dependent manner. This is largely supported by the data presented in the paper. However the authors should also discuss the possibility that RNA sequences could nevertheless contribute as only a uniform ORF has been tested. Could the promoter recruit Rvb1/2 similarly regardless of the ORF sequence tested? Please provide a sequence of the uniform ORF, discuss what this "uniformity" means and how a change in RNA sequence could affect the outcome of the experiment outlined in Figure 1A.The uniform ORF is CFP, which was also used for the promoter swap experiments in Figure 3B,C. These constructs were originally used in our previous manuscript (Zid and O’Shea 2014) and more detail is given there. We have also added these plasmids to Addgene, where they can be accessed (https://www.addgene.org/178307/). We see similar effects on mRNA localization and protein expression with alternative ORFs such as nanoluciferase used in Figure 4. We have also mixed the 5’UTRs and ORFs of Class I and Class II genes and seen that the promoter is the dominant factor for mRNA localization (Zid lab unpublished data).3) Figure 2: The authors use Pgk 1 in their ChIP control but this is not the appropriate control for the experiment as Pgk 1 is not nuclear and thus cannot demonstrate non-specific interaction with genetic regions of tested genes. Regardless, the data is convincing enough to support the model that Rvb1/2 are specifically recruited to the promoters of Class II stress-induced genes and not Class I stress-induced genes. GFP-NLS would be a better control. The authors should discuss in their Materials and methods section why they chose a cytoplasmic protein for their normalization control but preferably perform ChIP with GFP-NLS or other nuclear protein that could bind to chromatin non-specifically to further demonstrate the specificity of Rvb1/2 enrichment at Class II promoters.In the original hyper-ChIPability paper  they found enrichment on highly expressed regions even if there was no-tag present in the strain. We therefore used the highly abundant cytoplasmic protein Pgk1 as a control for pulldown specific enrichment effects, that were unrelated to actual protein binding, and added a note of this in the ChIP-seq methodology section.4) The authors claim that Rvb1/Rvb2 binding to transcripts leads to formation of granules that are non-colocalized with P-bodies and instead co-localized to SGs, but no SG fluorescent marker is used to demonstrate this claim. The authors should provide this data or remove this claim from their manuscript.We have removed this claim as we have had issues identifying a robust, endogenously tagged, 30-minute glucose starvation induced stress granule marker and instead refer to these granules as “P-body independent starvation-induced granules”5) Fluorescent images are fuzzy, very small and difficult to interpret. mRNA puncta are difficult to observe and it is hard to conclude which green puncta colocalize with P bodies and which do not (and how frequently). It is difficult to differentiate between the cytoplasm and nucleus. Consider adding DAPI overlay.We have added clearer images of mRNA puncta showing that there is minimal overlap between P-bodies and RVB-tethering induced RNA granules .6) The relevance of Figure 2B is not clear – please discuss.From the text “More generally we found that, for genes that show a greater than 3-fold increase in mRNA levels during glucose starvation, their promoters are significantly more enriched for Rvb2 binding. Previously we had found that Hsf1-binding sequences were sufficient to exclude mRNAs from mRNP granules during glucose starvation . Interestingly we found that glucose starvation induced Hsf1-target promoters have no difference in Rvb1/Rvb2 binding than an average gene, and significantly lower Rvb1/Rvb2 enrichment than stress induced non-Hsf1 targets .” This data is also referenced in the discussion “As we found that Rvb1/Rvb2 are generally enriched on the promoters of transcriptionally upregulated mRNAs we favor a model in which the default is for Rvb1/Rvb2 to be recruited to active transcription sites. This fits with previous data that Rvb1/Rvb2 are required to maintain expression of many inducible promoters including galactose-inducible transcripts . While Rvb1/Rvb2 are generally recruited to the promoters of induced mRNAs during glucose starvation, we find that Hsf1-regulated promoters circumvent this recruitment through an unknown mechanism, as the transcriptionally upregulated Hsf1 targets show reduced recruitment relative to non-Hsf1 targets . Intriguingly, Hsf1-regulated genomic regions have been found to coalesce during stressful conditions . It will be interesting to explore whether Rvb1/Rvb2 may be excluded from these coalesced regions in future studies.” Finally, this also serves as a control that Rvb1/Rvb2 are not just binding to transcriptionally induced promoters, as Hsf1 targets are induced just as strongly as Class II mRNAs.7) Figure 5A modeling adds little supporting evidence to the entire figure. The experimental results are more convincing. Consider moving to the Supplement.We have moved this to a Figure 4—figure supplement 10A,B.8) Figure 4 and 3B. The authors suggest that Rvb1/2 loaded by the promoters onto the mRNA determine accumulation of mRNAs to P bodies. To test this model, the authors tether Rvb1/2 onto the mRNA using MS2-MCP system and then look for co-localization of the mRNA with P bodies. However, if the authors' model is correct, this experiment could have been achieved already using the constructs in Figure 3B. The authors should look at the P body localization pattern using chimeras used in Figure 3B.This experiment was performed in our previous manuscript (Zid and O’Shea 2014) and showed that the promoter was sufficient to determine the cytoplasmic localization of the mRNA but did not show the mechanism for this promoter-controlled localization. One additional clarification is that our data supports Rvb1/2 driving localization to P-body independent starvation-induced granules, which is also the localization pattern we previously saw for Class II promoters.9) Figure 6: The authors present a model where mRNAs transcribed from Class II promoters are decorated with Rvb1/2 co-transcriptionally, exported into the cytoplasm, recruited to P bodies and translationally repressed. However, this model is not fully supported by the data shown. Specifically, the authors have not shown that localization of mRNAs to P bodies induces translational repression or whether the recruitment is a consequence of this repression. The authors should revise their model to reflect this uncertainty. Also, the numbering of steps 1,2 3 is confusing. Does it imply a temporal sequences? Some of these steps could be occurring simultaneously (like 1 and 3). How does step 3 lead from step 2? Please clarify this model.We have removed the numbering system as we agree that some of these steps, like 1 and 3 are probably happening at the same time. We have updated the text of the figure legend to “Then Rvb1/Rvb2 escort the interacting mRNAs to the cytoplasm and cause repressed translation and localization to cytoplasmic granules.” We have also updated the discussion with the following text pointing to the unclear causality of translational repression and P-body independent starvation-induced granule localization in the discussion – “We are uncertain whether Rvb1/Rvb2 tethering represses translation which directs mRNAs to mRNP granules, or if Rvb1/Rvb2 binding directly target the mRNA to the granule, which represses translation or some combination of both as these are very hard to disentangle”10) Consider showing data-points in Figure 1 figure supplement 1. The box/whisker plot doesn't give a good sense of the enrichment alone.We have added the number of replicates to the figure and have also included the data points in the accompanying supplemental data file for Figure 1 figure supplement 1. They are very close and end up overlapping when plotted on a graph together.11) Figure 1 Figure supplement 2 shows that the fluorophore seems to influence the % of cells with foci. Why is this the case?While we can’t be certain, one reason may be that mNeonGreen is ~4 times as bright as mRuby2 in yeast . This may make it easier to distinguish mNeonGreen Rvb foci versus non-foci fluorescence and the inherent autofluorescence of yeast.12) List gene names in Figure 2 Figure supp 5.We have revised Figure 2 Figure supp 5 by adding gene names.13) Throughout the paper the graph axis labels are very small and difficult to read.We have increased the font on many graph axis labels.14) Figure 4 Figure supplement 7C and 8E: on the y-axis the legend says proportion of cells (%), so the value on the y-axis might be 25, 50, 75 and not 0.25, 0.50 and 0.75.We have fixed these axis values.15) The last paragraph of the Introduction (page 2) detailed how Rvb1/Rvb2 are core components of the stress granule. Yet most experiments were conducted to relate