A systematic approach identifies p53-DREAM pathway target genes associated with blood or brain abnormalities

  1. Jeanne Rakotopare
  2. Vincent Lejour
  3. Carla Duval
  4. Eliana Eldawra
  5. Hugues Escoffier
  6. Franck Toledo

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Abstract

p53 (encoded by Trp53) is a tumor suppressor, but mouse models have revealed that increased p53 activity may cause bone marrow failure, likely through dimerization partner, RB-like, E2F4/E2F5 and MuvB (DREAM) complex-mediated gene repression. Here, we designed a systematic approach to identify p53-DREAM pathway targets, the repression of which might contribute to abnormal hematopoiesis. We used Gene Ontology analysis to study transcriptomic changes associated with bone marrow cell differentiation, then chromatin immunoprecipitation-sequencing (ChIP-seq) data to identify DREAM-bound promoters. We next created positional frequency matrices to identify evolutionary conserved sequence elements potentially bound by DREAM. The same approach was developed to find p53-DREAM targets associated with brain abnormalities, also observed in mice with increased p53 activity. Putative DREAM-binding sites were found for 151 candidate target genes, of which 106 are mutated in a blood or brain genetic disorder. Twenty-one DREAM-binding sites were tested and found to impact gene expression in luciferase assays, to notably regulate genes mutated in dyskeratosis congenita (Rtel1), Fanconi anemia (Fanca), Diamond–Blackfan anemia (Tsr2), primary microcephaly [Casc5 (or Knl1), Ncaph and Wdr62] and pontocerebellar hypoplasia (Toe1). These results provide clues on the role of the p53-DREAM pathway in regulating hematopoiesis and brain development, with implications for tumorigenesis.

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  5. Version published to 10.1242/dmm.050376
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    Reply to the reviewers

    We would like to thank the reviewers for their comments and suggestions, which were very helpful to improve our manuscript. The revised manuscript notably includes the following improvements:

    • To evaluate the relevance of identified candidate targets genes, we integrated an additional screening step in our method, corresponding to the analysis of RNAseq datasets specific of blood or brain cells. RNAseq data from irradiated hematopoietic stem cells or splenic cells were analyzed and included in the new Table S19, and RNAseq data from zika virus-infected neural progenitors were analyzed and included in the new Table S28. In addition, we also verified that the expression of a subset of blood related genes was decreased in the bone marrow cells of p53Δ31/Δ31 mice, known to exhibit increased p53 activity and to phenocopy dyskeratosis congenita (new Figure S8).
    • Luciferase data were expanded to show that, for promoters exhibiting a significant p53-mediated repression in luciferase assays, the p53-dependent regulation was abrogated after mutation of the putative DREAM binding site (new Figures 2e and 2i).
    • We found putative DREAM binding sites for 151 targets, and the predicted binding sites were precisely mapped relative to the position of ChIP peaks of DREAM subunits (E2F4 and LIN9) and to transcription start sites of target genes. These additional analyses, shown in the new Figures 3a and 3b, further suggest the reliability of our predicted binding sites. Notably, hypergeometric tests of the distribution of DREAM binding sites relative to E2F4/LIN9 ChIP peaks reveal a significant >1300-fold enrichment of these sites at ChIP peaks.
    • We now present a detailed comparison of our results with those reported in other studies, notably the predicted E2F and CHR sites from the Target gene regulation database (new Figure S11), or the list of candidate DREAM targets suggested from Lin37 KO cells (new Figure S10 and new Table S35). This also leads us to discuss the different types of DREAM binding sites (bipartite sites (e.g. CDE/CHR or E2F/CLE) vs sites composed of a single E2F or a single CHR motif).
    • We integrated updates of the Human phenotype ontology website to include the latest lists of genes related to blood or brain ontology terms in our analysis. In the previous version of the manuscript we had analyzed a total of 811 genes downregulated ≥ 1.5 fold upon bone marrow cell differentiation. Our revised manuscript now includes the analysis of 883 genes.
    • Several improvements were made to present our results more clearly and with more details : 1) additional evidence that the differentiation of Hoxa9ER cells correlates with p53 activation is now provided in the new Figure S1; 2) the precise values for gene expression after bone marrow cell differentiation, as well as p53 regulation scores from the Target gene regulation databases are included in the new Tables S1, S5, S8, S11, S14, S20 and S23; 3) A Venn-like diagram was included to summarize the different steps of our approach in the new Figure 3c, with detailed lists of genes selected at each step in new Tables S17 and S26; 4) for genes associated with blood or brain genetic disorders, bibliographic references describing gene mutations and clinical traits were included in a new Table S36; 5) Figure 4a and Table S37 were improved to include evidence that increased BRD8 in glioblastoma cells leads to a decreased expression of several genes transactivated by p53.

    Reviewer #1 (Evidence, reproducibility and clarity):

    Summary
    In this paper the authors describe a data driven approach to identify and prioritise p53-DREAM targets whose repression might contribute to abnormal haematopoiesis and brain abnormalities observed in p53-CTD deleted mice. The premise is that in these mice, (where they have previously demonstrated p53 to be hyperactive in at least a subset of tissues), that the p53-p21-E2F/DREAM axis is at least in part responsible for observed phenotypes due to the repression of E2F and CDE/CHE element containing genes. Their approach to home in on relevant genes is based on transcriptomic gene ontology analysis of genes repressed in these disease settings where they primarily use publicly available data from HOXA9-ER regulated model of HSC expansion wherein they observe increases on p53-p21 expression upon differentiation where they demonstrate that p53-p21 DREAM target genes are suppressed as we would expect in this scenario where p53-p21 is activating withdrawal from cell cycle. They then spend a lot of effort analysing this datasets combining "gene-ontology", "disease phenotype" and "meta-ChIP-seq" analysis of public data to support the observation that mutations of genes suppressed in this manner are disproportionately linked to heritable haematopoetic and brain disorders. While these results are interesting in terms of framing a hypothesis about how mutations in p53-p21-DREAM regulated targets contribute to such conditions, they are to be expected given the now very well described impact of p53-p21 on both E2F4/DREAM targets.

    We agree with the referee that the impact of p53-p21 on both E2F4/DREAM targets is well described. However, discussions with many scientists or clinicians specialized in bone marrow failure syndromes or microcephaly diseases led us to realize that most were not familiarized with the p53-DREAM pathway, so that a study that would bridge the gap between DREAM experts and bone marrow or microcephaly specialists would be particularly useful. In addition, we thought that strategies that would rely on disease-based ontology terms were likely to identify new targets, compared to previous studies that considered cell cycle regulation instead of disease phenotypes. Consistent with this, many genes we identified as candidate DREAM targets were not reported in previous studies. In addition, as detailed below, our positional frequency matrices led to identify DREAM binding sites that had not been predicted by previous approaches.

    The natural progression of this work would be to go on to show this occurs in relevant cells or tissues derived from the p53-CTD mice as well as look at modulating target genes to understand underlying mechanisms and consequences.
    Rather than this, they focus on validating that a sub-set of these targets are indeed suppressed by specific p53 activation by MDM2 inhibitor Nutlin-3A in MEFs by qPCR and that mutation of predicted CDE CHR elements in luciferase constructs leads to increase luciferase activity. While these findings support their predictions, the results are entirely expected based on what is known about such targets and demonstrating that this occurs in MEFs does not closely relate to haematopoietic and brain cells they suggest this regulation is important. In fact, in the discussion, the authors comment on the importance of cell type context specificity in terms of discordance between predictions of TF binding sites and public datasets.

    We agree that additional data from relevant cells or tissues were required to strengthen our conclusions. In the revised manuscript, we evaluated the relevance of candidate target genes related to blood ontology terms by integrating an additional screening step in our method, corresponding to the analysis of RNAseq datasets specific of blood cells. We analyzed dataset GSE171697, with RNAseq data from hematopoietic stem cells of unirradiated p53 KO, or unirradiated or irradiated WT mice, as well as dataset GSE204924, with RNAseq data from splenic cells of irradiated p53Δ24/- or p53+/- mice. The latter dataset appeared interesting because p53Δ24 is a mouse model prone to bone marrow failure and the spleen is a hematopoietic organ in mice. The analysis of these datasets is included in the new Table S19. In the datasets,increased p53 activity correlated with the downregulation of most of the 269 candidate DREAM targets. However, 56 genes which appeared upregulated in cells with increased p53 activity were considered poor candidate p53-DREAM targets and removed from further analyses, leading to a list of 213 genes that appeared as better candidate p53-DREAM targets related to blood abnormalities. Furthermore, we also verified that the expression of a subset of blood-related candidate genes was decreased in the bone marrow cells of p53Δ31/Δ31 mice (prone to bone marrow failure) compared to bone marrow cells from WT mice. This result is presented in the new Figure S8.

