Nutrient availability and stresses impact a cell’s decision to enter a growth state or a quiescent state. Acetyl-CoA stimulates cell growth under nutrient-limiting conditions, but how cells generate acetyl-CoA under starvation stress is less understood. Here, we show that general stress response factors, Msn2 and Msn4, function as master transcriptional regulators of yeast glycolysis via directly binding and activating genes encoding glycolytic enzymes. Yeast cells lacking Msn2 and Msn4 exhibit prevalent repression of glycolytic genes and a significant delay of acetyl-CoA accumulation and reentry into growth from quiescence. Thus Msn2/4 exhibit a dual role in activating carbohydrate metabolism genes and stress response genes. These results suggest a possible mechanism by which starvation-induced stress response factors may prime quiescent cells to reenter growth through glycolysis when nutrients are limited. Cell growth and proliferation are actively coordinated with extrinsic nutrient availability and intrinsic metabolic states . When nuHere, we exploited the yeast metabolic cycle (YMC) to study how transcriptionally regulated metabolism impacts cell growth program . In the Following this rationale, we developed a computational algorithm, DynaMO, to systematically predict which of the 175 yeast TFs that are linked to specific phases of the YMC . Briefly2 consumption) in the candidate TFs  than in control randomly selected TFs .   . We conf−5)  and intr strains . The numains −7; . On the ains −7; . To furte mutant . Collecte mutant , suggeste mutant . Interes mammals .msn2Δmsn4Δ double mutant compared to the WT strain  state of YMC by comparing it with multiple previously defined quiescent states, including starvation and limitation of one of the three essential nutrients, glucose, nitrogen or phosphate . We examMSN2 and MSN4 mutants is consistent with a transcriptional program in the quiescent phase which could be critical for subsequent cell proliferation. Our analyses suggest that Msn2/4 modulate the cellular programs by changing the intracellular acetyl-CoA levels, probably by promoting glycolysis. Metabolism has strong effects on cell growth and proliferation. Metabolites such as acetyl-CoA have been shown to function not only as building blocks for the synthesis of fatty acids, amino acids and nucleotides, but also as signals for gene expression and enzymatic activities 2SO4, 2 g/L KH2PO4, 0.5 g/L MgSO4•7H2O, 0.1 g/L CaCl2•2H2O, 0.02 g/L FeSO4•7H2O, 0.01 g/L ZnSO4•7H2O, 0.005 g/L CuSO4•5H2O, 0.001 g/L MnCl2•4H2O, 1 g/L yeast extract, 10 g/L glucose, 0.5 mL/L 70% H2SO4, and 0.5 mL/L Antifoam 204 (Sigma)  .All strains were generated from the CEN.PK background and manipulated by standard budding yeast protocols:Yeast strains:kanMX6 or hygMX) and 40 bp sequences flanking the target gene. Tagged-protein strains were generated similarly by integrating a cassette containing a protein tag and a drug resistance cassette at C terminus . Fast SYBR Green Master Mix  was used for real-time PCR and experiments were done on the platform of StepOnePlus Real-Time PCR System . RNA-seq libraries were prepared in the New York University Genome Technology Center using Illumina Trueseq RNAseq v2 library kit . PolyA beads were used for mRNA selection. 500 ng of RNA per sample was used as input and 12 cycles of PCR were run for amplification. Libraries were pooled together and sequencing was performed on Hiseq platform.2 OD cycling cells were collected and flash frozen. RNA was extracted with the Qiagen RNeasy Mini kit . First strand cDNA was synthesized by reverse-transcription using oligo(dT)AB_672215), Msn4 . Validation is provided on the manufacturer’s website and the antibodies were further tested by western blots of WT, msn2Δ and msn4Δ lysates  and 5 OD cells were spun down and lysed in 200 µl of 10% perchloric acid by bead beating. The lysate was spun down and the supernatant was neutralized to pH 6–8 with 3 M K bicarbonate, with vortexing and cooling on ice for 5 min. K bicarbonate was spun down and supernatants were used. For the second method, 5 OD cells were resuspended in 4 mL Quenching Solution M  and incubated at −40°C for 5 min. The cells were spin at 1000 g for 3 min at −10°C and washed once with Quenching Solution M. Pellets were resuspended in 1 mL Extraction Buffer M  and incubated at 80°C for 3 min. Mix was cooled on ice for 5 min and spun down. Supernatants were dried down in speedvac and dissolved in 30 µl water. The concentration of acetyl-CoA was measured using Acetyl-Coenzyme A Assay Kit .Cells in log or stationary phase were counted microscopically in a hemocytometer. A fixed number of cells was plated on YPD plates and the colonies were counted. The survival rate was the number of colonies/number of cells plated. Three biological replicates were examined.Log or stationary phase cells were harvested and washed with PBS twice. Forward scatter (FSC) was measured by BD Accuri C6 Flow Cytometer  and used as the indicator of cell size. Two biological replicated were examined and plotted.The DynaMO algorithm is described in detail in the manuscript .SCR_004694) annotation file (http://www.yeastgenome.org/download-data/curation#.UQFqKhyLFXY) with Fisher exact test. P values were converted into FDR by p.adjust to adjust for multiple testing.To evaluate the overlap between Msn2 and Msn4 binding sites , Msn2 anRNA-seq reads were mapped against SacCer2 genome using bowtie . Read cohttp://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeUwDnase. Then, the whole genome (chromosome Y excluded) was separated by 200 base pair bins (i.e. genomic loci). Bins with read counts larger than 10,000 in one or more cell types  and bins with read counts smaller than 10 in all cell types (noisy loci) were excluded. After filtering, 1,108,603 genomic loci with unambiguous DNase-seq signal in at least one cell type were retained.To examine the enrichment of Msn2 and Msn4 motif sites in different processes, yeast genes were extended by 1000 bp upstream from the start codon. Glycolysis, fatty acid oxidation and galactose metabolism genes were selected based on SGD annotation and previous study . We examhttp://hgdownload.cse.ucsc.edu/goldenPath/sacCer2/phastCons7way/. Six strains were used for alignment, Saccharomyces paradoxus, Saccharomyces mikatae, Saccharomyces kudriavzevii, Saccharomyces bayanus, Saccharomyces castelli, and Saccharomyces kluyveri. Promoter regions were calculated as 0–500 bp upstream of start codons and ORF regions were regions from start codons to stop codons. Scores were averaged across each base pair and further averaged across interesting groups of genes.PhastCons scores for multiple alignments of the yeast strains to the Saccaromyces cerevisiae genome are downloaded from Data of gene expression, nutrient-specific genes and genes required for specific nutrient starvation were downloaded from a previous study . Gene exhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72263).RNA-seq and ChIP-seq data have been deposited in the Gene Expression Omnibus database under accession code GSE72263 . Based on the results, the authors conclude that the two factors regulate key transition in this cycle by regulating the accumulation of acetyl-CoA.As you will see below, all three reviewers appreciated your study in providing interesting new data concerning the regulation of the metabolic cycle. However, there was also a serious concern about your claim of direct relationship between Msn2/4 regulation and acetyl-CoA pool levels (and production of this metabolite). Two of the reviewers were more supportive of this conjecture, yet even these reviewers requested that you will further validate your claims by analyzing the consequences of MSN2,4 over-expression, either transiently or by using a constitutive systemThe third reviewer, who is a metabolic expert, maintained that you should focus your study on the role of Msn2/4 in the shift between fermentation and respiration, and remove the claim about direct relationship between Msn2/4 regulation and acetyl-CoA pools. I would like to emphasize that since this reviewer is an expert in metabolism, we will not be accept the paper unless he/she accepts the arguments and claims presented.In addition, all three reviewers requested that you better discuss previous studies, and in particular refer to previous work on carbohydrate metabolism during nutrient deprivation.Reviewer #1:In this manuscript, Kuang and co-authors showed that the general stress responsive factors Msn2 and Msn4 play an important role in driving yeast metabolic cycle (YMC). They further revealed that it is through controlling the accumulation of acetyl-CoA and the same mechanism might generally contribute to cell growth regulation under prolonged nutrient limiting conditions.msn2/4 deletion to the progression of YMC is striking! (2) the proposed mechanism is based on careful genome-wide analysis and is convincing. (3) the potential relevance to cell growth regulation in general is intriguing. Therefore, I recommend the acceptance of this manuscript for publication in eLife provided the authors are able to address the major points below.I find this paper interesting in that (1) the effect of msn2/4 deletion) and the correlation analysis between metabolic enzyme expression or Msn2/4 genomic occupancy with YMC phases. It would significantly strengthen the paper if the authors can show some gain-of-function effects. For example, how does Msn2/4 overexpression affect YMC and the growth transition in general? This is especially important because most people in the field believe that activated Msn2/4 slow down cell growth.1) The conclusions are based primarily on loss of function mutant phenotypes (2) Msn2 and Msn4 are activated by dephosphorylation and translocation from the cytoplasm to the nucleus, therefore nuclear localization can serve as a reporter for Msn2/4 activity. In the paper, the authors only monitored the expression changes during YMC. It would serve as the direct supportive evidence for Msn2/4 activation if the authors can observe nuclear localization of Msn2/4 specifically during the RC phase. And if so, what would be the authors' speculation on the mechanisms of Msn2/4 activation? This will provide some clues on why Msn2/4 are activated specifically during the RC phase.3) The authors claim that Msn2/4 have a dual role in regulating carbohydrate metabolism genes and stress resistance genes. Under the conditions that they are focusing on (prolonged nutrient-limited condition), do the stress resistance genes also contribute to cell growth or they are unrelated by-products? An analysis or at least a discussion on this would be helpful.Reviewer #2:This paper describes analysis of how the transcription factors Msn2 and Msn4 regulates expression of genes encoding enzymes of the central carbon metabolism in yeast, with focus on regulation during the metabolic cycle of yeast. These TFs are known to play a central role in stress regulation, and they regulate expression of a very large number of genes in yeast (>100). The authors claim that based on their findings it can be suggested that Msn2/4 control the transition from reductive charging phase to oxidative phase by regulating the accumulation of AcCoA. Regulation of shifts in yeasts metabolism is highly complex, and it tends to the naive to suggest such a statement, and it is by no means supported by the data. Thus, the authors focus their discussion on how Msn2/4 binds (and control expression) to genes of the glycolysis, but these genes are controlled by a large number of other TFs. Furthermore, Msn2/4 themselves control a large number of other genes, including several other TFs, which is not considered in their analysis. A main line of argument is measurements of AcCoA, but this metabolite is present in several cellular compartments – mitochondria, cytosol, peroxisomes and the nucleus (probably equilibrated with the cytosolic pool), and there is no free transport of AcCoA between these compartments. Measuring the total level and linking this to enzymes that are operating in different compartments is not correct . Moreover, the authors show that RC phase gene expression closely resembles that of nutrient deprivation performed by other labs. Thus, data observed in the YMC is largely applicable to nutrient stress environments in general.The findings presented in the manuscript are of high significance and broaden the knowledge of factors and mechanisms that influence cell survival. In particular, recent publications on acetyl-CoA demonstrate that this a key metabolite in promoting cell growth and proliferation. This data demonstrates that well-established transcription factors support the metabolism of this critical metabolite. The data in this manuscript is clear and the writing is concise. In general, the conclusions support the main findings of the paper.The following are specific comments that can improve the manuscript:1) The authors could do a better job in integrating current literature on the importance of carbohydrate stores, particularly trehalose and glycogen, in survival in nutrient stress conditions and re-entry into the cell cycle from quiescence.2) Why did the authors focus on H3K9 acetylation, rather than gene expression, in the DynaMO prediction transcription factors that drive the YMC?  