Rvb1/Rvb2 with P-bodies. Maybe some information about the known roles Rvb1/Rvb2 play in the P-bodies in the Introduction section could help.We predominantly used a P-body marker because it gives a robust mRNP granule marker during glucose starvation, even at early timepoints. We see minimal colocalization between RVB-tethering induced RNA granules and P-bodies. This is in agreement with ours and others data that RVB doesn’t colocalize with P-bodies during glucose starvation. To our knowledge there is little information linking Rvb1/Rvb2 to P-bodies.Reviewer #2 (Significance (Required)):Ruvb helicase has been shown to regulate the formation of stress granules in human U2OS cells during oxidative stress . Thus, the authors suggest that Rvb proteins could have a broad and conserved role in the formation of RNA granules, which advances our understanding of how biomolecular condensates could form.C. elegans and Drosophila. These observations have gained considerable attention in the past 5 years and exact molecular principles behind this phenomenon are not entirely clear. Long and exposed RNA sequences are thought to be sufficient for this enrichment. The authors however suggest that specific proteins (Rvb1/2) could also trigger enrichment either directly by interacting with P bodies or indirectly by repressing translation and exposing RNA sequences. This finding will be particularly relevant to the field of biomolecular condensates.In addition, translationally-repressed mRNAs have been shown to preferentially recruit to diverse RNA granules, from stress granules P bodies in human cells as well as germ granules in My expertise is in the area of RNA biology, mRNA decay, RNA granules and mRNA localization.Reviewer #3 ):Dr. Brian Zid has previously published in Nature that, in response to glucose starvation, promoters of some genes ("class II") can control synthesis of mRNAs that are sequestered in cytoplasmic P bodies or Stress granules, away from the translation apparatus. In this paper, his group reports about the underlying mechanism. They have found proteins that bind preferentially class II promoters as well as their transcripts and are capable of repressing their translation and stimulating their assembly with P bodies. They found a correlation between the capacity of Rvb1/2 binding to promoters and binding to mRNAs. Using a tethering technique, they found that Rb1/Rvb2 recruitment to reporter mRNA (not class II) led to the association of the transcript with PBs and its translation repression. Interestingly, Binding of Rvb1/Rvb2 to the studied transcript increased transcription of its own gene, probably by remodeling the nearby chromatin.The paper uncovers a mechanism to sequester mRNAs as translationally repressed in RNA granules during starvation and warrants a publication in a good journal, after responding to various comments below.1. CoTrIP is a method to identify proteins that differentially bind plasmids carrying different promoters/genes. However, the claim that it identifies proteins bound to nascent mRNAs is an overreach, as the proteins bind both DNA and RNA and the purified plasmid contains both types of nucleic acids.Therefore, the title of section 1  should be changed to something like: Rvb1/Rvb2 are identified as proteins that are co-purified with a plasmid expressing alternative glucose metabolism genes.Description of CoTrIP and its results should be discussed throughout the manuscript accordingly.We changed the title of the section to: “Rvb1/Rvb2 co-purify with plasmids containing an alternative glucose metabolism gene promoter”2. The engineered Rvb1/Rvb2 tethering to mRNAs of choice is a potentially convincing way to show the causative effect of Rvb1/Rvb2 on RNA performance. Using this method, the authors show that attachment of Rvb1/Rvb2 to an engineered mRNA mediate its association with granules and inhibits its translation. However, this experiment takes Rvb1/2 out of its natural context such that its behavior in this case may not be exemplative of its endogenous function. The authors are encouraged to support their results by depleting Rvbs with AID and examine the outcome of this depletion on PBs formation and translation of class II genes (and class I as controls).We had extensively tried to use the AID system to deplete Rvbs, but had leaky depletion even without auxin addition with all AID systems we tried. We alternatively used CRISPRi and a gRNA directed to the promoter of RVB2 to downregulate expression of RVB2. This led to an almost 20-fold depletion of the RVB2 mRNA. From this strain we found increased protein induction for Class II genes during glucose starvation, but no effect on a Class I control. Even though the Class II genes had increased protein expression, we actually found decreased mRNA induction upon downregulated RVB2 expression during glucose starvation, further supporting the role of Rvb proteins affect transcription and translation. This data is presented as Figure 5.3. The tethering experiments, shown in Figure 4, would be more convincing by including an additional control. To rule out the possibility that any bulky protein that is recruited to the 3'-UTR by the PP7 element affects translation (not an unlikely possibility), they want to consider fusing irrelevant protein  to CP, in place of Rvb1/2.We have expressed GFP-PP7-CP plasmid in the HSP30prUTR-nLuc-PP7-MS2 yeast strain and found that this tethering has no significant effect on protein synthesis induction, mRNA induction, and translatability .4. The proposal that Rvb1 binds class II transcripts during transcription is a plausible possibility , but by no means demonstrated. This should be clearly addressed in the paper.We agree with the reviewer that this has not been explicitly shown and have tried to use appropriate language to convey this point that the data suggests and it is likely but not absolutely certain.Results: “This suggests that only the promoter itself can determine the transcribed mRNA’s interaction with Rvb1/Rvb2, further indicating that Rvb1/Rvb2 are likely to be co-transcriptionally loaded from the promoters to nascent mRNAs.”Discussion: “suggesting Rvb1/Rvb2 are loaded from enriched promoters to the nascent mRNAs.”5. An optional suggestion: The paper can be upgraded by performing ribosome profiling, as shown in Supplemental Figure 9, after a short depletion of Rvb1/2 by AID (see comment 2). This, in combination with the results already shown in Supp Figure 9, can demonstrate the role of Rvb1/2 in mRNA storage in granules and in translation shortly after glucose refeeding. The large data sets thus produced  can be used to try correlate the extent of ribosome occupancy (or the above mentioned ratio) with cis-element(s) or known trans-acting elements within the promoters. This may identify elements within the promoters that recruit (directly or indirectly) Rvb1/2. If successful, it can pave the way to demonstrate co-transcriptional RNA binding. I also suggest moving Supp Figure 9 as an additional panel of the main Figure 4.We agree that it would be an interesting direction to pursue ribosome profiling on the RVB2 depletion strain. Currently due to time constraints and graduating students we have not been able to perform this experiment.Minor point:1. The original reference about "mRNA imprinting" was published by Choder in Cellular logistics 2011.We have added this reference to the manuscript.2. The graph in 5B does not have error bars and the number of replicates is unclear.We have added the number of biological replicates to the figure legend (n=4) and statistical analysis to Figure 5B .Reviewer #3 (Significance (Required)):The paper uncovers a mechanism to sequester mRNAs as translationally repressed in RNA granules during starvation. This significantly advances our understanding of how gene expression in yeast responds to the environment and warrants a publication in a good journal, after responding to the various comments, indicated above.My expertise is regulation of gene expression.Referee Cross-commentingIn general all reviewers feel that the paper deals with a significant issue, each from his/her point of view, and is basically of high quality.I concur with all the comments of Reviewer 1 and 2. In particular, two comments drove my attention.Reviewer 1: Would be important to see increase in translation after glucose replenishment in one of the reporter constructs used in the paper, such as GL3 promoter driven CFP.We have tagged Class II mRNAs (GSY1 and HXK1) as well as Class I mRNAs (HSP30 and HSP26) and found that the Class II reporters show more robust induction upon glucose re-addition than Class I mRNAs further supporting our ribosome profiling data .Reviewer 2: The authors should look at the P body localization pattern using chimeras used in Figure 3B.This experiment was performed in our previous manuscript (Zid and O’Shea 2014) and showed that the promoter was sufficient to determine the cytoplasmic localization of the mRNA as the HSP26 promoter drove diffuse localization, while the GLC3 promoter led to RNA granule formation, that was mostly distinct from P-bodies, but sometimes overlapping. In the previous manuscript we were not able to show the mechanism for this promoter-controlled localization.