    As for genes related to brain development, we discussed in the previous version of the manuscript that most genes mutated in syndromes of microcephaly or cerebellar hypoplasia are involved in ubiquitous cellular functions (chromosome condensation, mitotic spindle activity, tRNA splicing…), which suggested that our analysis of transcriptomic changes associated with bone marrow cell differentiation might also be used to identify brain specific targets. However, we agree with the referee that confirmation of these brain specific targets in a more relevant cellular context was preferable. In the revised manuscript, we included the analysis of datasets GSE78711 and GSE80434, containing RNAseq data from human cortical neural progenitors infected by the Zika virus (ZIKV) or mock-infected, because ZIKV was shown to cause p53 activation in cortical neural progenitors and microcephaly. This analysis is detailed in the new supplementary Table S28. In both datasets, increased p53 activity correlated with the downregulation of most of the 226 candidate DREAM targets. Sixty-four genes which appeared more expressed in ZIKV-infected cells were considered poor candidate p53-DREAM targets and removed from further analyses, leading to a list of 162 candidate p53-DREAM targets related to brain abnormalities. We think this significantly increases the relevance of our analysis of brain-specific targets.

    Finally, they try and contextualise effects in glioblastoma data by correlating target gene expression with levels of BRD8 since it has recently been shown to attenuate p53 function in glioblastoma and show that some of the brain disease associated genes are expressed at higher levels in BRD8 high patient samples. It seems strange here that they do not also look at expression of p21 or other p53 targets that would help ascertain if p53 activity is indeed suppressed. Moreover, much more elegant methods for predicting transcription factor activity could be applied to this data.

    We agree with the referee. Indeed, when we had performed the analysis of glioblastoma cells, we first verified that increased BRD8 levels correlated with decreased p21 levels in these cells. However, we had not included this verification in the previous version of the manuscript. In this revision, we improved the Figure 4 (and Table S37) reporting the analysis of glioblastoma cells to address this point. In Figure 4a, we now show the variations in mRNA levels between BRD8Low and BRD8High tumors, for BRD8 itself, as well as 5 genes well-known to be transactivated by p53 (p21, MDM2, BAX, GADD45A and PLK3) and the 77 p53-DREAM targets associated with microcephaly or cerebellar hypoplasia. The data clearly show that tumors with high BRD8 exhibit a decrease in the expression of p53 transactivated targets, and an increase in p53-DREAM repressed targets.

    Major Comments
    The major result of this paper as it stands is the prioritisation of candidate genes in the p53-DREAM pathway involved in these conditions, and their refined approach used to identify and prioritise these genes and is such more of a starting point for further investigation. They fall short of demonstrating the relevance of their predictions physiologically in tissues from the mice and do not demonstrate functional importance of regulation of targets they put forward. Given that these genes will be co-ordinately regulated, without a mechanistic experiment in physiologically relevant model it is impossible to infer causality. For example, depleting individual targets in the HOXA9 model and evaluating impact on survival, proliferation and differentiation may be a (relatively) simple way to explore this, perhaps comparing to effects of p53 activating agents such as Nutlin-3A. Of note the authors (Jaber 2016 PMID: 27033104) and several other groups had (Fischer 2014 PMID: 25486564 McDade 2014 PMID: 24823795) previously demonstrated the link between p53-p21 and suppression of DNA-repair/Damage related genes (as is also observed here in particular FA-related genes that they discuss briefly here. I would have thought that this would be an obvious starting point for some mechanistic experiments and in fact I note this has been demonstrated before (Li et al 2018 PMID: 29307578)

    The starting point of our study is not the prioritization of DREAM target genes, but rather the detailed phenotyping of p53Δ31/Δ31 mice that we performed in previous publications (Simeonova et al. Cell Rep 2013, Toufektchan et al. Nat. Commun. 2016), in which we mentioned phenotypical traits typical of dyskeratosis congenita and Fanconi anemia, including notably bone marrow failure and cerebellar hypoplasia.

    We understand that depleting individual targets in the Hoxa9 system and evaluating impact on survival, proliferation and differentiation might seem appropriate to explore their potential causality. However, our previous work on Fanc genes leads us to think that this might not be informative. Regarding this, we now clearly discuss in the revised version of the manuscript : “Finding a functionally relevant [DREAM binding site] for Fanca, mutated in 60% of patients with Fanconi anemia [59,60], may help to understand how a germline increase in p53 activity can cause defects in DNA repair. Importantly however, we previously showed that p53Δ31/Δ31 cells exhibited defects in DNA interstrand cross-link repair, a typical property of Fanconi anemia cells, that correlated with a subtle but significant decrease in expression for several genes of the Fanconi anemia DNA repair pathway, rather than the complete repression of a single gene in this pathway [25]. Thus, the Fanconi-like phenotype of p53Δ31/Δ31 cells most likely results from a decreased expression of not only Fanca, but also of additional p53-DREAM targets mutated in Fanconi anemia such as Fancb, Fancd2, Fanci, Brip1, Rad51, Palb2, Ube2t or Xrcc2, for which functional or putative [DREAM binding sites] were also found with our systematic approach.” We further discuss in the manuscript how this may also apply to telomere-, ribosome-, of microcephaly-related genes.

    The analysis of brain specific targets and the link to BRD8 sits largely as an aside and the analysis of patient data from glioblastomas is underdeveloped as noted above.

    As we previously mentioned, the revised manuscript includes the analysis of RNAseq datasets from human cortical neural progenitors infected by the Zika virus (ZIKV) or mock-infected, which significantly increases the relevance of our analysis of brain-specific targets. Furthermore, we improved Figure 4 to present more clearly the impact of BRD8 levels on the expression of genes transactivated by p53 or repressed by p53-DREAM.

    The computational methods applied are robust, albeit predominantly coorelative, in terms of identifying regulation of potential causative target genes, validated across human and mouse cell lines, and this indicates a role of these genes in the relevant conditions. However, further validation through application in a bulk or single cell RNAseq patient cohort, or at least an in vivo model would strengthen these conclusions and complement the work presented here which is based on in vitro mouse and human cells. This is pertinent as this study improves upon previously published approaches by focusing on "clinically relevant target genes". Additionally, this would exhibit the potential applications of the findings presented.

    We thank the referee for this comment. As mentioned above, in the revised manuscript we analyzed RNAseq data from hematopoietic stem cells of unirradiated WT or p53 KO mice, or irradiated WT mice, and from splenic cells of irradiated p53D24/- or p53+/- mice, and quantified the expression of a subset of blood-related candidate genes in the bone marrow cells of p53Δ31/Δ31 mice (prone to bone marrow failure) and WT mice (new Figure S8 and Table S19). For genes related to brain development, we included the analysis of RNAseq data from human cortical neural progenitors infected by the Zika virus (ZIKV) or mock-infected (Table S28). These RNAseq analyses were added as an additional screening criterion in our approach, which significantly increased the relevance of the target genes identified.

    In terms of statistical analysis, the hypergeometric test should be applied to assess significant enrichment of genes for example with CDE/CHR regions within the previously identified lists.

    In the revised manuscript, we precisely mapped the DREAM binding sites in 50 bp windows within regions bound by E2F4 and/or LIN9, an analysis included in new Figure 3a. We then compared the distribution of DREAM binding sites at the level of ChIP peaks compared to their distribution over the entire genome and found a > 1300-fold enrichment of these sites at ChIP peaks. This significant enrichment (f=3 10-239 in a hypergeometric test) is most likely underestimated because mouse-human DNA sequence conservations were not determined for putative DBS over the full genome. These new analyses clearly reinforce our previous conclusions.

    Minor Comments
    References are required for the genes listed which play a role in the diseases of interest.

    In the revised manuscript, references are provided for genes which play a role in the diseases of interest. Due to the large number of added references, these were included in a new supplementary table, Table S36.

    This paper would benefit from the inclusion of summary schematics and tables throughout (rather than relying only on somewhat unwieldy heatmaps which show little other than all these genes are co-ordinately regulated), this could include summaries of the methods applied, gene or CDE/CHR inclusion criteria, and Venn diagrams indicating the subsets of final genes identified through this approach.

    We thank the referee for this suggestion. In the revised manuscript we provide a Venn-like diagram of the different steps of our approach (new Figure 3c), as well as tables listing the genes retained after each step of the selection (new Tables S17 and S26) and these additions improve the clarity of our manuscript.

    Reviewer #1 (Significance):

    In its current form this is a very limited study that would require significant additional work to move conclusions beyond correlation and hypothesis generation.
    Overall, while limited largely to target prioritisation, this research nicely exemplifies how genes affected by the p53-DREAM pathway can be robustly identified, providing a potential resource for individuals working on this pathway or on abnormal haematopoiesis and brain abnormalities. These results are complementary to work previously published by Fischer et al, which has been referenced throughout the analysis (highlighting Target Gene Regulation Database p53 and DREAM target genes) and discussion.

    This paper will be of interest to researchers of blood/neurological diseases who can assess if these genes are dysregulated in their datasets, or those investigating the p53-DREAM pathway. This work represents a useful resource detailing genes affected by this pathway in these disease settings, however researchers of the p53-DREAM pathway may find this paper useful when planning an approach to identify and prioritise genes of interest.

    We thank the reviewer for considering that our study represents a useful resource for researchers working on the p53-DREAM pathway, abnormal haematopoiesis and brain abnormalities, because it was exactly the purpose of our work. As mentioned above, we think that a study bridging the gap between DREAM experts and bone marrow or microcephaly specialists should be particularly useful.