Does the focus on H3K9ac provide a better assessment of gene expression that is responsive to acetyl-CoA?3) The Introduction states that TFs were deleted to validate their role in the YMC. This is a bit vague. Can the authors state which ones? Is Msn2 and 4 unique in terms of its YMC disruption?4) In the Introduction, the authors state that Msn2 and 4 are known to regulate stress genes. This statement should include relevant references, such as Gasch et al., 2000; Martínez-Pastor et al., 1996; Schmitt and McEntee, 1996).5) The authors may also want to discuss upstream factors that regulate Msn2 function. In particular Tor signaling, which is influence by nutrient availability.[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Nan Hao (Reviewer #1); Ashby J Morrison (Reviewer #2).Thank you for submitting your work entitled "Stress-response factors Msn2/4 control re-entry of quiescent cells into growth through glycolysis" for consideration by eLife.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife, however, we would have expected more solid insight into how one goes from altered glycolysis to changes in the regulatory molecule acetyl-CoA. This link, unfortunately, is not yet convincing enough and the revision did not help in solidifying this result.We found the data on Msn2/4 regulating glycolysis convincing and believe it can be published as is. For Reviewer #1:In the revision, the authors did not examine the YMC progression or acetyl-CoA level (which are where the major debates centered on) in the overexpression strain, because "the YMC requires coordination of various parameters"(?). Instead, they just checked the regrowth rate and got a variety of inconsistent results. This is not surprising given that they transformed yeast with a 2 micro plasmid (pRS425) expressing Msn2 or Msn4. The variability of plasmid copy numbers is huge, and therefore the kind of colony variability is expected no matter which output phenotypes (growth rate or others) you will be looking at. A genomic integration (with pRS305) will work way better.At this point, it seems my attempts to save the debated conclusion did not work out. However, given their striking findings summarized in my first review, I still support the publication of this manuscript, but would suggest the authors to tone down their conclusion and avoid claiming of direct causal or driving relationship.Reviewer #2:The authors have addressed my previously stated concerns in the revised manuscript. I believe the manuscript is a significant advancement in our understanding of the Msn2/4 involvement in quiescence to growth transitions and energy metabolism.Reviewer #3:The manuscript by Kuang et al., 2014 addresses transcriptional regulation during the yeast metabolic cycle. They attribute a key role of the transcription factors Msn2 and Msn4, well known for their role in the yeast stress response, to regulate the mRNA expression of glycolytic enzymes, during the metabolic cycle in the laboratory yeast strain CenPK (a popular model to synchronize yeast cells in their metabolic cycle). The authors conclude that transcriptional control of glycolytic enzymes though Msn2/Msn4 is causally linked to acetyl-CoA accumulation, triggering re-entry into cell growth.In principle, the storyline of the manuscript has two parts. The first is the one in which the study authors identify Msn2 and Msn4 as transcriptional regulators of glycolysis during the recovery phase of the metabolic cycle. There may be some little bumps here and there as pointed out by the other reviewers, and these warrant to be addressed. But overall I find this part convincing, and in fact impressive.Then there is the second part, the authors conclude from these transcriptional changes on how cells regulate metabolism via acetyl-CoA to drive cell growth. This part is not more than a (perhaps reasonable) speculation. It is intrinsically difficult to predict a change in the metabolome from transcriptional data, certainly one cannot conclude from the mRNA level of ~15 glycolytic enzymes on the acetyl-CoA concentration (that is no product of glycolysis) and its role in the recovery phase of the metabolic cycle.eLife. I think it perhaps does; eLife did aim for very high in the past, but one needs to see this in the light of the recent and effectual situation. One might recommend to the authors however to tone down the manuscript to the part that they actually show.In essence, the title of the manuscript should read something like 'Msn2 / Msn 4 regulate the expression of glycolytic enzymes during the recovery phase of the metabolic cycle'. I'm looking forward to discussing with the other reviewers whether this discovery on its own meets the bar set by eLife standard.Thanks for asking to re-consider the decision and sorry for the delay in getting back to you. The decision to reject the paper was based primarily on the worry, expressed by one of the reviewers and at least partly agreed to by the others, that, given the inability to establish the functional connection of MSN-related regulation to Acetyl-Co production, the significance of the paper may not reach the desired In particular, please:1) Tone-down the claims concerning acetyl-coA regulation. All reviewers were of the opinion that your study does not establish a functional connection from transcription regulation of glycolysis to the mechanism leading to altered acetyl-co levels.2) Validate the antibodies used, as requested by the new reviewer.3) Please see the other minor comments of the two new reviewers and perhaps emphasize some more the points that they emphasize as being mostly significant about your findings. These revisions are not essential but may improve your presentation.Reviewer #1[…] I do believe the authors present some important and novel findings worthy of reconsideration/publication. The data overall look pretty solid to me. It is not well-known that the stress response transcription factors Msn2/Msn4 also regulate glycolytic enzymes, and the evidence looks good that such genes may be induced by Msn2/4 to "prime" cells for rapid sugar utilization and re-entry into growth upon return to favorable nutrient conditions. I believe using the YMC system has enabled them to see this unanticipated relationship between Msn2/4 and glycolytic/carbohydrate metabolic enzymes, which may be obscured in traditional batch culture experiments. I also believe the RNA-seq and ChIP-seq could be high-quality datasets and will be of interest to those investigating both the overlapping and distinct roles for Msn2 and Msn4 in the regulation of stress-responsive gene transcription.msn2∆, msn4∆ lysates).I would ask them to improve Reviewer #2I am sorry about the delay. I had to read this a couple times to appreciate what I think is the importance of this work, but let me emphasize that I have not been up to speed on Ben Tu's most recent work or that of Linda Breeden who has also worked in this area. Hence, there may be things that are more broadly known that were new to me here.The importance of this paper to my mind is to broaden the appreciation of the roles of Msn2 and Msn4 from general stress resistance transcription factors to regulators of the metabolic cycle that have special roles in metabolism of cells that are coming out of quiescence that are not evident in standard growth conditions. The work is quantitative, incorporating multiple dimensions of analysis (though the magic sauce in DYNAMO was not described) and hence gives us a more comprehensive appreciation of the relevant targets of Msn2 and Msn4. As with any paper that provides a list of genes/proteins, there are some puzzles that are left unanswered such as why GIS1 is a target of this regulation. But on balance, the authors do a good job of drawing together logic behind the interactions described.In searching for broader relevance, the authors speculate that Myc might be the human equivalent for the regulation of tumor growth since they assert that tumors are nutrient limited. That assertion may be true, but it certainly isn't widely known and raised my skepticism about this point, which is not that important to the results of the paper, but should be checked.So why did I have to read it a couple times to get it? I think the authors could do a better job of emphasizing how different previous conditions used for the study of Msn2 and Msn4 are from the conditions encountered in the metabolic cycle. I would also advocate losing that damn acronyms for the three phases of the cycle as they are not commonly used and are disruptive to readers who are trying to understand this under explored region of metabolism. [Editors’ note: the author responses to the first round of peer review follow.]Reviewer #1:1) The conclusions are based primarily on loss of function mutant phenotypes (msn2/4 deletion) and the correlation analysis between metabolic enzyme expression or Msn2/4 genomic occupancy with YMC phases. It would significantly strengthen the paper if the authors can show some gain-of-function effects. For example, how does Msn2/4 overexpression affect YMC and the growth transition in general? This is especially important because most people in the field believe that activated Msn2/4 slow down cell growth.Because the YMC requires coordination of various parameters, it is not ideal for overexpression tests. Instead, we used inoculation of a stationary culture (similar in 2) Msn2 and Msn4 are activated by dephosphorylation and translocation from the cytoplasm to the nucleus, therefore nuclear localization can serve as a reporter for Msn2/4 activity. In the paper, the authors only monitored the expression changes during YMC. It would serve as the direct supportive evidence for Msn2/4 activation if the authors can observe nuclear localization of Msn2/4 specifically during the RC phase. And if so, what would be the authors' speculation on the mechanisms of Msn2/4 activation? This will provide some clues on why Msn2/4 are activated specifically during the RC phase.Besides the expression changes during YMC, we also observed the increase of ChIP-seq signals in the RC phase . Because3) The authors claim that Msn2/4 have a dual role in regulating carbohydrate metabolism genes and stress resistance genes. Under the conditions that they are focusing on (prolonged nutrient-limited condition), do the stress resistance genes also contribute to cell growth or they are unrelated by-products? An analysis or at least a discussion on this would be helpful.We think the stress resistance genes are primarily activated to improve survivability. However, we think some of the genes may also contribute to cell growth. For example, TPS2 and GAC1 are involved in synthesis of trehalose and glycogen. They are storage carbohydrates and important for stress resistance. They have also been shown to be metabolized during the exit of quiescence to fuel growth . We add discussions of these points in subsection “Regulatory network for Msn2/4 in YMC” and subsection “Systematic comparison between YMC and distinct quiescent states”.Reviewer #2:This paper describes analysis of how the transcription factors Msn2 and Msn4 regulates expression of genes encoding enzymes of the central carbon metabolism in yeast, with focus on regulation during the metabolic cycle of yeast. These TFs are known to play a central role in stress regulation, and they regulate expression of a very large number of