Cohesin, an essential protein complex for chromosome segregation, regulates transcription through a variety of mechanisms. It is not a trivial task to assign diverse cohesin functions. Moreover, the context-specific roles of cohesin-mediated interactions, especially on intragenic regions, have not been thoroughly investigated. Here we perform a comprehensive characterization of cohesin binding sites in several human cell types. We integrate epigenomic, transcriptomic and chromatin interaction data to explore the context-specific functions of intragenic cohesin related to gene activation. We identify a specific subset of cohesin binding sites, decreased intragenic cohesin sites (DICs), which are negatively correlated with transcriptional regulation. A subgroup of DICs is enriched with enhancer markers and RNA polymerase II, while the others are more correlated to chromatin architecture. DICs are observed in various cell types, including cells from patients with cohesinopathy. We also implement machine learning to our data and identified genomic features for isolating DICs from all cohesin sites. These results suggest a previously unidentified function of cohesin on intragenic regions for transcriptional regulation. Cohesin complex is essential for transcriptional regulation and chromosome folding. Here the authors provide a large-scale multi-omics analysis suggesting that cohesin bound on gene bodies has a specific role in transcriptional regulation. Recent studies suggest that cohesin also has a role in transcriptional regulation, maintenance of chromosome architecture2 and DNA repair3. Context-specific functions of cohesin have been investigated using chromatin immunoprecipitation followed by sequencing (ChIP-seq) and high-throughput chromosome conformation capture (Hi-C). The early study reported that most cohesin-binding sites overlap with CTCF to function as an insulator4. Conversely, a group of cohesin has been reported to be CTCF independent and co-bind with tissue-specific transcription factors (TFs) to contribute to transcriptional regulation6, possibly via mediating interactions between enhancers and promoters7. Other studies using Hi-C have shown that cohesin and CTCF are essential for the formation of topologically associated domains (TADs), evolutionarily conserved chromatin domains ranging from a few hundred kilobases to several megabases in length9. These studies focused on cohesin functions with respect to insulation, or the formation of enhancer-promoter interactions that implicitly assume the positive regulation of gene expression. In contrast, a recent report showed that transcription elongation within gene bodies causes displacement of cohesin binding from chromatin, leading to disruption of cohesin-mediated loops10. Thus, a subset of chromatin loops (either end of which may be located on intragenic regions) mediated by cohesin is suggested to be negatively correlated with gene activation. While modifications in intragenic regions affect transcriptional events13, the function of intragenic cohesin has hardly been discussed.Cohesin, a ring-shaped chromosome-bound protein complex, is required for holding sister chromatids together during certain phases of the cell cycle14, and in multiple types of cancers16. Our previous study found that the diagnostic phenotype of CdLS is very similar to that of CHOPS syndrome17, which is caused by missense mutations in AFF4, a core component of the super elongation complex. Given the diverse functions of cohesin in gene expression and chromatin folding, the underlying molecular mechanism responsible for the similarity between CdLS and CHOPS is yet unknown. Noteworthily, the CHOPS-related mutations in the super elongation complex are also associated with transcriptional regulation by cohesin, indicating a common pathogenetic mechanism of cohesin in CHOPS and CdLS. It could be a feasible hypothesis that intragenic cohesin has a distinct role that links the phenotypic similarity between CdLS and CHOPS.Mutations in the cohesin complex and its loader (NIPBL) are observed in the cohesinopathy Cornelia de Lange syndrome (CdLS), a multisystem developmental disorderHere, we conducted a large-scale epigenomic analysis to clarify the context-specific functions of cohesin sites, especially in intragenic regions. To investigate the perturbation of cohesin binding sites by gene activation, we generated RNA sequencing (RNA-seq) and ChIP-seq data for cohesin and several TFs in MCF-7 cells with or without transcription stimulus. We also used many publicly available datasets, including Hi-C, ChIP-seq, RNA-seq and chromatin interaction analysis by paired-end tag (ChIA-PET). First, we clarified that a subset of cohesin sites, which we refer to as ‘decreased intragenic cohesin sites’ (DICs), is distinct from the other groups of cohesin sites. Cohesin binding on DICs is negatively correlated with transcriptional activation and locus compaction of chromatin. A part of DICs exhibit a high preference for enhancer marks and paused RNA polymerase II, whereas others contribute to chromatin architecture. Second, we performed ChIP-seq and RNA-seq with cohesin-depleted cells and suggested that cohesin has an active function on DICs. Third, we applied machine learning and captured DICs with a distinct epigenomic landscape, which is predictable across cell types. Finally, we conducted plenty of ChIP-seq in other cell types. Importantly, DICs can be observed across multiple cell types, including cells derived from CdLS and CHOPS patients, in a cell-type-specific manner. The findings from our integrated analysis and machine learning approaches suggest an additional role for cohesin in the regulation of gene expression.18, is a widely used model for investigating the transcription-dependent perturbation6. We prepared ChIP-seq data of cohesin (Rad21), cohesin loader (MAU2), CTCF and several TFs  from MCF-7 cells treated with vehicle  or estradiol . The statistics and quality metrics of ChIP-seq and RNA-seq data generated in this study are summarized in Supplementary Data 19 > 0.5) was larger than the one that decreased (6.2%)   Fig. , bottom..5) Fig. . Such ‘c.5) Fig. , which i20. We did not observe a difference in the proportion of up- or down-regulated cohesin peaks between intergenic and intragenic sites . We then validated these genes by RNA-seq and confirmed that their expressions were mostly up-regulated in response to E2 stimulation  contained one or more DICs. We found that almost all (97.3% by RefSeq reference) DICs were located in intronic regions , whereas no difference (p = 0.38) was observed for all cohesin sites  and ICF  metrics26 to represent locus-specific changes in intra- and inter-chromosomal interactions, respectively. The difference (Δ) for DLR (or ICF) between two Hi-C samples represents chromatin compaction  or de-compaction . ΔDLR showed a positive value at DICs . Aggregate peak analysis (APA)tes Fig. . These rICs Fig. . In cont27  and high-CTCF ones for comparison with LC-DICs and HC-DICs. Using the APA analysis, we observed the weakened interactions in both LC- and HC-DICs, but not in CNCs or high-CTCF cohesin sites . We found that DICs could be clearly classified into two categories: HC-DICs (high CTCF binding) and LC-DICs (low CTCF binding), in which strong and weak (or no) CTCF peaks co-localized, respectively Fig. . LC-DICsMREG and PAK4; see Supplementary Fig. MREG locus, there were both HC-DICs and LC-DICs, the former co-localizing with strong CTCF signals but almost no TFs, while the latter corresponding to frequent bindings of many TFs yet without strong CTCF signals. Overall, at LC-DICs, the peak intensity of cohesin decreased after E2 stimulation, whereas that of many TFs increased. Consistently, for the E2-activated gene MREG  or all cohesin sites (p < 10−4). In contrast, HC-DICs exhibited a significantly higher occurrence with respect to Hi-C loops, as compared with LC-DICs (p < 10−4) or all cohesin (p = 0.0003). We also compared loops with CTCF ChIA-PET data (GSE39495) and found that over 81% of HC-DICs overlapped with CTCF loops (27% for LC-DICs). This result suggested that LC-DICs were anchored by chromatin loops with Pol2 and other TFs, and function as enhancers in a CTCF-independent manner. HC-DICs were more likely to interact with CTCF to form chromatin loops that participate in chromatin architecture independently of the Pol2 machinery.To explore DIC-mediated loops, we investigated what kind of chromatin loci interacted with LC-DICs and HC-DICs. Remarkably, when analyzing the Pol2-mediated chromatin loops identified by ChIA-PET (GSE33664), LC-DICs contained multiple Pol2 loops that interacted with the TSS of the host gene  from Hi-C