    We also agree with the referee that our approach could be used to identify DREAM targets relevant to other disease settings, and we now mentioned this clearly in the revised manuscript.

    While our results are complementary to work previously published by Fischer et al and included in the Target gene regulation database, in the revised manuscript we discuss the novelty of our results in more details, notably by performing additional analyses. For example, our method identified bipartite DREAM binding sites for 151 candidate DREAM targets (of which 56 genes were not previously mentioned by Fischer et al.) and we now provide a detailed mapping (using 50 bp windows) of the bipartite DREAM binding sites we identified relative to ChIP peaks for DREAM subunits, then performed a similar mapping of the E2F and CHR sites included in the Target gene regulation database. Our predicted DREAM binding sites coincided with ChIP peaks more frequently (Figure 3a) than the predicted E2F or CHR from the Target gene regulation database (Figure S11), which further indicates the usefulness of our study as a resource.

    Reviewer #2 (Evidence, reproducibility and clarity):

    The authors used various systems including Hoxa9-indubible BMCs, human and mouse cells, WT and p53 knockout MEF, glioblastoma cells to screen p53-DREAM targets and observed distinct finding for each system. Since different cell types have various p53 activation and p53 target genes expression, the authors might want to select proper cell type(s) to screen p53-DREAM target genes and design experiments to confirm that these genes are really p53-DREAM target genes.

    We agree that additional data from relevant cells or tissues were required to strengthen our conclusions. As mentioned in response to referee #1, in the revised manuscript we evaluated the relevance of candidate target genes related to blood ontology terms by integrating an additional screening step in our method, corresponding to the analysis of RNAseq dataset GSE171697, with data from hematopoietic stem cells of unirradiated or irradiated WT mice and unirradiated p53 KO mice , as well as RNAseq dataset GSE204924, with data from splenic cells of irradiated p53D24/- or p53+/- mice. As for genes related to brain development, we included the analysis of RNAseq datasets GSE78711 and GSE80434 for validation, two datasets from human cortical neural progenitors infected by the Zika virus or mock-infected. Together, the 4 datasets provide evidence for a p53-dependent downregulation in blood- and brain- relevant settings (new Tables S19 and S28).

    Importantly, in the revision we also compared our list of 151 genes appearing as the best p53-DREAM candidates with the results of Magès et al., who analyzed, in murine cells with a CRISPR-mediated KO of Lin37 (a subunit of DREAM), the transcriptomic changes that follow a reintroduction of Lin37. This comparison is detailed in the discussion section, with the new Figure S10 and Table S35. We mention: “Our list of 151 genes overlaps only partially with the list of candidate DREAM targets obtained with this approach, with 51/151 genes reported to be downregulated in Lin37-rescued cells [17]. To better evaluate the reasons for this partial overlap, we extracted the RNAseq data from Lin37 KO and Lin37-rescued cells and focused on the 151 genes in our list. For the 51 genes that Mages et al. reported as downregulated in Lin37-rescued cells, an average downregulation of 14.8-fold was observed (Figure S10, Table S35). Furthermore, when each gene was tested individually, a downregulation was observed in all cases, statistically significant for 47 genes, and with a P value between 0.05 and 0.08 for the remnant 4 genes (Table S35). By contrast, for the 100 genes not previously reported to be downregulated in Lin37-rescued cells, an average downregulation of 4.7-fold was observed (Figure S10, Table S35), and each gene appeared downregulated, but this downregulation was statistically significant for only 35/100 genes, and P values between 0.05 and 0.08 were found for 23/100 other genes (Table S35). These comparisons suggest that, for the additional 100 genes, a more subtle decrease in expression, together with experimental variations, might have prevented the report of their DREAM-mediated regulation in Lin37-rescued cells.”

    This comparison provides additional evidence that the 151 candidate target genes we identified are bona fide DREAM targets.

    Specific comments:
    The authors need to describe and define HSC and Diff in Figure 1.

    This has been corrected in the revised manuscript. “HSC” was replaced by “Hematopoietic Stem / Progenitor cells (+OHT)” and “Diff” was replaced by “Differentiated cells (5 days – OHT).

    Are Figure 1B and 1D list genes p53 targets in bone marrow cells?

    In the revised manuscript, we now analyzed RNAseq data to address this point. The question refers to lists of telomere-related genes (Figure 1b in both versions of the manuscript) and Fanconi-related genes (Figure 1d in the previous version, now Figure S2a), but could also apply to other lists of genes related to blood ontology terms (Figures S3-S5 in the revised manuscript). As mentioned in response to referee #1, in the revised manuscript we integrated an additional screening step in our method, corresponding to the analysis of RNAseq datasets specific of blood cells. We analyzed dataset GSE171697, with RNAseq data from hematopoietic stem cells of unirradiated WT or p53 KO mice, or irradiated WT mice, as well as dataset GSE204924, with RNAseq data from splenic cells of irradiated p53D24/- or p53+/- mice. The latter dataset appeared interesting because p53D24 is a mouse model prone to bone marrow failure and the spleen is a hematopoietic organ in mice. Furthermore, we also verified that the expression of a subset of blood-related candidate genes was decreased in the bone marrow cells of p53Δ31/Δ31 mice (prone to bone marrow failure) compared to bone marrow cells from WT mice, a result presented in the new Figure S8.

    Where is the detailed information for mouse and human cells in Figure 1 and Figure 2?

    In the first draft of the manuscript, supplementary tables provided precise values for ChIP binding. In the revised manuscript, we also provide the precise values for gene expression after bone marrow cell differentiation, as well as p53 regulation scores from the Target gene regulation databases. This additional information is included in the new Tables S1, S5, S8, S11, S14, S20 and S23.

    Are Figure 3B list genes also p53 target genes in other cell types such as bone marrow cells and glioblastoma?

    For genes in the Figure 3B of the previous version of the manuscript (now Figure 2B in the revised version), we now provide evidence that the blood-related genes are less expressed in the bone marrow cells of p53Δ31/Δ31 mice (mice with increased p53 activity and prone to bone marrow failure) compared to bone marrow cells from WT mice. This result is presented in the new Figure S8. For the brain-related genes of the same Figure, evidence of their p53-mediated regulation is provided by the RNAseq datasets GSE78711 and GSE80434, from human cortical neural progenitors infected by the Zika virus or mock-infected (analyzed in the new Table S28). Evidence of that a decreased p53 activity in glioblastomas correlates with increased expression of the brain-related genes of the same Figure is provided in supplementary Table S37.

    Does BRD8high has high p53 and p21?

    We now clearly show, in both Figure 4a and Table S37, that glioblastoma cells with high BRD8 exhibit a decreased expression of CDKN1A/p21 and other genes known to be transactivated by p53 (BAX, GADD45A, MDM2, PLK3), consistent with the fact that BRD8 attenuates p53 activity.

    Are genes listed in Figure 4B all p53 target genes? can some validation be done?

    For genes in Figure 4B, in the revision we focused on the genes that appeared more relevant, i.e. the 77 genes mutated in diseases with microcephaly or cerebellar hypoplasia. All the genes in Figure 4B are repressed in neural progenitors upon infection by the Zika virus, a virus known to cause p53 activation in those cells. This is reported in the new Table S28.

    Reviewer #2 (Significance):

    This is a potentially interesting study. The major limitation is the absence of validation from the screening. This study would definitely benefit the research community as long as some of the key findings are validated.

    We thank the referee for this comment. We hope the new evidence in this revision provide the validation requested by the referee.

    Reviewer #3 (Evidence, reproducibility and clarity):

    In their work submitted to Review Commons, Rakotopare et al. aim to identify p53-DREAM target genes associated with blood or brain abnormalities. To this end, they utilize published data generated with a cellular model that results in cell-cycle exit and differentiation of murine bone marrow progenitor cells upon inducible expression of Hoxa9. By analyzing this gene expression data set published by Muntean et al., they find that multiple of the 3631 genes which are downregulated more than 1.5-fold in differentiated BMCs are also mutated in several disorders connected to proliferation and differentiation defects during hematopoiesis and brain development. By screening ChIP-seq data sets available at ChIP-Atlas, they find that the promoters of many of these genes are bound by DREAM complex components, and most of them were identified as genes indirectly repressed by p53 before (Fischer et al. 2016, targetgenereg.org). They then use a computational approach to identify putative CDE/CHR DREAM-binding sites in the promoters of 372 genes associated with blood/brain abnormalities which are downregulated in differentiated BMCs and bound by DREAM components. Out of the 173 candidate genes, they select twelve to analyze whether mutation of the putative DREAM binding sites results in increased activity of the promoters in luciferase reporter assays. The authors conclude that their findings suggest a general role for the p53-DREAM pathway in regulating hematopoiesis and brain development.
    While the study supports a large body of publications proving that repression of cell cycle genes by the DREAM complex is crucial for cell cycle arrest and exit, it is noted that none of the main conclusions here are unexpected or particularly exciting. All the analyses are based on data sets that compare gene expression in highly proliferative cells with cells that underwent terminal cell cycle exit. Thus, a large portion of the genes that are downregulated in differentiated BMCs are cell cycle genes and well-established targets of DREAM and E2F:RB complexes. Furthermore, it is not surprising that some of these pro-proliferative genes are mutated in diseases connected to proliferation defects like anemias or microcephaly.