genes in yeast (>100). The authors claim that based on their findings it can be suggested that Msn2/4 control the transition from reductive charging phase to oxidative phase by regulating the accumulation of AcCoA. Regulation of shifts in yeasts metabolism is highly complex, and it tends to the naive to suggest such a statement, and it is by no means supported by the data. Thus, the authors focus their discussion on how Msn2/4 binds (and control expression) to genes of the glycolysis, but these genes are controlled by a large number of other TFs. Furthermore, Msn2/4 themselves control a large number of other genes, including several other TFs, which is not considered in their analysis. A main line of argument is measurements of AcCoA, but this metabolite is present in several cellular compartments – mitochondria, cytosol, peroxisomes and the nucleus (probably equilibrated with the cytosolic pool), and there is no free transport of AcCoA between these compartments. Measuring the total level and linking this to enzymes that are operating in different compartments is not correct  We agree with the reviewer that regulation of shifts in yeast metabolism is highly complex, and that is one reason that our study focuses on a single metabolite, acetyl-CoA. The concept that acetyl-CoA stimulates cell growth and proliferation has been reported in many systems such as yeast , embryonic stem cells  and cancer cells  and the role of acetyl-CoA is specifically in YMC to activate growth-promoting genes  and cell cycle genes . Based on these works, our study provides a mechanistic advance about the source of acetyl-CoA in the RC of YMC. We believe this is a critical extension to help understand the exact role of acetyl-CoA in YMC and general cell growth. We did not claim that acetyl-CoA accumulation is the only route by which Msn2/4 drives the transition. We discuss the role of Msn2/4 in activating stress-response genes to maintain survival and the storage carbohydrates  in stress resistance and regrowth (subsections “Regulatory network for Msn2/4 in YMC” and “Systematic comparison between YMC and distinct quiescent states” and in the Discussion section.).2) We agree with the reviewer that glycolysis genes are controlled by other TFs and Msn2/4 controls other TFs that in turn regulate glycolysis genes. We also added msn2msn4 mutant. (B) The expression level of ACS1 is significantly induced in the RC phase but not PDA1 (PDH complex), suggesting an enhancement of the cytosolic pathway. (C) Cai et al., (2011) have shown that adding different components of the cytocolic route, like acetate, ethanol, acetaldehyde, can immediately induce the transition from RC to OX. It is unlikely that these molecules can all be transported into the mitochondria and converted to acetyl-CoA there. (D) Cai et al., (2011) have shown that during the transition, histone acetylations are strongly increased and a gcn5 (histone acetyltransferase) mutant shows a similar YMC defect, suggesting the role of the nucleo-cytosolic pool of acetyl-CoA in driving regrowth. Our transcriptional analysis is consistent with the hypothesis of a key nucleocytosolic pool of acetyl-CoA. (E) Finally, we would like to clarify that our study focuses on the mechanism of acetyl-CoA generation and not the mechanism of how acetyl-CoA stimulates growth and we agree that it will be very important to elucidate the detailed mechanisms of how acetyl-CoA stimulates growth, including the roles of each pool of acetyl-CoA, but this is beyond the scope of this paper. And we would like to emphasize that the majority of the glycolytic pathway, from glucose transportation to pyruvate, occur in the cytosol and are all controlled by Msn2/4 (3) We agree with the reviewer that the total level of acetyl-CoA doesn’t reflect the levels in each compartment and it may change the interpretation. We updated y Msn2/4 .Reviewer #3:1) The authors could do a better job in integrating current literature on the importance of carbohydrate stores, particularly trehalose and glycogen, in survival in nutrient stress conditions and re-entry into the cell cycle from quiescence.Thank you for the good suggestion. We have expanded the discussion and added references to emphasize their important roles in regrowth (subsection “Regulatory network for Msn2/4 in YMC”).2) Why did the authors focus on H3K9 acetylation, rather than gene expression, in the DynaMO prediction transcription factors that drive the YMC?  Does the focus on H3K9ac provide a better assessment of gene expression that is responsive to acetyl-CoA?Sorry for the confusion. We added a description in the Results section to explain how the prediction works. H3K9ac not only marks the actively transcribed genes but also marks the transcription regulatory regions (promoters and enhancers). Based on the assumption that TFs bind to promoters and enhancers to function, a combination of spatial information of H3K9ac and TF motifs increases the prediction accuracy of TFBSs.We didn’t find a special advantage of H3K9ac in predicting genes that is responsive to acetyl-CoA. We used H3K9ac because it shows the highest dynamics across YMC. Other histone acetylations show similar trends, but are a lot less dynamic and thus the signal is not as strong.3) The Introduction states that TFs were deleted to validate their role in the YMC. This is a bit vague. Can the authors state which ones? Is Msn2 and 4 unique in terms of its YMC disruption?We added the names of the TFs we examined in subsection “DynaMO identifies TFs associated with specific phases of YMC”. Different defects were observed in different TF mutants. Among the examined TFs, the elongated RC phase was only observed in the Msn2 and Msn4 mutants.4) In the Introduction, the authors state that Msn2 and 4 are known to regulate stress genes. This statement should include relevant references, such as Gasch et al., 2000; Martínez-Pastor et al., 1996; Schmitt and McEntee, 1996).We have added the corresponding references.5) The authors may also want to discuss upstream factors that regulate Msn2 function. In particular Tor signaling, which is influence by nutrient availability.We added a discussion about TOR signaling as a potential upstream factor that regulate Msn2/4 (Discussion section). We think it might be an interesting future direction.[Editors’ note: the author responses to the second round of peer review follow.]Reviewer #1:In the revision, the authors did not examine the YMC progression or acetyl-CoA level (which are where the major debates centered on) in the overexpression strain, because "the YMC requires coordination of various parameters"(?). Instead, they just checked the regrowth rate and got a variety of inconsistent results. This is not surprising given that they transformed yeast with a 2 micro plasmid (pRS425) expressing Msn2 or Msn4. The variability of plasmid copy numbers is huge, and therefore the kind of colony variability is expected no matter which output phenotypes (growth rate or others) you will be looking at. A genomic integration (with pRS305) will work way better.At this point, it seems my attempts to save the debated conclusion did not work out. However, given their striking findings summarized in my first review, I still support the publication of this manuscript, but would suggest the authors to tone down their conclusion and avoid claiming of direct causal or driving relationship.We have revised the text including the title and text to avoid implying any direct causal relationship between glycolysis regulation and acetyl-CoA accumulation.Reviewer #3:[…] In essence, the title of the manuscript should read something like 'Msn2 / Msn 4 regulate the expression of glycolytic enzymes during the recovery phase of the metabolic cycle'. I'm looking forward to discussing with the other reviewers whether this discovery on its own meets the bar set by eLife. I think it perhaps does; eLife did aim for very high in the past, but one needs to see this in the light of the recent and effectual situation. One might recommend to the authors however to tone down the manuscript to the part that they actually show.We have revised the text including the title to avoid any direct causal relationship between glycolysis regulation and acetyl-CoA accumulation.In particular, please:1) Tone-down the claims concerning acetyl-coA regulation. All reviewers were of the opinion that your study does not establish a functional connection from transcription regulation of glycolysis to the mechanism leading to altered acetyl-co levels.We have revised the text including the title to remove any explicit functional connection between the transcriptional regulation of glycolysis and altered acetyl-CoA levels. In the updated presentation, we only project that the transcription regulation of glycolysis is a potential mechanism by which acetyl-CoA is accumulated.2) Validate the antibodies used, as requested by the new reviewer.We have added western blot results of the WT, msn2 and msn4 lysates against Msn2 and Msn4 antibodies in 3) Please see the other minor comments of the two new reviewers and perhaps emphasize some more the points that they emphasize as being mostly significant about your findings. These revisions are not essential but may improve your presentation.We have addressed each comment of all the reviewers. Please see our point-by-point response below.Reviewer #1[…] I would ask them to improve Saccharomyces strains than coding regions of other genes or promoter regions of glycolytic genes  and hence gives us a more comprehensive appreciation of the relevant targets of Msn2 and Msn4. As with any paper that provides a list of genes/proteins, there are some puzzles that are left unanswered such as why GIS1 is a target of this regulation. But on balance, the authors do a good job of drawing together logic behind the interactions described.We have a short description of DynaMO in the end of introduction and beginning of the result section since it is not the main focus of this manuscript. We believe it will be a valuable tool to study transcription regulation in dynamic processes and the method paper describing DynaMO is currently under revision and will likely publish around the same time.msn2Δmsn4Δ double mutant. Based on this negative result we didn’t include it as a core target of Msn2/4.We described GIS1 as another TF identified by DynaMO as a candidate TF regulating RC phase and it shows similar targets as Msn2/4 . WhetherIn searching for broader relevance, the authors speculate that Myc might be the human equivalent for the regulation of tumor growth since they assert that tumors are nutrient limited. That assertion may be true, but it certainly isn't widely known and raised my skepticism about this point, which is not that important to the results of the paper, but should be checked.The nutrient environment of cancer cells is complex and dynamic. We have revised the words to clarify the conditions under which cancer cells survive under nutrient limited conditions. We have also added a reference that specifically demonstrates that acetyl-CoA synthetase is important to fuel cancer cells under nutrient limited conditions. We also carefully described the connection between Msn2/4 and Myc. We only speculate that Myc might be a functional equivalent of Msn2/4.So why did I have to read it a couple times to get it? I think the authors could do a better job of emphasizing how different previous conditions used for the study of Msn2 and Msn4 are from the conditions encountered in the metabolic cycle. I would also advocate losing that damn acronyms for the three phases of the cycle as they are not commonly used and are disruptive to readers who are trying to understand this under explored region of metabolism.We have carefully compared the previous conditions and the YMC condition in the discussion and analyzed the reason that YMC can be a good platform for revealing the roles of Msn2/4 in transitions from starvation to growth. We apologize for the difficult to remember acronyms  but these are the abbreviations used in all the other papers. To help readers understand the three phases of the cycles, we have used metaphors in the introduction to liken OX, RB and RC as growth, proliferation and quiescence. We have also sprinkled these pseudo-synonyms throughout the text to remind readers about the meanings of the three phases.