data, for which a lower value indicates more insulated regions, e.g., TAD boundaries. Although the IS profile showed a clear valley at all, intergenic and intragenic cohesin sites Fig. , it peak12. To test whether DICs can function as roadblocks, we investigated the Pol2 enrichment at DICs using our Pol2 (unphosphorylated CTD) and Pol2ser2 (ser2 phosphorylated CTD) ChIP-seq data . For example, the cohesin KD under E2 , but significant changes at TSS_E2res (p < 10−32). These results suggested that cohesin binding on LC-DICs is not passive and plays a role related to the Pol2 binding level. Pol2 binding in KD_Ctrl cells was not affected by E2-stimulation  and presence  of Rad21-specific siRNA (siRad21) to generate Rad21 knockdown . After E2 treatment, siRad21 moderately affected gene expression . Indeed, only a small subset (~10%) of LC-DIC-host genes (Fisher’s exact test p > 0.1 compared with other E2-response genes) were identified as differentially expressed genes. It is possible because only a subset of Pol2 that had paused on LC-DICs represented productive Pol2. We also quantitatively compared Pol2 and Pol2ser2 signals under four different conditions on various cohesin sites . Such a tendency was distinct from those involving the TSSs of E2-responsive genes, up-regulated and non-changed intragenic cohesin, other cohesin sites, and also other enhancer sites. Our results suggested the role of cohesin at LC-DICs which is different from the known roles of cohesin sites.Interestingly, siRad21 did not largely affect Pol2ser2 binding. Pol2ser2 levels on LC-DICs were not obviously different between WT and siRad21 cells Fig. . In KD_Ctes Fig. . The resIn Fig. Although we manually defined the criteria for DICs in the analysis above, we also wondered whether DICs can be automatically isolated based on various genomic features obtained from our multi-omics information. To this end, we implemented machine learning (ML)  to the matrix and obtained 10 clusters for all cohesin sites  at which many TFs co-localize. In contrast, cluster 7 (HC-DICs) consists of a cluster of intragenic cohesin sites that tend to be localized to open chromatin, are highly de-compacted and contain loops but are strongly negatively correlated with TFs. Therefore, they may be associated with a more universal chromatin structure that is required for proper gene transcription.Similar to the previous study of CNCs33 to deal with such “imbalanced” classifications. The trained model that was based on logistic regression achieved the best performance overall as compared with the others ;35 (3) tissue-specific regulators . Both LC-DICs and HC-DICs were positively associated with open chromatin  and chromatin de-compaction (ΔDLR), and were negatively associated with H3K27me3 and CpG island levels. HC-DICs, in particular, showed positive features of Hi-C loops but negative features of Pol2 loops and TF binding, which is consistent with our analysis above, indicative of the TF-independent chromatin de-compaction. Taken together, the application of machine learning successfully isolated a special subset of cohesin sites corresponding to DICs, which also provided additional characteristics for DICs.To further explore the importance of genomic features related to DICs, we applied modeling of supervised ML  to predict LC- and HC-DICs from all cohesin sites in a binary manner (labeled by 0 or 1). In this analysis, the input matrix consisted of 168 features . We selected chromosomes 16 to 22 for testing, and the remaining chromosomes were divided into training and validation by five-fold cross-validation (see Methods). Because DICs are a small subset of all cohesin sites, we used SMOTE over-sampling37, we then asked whether the ubiquitous HC-DICs, which have high-level CTCF, can affect transcription across cell types. As a result, the peak intensities for Rad21 at HC-DICs were negatively correlated with transcription levels of their host genes , B-cells (lymphocytes), human skin fibroblast cells, RPE  cells, and HeLa cells . Cohesin peaks at MCF-7 derived LC-DICs were more specific in MCF-7 cells, whereas cohesin peaks at HC-DICs were more ubiquitous across cell types Fig. . Thus, Lnes Fig. , suggest38, respectively. First, we tested if the ML model trained by MCF-7 data was applicable to the RPE data. We used 25 features that were available for both MCF-7 and RPE cells to predict whether the binding of intragenic cohesin was decreased or not after transcription stimulation , indicating that DICs exhibited some common rules across cell types. Then we identified DICs of stimulation-responsive genes in RPE cells  and DRB , which function as a stimulator and inhibitor of transcriptionion Fig. . The pre17. We overlapped the binding sites of intragenic cohesin in different cell types , suggesting that the decreased cohesin binding at DICs was correlated with upregulated gene expression in both CdLS and CHOPS. We classified DICs into 185 LC-DICs and 147 HC-DICs based on CTCF signal (from the ENCODE project: id ENCFF757GIM), and we assessed the binding of TFs pes Fig. . WhereasTFs Fig. . InteresTFs Fig. . Enhance40. Cohesin also participates in the formation of chromatin topological structures via the loop extrusion model8. A recent paper reported that transcription stimuli such as IFN-beta in THP-1 cells can displace cohesin from chromatin10, which attracted our interest. Here, we focused on intragenic cohesin, a subset of cohesin that has not been discussed by previous research. Of note, we emphasized the negative regulation of gene expression by cohesin-mediated chromatin loops, whereas most of the previous studies implicitly assumed the positive regulation. DICs were negatively associated with activated transcription and chromatin compaction. LC-DICs were highly enriched with enhancer markers and paused Pol2, whereas HC-DICs were more involved in the features of chromatin architectures. Importantly, DICs could be found in multiple cell types, especially in CdLS and CHOPS cells, which partly supported the similarities between CdLS and CHOPS.Cohesin is thought to be responsible for transcriptional regulation and chromatin folding. Several models have been proposed to explain its functions. Cohesin can mediate enhancer-promoter loops with the mediator complex or function as a blocker between enhancer and promoter in conjunction with insulator factor CTCF41. By using siRad21 cells, we observed that the release of Pol2 was related to the loss of cohesin at LC-DICs, which supported our model that intragenic loops formed by cohesin paused Pol2 and that transcription elongation from TSSs could remove such cohesin and then release the paused Pol2 . Across different cell types and genes, the Rad21 signal at HC-DICs was negatively correlated with the expression of host genes, indicating the role of HC-DICs in restraining transcription. Whereas we observed that more than half of HC-DIC−mediated loops anchored intronic regions of two genes, it was difficult to infer the biological meaning of this because HC-DICs scarcely overlapped with any other TFs. Further biological approaches such as genome editing of HC-DICs of activated genes could be promising in the future.Chromatin interactions are required not only to facilitate transcription but also for Pol2 pausingol2 Fig. . Velasco11; Intragenic microRNAs affect the expression of their host genes13. Here we present a specific study focusing on intragenic cohesin sites. We also used penalty linear regression followed by univariate linear regression to better understand the changes of cohesin binding in intragenic regions  were related to enhancers and the accumulated Pol2, whereas others (HC-DICs) contributed to chromatin architecture and might attenuate transcription. Our integrated analysis and machine learning approaches indicated distinct characteristics that distinguish DICs from other cohesin binding sites. Based on these genomic, epigenomic and transcriptomic characteristics, we can infer that DICs have distinct functions as compared with other cohesin sites.45, MCF-7 cells (JCRB Cell Bank) and immortalized fibroblast cells (generated in our previous study17) were cultured in DMEM containing 10% FBS and 1% penicillin/streptomycin. Before subsequent treatments, RPE cells were cultured in serum-free medium for 48 h and then were incubated in DMEM containing 10% FBS for 30 min. MCF-7 cells were maintained in phenol red−free medium containing charcoal-dextran−stripped FBS (Life Technologies) at 70−80% confluency for 2 days before treatment with 50 nM E2  for the indicated times. Rad21 stealth siRNAs UUCCACUCUACCUGAUUCAAGCUG  were transfected using Lipofectamine RNAiMax  according to the manufacturer’s instructions at 40 h before treatment with E2. DRB  was added at 1.5 h before treatment with E2. The effect of cohesin (Rad21)-deficiency was verified by western blot as shown in Supplementary Fig. RPE cells46. ChIP-seq libraries were prepared using NEBNext ChIP-seq Library Prep Master