    We agree with the referee that the DREAM complex is well known to regulate cell cycle genes – in fact, this is what we mention in the first sentence of our introduction in both versions of our manuscript. However, as we already pointed out in response to Referee #1, many scientists or clinicians specialized in bone marrow failure syndromes or microcephaly diseases are not familiarized with the p53-DREAM pathway, and we think our study will be particularly useful to them. Furthermore, our strategy relying on disease-based ontology terms rather than cell cycle regulation led to identify many DREAM targets that were not reported in previous studies, and our positional frequency matrices led to identify DREAM binding sites not predicted by previous approaches. As discussed below, our revised manuscript provides a more detailed comparison of our findings with those from previous studies.

    Additionally, I am not very enthusiastic about this manuscript because of several major concerns:

    1. The authors draw conclusions about the p53-DREAM pathway based on data that was generated in a cellular differentiation model without convincingly showing that p53 plays a central role in gene repression in this experimental setup.
      (A) Rakotopare et al. define p53-DREAM target genes based on RNA expression data from proliferating precursor cells and non-proliferating, differentiated BMCs (Muntean et al., 2010). This paper has not studied whether p53 gets activated in the particular experimental setup during Hox9a-induced BMC differentiation. On page 4 of their manuscript, the authors state: "Consistent with the fact that BMC differentiation strongly correlates with p53 activation..." without citing any literature or explaining why this is supposed to be a fact. Furthermore, they imply that cell cycle gene repression in this model system depends on p53 because mRNA expression of the p53 targets p21 and Mdm2 was found to be increased in the differentiated cells (Fig. 1A, 5-fold and 2-fold, respectively). However, defining a large set of "p53-DREAM target genes" based on the moderate increase in mRNA levels of two genes that are known to be activated by p53 without showing any evidence that p53 is even involved in this effect during BMC differentiation is not appropriate.

    We agree that Muntean et al. did not study whether p53 gets activated when BMCs differentiate in the Hox9a-ER system. We previously mentioned: “We observed that p53 activation correlated with cell differentiation in this system, because genes known to be transactivated by p53 (e.g. Cdkn1a, Mdm2) were induced, whereas genes repressed by p53 (e.g. Rtel1, Fancd2) were downregulated after tamoxifen withdrawal (Figure 1a)”. We had provided examples for 2 genes transactivated and 2 genes repressed, but clearly mentioned that they were given as examples. In the revised manuscript, we provide additional evidence with a new supplementary Figure that includes changes in expression for 15 additional genes known to be transactivated by p53, and 5 additional genes known to be repressed by p53 (Figure S1). In total, we now correlate HSC differentiation with p53 activation based on the expression of 24 well-known p53-regulated genes, which we hope is more convincing.

    In addition, we changed our phrasing and mention “Consistent with the notion that BMC differentiation strongly correlates with p53 activation in this system, 72 of these 76 genes have negative score(s) in the Target gene regulation (TGR) database”.

    (B) Interestingly, p53 is among the genes that get repressed on mRNA level in differentiated BMCs (Fig. 1B; Trp53), and the authors also identify the DREAM components E2F4 and LIN9 as bound to the p53 promoter by screening ChIP-Atlas data (Fig. 1C). Given that p53 has never been described as a DREAM target, I find this rather surprising and it makes me wonder whether appropriate parameters were selected for analyzing the ChIP data, particularly since the authors do not provide binding data for sets of non-cell cycle genes as a negative control.

    We retrieved ChIP data from the ChIP Atlas database without any specific parameters, thus in a completely unbiased manner. Importantly however, for reasons detailed in the manuscript, we clearly mentioned that total ChIP scores <979/4000 were considered too low to reflect significant DREAM binding. The ChIP score for Trp53 was 630, which rapidly led us to eliminate this gene from our screen.

    This ChIP score criterion was already mentioned in the previous version of our manuscript, but we think the addition of a Venn-like diagram (Figure 3c) and summary tables (S17 and S26) in the revised manuscript will probably make it easier to understand.

    (C) Finally, the authors utilize the targetgenereg.org database to show that many of the genes they describe as p53-repressed were already identified as p53 targets. This database (Fischer et al. 2016) was created by performing a meta-analysis integrating a plethora of RNA-seq and ChIP-seq datasets with the aim to identify whether a particular gene gets up- or downregulated by p53, shows cell-cycle-dependent expression, is a DREAM/MuvB or E2F:RB target, etc. For example, 57 datasets analyzing p53-dependent RNA expression in human and 15 datasets generated with mouse cells were included, and a positive or negative score shows in how many of these experiments the gene was found to be up (positive score) or downregulated (negative score). Combining a large number of datasets in such a study is very helpful to get an idea if a gene is indeed generally regulated by a transcription factor, or if it just showed up in a few experiments - either as a false positive or because the regulation depends on a particular biological setting. The authors find most of the genes they identify as repressed in differentiated BMCs also as downregulated by p53 in targetgenereg.org, however, it remains unclear what parameters they used to define a gene as p53-repressed. For example, in the caption of Fig. 1C, they state: "According to the Target gene regulation database, 72/76 genes are downregulated upon mouse and/or human p53 activation." The four exemptions are SLX1B (human score: 0, mouse score : na), PML (+41, +9), RAD50 (0, na), and TNKS2 (+17, +4). However, there are several other genes that do not appear to be generally repressed by p53, e.g. HMBOX1 (+4, -2); UPF1 (+1, -2), SMG6 (+18, -2), CTC1 (-5, +11), etc. Thus, without providing details regarding the parameters they use to define p53-target genes, such statements are rather misleading. An easy way to solve this problem would be to show the p53 scores in the tables together with the E2F4/LIN9 ChIP data.

    All the genes mentioned as downregulated by p53 had a negative TGR score in human and/or mouse cells. In the revised manuscript, we mention clearly what a negative TGR score means, by stating: “Consistent with the notion that BMC differentiation strongly correlates with p53 activation in this system, 72 of these 76 genes have negative p53 expression score(s) in the Target gene regulation (TGR) database [23], which indicates that they were downregulated upon p53 activation in most experiments carried out in mouse and/or human cells (Figure 1b, Table S1).” We agree with the referee that adding precise TGR scores is informative. In the revised manuscript, we provide the TGR scores for all the genes analyzed, as part of the new supplementary Tables S1, S5, S8, S11, S14, S20 and S23, together with their expression levels in undifferentiated or differentiated cells (as requested by Referee #2). The ChIP data are provided in separate tables (Tables S2, S3, S6, S7, S9, S10, S12, S13, S15, S16, S21, S22, S24 and S25).

    1. The authors define a large set of genes containing "CDE-CHR" promoter elements and thereby ignore how these elements are defined and what properties they have.
      (A) At the beginning of the introduction, the authors state: "The DREAM complex typically represses the transcription of genes whose promoter contain a bipartite CDE/CHR binding site, with a cell cycle-dependent element (CDE) bound by E2F4 or E2F5, and a cell cycle gene homology region (CHR) bound by LIN54, the DNA binding subunit of MuvB (Zwicker et al., 1995; Müller and Engeland, 2010)."
      This statement is incorrect. The authors ignore that the CDE/CHR tandem site is just one of four promoter elements that have been shown to recruit DREAM for the transcriptional repression of several hundred genes. It has been studied in detail that DREAM can bind to the following promoter sites:
      (I) CHR elements - bound by DREAM via LIN54; also bound by the activator MuvB complexes B-MYB-MuvB and FOXM1-MuvB which results in maximum gene expression in G2/M
      (II) CDE-CHR tandem elements - like (I) but binding of DREAM can be stabilized via E2F4/DP interacting with a truncated E2F binding site. Since CDE elements do not represent functional E2F sites, E2F:RB complexes do not bind.
      (III) E2F binding sites - bound by DREAM via E2F4/DP; also bound by E2F:RB complexes and activator E2Fs which results in maximum gene expression in G1/S
      (IV) E2F-CLE tandem elements - like (III) but binding of DREAM can be stabilized via LIN54 interacting with a non-canonical CHR-like element. Since CLE elements do not represent functional CHR sites, B-MYB-MuvB and FOXM1-MuvB do not bind.
      Thus, these promoter sites have different functions and can be clearly distinguished from each other based on their properties - a fact that is completely ignored by the authors. Since the authors do not differentiate between G1/S and G2/M expressed genes and (CDE)-CHR and E2F-(CLE) sites, they identify CDE-CHR elements in G1/S genes that are functional E2F-(CLE) sites. A good example of this is the Rad51ap1 gene (and also the Rad51 gene that the Toledo lab described before as a CDE-CHR gene (Jaber et al. 2016)): these genes get expressed in G1/S and the promoters contain highly conserved E2F sites (parts of which the authors define as CDEs), and CLEs (which the authors define as CHRs). Furthermore, E2F:RB complexes bind to the promoters. Again: even though (CDE)-CHR and E2F-(CLE) sites both bind DREAM, they are otherwise functionally different in their ability to recruit non-DREAM complexes.