MYC is a hallmark of many human cancers. The MYC oncogene has long been thought to execute its neoplastic functions by acting as a classic transcription factor, deregulating the expression of a large number of specific target genes. However, MYC’s influence on many of these target genes is rather modest and there is little overlap between MYC regulated genes in different cell types, leaving many mechanistic questions unanswered. Recent advances in the field challenge the dogma further, revealing a role for MYC that extends beyond the traditional concept of a sequence-specific transcription factor. In this article, we review MYC’s function as a regulator of the cancer epigenome and transcriptome. We outline our current understanding of how MYC regulates chromatin structure in both a site-specific and genome-wide fashion, and highlight the implications for therapeutic strategies for cancers with high MYC expression.Overexpression of  MYC family is comprised of c-MYC , N-MYC, and L-MYC, which encode for basic helix–loop–helix leucine zipper (bHLH-Zip) transcription factors [The importance of MYC in human development and disease has generated intense research interest over the past 30 years, resulting in numerous original articles and reviews . Th factors  that hav factors ,6,7,8. T factors ) and the factors  have madMYC exerts its neoplastic features by increasing autonomous cellular proliferation, growth, angiogenesis, and genomic destabilization while blocking differentiation see   from pausing.An increasing number of chromatin modifying co-factors including chromatin “writers”, “readers”, and “erasers” have been found to interact directly or indirectly with MYC–MAX complexes  as a stabilizing co-activator that promotes transactivation of canonical target genes . p300 anDrosophila MYC (dMYC). The Trithorax group protein dKDM5/LID that belongs to the JARID1 family of histone H3 lysine 4 (H3K4) demethylases was found crucial for dMYC-promoted cell growth [In addition to histone acetyltransferases, there is increasing evidence that MYC recruits lysine-specific histone demethylases (KDM) to activate transcription of canonical target genes, even though the exact mechanism of how histone methylation contributes to MYC regulated gene expression remains unknown. First hints that MYC-dependent transactivation involves KDMs came from l growth . Howeverl growth ,44. KDM4l growth . This mel growth ,44, indiPIM1) oncogene, a constitutive active serine/threonine kinase , a core subunit of the SWI/SNF chromatin remodeling complex ,55. The The transactivation of E-box target genes by MYC–MAX can be antagonized by MAX-Dimerization (MXD) proteins. MXD family members such as MXD1 and MAX Network Transcriptional Repressor (MNT) also form heterodimeric complexes with MAX, competing with MYC–MAX for binding to the same E-box sequences, but subsequently repress the corresponding gene ,60. WhilAdding another level of complexity, the transactivating effect of MYC–MAX on E-box regulated genes has recently been found to be antagonized by MIZ-1 . While tMYC’s function as a site-specific transcription factor includes not only the activation but also the repression of genes (both protein-coding as well as noncoding RNAs). In fact, both mechanisms are essential for MYC-driven tumor initiation and maintenance ,63, and V394D, which is selectively deficient in its ability to interact with MIZ-1, while still dimerizing with MAX to transactivate canonical target genes. MYCV394D is unable to repress wild-type MYC targets such as Cyclin Dependent Kinase Inhibitor 1A (CDKN1A/p21CIP) and Cyclin Dependent Kinase Inhibitor 2B (CDKN2B/p15INK4B), in a transgenic lymphoma model [The interaction with MIZ-1 is the best characterized example of how MYC represses transcription. MIZ-1 recognizes a consensus sequence known as INR in core promoters and, in the absence of MYC, activates the transcription of the corresponding gene see . Howeverma model . Furtherma model . This swCDKN1A (p21CIP) and CDKN2B (p15INK4B) [RASSF1A), silencing its expression through DNA hypermethylation [In addition to the displacement of co-activators, MYC-mediated gene repression is associated with the recruitment of co-repressors. MYC–MAX/MIZ-1 complexes have been shown to recruit the de novo DNA methyltransferase 3a (DNMT3A) to non-canonical targets such as 15INK4B) , increas15INK4B) . This is15INK4B) ,66,69. T15INK4B) . In non-hylation ,72. The hylation ,74,75,76tTG) [ID-2) and Growth Arrest and DNA Damage-Inducible Protein GADD153 (GADD153) [MIR-15A/MIR-16-1 and MIR-29). The fact that those microRNAs have tumor-suppressive function in turn strengthens the notion that transrepression of genes is critical for MYC-dependent transformation [While the role of histone acetylation in MYC-dependent transcriptional repression is less well characterized, two HDACs, HDAC1 ,78,79 anGADD153) ,81, corrormation . In the ormation ,84,85.CDKN1A (p21CIP) and Clusterin (CLU), two tumor suppressor genes, thereby functionally cooperating with N-MYC in neuroblastoma initiation and progression [Histone KDMs are a double-edged sword in MYC-dependent transcriptional regulation, as they are involved in both repression and activation of MYC-target genes. Lysine-Specific Histone Demethylase 1A (LSD1)  cooperates with N-MYC to repress tumor suppressor genes, contributing to tumor maintenance in neuroblastoma . LSD1 hagression . NCL) and Carbamoyltransferase-dihydroorotase (CAD), causing cycles of methylation and demethylation of lysine 4 in histone H3 (H3K4me2/me3), before H4 acetylation increases [2O2 causing the recruitment of oxidative repair enzymes to E-box genes [Interestingly, by interacting with different co-factors, LSD1 also seems capable of activating genes. MYC-dependent transactivation of E-box genes in rat fibroblasts has been proposed to involve LSD1-mediated demethylation of H3K4 . Given tncreases . These tox genes . The conMYC's physiologic functions are important for embryonic stem cells (ESC), and its ectopic expression contributes to the reprogramming of somatic cells into induced pluripotent stem cells (iPSC). In ESCs, MYC is thought to fulfill two important functions, contributing to self-renewal through maintaining an undifferentiated state and promoting cell cycle progression. While the exact molecular mechanism of how MYC regulates stemness has not yet been fully elucidated yet, MYC has been found to regulate the expression and the recruitment of Polycomb Repressive Complex 2 (PRC2) proteins, providing a mechanism how it suppresses developmental genes in ESCs . PRC2 isMYC utilizes the repressive PRC2 machinery in ESCs, by increasing the transcription of all core components of PRC2  as well as PRC2-associated factors typically found in ESCs (including Jumonji and AT-Rich Interaction Domain Containing 2 (JARID2), esPRC2p48, and Metal Response Element Binding Transcription Factor 2 (MTF2) through binding E-box sites in their promoter regions . FurtherEμ-myc-driven B-cell lymphoma by restricting self-renewal capabilities [CLU [MYC and PRC2 cooperate not only in ESCs, but also during tumorigenesis; however, the role of PRC2 in cancer is more complex, as its function depends on the tissue context. Loss-of-function mutations in EZH2, EED, and SUZ12 have been reported for T-cell acute lymphoblastic leukemia (T-ALL) and MDS ,100, sugbilities . In conties [CLU . Furtheries [CLU . Togetheies [CLU .Another class of co-repressors that is recruited by MYC are 48 KDa TBP-Interacting Protein (TIP48)/49 KDa TBP-Interacting Protein (TIP49) , which fMYC oncogene influences chromatin structure in a global fashion , these changes were thought to be a reflection of the proliferative state of the cell rather than a direct consequence of MYC’s action. Only recently has light been shed on the mechanism by which MYC utilizes the dual function of histone methylation as an activator or repressor of transcriptome. SIN3B), HMG-box Transcription Factor 1 (HBP1), suppressor of Variegation 4–20 Homolog 1 (SUV420H1), and B Cell Translocation Gene 1 (BTG1) through miR-17-92 [MYC has been uncovered to suppresses the SIN3 Transcription Regulator Family Member B  and SMADs. The TGF-β/SMAD pathway can have tumor promoting or suppressing functions depending on the context . Hence,iewed in . SUV39H1iewed in . Howeverferation ,129.CTCF), which is thought to allow for transcriptional alterations of wide regions of the genome by blocking facultative heterochromatin [MTA1), a component of the nucleosome remodeling and deacetylating (NuRD) co-repressor complex [Furthermore, MYC’s influence on chromatin structure extends beyond gene regulatory or genic regions and also includes expansive intergenic regions. Genomic location analyses suggest that the inactivation of N-MYC depletes large portions of active histone marks in the genome (H3K9 acetylation and H3K4 methylation) . These rhromatin ,132. MYC complex . Since M complex , it has Fueling the oncogene’s enigmatic reputation further and intensifying research interest, recent reports reveal that MYC deregulates large gene expression programs in tumor cells through transcriptional amplification. When overexpressed, MYC accumulates in the promoter regions of actively transcribed genes and acts as a general amplifier, further increasing the transcriptional output, boosting a cell's gene expression profile . Thus, rThe classic model of MYC as a sequence-specific transcription factor leaves important questions unanswered. It is unable to explain the diversity of transcriptional responses MYC provokes in different cell types, indicating an inherent plasticity in MYC’s selection of target genes. Hence, additional mechanisms must exist that determine which genomic loci are occupied by MYC in a given cell type. It has been speculated that the binding of MYC to its target genes is not solely determined by a specific DNA sequence, but that MYC binding site recognition depends on chromatin structure. Early clues came from large-scale location analyses revealing that the active H3 lysine 4 (K4) and 79 (K79) methylation marks are a strict prerequisite for recognition of any target site by MYC, suggesting that distinct histone variants or modifications can modulate protein recognition ,140. ThiThe notion that inactivation of MYC can cause tumor regression in animal models by eliciting oncogene addiction, makes it an attractive target for therapeutic anti-cancer strategies. However, despite considerable efforts in that regard, a pharmacologic inhibitor that directly targets MYC and can be used to translate these findings into the clinical setting remains elusive. Recently, novel therapeutic strategies have emerged that promise to take advantage of the reversibility of chromatin modifications to adjust the epigenome in ways that slow cell proliferation and induce cellular senescence or apoptosis in MYC-deregulated cancers. Targeting MYC through chromatin associated co-factors that affect either the expression and/or the function of the oncogene has proven to be effective in various cell lines and animal models  and its analog 5-aza-2′-deoxycytidine (decitabine) ,76. Azaclymphoma . Azacytilymphoma ,154. Deslymphoma . This prAcetylation of nucleosomal histones is a key mechanism through which MYC facilitates transactivation of genes important for tumor development and progression. Consequently, histone acetyltransferases inhibitors have been explored as therapeutic strategy for MYC-associated tumors. Broadly acting HAT inhibitors such as PU139 and PU141 decreased proliferation and activated apoptosis in vitro in various cancer types including ovarian carcinoma, hepatocellular carcinoma, and colon adenocarcinoma through blocking GCN5, p300/CBP, and cAMP-Responsive Element-Binding Protein 1 (CREB) activity. Intriguingly, both inhibitors also affected growth of neuroblastoma in xenograft models . HoweverThe function of p300/CBP has been linked to the development