Mix Set for Illumina . Rabbit polyclonal antibody for Rad21  was obtained from Eurofins Genomics and has been described in47. Antibodies for MAU2  and SA1  were from Abcam. Antibodies for TAF1  and AFF4  were from Bethyl Laboratory. CTCF  antibody was from Merck Millipore. Antibodies (2.5 ug/million cells) for unphosphorylated Pol2 (CMA601), Pol2ser2 (CMA602) and H3K27ac (CMA309) were kindly provided by Dr. H Kimura (TITech), which were used in previous studies48. Antibody for CBP  was from BioLegend. Antibodies for P300  and Med1  were from Santa Cruz Biotechnology.Cells were fixed in medium or phosphate buffered saline with 1% formaldehyde at room temperature for 10 min. ChIP experiments were performed as described49, ChIP-seq reads were aligned to the human reference genome (hg38) using Bowtie50 version 1.2.2 with “-n2 -m1” parameters, by which we considered only uniquely mapped reads and allowed two mismatches in the first 28 bases per read. Peak calling was performed using MACS251 version 2.2.6 with default settings. We used DROMPA52 version 3.7.2 to conduct statistical analysis and visualization. For visualization of ChIP-seq binding to particular chromatin regions, reads were normalized relative to total read number, and gene annotation was obtained from NCBI reference sequences . Read profiles around the sites of interest were plotted with the PROFILE mode of DROMPA, whereas the heatmap of target sites (2.5 kb around the peak summit) was plotted using HEATMAP mode. Genomic distribution in Fig. 53. Downstream analysis, such as peak overlap, was performed by Bedtools54 version 2.29.2 and Samtools55 version 1.9. Sources for all ChIP-seq data and other next-generation sequencing data (including our data and public data) are listed in Supplementary Tables After quality check by FastQC and SSP25 version 1.11.04. All contact matrices were normalized by the KR method in Juicer. Chromatin loops were annotated using the HiCCUPS algorithm with default parameters25. The loop regions we used were merged from the results of 5-kb,10-kb and 25-kb resolutions. Aggregate peak analysis (APA) was performed using the ‘apa’ mode of Juicer (5-kb resolution), to measure the enrichment of the Hi-C signal around a set of peaks. The visualization of the contact matrix on the MREG locus was accomplished by Matplolib. After correction and normalization, comparable contact matrices were plotted at a 5-kb resolution. We merged two adjacent bins for smoothing. Other Hi-C analyses were performed using HOMER10. We made the Tag directory with the “GATC” restriction site sequence. Chromatin compaction scores ΔDLR and ΔICF were calculated for each 5-kb region across the genome (-res 5000) from a 15-kb window size . Other metrics including PC1, insulation score and TAD boundaries were obtained using HOMER with default parameters. We used the WashU epigenome browser56 to visualize Supplementary Fig. All in-situ Hi-C data (control or E2-treated MCF-7 cells with two replicates) were aligned to the hg38 human reference genome. Further analysis was carried out mainly by Juicer57 with default parameters, based on the hg38 reference genome. ChIA-PET interactions were visualized by DROMPA with the parameter ‘-inter’.RNA polymerase II−bound chromatin interactions in MCF-7 cells were extracted from ChIA-PET data (GSE33664). All fastq files were applied to the published pipeline Mango58 version 2.2.0, we aligned paired-end RNA-seq reads to the index established from the hg38 reference genome. The output SAM files were converted to BAM files by Samtools. Htseq59 version 0.11.3 was then used with default parameters to generate a count table, which describes the number of reads on each gene. We used a GTF file (GRCh38.p12) from GENCODE for gene annotation. Subsequent differential expression analysis was achieved using DESeq260, with its internal normalization. For GRO-seq, alignment was carried out using Bowtie with “-n2 -m1” parameters. The output was preprocessed and visualized using DROMPA.Using HISAT2corrplot. We used scikit-learn version 0.22.1 to perform machine learning. Overall, the parameters used in scikit-learn were optimized by grid search with 5-fold cross-validation. For unsupervised learning (k-means), all 175 features were used to fit models. For supervised learning , we omitted Mvalue, cohesin location and CTCF signal information and then used the remaining 168 features as independent variables All datasets used in machine learning are listed in Supplementary Table 61, to reveal which features contributed to negative or positive Mvalue (log ratio of cohesin peak intensity between E2 and control) in intragenic cohesin (26066 sites). We used 169 features  as independent variables We also applied penalized regression followed by univariate linear regression as described19 version 1.3.0 with default parameters. This results in the normalized Mvalue, a quantitative measure of differential binding for all cohesin sites. To acquire more comprehensive cohesin binding sites, we combined our peak results with high-quality ChIP-seq data from E-TABM-8286. We excluded the cohesin sites with peaks width >3 kb and selected decreased peaks as M value < −0.5. Next, we used RefSeq genome annotations as the reference to obtain intragenic regions. As described in Supplementary Fig. Quantitative comparison of Rad21 binding events was performed using MAnormfindMotifsGenome.pl was used to discover the motif. The results included known motifs as well as de novo discovered motifs. The size of the region used for motif finding was set to 200 bp. The top 10 motifs with the lowest q values (Benjamini-Hochberg) are shown.Motifs were analyzed using HOMER. Briefly, peak files in standard bed format were converted to HOMER peak files, and then the command All analyses were based on Ubuntu 18.04.4 with Python 3.6.9 and R 3.6.3. Data were processed using R base package or Numpy (v 1.17.2) as well as Pandas (v 0.25.1) in Python. Figures were drawn with DROMPA (v 3.7.2), Matplotlib (v 3.1.1), ggplot2 and R base plotting.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


In silico analysis of promoter regions of Jun targets identifies stronger over-representation of AP1-like sites than CRE-like sites, although CRE sites were also over-represented in regions flanking AP1 sites. Strikingly, in motor neurons lacking Jun, an alternative SRF-dependent gene expression program is initiated after axotomy. The promoters of these newly expressed genes exhibit over-representation of CRE sites in regions near to SRF target sites. This alternative gene expression program includes plasticity-associated transcription factors and leads to an aberrant early increase in synapse density on motor neurons. Jun thus has the important function in the early phase after axotomy of pushing the injured neuron away from a plasticity response and towards a regenerative phenotype.The regeneration-associated gene (RAG) expression program is activated in injured peripheral neurons after axotomy and enables long-distance axon re-growth. Over 1000 genes are regulated, and many transcription factors are upregulated or activated as part of this response. However, a detailed picture of how RAG expression is regulated is lacking. In particular, the transcriptional targets and specific functions of the various transcription factors are unclear. Jun was the first-regeneration-associated transcription factor identified and the first shown to be functionally important. Here we fully define the role of Jun in the RAG expression program in regenerating facial motor neurons. At 1, 4 and 14 days after axotomy, Jun upregulates 11, 23 and 44% of the RAG program, respectively. Jun functions relevant to regeneration include cytoskeleton production, metabolic functions and cell activation, and the downregulation of neurotransmission machinery.  