    We agree that in the previous version of our manuscript we should have presented in more details the different types of DREAM binding sites and have corrected this in the revised manuscript. We now mention in the introduction that “The DREAM complex was initially reported to repress the transcription of genes whose promoter sequences contain a bipartite binding motif called CDE/CHR [19,20] (or E2F/CHR [21]), with a GC-rich cell cycle dependent element (CDE) that may be bound by E2F4 or E2F5, and an AT-rich cell cycle gene homology region (CHR) that may be bound by LIN54, the DNA-binding subunit of MuvB [19,20]. Later studies indicated that DREAM may also bind promoters with a single E2F binding site, a single CHR element, or a bipartite E2F/CHR-like element (CLE), and concluded that E2F and CHR elements are required for the regulation of G1/S and G2/M cell cycle genes, respectively [14,22].”

    We hope that the referee will agree with this complete yet concise way of presenting DREAM binding sites. Importantly, we agree that CDE/CHR and E2F/CLE are sites bound by different non-DREAM complexes, but both sites are bound by DREAM, so it makes perfect sense to use them together to define positional frequency matrices for DREAM binding predictions. We would also like to point out that terms used to define DREAM binding sites may vary in the literature. For example, to our knowledge Müller et al. were the first to propose a clear distinction between “CDE/CHR” and “E2F/CLE” sites (Müller et al. (2017) Oncotarget 8, 97737-97748), yet Müller recently co-authored a review in which these two distinct terms were not used, but were replaced by a single, apparently more generic term of “E2F/CHR” (Fischer et al., (2022) Trends Biochem. Sci. 47, 1009-1022). In the revised manuscript we now clearly mention that we designed our positional frequency matrices to search for “bipartite DREAM binding sites”, i.e. sites that might be referred to as CDE/CHR, E2F/CLE or E2F/CHR sites in various publications.

    (B) The authors identified putative CDE-CHR in the promoters of genes by building two position weight matrices (PWMs) based on 10 or 22 "validated CDE-CHR elements". However, since they include several genes that are clearly expressed in G1/S and contain E2F-(CLE) sites (e.g. Mybl2/B-myb, Rad51, Fanca, Fen1), it is not surprising that they identify a lot of putative CDE-CHR sites in genes that do not contain such elements.

    As discussed above, both CDE/CHR and E2F/CLE are bipartite DREAM binding sites, and we now clearly state that we used bipartite DREAM binding sites to generate our positional frequency matrices and predict DREAM binding.

    (C) Finally, in the discussion, the authors state: "A recent update (2.0) of the Target gene regulation database of p53 and cell cycle genes (www.targetgenereg.org) was recently reported to include putative DREAM binding sites for human genes (Fischer et al., 2022). However, this update only suggests potential E2F or CHR binding sites independently, a feature of little help to identify CDE/CHR elements. For example, targetgenereg 2.0 suggests several potential E2F sites, but no CHR site close to the transcription start site of FANCD2, despite the fact that we previously identified a functionally CDE/CHR element near the transcription start site of this gene (Jaber et al., 2016)." This statement highlights again that the authors don't seem to be aware of what specific properties distinct DREAM binding sites have, and that analyzing promoters for CHR and E2F sites separately generates much more meaningful results than the approach they chose. Also, the FANCD2 promoter binds DREAM as well as E2F:RB complexes and contains a highly conserved E2F binding site - which Jaber et al. mutated together with a potential downstream CLE element and named it "CDE/CHR".

    In the revised manuscript, we provide a more detailed comparison between the bipartite DREAM binding sites predicted with our positional frequency matrices for 151 genes and the separate E2F and CHR predicted sites reported in the Target gene regulation database for the same set of genes. We now mention: “The Target gene regulation (TGR) database of p53 and cell-cycle genes was reported to include putative DREAM binding sites for human genes, based on separate genome-wide searches for 7 bp-long E2F or 5 bp-long CHR motifs [23]. We analyzed the predictions of the TGR database for the 151 genes for which we had found putative bipartite DBS. A total of 342 E2F binding sites were reported at the promoters of these genes, but only 64 CHR motifs. The similarities between the predicted E2F or CHR sites from the TGR database and our predicted bipartite DBS appeared rather limited: only 14/342 E2F sites overlapped at least partially with the GC-rich motif of our bipartite DBS, while 27/64 CHR motifs from the TGR database exhibited a partial overlap with the AT-rich motif. Importantly, most E2F and CHR sites from the TGR database mapped close to E2F4 and LIN9 ChIP peaks, but only 16% of E2Fs (54/342), and 33% of CHRs (21/64) mapped precisely at the level of these peaks (Figure S11), compared to 55% (83/151) of our bipartite DBS (Figure 3a). Thus, at least for genes with bipartite DREAM binding sites, our method relying on PFM22 appeared to provide more reliable predictions of DREAM binding than the E2F and CHR sites reported separately in the TGR database. Importantly however, predictions of the TGR database may include genes regulated by a single E2F or a single CHR that would most likely remain undetected with PFM22, suggesting that both approaches provide complementary results.”

    1. The experimental approach chosen to validate CDE-CHR elements in a set of twelve promoters by luciferase reporter assays is not adequate.
      (A) Since the authors introduce point mutations in putative CDE and CHR elements in parallel, it is impossible to identify functional CDE elements. As explained above, a functional CDE is not required for binding of MuvB complexes and gene repression, and mutating the CHR alone would already lead to a loss of DREAM binding and to de-repression of a promoter. Thus, without mutating both sites of CDE-CHR elements separately, it is impossible to provide evidence that a putative CDE is functional.
      (B) As the putative CDE-CHR elements identified by the authors with a computational approach can overlap with functional E2F-(CLE) elements, the authors inactivate such sites by introducing mutations which leads to loss of DREAM binding and upregulation of the promoters, however, because of the problems described above, this experimental approach in the best case identifies DREAM binding sites, but does not differentiate between (CDE)-CHR and E2F-(CLE) elements.

    Yes, we agree with this comment. As discussed above, our goal was to identify DREAM-binding sites, not to differentiate between CDE/CHR and E2F/CLE elements. In other words, we wanted to identify genes regulated by p53 and DREAM, but not distinguish between genes regulated by p53, DREAM and E2F/Rb versus those regulated by p53, DREAM and BMyb-MuvB or FoxM1-MuvB.

    (C) The authors analyze the activities of wild-type and mutant promoters in proliferating NIH3T3 cells. Since the mutated promoters showed increased activity (about 2-3 fold), which would be expected when binding of DREAM gets abolished, they conclude: "...these experiments indicated that we could identify functional CDE/CHRs for 12/12 tested genes." In addition to the problems described above, a slight upregulation of promoter activities caused by the introduction of multiple point mutations close to the TSS is not sufficient to verify these elements. The increase in activity could occur independent of DREAM-binding by unrelated mechanisms. The authors should at least analyze the activities of the promoters with and without induction of p53. A loss of p53-dependent repression of the mutated promoters would prove that the elements are essential for p53-dependent repression. Furthermore, there are several experimental approaches to analyze whether DREAM binds to the putative promoter element and whether the introduced mutations disrupt binding (ChIP, DNA affinity purification, etc.).

    In the revised manuscript, we show that the promoters of 7 of the tested genes, when cloned in luciferase reporter plasmids and transfected into NIH3T3 cells, exhibited a significant (> 1.4 fold) repression upon p53 activation by cell treatment with Nutlin, the Mdm2 antagonist. For these promoters, we showed that the p53-dependent repression was abrogated by mutating the identified DREAM binding site, which provided direct evidence that our positional frequency matrices can identify functionally relevant DREAM binding sites essential for p53-mediated repression. These experiments were added in Figures 2e and 2i.

    Furthermore, as previously mentioned in response to referee #1, in the revised manuscript we precisely mapped the predicted DREAM binding sites for 151 genes in 50 bp windows within regions bound by E2F4 and/or LIN9, an analysis included in new Figure 3a. The distribution of these peaks clearly indicates that most predicted DREAM binding sites map precisely within a 50 bp-window encompassing the ChIP peaks, which represents an enrichment of at least a 1300-fold compared to the rest of the genome. This mapping strongly suggests that our predicted DREAM binding sites are functionally relevant.

    Importantly, as shown in the new Figure S11, we carried out a similar mapping of the predicted E2F and CHR sites reported in the Target gene regulation (TGR) database and found that our predicted DREAM binding sites co-mapped with E2F4/LIN9 ChIP peaks more frequently than the E2F and CHR sites of the TGR database, which supports the conclusion that our positional frequency matrices bring new and improved predictions for DREAM binding.

    1. Taken together, while over-simplifying mechanisms of cell cycle gene regulation, the authors largely ignore recent findings and publications regarding gene regulation by p53, E2F:RB, and DREAM/MuvB complexes:
      (A) Publications that show how DREAM binds to (CDE)-CHR sites and that experimentally defined a consensus motif for CHR elements (e.g. PMID: 27465258, PMID: 25106871).
      (B) Publications that identify p53-DREAM target genes by activating p53 in cells with or without functional DREAM complex (e.g. PMID: 31667499, PMID: 31400114).
      (C) Identification and comparison of (CDE)-CHR and E2F-(CLE) DREAM binding sites that have distinct functions in the activation of cell-cycle expression in G1/S and G2/M (e.g. PMID: 29228647, PMID: 25106871).
      These findings have been summarized in several review articles (e.g. PMID: 29125603, PMID: 28799433, PMID: 35835684). All of them describe the mechanisms I have mentioned above in detail, and since Rakotopare et al. cite one of the papers (Engeland 2018), I wonder even more why they did not design their experiments based on current knowledge.