of various human cancers, including solid tumors and hematological malignancies. p300/CBP is critical for MYC to drive cell proliferation ; howeverCCNG2), which ordinarily blocks the progression of the cell cycle, in neuroblastoma and pancreatic cancer cells to drive proliferation [tTG), which normally promotes apoptosis and induces differentiation, through recruitment of HDAC1 [While inhibiting MYC-associated HAT activity aims at antagonizing MYC-driven transactivation of tumor-promoting genes, the goal of targeting HDAC activity is to release the MYC-driven transrepression of tumor suppressor genes. HDACs have been proven to have multiple functions in driving proliferation in high MYC-expressing cancers. MYC causes overexpression of HDAC2, contributing to tumor cell proliferation, while inactivating mutations in HDAC2 reduce the frequency of tumors in mice ,163. MYCferation . Inhibitferation . Additioof HDAC1 . HDAC inInhibitors for different classes of HDACs (primarily class I and II due to their overexpression in various cancers) have been exploited as a broad strategy to reactivate tumor suppressor genes in various cancer types ,167,168.Histone methyltransferases inhibitors have proven to be a successful potential therapeutic strategy. EZH2, a HMT contributor to PRC2, is overexpressed in a variety of human cancers including those that express high MYC levels. EZH2 overexpression contributes to colony formation, EMT, inhibition of DNA repair regulators, and inactive H3K27 methylation marks . The S-aLoss of another HMT, SUV39H1, in MYC-driven myeloid leukemia prevented chromosomal instability in mouse models, indicating that it may be a potentially useful therapeutic target . NV10, aNCL [Similar to targeting HATs or HDACs, the reasoning for inhibiting MYC-associated HMT and KDM activity is based on antagonizing either MYC’s transactivating or transrepressing capabilities. KDMs, particularly those that remove H3K4 and H3K27 methylation are often mutated in cancer, indicating potential therapeutic targets for these enzymes. Histone demethylase inhibitors target important KDMs in MYC-expressing cancers, such as LSD1 (KDM1) and KDM4. H3K4me2 indicates active genes and LSD1 is responsible for removing mono- and di-methylated H3K4 and H3K9 marks. Therapeutic potential arose when it was discovered that the silencing of LSD1 reduced the transcription of MYC-target genes such as NCL . InhibitNCL ,178. SomNCL ,180.MIR17HG, CDC25A, SOX2, KITLG, VCAN, and SDC1 [MET oncogene [The H3K9 demethylase, KDM4, has shown promise as a therapeutic target due to the discovery that KDM4B knockout in MYC-driven neuroblastoma cells resulted in downregulation of important MYC target genes, such as and SDC1 . Inhibitand SDC1 . Anotheroncogene . BecauseMYC, and are more susceptible to BET bromodomain inhibition [More recently, strategies targeting epigenetic “readers” in cancers that harbor MYC overexpression have received much attention. The discovery of super enhancers and their role in tumorigenesis led to the emergence of BET bromodomain inhibitors (BETi) as a therapeutic strategy for various cancer types . BETi, shibition . BET inhhibition ,186,187.hibition ,189. Comhibition .Due to the role of WDR5 in recruiting MYC to E-boxes and in driving DNA replication, the potential of inhibiting WDR5 for therapy has been explored. Recent reports indicate that WDR5 is needed for proliferation and DNA replication. Two available WDR5 inhibitors with low efficacy, OICR-9429 and MM-401, show that WDR5 inhibition slowed proliferation in pancreatic cancer . OICR-94Thus far, targeting chromatin modifiers associated with MYC’s oncogenic function in cancer has proven successful and effective; however, there is still much progress to be made to improve inhibitors as well as clinical efficacy for patients. Targeted strategies against tumor-cell-specific proteins hold the promise to reduce cytotoxic effects on normal tissue. The difficulty of designing small molecules that block protein–protein interactions between MYC and distinct co-factors lies in the complexity of MYC being a central hub in tumorigenesis and its co-factors’ interactions with other effector molecules. Regardless, the use of chromatin modifying drugs targeting MYC, either through directly affecting MYC expression or through affecting the cofactors MYC requires for its function, can provide an avenue for exploration into promising therapeutic strategies.It has become evident that MYC’s ability to deregulate a wide variety of cellular processes culminating in neoplastic transformation cannot be attributed to one single trait, but instead is the product of multiple molecular mechanisms. MYC acts as a site-specific transcription factor on selective target genes by recruiting chromatin-modifying co-factors that remodel chromatin structure in the vicinity of its binding sites. However, MYC’s role as global regulator of the epigenome and transcriptome extends its function beyond the traditional concept of a classic transcription factor. MYC has been found to control the expression of an increasing number of epigenetic modifiers that facilitate widespread changes in chromatin architecture. The resulting epigenetic landscape provides the framework for MYC as a global transcriptional amplifier, boosting transcription of already active genes. MYC would not be MYC if there were not questions that remain unanswered. There is the chicken or the egg causality dilemma: What came first, MYC or open chromatin? Is a preexisting epigenetic state the prerequisite for MYC to further remodel the epigenome to a cancer-cell-specific pattern? Despite elusive answers to these mechanistic questions, there is strong evidence that MYC is a highly promising and effective therapeutic target. Unfortunately, MYC has also been deemed “undruggable” in clinic, so alternative strategies are needed. Targeting MYC indirectly via key chromatin modifiers in its network, taking advantage of the reversibility of epigenetic marks, provides an attractive new approach. Last but not least, one thing remains the same, regardless of the efforts over the years to understand the mechanisms underlying MYC’s neoplastic properties: the oncogene seems to preserve its enigmatic reputation.


Turning genes on and off is essential for development and homeostasis, yet little is known about the sequence and causal role of chromatin state changes during the repression of active genes. This is surprising, as defective gene silencing underlies developmental abnormalities and disease. Here we delineate the sequence and functional contribution of transcriptional repression mechanisms at high temporal resolution. Inducible entry of the NuRD-interacting transcriptional regulator Ikaros into mouse pre-B cell nuclei triggered immediate binding to target gene promoters. Rapid RNAP2 eviction, transcriptional shutdown, nucleosome invasion, and reduced transcriptional activator binding required chromatin remodeling by NuRD-associated Mi2beta/CHD4, but were independent of HDAC activity. Histone deacetylation occurred after transcriptional repression. Nevertheless, HDAC activity contributed to stable gene silencing. Hence, high resolution mapping of transcriptional repression reveals complex and interdependent mechanisms that underpin rapid transitions between transcriptional states, and elucidates the temporal order, functional role and mechanistic separation of NuRD-associated enzymatic activities.DOI:http://dx.doi.org/10.7554/eLife.22767.001 Transcriptional gene activation and repression are essential for cell commitment, differentiation and homeostasis in metazoans. The dynamics of inducible gene expression have been characterised in exquisite detail . CompareMyc and the prototypic pre-B cell gene Igll1 are direct Ikaros target genes , which initially drives pre-B cell proliferation , any increase in Ikaros-DNA binding comes at the expense of EBF-DNA binding  showed that the nuclear translocation of Ikaros abolished RNAP2 binding to promoters and gene bodies of the target genes d Vpreb1  that ared Vpreb1 . RNAP2 oy Ikaros .10.7554/Igll1 promoter showed a nucleosome-free region that was occupied by RNAP2. Upon nuclear translocation of Ikaros this region became occluded by nucleosomes, and RNAP2 occupancy was reduced . Prior to nuclear translocation of Ikaros the  reduced . Reducedng sites .Igll1 and Myc. ChIP-PCR demonstrated a rapid decrease in RNAP2 occupancy at the promoters, gene bodies and transcription termination sites (TTS) of Ikaros-repressed genes  transcripts, which are short-lived and serve as an indicator of transcriptional activity . The abuccupancy . These cy (Zfp36 .Igll1 and Myc promoters (Rex1 promoter is shown as a negative control). Loss of RNAP2 reduced promoter occupancy by the basal transcription factor TFIIB  and vice versa . Conversely, Ikaros-repressed genes were depleted among BRG1-repressed genes (Odds ratio = 0.62) and vice versa . Histone modifications are therefore not likely drivers for early events in Ikaros-mediated repression.These data demonstrate that - unexpectedly - HDAC activity is dispensable for Ikaros-induced repression. Consistent with this conclusion, ChIP experiments showed that although Ikaros reduced the acetylation of histone H3 and H4 at the target genes nscripts . We saw nscripts  at the IWhile the immediate transcriptional downregulation of Ikaros target genes was unaffected by HDAC inhibition , we founIgll1 promoter and TSS using PCR amplicons of defined length  were associated with γ-satellite DNA (red), the repeat unit of mouse pericentromeric heterochromatin  of TSA  of hromatin . In conter 4-OHT , indicat) of TSA . We note) of TSA  but did ) of TSA .Igll1, partial MNase protection of Igll1 promoter DNA and incomplete repositioning of Igll1 alleles to pericentromeric heterochromatin in the presence of HDAC inhibitors indicate that HDAC activity is required for full Igll1 silencing, even though it is dispensable for the early events in transcriptional repression  by knockdown of Chd4 . Chd4-depleted cells showed a steeper rise in Ikaros binding between 5 and 15 min than control cells. Strikingly, the decrease in EBF1 binding seen in control cells was significantly blunted in the absence of CHD4. In this regard, the binding kinetics of Ikaros and EBF1 in the absence of CHD4 resembled a 3-state model  and TEV activity was induced by rapamycin . Nuclear translocation was monitored by anti-HA (Covance MMS-101R) immunofluorescence staining and confocal microscopy as described and images were processed using Leica and Image J software.Where indicated, cells were treated with 1 μM triptolide (Sigma), 1 ng/ml trichostatin A  or 10 μM MS-275 (Selleckchem).Chd4 (5’-GACTACGACCTGTTCAAGCAG-3’) and pQsupR control plasmid were used as advised by the manufacturer (Invitrogen) and media were supplemented with 20 μM pan caspase inhibitor .shRNA for mRNA extraction, reverse transcription PCR and immunoblotting was as described. We used antibodies to histone H3 (Abcam AB1791), acetylated histone H3 (Abcam AB47915), acetylated histone H4 (Millipore 06–866), RNAPII (Santa Cruz sc-899), TFIIB (Santa Cruz sc-225), CHD4 (Abcam AB72418), and tubulin (Sigma T9026).2+ buffer  and digested with MNase  at in room temperature for 10 min. Digestion was stopped by 20 µl 100 mM EDTA, 10 mM EGTA per 2 × 106 cells. As a control, an equal number of cells were treated in parallel without adding MNase. Samples were reverse crosslinked, DNA purified by phenol/chloroform and quantified .MNase digestion was done as described . Cells wRex1 or αActin promoter.For MNase-qPCR, 2 ng DNA were used per qPCR reaction. Results were normalised to undigested controls and compared to the inactive For MNase-seq, 50 ng DNA after MNase digestion (carrier ethanol treatment for 6 hr or 0.5 μM 4-OHT treatment for 6 hr) were used to prepare MNase-seq samples  without size selection steps. Libraries from three biological