Neurons axotomized by peripheral nerve injury initiate a gene expression program, which not only facilitates long-distance regeneration in peripheral nerves, but can also achieve growth in inhibitory areas such as sites of spinal cord injury . A stronThus, although a fairly complete picture of the regulated genes in regenerating neurons has been acquired , many TFJun was the first TF to be identified as regulated during regeneration  and the Jun homo- and heterodimers can act via AP1 sites and CRE sites , with thin silico analysis of the promoter regions of Jun-dependent RAGs reveals that these promoters display over-representation of AP1 sites, much more than of CRE sites, although these are found more frequently near to AP1 sites. Furthermore, we show that in the absence of Jun the neuronal response to axotomy begins with a plasticity-type response involving an increase in synapses on the motor neurons. In facial motor neurons, Jun thus has a specific function in pushing the neuronal injury response towards regeneration rather than synaptic plasticity.In this study, we have carried out gene expression profiling by microarray of regenerating mouse facial motor neurons in which Jun was knocked out. We have made a comprehensive determination of the contribution of Jun to successful regeneration in facial motor neurons, in terms of target genes, functions and regulatory mechanisms at the promoter level. We quantify the contribution of Jun to the RAG program and show it is essential for the upregulation of 254 genes. Jun target genes are found in a variety of functional categories. Deep Floxed Jun mice crossed with nestin-cre mice (referred to as KO), in which Jun is deleted in the central nervous system, and cre-negative littermates (referred to as WT) received a facial nerve injury. Facial motor nuclei, visualized by cresyl violet staining , were exPrinciple component analysis of the 500 most variable genes , and hiede novo regulation of other genes . Differentially expressed genes were thus categorized into four classes at each time point, based on the regulation induced by axotomy in wild type animals and the effect of Jun deletion on expression  were differentially expressed between WT and KOs uninjured animals  over-representation analysis on the genes in the JunUP, JunDOWN and AltUP classes. Over-represented GO classes were grouped into broader categories for summarization  The ful.In the JunUP class, most identified categories of function were regeneration-related activities (labeled pink in In the JunDOWN set of genes (Jun-dependent axotomy-downregulated genes), over-representation of genes related to neurotransmission was seen at 4 days  revealed GO classes related to synaptic plasticity and learning and memory (labeled blue in in situ hybridization (ISH) or immunohistochemistry for a number of genes in the JunUP and AltUP groups. Targets for validation were selected that showed clear differential expression between WT and KO animals. Targets in The nestin-Cre mouse line used here should delete Jun in all cells of the facial motor nucleus and not just motor neurons. Jun expression is confined to motor neurons after axotomy , so the n = 2–3). The time point shown for each gene was chosen where the differential regulation between genotypes was the strongest according to the gene expression profiles. Quantification of gene expression and protein expression differences observed by ISH and immunohistochemistry, expressed as the ratio of intensities in facial motor neurons in WT and KO sections, is shown in All selected targets were upregulated specifically in motor neurons, in a manner consistent with the gene expression profiling. Lastly, in silico analysis of sequences up to 5 kb upstream of the transcription start sites (TSS). These sequences include the core promoters (35–50 bp upstream of the TSS), the proximal promoters (up to 250 bp upstream) and distal promoter elements (over 250 bp upstream). We refer to the sequences up to 5 kb upstream of the TSS as ‘promoter regions’. These regions are important sites of transcriptional regulation. Although regulation also takes place outside these regions in distal enhancers and also in downstream sequences, analysis of these regions can shed light on the subset of regulatory mechanisms that take place in these areas. We sought evidence for the presence of AP1-like or CRE-like sites in the promoter regions of Jun-dependent RAGs, and we aimed, where possible, to determine if motifs corresponding to particular dimerization partners were preferred. We also looked for binding site motifs present in promoter regions of de novo upregulated genes in the AltUP class. Sequence motifs of typical AP1 and CRE sites are shown for comparison in In order to learn more about possible mechanisms of Jun-dependent RAG regulation, we performed an We looked for over-representation (OR) of target sites in promoters of interest compared to a control set of promoters of unregulated genes. We found that using a flexible scoring threshold  improved sensitivity. The efficacy of this approach is demonstrated in We also incorporated a requirement for conservation across multiple species  . We searched for over-representation of AP1 sites and of ATF2, ATF3 and CREB1 binding sites , optimizing first for over-representation ratio and then for ‘Additional Sites’ (AS) i.e. the increase in number of sites found over that expected based on the control promoters. We searched exhaustively, varying conservation (in 0–5 species besides mouse) and promoter length. AP1 site over-representation was found at all time points in the JunUP promoters , while CWe examined AS scores for Jun dimers with specific partners , and ploWe then tested all binding site matrices for the AS measure in JunUP promoters . At day P = 10−7, binomial test; false discovery rate (FDR) = 0.24). At day 4 and day 14, SRF sites were still over-represented, but CEBP family sites showed the greatest AS score  in these promoters. At day 1, this was the only site showing significant over-representation  of AP1 sites in JunUP promoters, and of SRF sites in AltUP promoters. At day 1 and day 4 we found over-representation of CRE sites near AP1 sites . Lastly,GO analysis of the AltUP program, containing genes usually suppressed by Jun, revealed GO classes related to synaptic plasticity, learning and cognition. We found SRF binding sites over-represented in AltUP promoters, and indeed AltUP members Fos, Egr1 and Egr2 are known targets of SRF . FurtherThe synaptic plasticity functional class included three out of eight TFs in the AltUP class, and Egr1 in particular was one of the most strongly upregulated genes. Several other strongly upregulated genes  may affect synaptic plasticity ,41 and aThe existence of a plasticity response in axotomized facial motor neurons is of interest because neuronal plasticity, rather than regeneration, is characteristic of the CNS response to injury (e.g. stroke or traumatic brain injury). Motor neurons are located in the CNS but normally respond with a regeneration response following injury to their axons. The existence of this plasticity response in KO facial motor neurons is significant because it suggests Jun has evolved a specific role to favor regeneration in these motor neurons. We therefore chose to study this unexpected response further.We first investigated whether increased plasticity was detectable in the axotomized facial nuclei in Jun mutants, by quantifying synapse density on motor neuron profiles and on perineuronal dendrites.Axotomized (1 day post-injury) and uninjured facial nuclei were immunostained for synaptophysin and MAP2, along with a fluorescent Nissl stain to identify motor neurons. Motor neuron profiles were identified using the neural network U-NET  via its P < 0.05). In wild type mice, synaptic density appears to decrease, consistent with the known phenomenon of synaptic stripping . Synaptic density decreased in the first day after axotomy and then remained stable, although this initial decrease is not significant. Strikingly, in KO motor neurons a significant increase in synaptic density was observed on cell bodies at day 1 .Surprisingly, in Jun KO motor neurons, the baseline synaptic density was significantly lower than in WT mice  after axotomy (P = 0.008). The change in synaptic density can be seen in the images of motor neurons shown in Synapse density on dendrites close to motor neuron cell bodies showed a similar pattern. Synapse density on dendrites increased significantly  construct in the KO facial nucleus, using adeno-associated viral (AAV) vectors. A comparison of AAV serotypes revealed that AAV6 gave robust neuron-specific transduction in the mouse facial nucleus . AAV6 veImmunohistochemistry revealed that dnSRF does indeed block expression of Fos  and Egr2P = 0.018; P = 0.013) indicating the two groups behave differently. Synapse density changes can be seen in the images of GFP-positive motor neurons in Synaptic density measurements in uninjured animals and at 1 day after axotomy were performed on GFP-positive motor neurons and on GFP-positive perineuronal dendrites. As shown in P = 0.013). In the GFP-only virus group, the increase in synaptic density is not significant. However, a significant decrease is seen in the dnSRF group (P = 0.006), indicating that synaptic stripping is taking place. These results show that the plasticity response in response to axotomy, resulting in new synapse formation on motor neurons, is driven by SRF target genes.Synaptic density on dendrites shows a similar response , with a In this study, we have defined the role of Jun in the regeneration-associated gene program of injured facial motor neurons, identifying its regulatory targets, its biological functions and modes of gene regulation, and revealing its key importance in directing the axotomized facial motor neuron towards a regenerative response over a plasticity response. Currently, while many TFs have been linked to functional effects on axon regeneration ,22,24,48We find that Jun contributes to upregulation of a significant fraction of the RAG program in facial