    The points (A) and (C) of this comment were largely discussed in our response to points 2 and 3 of the same referee. Briefly, in the revised manuscript we clearly mention CDE/CHR, E2F/CLE and E2F/CHR sites, as well as the functional differences between E2F and CHR sites with regards to cell cycle regulation, but all these sites were considered together in our positional frequency matrices because our goal was to identify genes regulated by p53 and DREAM, not to distinguish between genes regulated by p53, DREAM and E2F/Rb versus those regulated by p53, DREAM and BMyb-MuvB or FoxM1-MuvB.

    Regarding point (B) of this comment, in the revised manuscript we performed a detailed comparison of our results with those of Mages et al. who analyzed, in murine cells with a CRISPR-mediated KO of Lin37 (a subunit of DREAM), the transcriptomic changes that follow a reintroduction of Lin37 (Mages et al. (2017) elife 6, e26876). This comparison is detailed in the discussion section, with New Figure S10 and Table S35. As mentioned in response to referee #2, this comparison is perfectly consistent with DREAM regulating the 151 genes for which we identified DREAM binding sites.

    Minor concerns:

    1. The authors state: "Importantly however, the relative importance of the p53-p21-DREAM pathway (called below p53-DREAM) remains controversial, because multiple mechanisms were proposed to account for p53-mediated gene repression (Peuget and Selivanova, 2021)." Even though Peuget & Selivanova do not agree that genes get repressed in response to p53 activation exclusively by the p21-DREAM pathway, they do not question that this mechanism is essential for the p53-dependent repression of a core set of cell cycle genes. Since I am also not aware of any publications that challenge the importance of the p53-p21-DREAM pathway, I do not agree with this statement.

    As the referee pointed out, in the first version of the manuscript we wrote that “the relative importance of the p53-p21-DREAM pathway (called below p53-DREAM) remains controversial, because multiple mechanisms were proposed to account for p53-mediated gene repression (Peuget and Selivanova, 2021)”. The term “relative” was crucial in this sentence, because we wanted to say that the relative proportion of genes regulated by DREAM remained controversial. It seems to us that the title of the review by Peuget & Selivanova (“p53-dependent repression: DREAM or reality?”) emphasizes this controversy. Nevertheless, in the revised manuscript, we now mention : “The relative importance of this pathway remains to be fully appreciated, because multiple mechanisms were proposed to account for p53-mediated gene repression [18]”. We hope the referee will find this phrasing more acceptable.

    1. Some parts of the manuscript are tiring to read - for example, pages 6, 7, and 8 which contain long listings and numbers of genes that are downregulated in differentiated BMC, found to be mutated in various disorders, bind DREAM components, were identified as downregulated by p53, etc. The authors may consider combining central parts of these data in a table that they show in the main manuscript which would make it easier to digest the information and at the same time significantly shorten the manuscript.

    We apologize if some parts of the article were tiring to read. We hope that the addition of Tables S17 and S26, as well as the Venn-like diagram in Figure 3c, will improve the reading of the manuscript.

    1. The supplementary tables (S1-S26) are combined in one Excel file with multiple tabs. The authors should label the tabs accordingly to make it easier for the reader to find a particular table.

    We labelled the Excel tabs in the revised manuscript, as suggested.

    1. At the end of page 6, the authors show that 17 genes found to be downregulated in differentiated BMCs are mutated in multiple bone marrow disorders, however, since they don't include references, it remains unclear where these mutations were originally described.

    In the revised manuscript, we included a supplementary table (Table S36) with appropriate references for blood and/or brain related phenotypes for the 106 genes associated with blood or brain abnormalities.

    1. On page 9, the authors state: "As a prerequisite to luciferase assays, we first verified that the expression of these genes, as well as their p53-mediated repression, can be observedin mouse embryonic fibroblasts (MEFs), because luciferase assays rely on transfections into MEFs (Figure 3b)." The authors don't explain why luciferase assays rely on transfections into MEFs and based on the caption of Fig. 3C, the luciferase assays were not performed in MEFs, but in NIH3T3 cells: "WT or mutant luciferase reporter plasmids were transfected into NIH3T3 cells..."

    According to the American Type Culture Collection (ATCC), the NIH3T3 cell line is a mouse embryonic fibroblastic (MEF) cell line, which explains why we had tested the expressions of candidate target genes in MEFs. However, as we now clearly mention in the manuscript, this cell line exhibits an attenuated p53 pathway, which improves cell survival after transfection but leads to decreased p53-mediated repression. These points are now clearly mentioned in the text and in a new supplemental Figure (Figure S9).

    Reviewer #3 (Significance):

    While the study supports a large body of publications proving that repression of cell cycle genes by the DREAM complex is crucial for cell cycle arrest and exit, it is noted that none of the main conclusions here are unexpected or particularly exciting. All the analyses are based on data sets that compare gene expression in highly proliferative cells with cells that underwent terminal cell cycle exit. Thus, a large portion of the genes that are downregulated in differentiated BMCs are cell cycle genes and well-established targets of DREAM and E2F:RB complexes. Furthermore, it is not surprising that some of these pro-proliferative genes are mutated in diseases connected to proliferation defects like anemias or microcephaly.

    Again, we agree with the referee that the DREAM complex is well known to regulate cell cycle genes, but many scientists or clinicians specialized in bone marrow failure syndromes or microcephaly diseases are not familiarized with the p53-DREAM pathway, and we think our study will be particularly useful to them. As for DREAM specialists, our strategy relying on disease-based ontology terms rather than cell cycle regulation led to identify many DREAM targets that were not reported in previous studies, and our positional frequency matrices led to identify DREAM binding sites not predicted by previous approaches. We hope that, by considering all these points together, the referee will acknowledge that our study provides a valuable resource for different types of readerships.

  7. Review Commons

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    Referee #3

    Evidence, reproducibility and clarity

    In their work submitted to Review Commons, Rakotopare et al. aim to identify p53-DREAM target genes associated with blood or brain abnormalities. To this end, they utilize published data generated with a cellular model that results in cell-cycle exit and differentiation of murine bone marrow progenitor cells upon inducible expression of Hoxa9. By analyzing this gene expression data set published by Muntean et al., they find that multiple of the 3631 genes which are downregulated more than 1.5-fold in differentiated BMCs are also mutated in several disorders connected to proliferation and differentiation defects during hematopoiesis and brain development. By screening ChIP-seq data sets available at ChIP-Atlas, they find that the promoters of many of these genes are bound by DREAM complex components, and most of them were identified as genes indirectly repressed by p53 before (Fischer et al. 2016, targetgenereg.org). They then use a computational approach to identify putative CDE/CHR DREAM-binding sites in the promoters of 372 genes associated with blood/brain abnormalities which are downregulated in differentiated BMCs and bound by DREAM components. Out of the 173 candidate genes, they select twelve to analyze whether mutation of the putative DREAM binding sites results in increased activity of the promoters in luciferase reporter assays. The authors conclude that their findings suggest a general role for the p53-DREAM pathway in regulating hematopoiesis and brain development.

    While the study supports a large body of publications proving that repression of cell cycle genes by the DREAM complex is crucial for cell cycle arrest and exit, it is noted that none of the main conclusions here are unexpected or particularly exciting. All the analyses are based on data sets that compare gene expression in highly proliferative cells with cells that underwent terminal cell cycle exit. Thus, a large portion of the genes that are downregulated in differentiated BMCs are cell cycle genes and well-established targets of DREAM and E2F:RB complexes. Furthermore, it is not surprising that some of these pro-proliferative genes are mutated in diseases connected to proliferation defects like anemias or microcephaly.

    Additionally, I am not very enthusiastic about this manuscript because of several major concerns:

    1. The authors draw conclusions about the p53-DREAM pathway based on data that was generated in a cellular differentiation model without convincingly showing that p53 plays a central role in gene repression in this experimental setup.

    (A) Rakotopare et al. define p53-DREAM target genes based on RNA expression data from proliferating precursor cells and non-proliferating, differentiated BMCs (Muntean et al., 2010). This paper has not studied whether p53 gets activated in the particular experimental setup during Hox9a-induced BMC differentiation. On page 4 of their manuscript, the authors state: "Consistent with the fact that BMC differentiation strongly correlates with p53 activation..." without citing any literature or explaining why this is supposed to be a fact. Furthermore, they imply that cell cycle gene repression in this model system depends on p53 because mRNA expression of the p53 targets p21 and Mdm2 was found to be increased in the differentiated cells (Fig. 1A, 5-fold and 2-fold, respectively). However, defining a large set of "p53-DREAM target genes" based on the moderate increase in mRNA levels of two genes that are known to be activated by p53 without showing any evidence that p53 is even involved in this effect during BMC differentiation is not appropriate.