replicates were sequenced  to ~500–600 M reads per condition. Reads were aligned to mouse genome mm9 and reconstructed into complete fragments in silico. To calculate normalised coverage per base for MNase data, fragments with length between 110 bp and 170 bp were normalised to total reads outside of mm9 blacklisted regions.6 cells/ml on ice for 15 min, centrifuged at 900 rcf for 10 min at 4°C, resuspended in 140 mM NaCl, 50 mM HEPES, 1 mM EDTA, 1% Triton X-100, 0.1% SDS, 0.1% sodium deoxycholate, pH 7.9 and sonicated for 25 min at 4°C with 30 s on, and 30 s off . Sonicated chromatin was recovered as supernatant after centrifugation at 25,000 rcf for 15 min at 4°C and incubated with antibodies to histone H3 (Abcam AB1791), acetylated histone H3 (Abcam AB47915), acetylated histone H4 (Millipore 06–866), RNAP2 (Santa Cruz sc-899), TFIIB (Santa Cruz sc-225), CHD4 (Abcam AB70469), MBD3 (Bethyl A302-528A) or Brg1  on a rotating platform at 4°C overnight, washed sequentially in 140 mM NaCl, 50 mM HEPES, 1 mM EDTA, 1% Triton X-100, 0.1% SDS, 0.1% sodium deoxycholate, pH 7.9 followed by 500 mM NaCl, 50 mM HEPES, 1 mM EDTA, 1% Triton X-100, 0.1% SDS, 0.1% sodium deoxycholate, pH 7.9 followed by 250 mM LiCl, 50 mM HEPES, 1 mM EDTA, 0.5% NP-40, 0.5% sodium deoxycholate, pH 8.0 and finally 10 mM Tris-HCl, 1 mM EDTA, pH 7.5. Reverse crosslinked DNA was purified by phenol-chloroform extraction. For ChIP-seq, 10 ng ChIP and input DNA were used to prepare libraries  with size selection for fragments between 100 and 300 bp. After 50 bp single-end sequencing , reads were aligned to mm9 using BWA version 0.7.5. SAM files were generated, aligned, sorted and indexed with samtools version 0.1.18. Duplicate reads were marked using Picard MarkDuplicates. The quality of the datasets and predicted fragment lengths were analysed using the ChIPQC Bioconductor package. Normalised BigWig files were generated using rtracklayer (version 1.3.2) with signal normalised to total mapped fragments per sample. ChIP-seq sample quality was assessed using ChIPQC (version 1.8.7)  . For then 1.8.7) . MNase sn 1.8.7)  was calcn 1.8.7)  and diffn 1.8.7) . DiffereTo assess the impact of nuclear Ikaros on Ikaros-EBF1 competition, we implemented kinetic models of increasing complexity. First, we considered a 2-state model where DNAE and DNAI correspond to DNA bond by EBF1 and Ikaros with the binding rate constants are kE and kI .(1)eLife. Your revised article has been favorably evaluated by Jessica Tyler as Senior editor and a Reviewing editor.Thank you for resubmitting your work entitled "A high-resolution map of transcriptional repression" for further consideration at The manuscript has been improved but there are four remaining issues that need to be addressed before acceptance, as outlined below:In this revision of Liang et al., the authors have addressed the points raised in the first round of review. Regarding data reproducibility, the authors have indicated in figure legends which datasets are averages of multiple biological experiments, and which are representative. Additional experiments were performed for 3D FISH, and a The reviewers had asked for more specific analysis of genome-wide gene expression, since only a minority of genes showed the Pol II decrease found at the two promoters of interest. In the response to the reviewers, the authors reanalyze the data, and conclude that for a majority of promoters, it appears that Pol II decrease at the promoter is the trend, and that differences in sensitivity of measuring mRNA vs. Pol II occupancy may explain some of the discrepancy.1) In addition to having this information in the letter of response, the manuscript should indicate that they believe the majority of genes are showing reductions in Pol II, but that differential sensitivity may be an issue. Otherwise it seems the authors are content to ignore what might be a mechanism that impacts the majority of repressed genes.The use and interpretation of the models were questioned; in response, the authors provide more information on modeling, and emphasize that their many biological measurements point away from the simple two-state model, where Ikaros directly dislodges EBF1. This conclusion seems to be strongly supported by the data. They emphasize the failure to find reasonable parameters for the simple model, rather than over-interpreting specific parameters for more complex models, which seems a reasonable conclusion.The nature and quality of experiments from genome-wide experiments is better explained and illustrated by the additional supplemental figure noted above, by correlation analyses in the response letter, and data deposited in GEO.In response to the question about how endogenous Ikaros figures into this system, the authors note that they find similar responses in CRISPR'd cells lacking endogenous Ikaros, and show preliminary data in the letter. As asked, they also carry out a Western blot showing the levels of native and induced Ikaros over the time course, showing that the levels of the inducible form is approximately the same as that of the endogenous protein.2) The Western blot data should be included in the manuscript as part of, or attached to, The interpretation of a "fragile" nucleosome state was questioned, based on the limited probing with one concentration of MNase. The authors carry out a titration, showing a similar trend, but also soften their conclusion, noting that it appears that it is either nucleosome depletion or movement that is affecting the repressed promoter.A number of minor points were addressed including provenance of antibodies, figure labeling, and explanation in the figure legend that nascent RNA was measured with intron-exon boundary spanning primers.3) The text still refers to "intron-spanning primers" – that needs to be fixed.4) Typo  Essential revisions:1) Data reproducibility. A major point raised in the review process was whether the ChIP data shown represents biologically independent measurements or technical replicates from single preparations of chromatin. A number of experiments , 4, 5, 6The number of experiments refers to independent biological replicates in all cases. Everything from cells/chemical treatment, chromatin/RNA isolation/digestion, ChIP/reverse transcription/PCR, library preparation and sequencing was done in the form of independent repeats on different days for the number of times indicated in the figure legends. We have added the term “independent biological replicates” to the revised figure legends to indicate this fact. The data are highly reproducible, as documented in the new The difference between the drop in RNAP2 in 2) Relationship of findings to prior knowledge about Ikaros. A second point from the review was need to set the authors' findings in a more general context, to show how information about HDAC, NuRD, and chromatin structure in regulation of Myc and Ipll1 supports or contradicts previous studies on Ikaros action. For instance, earlier work from Georgopoulos showed that on the CD4 intronic enhancer, Ikaros and NuRD appear to antagonize each other's activity, such that loss of CHD4 results in enhanced, not reduced Ikaros, activity. Similarly, previous studies have implicated basal factor pTefb-Ikaros interactions, as well as polycomb. Other studies have shown concomitant binding of Ikaros and Ebf1 at the Igll1 promoter in large pre-B cells where the gene is expressed, raising the question of whether Ikaros activates in this context. The paper would be much more valuable to the community if the authors integrate their findings better with previous work, and in the Discussion propose how similarities or differences are to be reconciled. As it stands, the main summary concluding the paper is rather superficial.The paragraph above raises several important issues, and we have taken the liberty to structure it into 5 different points (i) to (v).i) The data reported in our manuscript do not in our view contradict previous reports, largely because no previous reports have examined the impact of IKAROS and NuRD on the time scale investigated here. However, it is worth noting that previous reports occasionally contradict each other. For example, there is marked disagreement between the Georgopoulos  and Busslinger labs . We did not examine CHD4 positioning in Ikzf1-deficient cells, nor did we investigate the role of IKAROS, HDACs, or CHD4 at the proximal Cd4 enhancer , which is active in T-cells but not in B-cells. Instead, we focus on Igll1 and Myc, which are repressed by IKAROS with fast kinetics, and by a mechanism that primarily depends on CHD4, and secondarily on HDAC activity. Importantly, not all IKAROS target genes are repressed by IKAROS. Our manuscript illustrates this by showing increasing chromatin accessibility, RNAP2 occupancy and transcriptional output for the IKAROS target gene Zfp36 in ii) The link between IKAROS-mediated gene activation and P-TEFb  is very interesting. We have not explored the biochemical interaction between IKAROS and P-TEVb directly. Among IKAROS-repressed genes, few are regulated at the level of RNAP2 elongation . Howeveriii) We have analysed the potential impact of the Polycomb repressive complex PRC2  and found no increase in the PRC2-catalysed H3K27me3 mark at IKAROS-repressed genes within the 24 hour timeframe analysed in our study. This fact is indicated by the following sentence in our manuscript: “We saw no detectable increase in the trimethylation of H3K27 by PRC2  at the Igll1 and Myc promoters 24h after nuclear translocation of Ikaros”.iv) Our previous data provide support for the idea that both IKAROS and EBF1 bind the Igll1 promoter in large pre-B cells . As pointed out by the referees, binding of Ikaros to the Igll1 promoter in large pre-B cells could potentially indicate a positive effect of IKAROS on Igll1 in large pre-B cells. To test this idea, we expressed a dominant negative form of Ikaros (IKAROS 159A). If IKAROS activates Igll1 in large pre-B cells, IKAROS 159A would be predicted to reduce Igll1 expression in large pre-B cells. Conversely, if IKAROS represses Igll1 in large pre-B cells, IKAROS 159A would be predicted to increase Igll1 expression. IKAROS 159A increased the expression of Igll1 , demonstrating that Ikaros does not activate Igll1 in large pre-B cells.v) To address this point, we have changed our Results section to explicitly state that reduced RNAP2 occupancy, reduced accessibility and transcriptional repression “were selective for Ikaros-repressed genes, as illustrated by comparison with the Ikaros-induced gene Zfp36 ” and mod3) Presentation and use of genome-wide data. The reviewers found that the integration of genome-wide information was potentially useful but incomplete; the quality of genome-wide information should be indicated with standard statistical measures (e.g. how much agreement is found among the triplicate MNase experiments? reproducibility of peaks etc.), even if the analysis of the entire dataset is being prepared in a separate study. MNase digestion patterns at two loci discussed, but overall significance is difficult to assess; is variation indicative of a change in nucleosome position, occupancy or other aspect of the individual experiments? It is not clear if the RNA pol II genome-wide study was similarly performed in triplicate. A related point to consideration of genome-wide data is related to point 2; it is reported that out of the 924 genes repressed upon increase in Ikaros expression, 372 show a reduction in RNAP2. How are the other 550 genes regulated? Are these not directly repressed by the Ikaros-NuRD complex? It would help this paper to provide enough information to understand Author response Author response across all samples, including replicates and experimental groups. ** Correlation values between RNAP2 ChIP-seq at all transcription start sites (TSS) and gene bodies that showed statistically significant differences in RNAP2 