motor neurons, increasing over time from about 10% at day 1 to around 40% of the RAG genes at 14 days. Approximately half of these genes are absolutely dependent on Jun for their upregulation in response to axon injury. The initial phase of the response to axotomy appears to proceed by multiple pathways, since the upregulation of most other TFs associated with axotomy appears to be largely unaffected in the first 24 hours, but Jun appears to drive expression of 10–20% of the transcription factors going up after axotomy  at later post-lesion time points. In a gene regulatory network, key TFs at the network hubs drive expression of other TFs, and this appears to be the case for Jun.Successful regeneration occurs after axotomy in multiple types of neurons, e.g. sensory and autonomic neurons in the peripheral nervous system and spinal motor neurons. It remains to be seen whether Jun deletion has s similar impact on RAG expression in these other regenerating cell types.in silico analysis, heavier over-representation of AP1 sites than CRE sites in Jun-dependent RAG promoter regions. While such an analysis does not allow firm conclusions to be drawn about regulatory mechanisms, these findings suggest that, in regions upstream of transcription start sites, direct Jun regulation of RAG targets may occur predominantly via AP1 sites rather than CRE sites, although we find evidence for some CRE-dependent targets as well as regulation via adjacent AP1 and CRE sites. This is in contrast to a recent Chip-SEQ analysis of Jun binding-sites in regenerating neurons, which did not clearly favor AP1 or CRE sites , AP1As well as acting via promoters, it is likely that Jun activity at enhancer sequences also contributes to regulation after axotomy. Indeed, a ChipSEQ study of Jun binding in sensory neurons showed changes in Jun binding at enhancers after axotomy . RegulatJun target genes have a broad range of functions relevant for regeneration, including many signaling pathways and cell adhesion. Furthermore, Jun appears to have a general activating effect on the cell, increasing both metabolism and cytoskeleton production. Jun also appears to control the downregulation of neurotransmission machinery, such as post-synaptic components (including GABA receptors), which may be the gene expression correlate of synaptic stripping, and potassium channels, which may be related to the changes in electrical properties that occur post-axotomy ,51. SomeFinally, as summarized in The increase in synapse density is accompanied by expression of the plasticity-linked SRF target genes Fos, Egr1 and Egr2. Activation of SRF after axotomy is known to occur via phosphorylation of the cofactor Elk1, likely by axoplasmic ERK ,54. HoweIt is likely that, at least in the mammalian CNS, neurons have evolved to favor plasticity over axon regeneration by default, as this is an effective response to stroke and traumatic brain injury, and also given the importance of learning as a CNS function. Facial motor neurons, surprisingly, seem to express the pathway that leads to a plasticity response to axotomy, although it is normally latent. In this case, Jun, as well as coordinating a large part of the RAG program, appears to have the additional role of suppressing this pathway in favor of regeneration. It will be interesting to determine if the plasticity response would also manifest in other motor neuron types when Jun is knocked out, and in sensory neurons, which lie outside the CNS. Interestingly, the activation of SRF by axotomy was demonstrated in mouse sensory neurons . OverallIn summary, in this work, we have fully defined the role of a specific TF in the control of the regeneration gene expression program in facial motor neurons. A similar approach applied to other factors, and the promoter analysis techniques employed here, will lead to a more complete understanding of the control of the RAG program, and thus enable development of rational TF-based strategies to promote axon regeneration in the injured CNS.Homozygous floxed Jun mice  were croAll procedures were carried out in accordance with local animal experimentation rules. Animals operated in the UK were carried out with approval under the Scientific Procedures Act in the United Kingdom and in the Netherlands experimental procedures were approved by the local laboratory animal welfare committee and performed in accordance with European guidelines (2010/63/EU).Animals that received facial nerve injury were anaesthetized with Avertin or isoflurane and the right facial nerve was exposed and cut near the stylomastoid foramen, taking care to cut also the retroauricular branch. The wound was closed with suture clips and the animals were allowed to recover in a heated chamber.For gene expression profiling, a total of 37 animals were utilized. 29 animals were operated on. After 1 day , 4 days  and 14 days , animals were anaesthetized with isoflurane and sacrificed. Brains were removed and frozen in OCT (Finetek) on dry ice. Brains of 8 uninjured animals  were also obtained and frozen in the same way.For validation of gene expression with histology and for quantification of synapse density, a further 30 animals were operated and 10 unoperated animals were sacrificed (n = 5 for each condition). At 1 day, 4 days or 14 days after surgery, animals were euthanized with pentobarbitone and perfused transcardially with PBS followed by 4% paraformaldehyde (PFA).n = 7) or were sacrificed uninjured (n = 6). Animals were euthanized with pentobarbitone and perfused with PFA as before.For experiments with adeno-associated viral vectors (AAV) expressing dominant negative SRF, 26 animals were used. Animals were injected with AAV6 expressing farnesylated GFP (fGFP) and dominant negative SRF or fGFP only. After allowing 2 weeks for transgene expression, animals either received a facial nerve injury (12 genomic copies/ml) was injected over 5 min. After a further 2 min delay, the needle was removed and the wound closed, post-operative analgesia was given, and the animals were allowed to recover.AAV delivery to the facial nucleus was carried out as follows. Mice were anaesthetized with isoflurane, an incision was made in the scalp along the midline, and the animal was mounted in a stereotaxic frame. The skull was positioned such that bregma and lambda were level to within 50 μm. The rostro-caudal position for injection was calculated as the midpoint of 1.75 mm caudal to bregma and 6.08 mm caudal to lambda. The other coordinates used for injection were 1.25 to the right of midline and to a depth of 6.05 mm. The distance from lambda to the caudal skull suture was measured and used to scale the coordinates appropriately, the standard measure being 7.75 mm. A hole was drilled in the skull, the dura mater opened with a fine needle and a glass needle containing AAV vector, mounted on the frame, inserted into the brain to the required depth. After a 3 min delay, 0.5 μL of viral vector (titer 1 × 10Sections of brainstem were cut on a cryostat at 20 μm thickness onto PALM PEN–coated slides (Carl Zeiss b.v.), allowed to dry and stored at −80°C. To visualize the facial nuclei, slides were transferred to 70% alcohol containing 0.1% Cresyl Violet for 5 min at 4°C. Slides were then dehydrated in 100% EtOH at 4°C (2 × 5 min) and dried at room temperature. This allowed clear visualization of the facial nucleus, which was then excised using a PALM Laser Microbeam Microdissection (Carl Zeiss b.v.). RNA was extracted using Trizol and RNeasy microcolumns as described . RNA samSamples were amplified and labeled with the Agilent Low RNA Input Fluorescent Linear Amplification Kit (Agilent Technologies) using Cy3-CTP or Cy5-CTP (Perkin Elmer). Samples were hybridized to Agilent 44 k Mouse Whole Genome Arrays (part no. G4122F) and scanned using an Agilent DNA Microarray Scanner. Amplification, labeling and scanning were carried out as described . ExpressAt each time point, all genes were classified first by their expression change in the WT animals following axotomy, as either upregulated (i.e. RAGs), downregulated or not regulated, compared to intact facial motor nuclei. Genes were then classified by the effect of Jun deletion on the expression profiles, as either higher in the KO, lower in the KO, or unchanged. Genes were then grouped as shown in http://www.informatics.jax.org/) and the European Bioinformatics Institute (https://www.ebi.ac.uk/GOA/) and the two sources combined. The background gene list was taken as all genes with probes present on the array. Only over-represented GO classes with P < 0.05, an over-representation ratio > 2, and over 4 steps from the root node were considered. For a group with n genes classes with fewer than loge  n genes or fewer than 2 genes were discarded. The FDR was determined for each significant GO class in a gene list by random resampling of the background gene set and determining how many GO classes were found to be over-represented, meeting the criteria above, with a