    (B) Interestingly, p53 is among the genes that get repressed on mRNA level in differentiated BMCs (Fig. 1B; Trp53), and the authors also identify the DREAM components E2F4 and LIN9 as bound to the p53 promoter by screening ChIP-Atlas data (Fig. 1C). Given that p53 has never been described as a DREAM target, I find this rather surprising and it makes me wonder whether appropriate parameters were selected for analyzing the ChIP data, particularly since the authors do not provide binding data for sets of non-cell cycle genes as a negative control.

    (C) Finally, the authors utilize the targetgenereg.org database to show that many of the genes they describe as p53-repressed were already identified as p53 targets. This database (Fischer et al. 2016) was created by performing a meta-analysis integrating a plethora of RNA-seq and ChIP-seq datasets with the aim to identify whether a particular gene gets up- or downregulated by p53, shows cell-cycle-dependent expression, is a DREAM/MuvB or E2F:RB target, etc. For example, 57 datasets analyzing p53-dependent RNA expression in human and 15 datasets generated with mouse cells were included, and a positive or negative score shows in how many of these experiments the gene was found to be up (positive score) or downregulated (negative score). Combining a large number of datasets in such a study is very helpful to get an idea if a gene is indeed generally regulated by a transcription factor, or if it just showed up in a few experiments - either as a false positive or because the regulation depends on a particular biological setting. The authors find most of the genes they identify as repressed in differentiated BMCs also as downregulated by p53 in targetgenereg.org, however, it remains unclear what parameters they used to define a gene as p53-repressed. For example, in the caption of Fig. 1C, they state: "According to the Target gene regulation database, 72/76 genes are downregulated upon mouse and/or human p53 activation." The four exemptions are SLX1B (human score: 0, mouse score : na), PML (+41, +9), RAD50 (0, na), and TNKS2 (+17, +4). However, there are several other genes that do not appear to be generally repressed by p53, e.g. HMBOX1 (+4, -2); UPF1 (+1, -2), SMG6 (+18, -2), CTC1 (-5, +11), etc. Thus, without providing details regarding the parameters they use to define p53-target genes, such statements are rather misleading. An easy way to solve this problem would be to show the p53 scores in the tables together with the E2F4/LIN9 ChIP data.

    1. The authors define a large set of genes containing "CDE-CHR" promoter elements and thereby ignore how these elements are defined and what properties they have.

    (A) At the beginning of the introduction, the authors state: "The DREAM complex typically represses the transcription of genes whose promoter contain a bipartite CDE/CHR binding site, with a cell cycle-dependent element (CDE) bound by E2F4 or E2F5, and a cell cycle gene homology region (CHR) bound by LIN54, the DNA binding subunit of MuvB (Zwicker et al., 1995; Müller and Engeland, 2010)."

    This statement is incorrect. The authors ignore that the CDE/CHR tandem site is just one of four promoter elements that have been shown to recruit DREAM for the transcriptional repression of several hundred genes. It has been studied in detail that DREAM can bind to the following promoter sites:

    (I) CHR elements - bound by DREAM via LIN54; also bound by the activator MuvB complexes B-MYB-MuvB and FOXM1-MuvB which results in maximum gene expression in G2/M

    (II) CDE-CHR tandem elements - like (I) but binding of DREAM can be stabilized via E2F4/DP interacting with a truncated E2F binding site. Since CDE elements do not represent functional E2F sites, E2F:RB complexes do not bind.

    (III) E2F binding sites - bound by DREAM via E2F4/DP; also bound by E2F:RB complexes and activator E2Fs which results in maximum gene expression in G1/S

    (IV) E2F-CLE tandem elements - like (III) but binding of DREAM can be stabilized via LIN54 interacting with a non-canonical CHR-like element. Since CLE elements do not represent functional CHR sites, B-MYB-MuvB and FOXM1-MuvB do not bind.

    Thus, these promoter sites have different functions and can be clearly distinguished from each other based on their properties - a fact that is completely ignored by the authors. Since the authors do not differentiate between G1/S and G2/M expressed genes and (CDE)-CHR and E2F-(CLE) sites, they identify CDE-CHR elements in G1/S genes that are functional E2F-(CLE) sites. A good example of this is the Rad51ap1 gene (and also the Rad51 gene that the Toledo lab described before as a CDE-CHR gene (Jaber et al. 2016)): these genes get expressed in G1/S and the promoters contain highly conserved E2F sites (parts of which the authors define as CDEs), and CLEs (which the authors define as CHRs). Furthermore, E2F:RB complexes bind to the promoters. Again: even though (CDE)-CHR and E2F-(CLE) sites both bind DREAM, they are otherwise functionally different in their ability to recruit non-DREAM complexes.

    (B) The authors identified putative CDE-CHR in the promoters of genes by building two position weight matrices (PWMs) based on 10 or 22 "validated CDE-CHR elements". However, since they include several genes that are clearly expressed in G1/S and contain E2F-(CLE) sites (e.g. Mybl2/B-myb, Rad51, Fanca, Fen1), it is not surprising that they identify a lot of putative CDE-CHR sites in genes that do not contain such elements.

    (C) Finally, in the discussion, the authors state: "A recent update (2.0) of the Target gene regulation database of p53 and cell cycle genes (www.targetgenereg.org) was recently reported to include putative DREAM binding sites for human genes (Fischer et al., 2022). However, this update only suggests potential E2F or CHR binding sites independently, a feature of little help to identify CDE/CHR elements. For example, targetgenereg 2.0 suggests several potential E2F sites, but no CHR site close to the transcription start site of FANCD2, despite the fact that we previously identified a functionally CDE/CHR element near the transcription start site of this gene (Jaber et al., 2016)." This statement highlights again that the authors don't seem to be aware of what specific properties distinct DREAM binding sites have, and that analyzing promoters for CHR and E2F sites separately generates much more meaningful results than the approach they chose. Also, the FANCD2 promoter binds DREAM as well as E2F:RB complexes and contains a highly conserved E2F binding site - which Jaber et al. mutated together with a potential downstream CLE element and named it "CDE/CHR".

    1. The experimental approach chosen to validate CDE-CHR elements in a set of twelve promoters by luciferase reporter assays is not adequate.

    (A) Since the authors introduce point mutations in putative CDE and CHR elements in parallel, it is impossible to identify functional CDE elements. As explained above, a functional CDE is not required for binding of MuvB complexes and gene repression, and mutating the CHR alone would already lead to a loss of DREAM binding and to de-repression of a promoter. Thus, without mutating both sites of CDE-CHR elements separately, it is impossible to provide evidence that a putative CDE is functional.

    (B) As the putative CDE-CHR elements identified by the authors with a computational approach can overlap with functional E2F-(CLE) elements, the authors inactivate such sites by introducing mutations which leads to loss of DREAM binding and upregulation of the promoters, however, because of the problems described above, this experimental approach in the best case identifies DREAM binding sites, but does not differentiate between (CDE)-CHR and E2F-(CLE) elements.

    (C) The authors analyze the activities of wild-type and mutant promoters in proliferating NIH3T3 cells. Since the mutated promoters showed increased activity (about 2-3 fold), which would be expected when binding of DREAM gets abolished, they conclude: "...these experiments indicated that we could identify functional CDE/CHRs for 12/12 tested genes." In addition to the problems described above, a slight upregulation of promoter activities caused by the introduction of multiple point mutations close to the TSS is not sufficient to verify these elements. The increase in activity could occur independent of DREAM-binding by unrelated mechanisms. The authors should at least analyze the activities of the promoters with and without induction of p53. A loss of p53-dependent repression of the mutated promoters would prove that the elements are essential for p53-dependent repression. Furthermore, there are several experimental approaches to analyze whether DREAM binds to the putative promoter element and whether the introduced mutations disrupt binding (ChIP, DNA affinity purification, etc.).

    1. Taken together, while over-simplifying mechanisms of cell cycle gene regulation, the authors largely ignore recent findings and publications regarding gene regulation by p53, E2F:RB, and DREAM/MuvB complexes:

    (A) Publications that show how DREAM binds to (CDE)-CHR sites and that experimentally defined a consensus motif for CHR elements (e.g. PMID: 27465258, PMID: 25106871).

    (B) Publications that identify p53-DREAM target genes by activating p53 in cells with or without functional DREAM complex (e.g. PMID: 31667499, PMID: 31400114).

    (C) Identification and comparison of (CDE)-CHR and E2F-(CLE) DREAM binding sites that have distinct functions in the activation of cell-cycle expression in G1/S and G2/M (e.g. PMID: 29228647, PMID: 25106871).

    These findings have been summarized in several review articles (e.g. PMID: 29125603, PMID: 28799433, PMID: 35835684). All of them describe the mechanisms I have mentioned above in detail, and since Rakotopare et al. cite one of the papers (Engeland 2018), I wonder even more why they did not design their experiments based on current knowledge.