ChIP-seq signal between experimental groups.We have added Our study reports on 4 independent biological replicates of RNAP2 ChIP-seq data. We apologise for not making this clear in the original manuscript, and now indicate this clearly in the revised figure legends.The focus of our analysis is on downregulated genes that show a significant reduction in RNAP2 binding, and the great majority of these show no evidence for increased RNAP2 pausing. The referees point out that of 924 genes downregulated at adj. p<0.05, only 372 (40.3%) showed a significant reduction in RNAP2 ChIP-seq signal. For most of the remaining downregulated genes, the reduction in RNAP2 binding did not reach statistical significance. To test whether this is due to a difference in sensitivity between RNA-seq and RNAP2 ChIP-seq, we repeated our analysis considering only genes that were significantly downregulated and also showed a minimal log2 fold- change of 2. Of 174 genes that met these criteria, 95 showed a significant reduction in RNAP2 ChIP- seq signal (55%). The percentage of downregulated genes with significantly reduced RNAP2 ChIP- seq signal further increased to 62% when considering only genes downregulated with adj. p<0.01. The finding that the majority of strongly downregulated genes had reduced RNAP2 ChIP-seq signals supports the idea that downregulated expression without reduced RNAP2 ChIP-seq signals is due at least in part to a difference in sensitivity between RNA-seq and RNAP2 ChIP-seq. Regardless of the criteria applied to define downregulated genes, reduced RNAP2 elongation remained an exception among IKAROS/NuRD-repressed genes: 4% (15/372) at adj. p<0.05, 5.4% (5/93) at log2FC>2 and p<0.05, and 4.6% (4/87) at log2FC>2 and p<0.01.Differential gene expression was defined by standard statistical criteria, namely a p-value of <0.05 after Benjamini-Hochberg correction for multiple testing (adj. p<0.05). We have added this information to the revised legend of 4) Modeling. The reviewers had differing opinions about the modeling; some asked for further validation through additional measurements of actual nucleosome positioning and Ikaros occupancy at individual sites, while another view was that the models are useful enough to point toward future studies of mechanism. A justification of the application of the modeling would strengthen this aspect of the study. In addition, a clarification of the actual mechanism suggested is requested: Ikaros has been variously described as a recruiter of NuRD, or an antagonist, or binding in an overlapping manner at promoters but not enhancers. The kinetics of binding shown in 4D indicate that a model with simple, stable recruitment of NuRD by Ikaros is too simple. But it is not clear what mechanism the authors are proposing, based on their findings.Chd4 experiments shown in The experimental finding that Ikaros binding increases prior to the displacement of EBF1 is based on 9 separate chromatin preparations and 9 separate ChIP experiments for each factor  Endogenous Ikaros vs. induced form. An increase in Ikaros nuclear localization is assumed from the immunofluorescence data but this is not a very quantitative method. A western blot analysis for Ikaros in the nuclear vs, cytoplasmic fractions at the different time points should be performed. The way the ChIP data is presented, it is not clear how much endogenous Ikaros is already present at the target promoters before the induction of the tamoxifen-regulated protein, and how this compares with the induced levels. Previous studies have already noted that Ikaros is present at these genes, so how much of the modifications are due to overexpression of the protein? Why would nucleosome changes results only from overexpression; is this a function of rapid interconversion between active and inactive states on the genes with only endogenous Ikaros? In addition, the paper is silent about the Ikaros-like Aiolos protein; is it present at these genes, and does it play a role in regulation under these conditions?As requested, We have added the occupancy by endogenous Ikaros to the excel spread sheets listing the primary data for Ikaros dosage in pre-B cells drives changes in gene expression that resemble the in vivo differentiation from large (Fr.C') to small (Fr.D) pre-B cells . Ikaros is essential for the differentiation of large (Fr.C') to small (Fr.D) pre-B cells , and the majority of Ikaros target genes are already bound by Ikaros in large (Fr.C') pre-B cells . The Ikaros family member Ikzf3 is upregulated between the large (Fr.C') and small (Fr.D) pre-B cell stage , and increased Ikaros/Aiolos expression drives increased binding to target gene promoters . Hence, Ikaros dosage is critical for the impact of Ikaros on gene expression. Inducible translocation of Ikaros into the nucleus is intended to model the increase in the expression of Ikaros family proteins during the differentiation of large (Fr.C') to small (Fr.D) pre- B cells. Accordingly, our system aims to moderately increase Nuclear Ikaros dosage, as illustrated by western blotting for Ikaros in whole cell lysate, nuclear and cytoplasmic fractions before and after 4- OHT addition .These data indicate that nuclear Ikaros dosage translates into the regulation of Ikaros target genes, including Igll1, Myc, and other genes where Ikaros binding is seen in large pre-B (Fr.C') cells.Nucleosome changes and repression of Igll1 and Myc result from an increase in the nuclear dosage and increased target gene binding of Ikaros and/or Aiolos. In B cell differentiation in vivo, both Igll1 and Myc are downregulated as Fr.C' cells progress to Fr.D (www.immgen.org).Both (highly expressed) IKAROS-IRES-GFP and (moderately expressed) IKAROS-IRES-Cherry result in increased nucleosome occupancy, reduced RNAP2 occupancy and reduced transcriptional output. This indicates that a moderate increase in Ikaros expression and binding is sufficient to trigger the described responses.To explore the relationship between endogenous and exogenous Ikaros we repeated Ikaros induction experiment in B3 cells in which we targeted both endogenous Ikzf1 alleles by CRISPR/Cas9. In these cells, repression of Igll1 and Myc (and induction Zfp36) occurred after Ikaros-ERt2 induction with kinetics similar to those we found in B3 cells with intact endogenous Ikzf1 alleles . These d± SE of 3 independent biological replicates.</BLNK-mediated upregulation of Ikzf3 results in the repression of Igll1 , and Ikaros and Aiolos both repress Myc .6) MNase interpretation. The purported identification of "fragile" sites by MNase tests was not viewed as very strong, for a number of reasons. To identify such changes as an alternatively-bound form of the nucleosome, one would wish for a titration of MNase, not just a single digestion. In addition, it would be important to know if the change is due to a nucleosome shifting in position, rather than being weakly bound. Finally, the differential amplification in 6B with "short" and "long" amplicons may or may not be significant – it is not clear if the n=3 is a technical replicate, in which case, the support is weak.As discussed in section 1, our conclusions are based on independent biological replicates and the data are solid. MNase titration followed by quantitative PCR confirmed that TSA treatment reduced the protection of long amplicons at the Igll1 promoter 24h after Ikaros induction . NeverthA number of technical points relating to reagents, methods, and data presentation should be addressed:7) The antibodies for Ikaros and EBF1 are not described; in the Materials and methods, a reference is made to Ferreiros-Vidal 2013, but EBF1 is not used there. What is the measure of specificity for these reagents? Is there other data to support the EBF1 binding shown here?We apologise for these omissions and have added the following sentences to the methods section: “Ikaros ChIP was as described  using a C-terminal Ikaros antibody provided by Dr Stephen Smale that has been extensively characterised in ChIP-seq experiments . We used an EBF1 antibody (Santa Cruz sc- 137065) previously characterised by ENCODE for EBF1 ChIP-seq”. Additional support for the Ikaros and EBF1 binding shown here comes from Schwickert et al., 2014, for EBF1 (using a V5 epitope tag- specific reagent), Ferreiros-Vidal et al., 2013, for HA-Ikaros (using a HA epitope tag-specific reagent), Ferreiros-Vidal et al., 2013, for endogenous Ikaros  and Bossen et al., 2015, for endogenous Ikaros  as summarised in 8) In </9) For Pol II and TFIIB measurements, the manuscript shows directly the% input recovered from ChIP, which is a preferable approach for chromatin aficionados . The Y-axis is labeled "enrichment", however, which is confusing, because presumably the% input was not normalized to signal at another locus.All ChIP-PCR data in our manuscript are% input or fold-changes thereof. “Enrichment” was added to the Y-axes simply to indicate that these are ChIP experiments, not to indicate normalisation to a reference locus. We have changed the relevant axes to “% input”.10) In The main points of this experiment are to show (i) that Igll1 promoter accessibility is maintained in the absence of RNAP2 and (ii) Ikaros induction can still induce chromatin remodeling in triptolide-treated cells. 11) As the referees point out, TSA treatment can have moderate effects on local histone acetylation prior to Ikaros induction , as well12) The differences for pericentric chromatin localization +/- TSA (6D) and loading of Ikaros with or without CHD4 (7B) may or may not be significant; it is not clear how reproducible these differences are in independent experiments. (See Point 1). In 6D, apparently two experiments were conducted; is this data from one?Chd4 cells. The p-values as calculated based on replicates using GLM  were 9.54x10-18 for the effect of TSA and 5.54x10-38 for the effect of Chd4 knockdown, indicating that the effect of TSA and Chd4 knockdown on the positioning of Igll1 alleles were statistically significant. The figures, legends and source files of the revised manuscript have been updated accordingly.We have performed additional DNA-FISH experiments, and the revised 13) It is not clear why the KinTec modeling used the specific parameters indicated in Materials and methods. Have these been measured in a particular system?As discussed in detail in section 4, the models were largely robust to parameter choices, and our choices were based on the greater stability of nucleosomes compared to transcription factors, and the knowledge that Ikaros binds as an obligate dimer/facultative multimer. The rate of nuclear Ikaros was estimated based on semi-quantitative confocal microscopy.14) In subsection “Loss of RNAP2, reduced promoter accessibility, and transcriptional repression are early and near-simultaneous events” the authors state that they use primer pairs that span introns as a means to measure unspliced transcripts. They presumably mean primers that span individual exon/intron junctions? The primers used should be included in Materials and methods.Thank you for pointing out the inaccurate phrasing. The primer pairs used were “designed to span an intron-exon boundary”. We have corrected this in the revised manuscript. Primer sequences are listed in the Methods section of the revised manuscript.The reviewers had asked for more specific analysis of genome-wide gene expression, since only a minority of genes showed the Pol II decrease found at the two promoters of interest. In the response to the reviewers, the authors reanalyze the data, and conclude that for a majority of promoters, it appears that Pol II decrease at the promoter is the trend, and that differences in sensitivity of measuring mRNA vs. Pol II occupancy may explain some of the discrepancy.1) In addition to having this information in the letter of response, the manuscript should indicate that they believe the majority of genes are showing reductions in Pol II, but that differential sensitivity may be an issue. Otherwise it seems the authors are content to ignore what might be a mechanism that impacts the majority of repressed genes.We have changed the narrative to “Genome-wide, RNAP2 occupancy was significantly reduced at 372 downregulated genes 6 hours after Ikaros translocation ” and add2) The Western blot data should be included in the manuscript as part of, or attached to, We have added a western analysis showing the levels of native and induced Ikaros over time to 3) The text still refers to "intron-spanning primers" – that needs to be fixed.We have fixed the offending sentence on p10 of the manuscript to read: “We used quantitative RT-PCR with primers spanning intron-exon boundaries to quantify the abundance of unspliced (primary) transcripts…”4) Typo We have corrected the typo in