p-value lower than that of the GO class under test, taking the geometric mean of 100 trials. For presentation and classification, GO classes were grouped according to their parent–child relationships in the GO tree structure. Classification of GO classes into broader categories was performed manually.GO over-representation analysis was performed in R  with thecDNA for qPCR was made with random hexamer primers and MMLV Reverse Transcriptase . Quantitative PCR was carried out using 2x SYBR green mastermix (Applied Biosystems) and ABI 7300 real-time PCR system. qPCR primers used for validation are given in n = 2 or 3.Targets for validation of gene expression were chosen to represent over-represented GO functions in the JunUP class and genes in the AltUP class, including plasticity related TFs. Validation was performed at all time points with Sequences used to generate probes for ISH are detailed in ISH was carried out as previously described , with thFor combined localization of fGFP with ISH signal, sections were first coverslipped with PBS containing RNaseOUT (Promega) diluted 1:50 and 500 ng/ml ethidium bromide (as a Nissl stain), and fluorescent microscopy images were taken of GFP native fluorescence and the red Nissl stain. Coverslips were then removed and slides placed in cold 70% EtOH prior to ISH being carried out. Subsequently images of ISH and fluorescence were aligned with ImageJ (plug-in ‘Align Image by line ROI’).The following antibodies and final dilutions were used: rabbit anti-Fos , rabbit anti-Egr2 ; mouse anti-MAP2 , rabbit anti-synaptophysin , chicken anti-GFP . All primary antibodies were incubated overnight, and all antibodies were diluted in IHC blocking medium  for incubation. Fos and Egr2 incubations were followed with biotinylated anti-rabbit , followed by ABC kit  and visualization with 3,3’-Diaminobenzidine (DAB) .MAP2 staining in combination with synaptophysin, Nissl staining and GFP was carried out as follows. To avoid cross-reactivity with endogenous immunoglobulins when using the mouse MAP2 antibody, this antibody was first incubated at 10× concentration with an F’ab fragment anti-mouse secondary antibody conjugated to DyeLight 649 (Jackson Immunoresearch Labs) at 1:50 in PBS. After 2 hours, mouse serum was added to 10% volume. After 1 more hour, the mixture was diluted 1:10 in IHC blocking medium, the synaptophysin and GFP antibodies added, and the mixture applied to the sections. After an overnight incubation and washes, the secondary antibodies anti-rabbit AlexaFluor594 and anti-chicken AlexaFluor 488  were applied for 2 h.For MAP2/synaptophysin/Nissl staining, ethidium bromide was applied at 500 ng/ml for 5 min to provide a red fluorescent Nissl stain , before Images were acquired on a Zeiss Axioplan 2 microscope with a Retiga2000DC camera (fluorescence) or an Evolution MP color camera (brightfield), where a standard gamma correction was applied to color brightfield images, and on an Axioscan Z1 slide scanner.Quantification of validation stainings  was perfFor AAV serotype testing in the facial nucleus AAV vectors packaged with pTR CGW  (contain12 genomic copies/ml for injection.Dominant negative SRF (dnSRF) was generated by fusing the engrailed transcriptional repressor  to SRF. Seven training images of fluorescent Nissl-stained facial nuclei and 2 test images were manually segmented for facial motor neuron cell bodies and were used to train the U-NET neural network for image segmentation  using thSynapse density was expressed as a proportion of the area measured. Since proportions are not normally distributed, these were analyzed using quasibinomial general linear models using R . Where cTranscription factor binding site (TFBS) position-frequency matrices were obtained from the CORE and PBM collections of JASPAR2020  and fromMany algorithms are available for identification of TFBS in promoter sequences. Almost all algorithms employ fixed scoring thresholds to determine whether a sequence matches a given PWM, typically either the same fixed threshold for all PWMs, or a pre-calculated one for each PWM. For example, TRANSFAC offered thresholds for each PWM to maximize false positives, based on the presumed lack of functional binding sites in the second exon of coding mRNAs, or false negatives, or both . The scoAn alternative approach, used by the F-match algorithm , is to oA control promoter set was chosen of genes that showed no significant difference in expression due to Jun knockout and that also showed fold-change differences between genotypes of less than 1.3 fold. In addition, only genes with a maximum expression level over 7 (approximately the mean baseline expression level) were included, to exclude promoter regions, which might be in heterochromatin regions and thus inaccessible.The following matrices were used to look for over-representation of AP1 and CRE sites in JunUP promoters: V$AP1_C from TRANSFAC; MA0018.3 (CREB1); MA0605.2 (ATF3); MA1632.1 (ATF2) .For each PWM representing a TF, scores at potential match sites were generated for the test promoter set and the control promoter set. Next, an optimized threshold was determined. This is achieved by compiling all the scores for a given PWM in the test and control promoter regions, to create the list of all possible thresholds, which are then tested exhaustively. For each possible threshold, the over-representation (OR) ratio, the ‘Additional Sites’ (AS) score, and the significance were calculated. Only scores were considered that gave a site frequency in the control promoter set of 0.01–0.2 sites/kilobase.OR ratio was calculated as (A/lenA)/(B/lenB), where A is the number of matches in the promoter set under test, lenA is the total length of the promoters under test, B is the number of matches in the control promoter set, lenB is the total length of the control promoters. The ‘Additional Sites’ measure is calculated as A-(B*lenA/lenB).Statistical significance of TFBS over-representation and under-representation was calculated with one-sided binomial tests, using the number of sites found in the test promoters and in the control promoters and the total respective lengths of each promoter set.To further improve sensitivity and specificity, we incorporated cross-species conservation in the scoring method. Functional binding sites are likely to be conserved by evolution, so much of the noise of false positives due to random occurrence of sequences can be removed by considering only conserved binding sites, a fact used by several available tools for identifying regulatory elements . Whole gn species, the scores across all the genomes are ranked in descending order and the nth score is taken. Whatever threshold is chosen, the site will meet the conservation requirement if and only if this conserved score is over the threshold. Thus, the multiple scores from the various species at each position are reduced to a single score at that position, and the algorithm can proceed as for the single species case, as described above.At each initial match in the mouse genome, the ‘conserved score’ was calculated as follows. If the requirement is that a site is conserved in Because many of these TFBS scoring matrices are similar, matrices were grouped if they identified sites in identical locations. If at least 50% of a given PWM’s hits were in the same location (within 4 bp) as those of another PWM they were grouped together.a promoters of interest to b control promoters, each test run compared a random promoters from the combined promoter set to the remaining b promoters. Over-represented sites were grouped by location as above, 100 test runs were performed and the average number of groups with an equal or better optimum value of the score under optimization was calculated. All reported results have a group-wise FDR < 0.05 unless otherwise stated.The FDR was calculated for binding site groups (group-wise FDR) for each analysis by a resampling method. For each resampling run, the analysis was carried out on randomized sets of genes drawn from the combined sets of test and control genes. If an analysis compared Regions flanking 100 bp on either side of previously identified sites matching given PWMs were used, where all initial matching sites were excluded, and random 200 bp fragments from the control promoter set were used for the comparison.AAVadeno-associated vectorASadditional sitesDABdiaminobenzidineDnSRFDominant negative SRFfGFPfarnesylated GFPGOgene ontologyISHin situ hybridizationORover-representationPFAparaformaldehydePWMposition weight matrixRAG TFregeneration-associated transcription factorRAGregeneration-associated geneSRFserum response factorTFtranscription factorTFBStranscription factor binding siteTSStranscription start siteCode for the analysis of promoter regions will be made available on request.Supplementary_Tables_and_Figs_ddab315Click here for additional data file.Supp_Data_1_ddab315Click here for additional data file.Supp_Data_2_ddab315Click here for additional data file.