    Minor concerns:

    1. The authors state: "Importantly however, the relative importance of the p53-p21-DREAM pathway (called below p53-DREAM) remains controversial, because multiple mechanisms were proposed to account for p53-mediated gene repression (Peuget and Selivanova, 2021)." Even though Peuget & Selivanova do not agree that genes get repressed in response to p53 activation exclusively by the p21-DREAM pathway, they do not question that this mechanism is essential for the p53-dependent repression of a core set of cell cycle genes. Since I am also not aware of any publications that challenge the importance of the p53-p21-DREAM pathway, I do not agree with this statement.
    2. Some parts of the manuscript are tiring to read - for example, pages 6, 7, and 8 which contain long listings and numbers of genes that are downregulated in differentiated BMC, found to be mutated in various disorders, bind DREAM components, were identified as downregulated by p53, etc. The authors may consider combining central parts of these data in a table that they show in the main manuscript which would make it easier to digest the information and at the same time significantly shorten the manuscript.
    3. The supplementary tables (S1-S26) are combined in one Excel file with multiple tabs. The authors should label the tabs accordingly to make it easier for the reader to find a particular table.
    4. At the end of page 6, the authors show that 17 genes found to be downregulated in differentiated BMCs are mutated in multiple bone marrow disorders, however, since they don't include references, it remains unclear where these mutations were originally described.
    5. On page 9, the authors state: "As a prerequisite to luciferase assays, we first verified that the expression of these genes, as well as their p53-mediated repression, can be observed
      in mouse embryonic fibroblasts (MEFs), because luciferase assays rely on transfections into MEFs (Figure 3b)." The authors don't explain why luciferase assays rely on transfections into MEFs and based on the caption of Fig. 3C, the luciferase assays were not performed in MEFs, but in NIH3T3 cells: "WT or mutant luciferase reporter plasmids were transfected into NIH3T3 cells..."

    Significance

    While the study supports a large body of publications proving that repression of cell cycle genes by the DREAM complex is crucial for cell cycle arrest and exit, it is noted that none of the main conclusions here are unexpected or particularly exciting. All the analyses are based on data sets that compare gene expression in highly proliferative cells with cells that underwent terminal cell cycle exit. Thus, a large portion of the genes that are downregulated in differentiated BMCs are cell cycle genes and well-established targets of DREAM and E2F:RB complexes. Furthermore, it is not surprising that some of these pro-proliferative genes are mutated in diseases connected to proliferation defects like anemias or microcephaly.

  8. Review Commons

    Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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    Referee #2

    Evidence, reproducibility and clarity

    The authors used various systems including Hoxa9-indubible BMCs, human and mouse cells, WT and p53 knockout MEF, glioblastoma cells to screen p53-DREAM targets and observed distinct finding for each system. Since different cell types have various p53 activation and p53 target genes expression, the authors might want to select proper cell type(s) to screen p53-DREAM target genes and design experiments to confirm that these genes are really p53-DREAM target genes.

    Specific comments:

    The authors need to describe and define HSC and Diff in Figure 1.

    Are Figure 1B and 1D list genes p53 targets in bone marrow cells?

    Where is the detailed information for mouse and human cells in Figure 1 and Figure 2?

    Are Figure 3B list genes also p53 target genes in other cell types such as bone marrow cells and glioblastoma?

    Does BRD8high has high p53 and p21?

    Are genes listed in Figure 4B all p53 target genes? can some validation be done?

    Significance

    This is a potentially interesting study. The major limitation is the absence of validation from the screening. This study would definitely benefit the research community as long as some of the key findings are validated.

  9. Review Commons

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    Referee #1

    Evidence, reproducibility and clarity

    Summary

    In this paper the authors describe a data driven approach to identify and prioritise p53-DREAM targets whose repression might contribute to abnormal haematopoiesis and brain abnormalities observed in p53-CTD deleted mice. The premise is that in these mice, (where they have previously demonstrated p53 to be hyperactive in at least a subset of tissues), that the p53-p21-E2F/DREAM axis is at least in part responsible for observed phenotypes due to the repression of E2F and CDE/CHE element containing genes. Their approach to home in on relevant genes is based on transcriptomic gene ontology analysis of genes repressed in these disease settings where they primarily use publicly available data from HOXA9-ER regulated model of HSC expansion wherein they observe increases on p53-p21 expression upon differentiation where they demonstrate that p53-p21 DREAM target genes are suppressed as we would expect in this scenario where p53-p21 is activating withdrawal from cell cycle. They then spend a lot of effort analysing this datasets combining "gene-ontology", "disease phenotype" and "meta-ChIP-seq" analysis of public data to support the observation that mutations of genes suppressed in this manner are disproportionately linked to heritable haematopoetic and brain disorders. While these results are interesting in terms of framing a hypothesis about how mutations in p53-p21-DREAM regulated targets contribute to such conditions, they are to be expected given the now very well described impact of p53-p21 on both E2F4/DREAM targets. The natural progression of this work would be to go on to show this occurs in relevant cells or tissues derived from the p53-CTD mice as well as look at modulating target genes to understand underlying mechanisms and consequences.
    Rather than this, they focus on validating that a sub-set of these targets are indeed suppressed by specific p53 activation by MDM2 inhibitor Nutlin-3A in MEFs by qPCR and that mutation of predicted CDE CHR elements in luciferase constructs leads to increase luciferase activity. While these findings support their predictions, the results are entirely expected based on what is known about such targets and demonstrating that this occurs in MEFs does not closely relate to haematopoietic and brain cells they suggest this regulation is important. In fact, in the discussion, the authors comment on the importance of cell type context specificity in terms of discordance between predictions of TF binding sites and public datasets.
    Finally, they try and contextualise effects in glioblastoma data by correlating target gene expression with levels of BRD8 since it has recently been shown to attenuate p53 function in glioblastoma and show that some of the brain disease associated genes are expressed at higher levels in BRD8 high patient samples. It seems strange here that they do not also look at expression of p21 or other p53 targets that would help ascertain if p53 activity is indeed suppressed. Moreover, much more elegant methods for predicting transcription factor activity could be applied to this data.

    Major Comments

    The major result of this paper as it stands is the prioritisation of candidate genes in the p53-DREAM pathway involved in these conditions, and their refined approach used to identify and prioritise these genes and is such more of a starting point for further investigation. They fall short of demonstrating the relevance of their predictions physiologically in tissues from the mice and do not demonstrate functional importance of regulation of targets they put forward. Given that these genes will be co-ordinately regulated, without a mechanistic experiment in physiologically relevant model it is impossible to infer causality. For example, depleting individual targets in the HOXA9 model and evaluating impact on survival, proliferation and differentiation may be a (relatively) simple way to explore this, perhaps comparing to effects of p53 activating agents such as Nutlin-3A. Of note the authors (Jaber 2016 PMID: 27033104) and several other groups had (Fischer 2014 PMID: 25486564 McDade 2014 PMID: 24823795) previously demonstrated the link between p53-p21 and suppression of DNA-repair/Damage related genes (as is also observed here in particular FA-related genes that they discuss briefly here. I would have thought that this would be an obvious starting point for some mechanistic experiments and in fact I note this has been demonstrated before (Li et al 2018 PMID: 29307578)
    The analysis of brain specific targets and the link to BRD8 sits largely as an aside and the analysis of patient data from glioblastomas is underdeveloped as noted above.
    The computational methods applied are robust, albeit predominantly coorelative, in terms of identifying regulation of potential causative target genes, validated across human and mouse cell lines, and this indicates a role of these genes in the relevant conditions. However, further validation through application in a bulk or single cell RNAseq patient cohort, or at least an in vivo model would strengthen these conclusions and complement the work presented here which is based on in vitro mouse and human cells. This is pertinent as this study improves upon previously published approaches by focusing on "clinically relevant target genes". Additionally, this would exhibit the potential applications of the findings presented.
    In terms of statistical analysis, the hypergeometric test should be applied to assess significant enrichment of genes for example with CDE/CHR regions within the previously identified lists.

    Minor Comments

    References are required for the genes listed which play a role in the diseases of interest. This paper would benefit from the inclusion of summary schematics and tables throughout (rather than relying only on somewhat unwieldy heatmaps which show little other than all these genes are co-ordinately regulated), this could include summaries of the methods applied, gene or CDE/CHR inclusion criteria, and Venn diagrams indicating the subsets of final genes identified through this approach.

    Significance

    In its current form this is a very limited study that would require significant additional work to move conclusions beyond correlation and hypothesis generation.

    Overall, while limited largely to target prioritisation, this research nicely exemplifies how genes affected by the p53-DREAM pathway can be robustly identified, providing a potential resource for individuals working on this pathway or on abnormal haematopoiesis and brain abnormalities. These results are complementary to work previously published by Fischer et al, which has been referenced throughout the analysis (highlighting Target Gene Regulation Database p53 and DREAM target genes) and discussion.

    This paper will be of interest to researchers of blood/neurological diseases who can assess if these genes are dysregulated in their datasets, or those investigating the p53-DREAM pathway. This work represents a useful resource detailing genes affected by this pathway in these disease settings, however researchers of the p53-DREAM pathway may find this paper useful when planning an approach to identify and prioritise genes of interest.

    My expertise is in the field of transcription factor and p53 family biology in cancer and disease. Our group utilises functional genomics and computational approaches to harness this information to identify causal regulators of downstream effects or indeed novel ways to exploit p53 family

  10. Version published to 10.1101/2023.06.21.545932v1 on bioRxiv
  11. Version published to 10.1101/2023.06.21.545932v2 on bioRxiv