Salmonella enterica serovar Typhimurium at different stages of the growth cycle, a surprising disconnection between gene dosage and transcriptional output was revealed. During exponential growth, gene output occurred chiefly in the Ori (origin), Ter (terminus) and NSL (non-structured left) domains, whereas the Left macrodomain remained transcriptionally quiescent at all stages of growth. The apparently high transcriptional output in Ter was correlated with an enhanced stability of the RNA expressed there during exponential growth, suggesting that longer mRNA half-lives compensate for low gene dosage. During exponential growth, RNA polymerase (RNAP) was detected everywhere, whereas in stationary phase cells, RNAP was concentrated in the Ter macrodomain. The alternative sigma factors RpoE, RpoH and RpoN were not required to drive transcription in these growth conditions, consistent with their observed binding to regions away from RNAP and regions of active transcription. Specifically, these alternative sigma factors were found in the Ter macrodomain during exponential growth, whereas they were localized at the Ori macrodomain in stationary phase.We have investigated the connection between the four-dimensional architecture of the bacterial nucleoid and the organism's global gene expression programme. By localizing the transcription machinery and the transcriptional outputs across the genome of the model bacterium  ChIP, chromatin immunoprecipitation; ChIP-chip, chromatin immunoprecipitation-microarray; NSL, non-structured left; NSR, non-structured right; Ori, origin; RNAP, RNA polymerase; RNA-seq, RNA sequencing; Ter, terminus.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97283) and GSE97161 .1. Microarray datasets have been deposited at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database; accession numbers GSE97283  and normalized ‘IGB files’ (.gr file format) are available at NCBI GEO database; accession number GSE49829.2. RNA sequencing data analysed in this manuscript was previously published at DOI: Salmonella enterica chromosome. The multicopy gene effect created by chromosome replication is superseded by regional differences in transcription activity, including the existence of a consistently low activity macrodomain and the apparent evolution of extended RNA half-lives near the replication terminus that may counterbalance multicopy effects nearer the origin of replication. Most surprising is the discovery that inactive sigma factors bind to DNA away from RNA polymerases (RNAPs), then reposition to locations diametrically opposed to RNAPs as transcriptional activity shifts through growth. This research hints at large-scale spatial organization of a bacterial chromosome consistent with the macrodomains described in Escherichia coli. Charting the dynamic nature of chromosome spatial structure is an important step in identifying the evolutionary constraints that shape bacterial chromosomes and will contribute to the rational design of synthetic chromosomes.This study employs a top-down analysis of genome-wide transcriptional activity to identify broad geographical and temporal patterns of gene expression and transcription machinery distribution around the The bacterial chromosome is more than simply a carrier of genetic information. It is becoming apparent that a blueprint for the gene expression programme of the cell may be written into the geography and architecture of the folded chromosome . This prSalmonella enterica serovar Typhimurium has been studied for many decades as an aid to understanding the link between gene position and bacterial physiology [S.enterica serovar Typhimurium is that the chromosome of this pathogen is closely related in size, gene content and gene order to the chromosome of the commensal bacterium Escherichia coli, arguably the best understood of all living cells [E. coli chromosome is divided into a small number of distinct domains was an important advance in our understanding of bacterial chromosomal architecture [The chromosome of the model bacterium ysiology . An advang cells . The disitecture . An analitecture  , and the position of a gene along a replichore between the origin and terminus of replication can affect its level of expression simply due to copy number effects: in rapidly-growing bacteria, there are more copies of genes close to the origin than there are copies of genes near the terminus . HoweverS.enterica serovar Typhimurium chromosome. Our data illustrate shifting patterns of RNA polymerase (RNAP) and sigma factor localization as a function of growth stage, and show that assumptions based on gene dosage in growing cells are an unreliable guide to predicting gene outputs.In this study, we performed what is to the best of our knowledge the first investigation of the genome-wide distribution of transcription machinery and transcript output in the context of the macrodomain structure of the S. enterica serovar Typhimurium SL1344 was divided into six even-sized domains of 813 002 bp, oriented such that the Ori domain was centred on the origin of replication (position 4 105 236). Similarly, the 4 639 675 bp E. coli K-12 substrain MG1655 chromosome was divided into six even-sized domains of 773 279 bp, oriented such that the Ori domain was centred on the origin of replication (position 3 923 883). Genes that overlapped domain boundaries were assigned to the domain that contained the majority of the gene . Transcripts-per-million counts from published data [The 4 878 012 bp chromosome of hed data  were sumhed data .Chromatin immunoprecipitation (ChIP) experiments were conducted as previously described , using tE. coli MG1655 and S. enterica serovar Typhimurium SL1344 are very similar in size, gene content and gene order . The gene insertions and deletions that have occurred since the divergence of E. coli and S. enterica from their last common ancestor are evenly distributed around their chromosomes [S. enterica chromosome the broad outlines of the macrodomain organization of E. coli. To observe genome-wide transcriptional activity in the context of chromosome macrodomain architecture, we divided the S. enterica chromosome into six equal-sized zones that were approximately equivalent to the six domains of the E. coli chromosome elucidated by Valens et al. [S. enterica serovar Typhimurium. This was done by comparing whole-genome tiled oligonucleotide microarray data to cDNA sequencing  data to quantify transcripts from protein-encoding genes in two phases of growth, late exponential phase (OD600 0.3) and early stationary phase (OD600 2.0) in rich medium . Quantification of transcripts by microarray and RNA-seq methods yielded similar measures of gene expression, with remarkably similar transcriptional peaks being observed in late exponential phase  and rpsJ (SL3408)  . The mRNS.enterica serovar Typhimurium. It also allowed the mRNA output to be assessed in the context of expectations arising from gene-dosage effects on transcript abundance. When a chromosome is replicating as fast as possible, a fresh round of replication will initiate before the previous copy is complete. Thus, a chromosome can have up to six active replication forks, which creates a 6.5 : 2.2 ratio of Ori-proximal to Ter-proximal gene copies during rapid growth [rrsG ribosomal operon at its boundary with the NSL domain  in the NSL domain from the dataset because these 28 genes alone account for 22 % (EP), 19 % (LEP1) and 14 % (LEP2) of genome-wide transcription from protein-encoding genes, corresponding to 61 % (EP), 58 % (LEP1) and 48 % (LEP2) of transcripts originating from the NSL domain. 600 0.3), the Ter domain had surpassed the Ori domain for transcriptional output. In stationary phase, more transcripts originated from the Ter macrodomain than from any other domain  would be concentrated in macrodomains according to transcriptional output, and that transcription machinery would concentrate in different domains as the observed transcriptional output changes as a function of the growth cycle. ChIP-chip analysis revealed that during exponential growth, RNAP was relatively evenly distributed across the chromosome except at the Ori and Ter domains where it was less abundant . Conversl growth . The higl growth . Work wiromosome . We obseromosome . Thus, iifically . Our datifically , perhaps70) was concentrated in the Ori, NSR and NSL domains  and RpoH (heat shock) did not bind to DNA showing matches to their binding site consensus sequences, indicating that binding may have been non-specific. Genes belonging to the  regulon  were mor regulon  were als regulon .Salmonella and E. coli over the past decades. We anticipate that similar transcriptional and polymerase dynamics will be observed in E. coli and other bacteria because the E. coli macrodomain structure has also been successfully extrapolated to another enteric bacterium, Dickeya dadantii (Erwinia chrysanthemi) [Transcript abundance does not reflect gene dosage either during rapid growth or in stationary phase. Instead, mRNA output displays a domain-specific pattern in which Ter, the macrodomain with the lowest gene dosage, has a higher output than either the Left or Right macrodomains in rapidly growing cells and exceeds that of Ori in non-growing cells. These unexpected findings may reflect differences in mean half-lives for mRNAs expressed in different domains, with extended half-lives associated with Ter transcripts counterbalancing the gene-dosage effect. During rapid growth, transcription and RNAP are not well correlated, probably reflecting the inability of our detection method to distinguish between specific and non-specific RNAP binding; but in stationary phase, transcription and RNAP localize around Ter. The pattern seen in rapid growth may reflect the longer half-life of mRNA originating in Ter, while the stationary phase pattern may reflect a role for Ter as a store of non-transcribing RNAP during periods when the cell is not growing. Although chemostat cultures and synchronized cell studies will be useful to disentangle the effects of growth rate and culture conditions, in the present study cells were grown in rich medium in batch culture conditions to support comparisons with other broad-scale studies conducted in these same growth conditions in anthemi) .Under growth conditions that do not select for their activities, the alternative sigma factors RpoE, RpoH and RpoN display a distribution pattern that is the reciprocal of that seen for RpoD and RNAP. The alternative sigma factors bind to chromosomal domains that are transcriptionally quiescent. The consistently low transcriptional output of the Left domain is intriguing as this domain has several unusual traits, including a relatively low A+T base content, a lower propensity to form curved DNA than other domains and low levels of binding by the transcription silencing nucleoid-associated protein H-NS . DespiteCameron ADS, Dillon SC, Kröger C, Beran L, Dorman CJ. Gene Expression Omnibus GSE97283 (2017).Cameron ADS, Dillon SC, Kröger C, Beran L, Dorman CJ. Gene Expression Omnibus GSE97161 (2017).