Protein-DNA binding sites of ChIP-seq experiments are identified where the binding affinity is significant based on a given threshold. The choice of the threshold is a trade-off between conservative region identification and discarding weak, but true binding sites.We rescue weak binding sites using MSPC, which efficiently exploits replicates to lower the threshold required to identify a site while keeping a low false-positive rate, and we compare it to IDR, a widely used post-processing method for identifying highly reproducible peaks across replicates. We observe several master transcription regulators  and HDAC2-GATA1 regulatory networks on rescued regions in K562 cell line.rescued by MSPC. An implementation of the proposed extended MSPC methodology and the scripts to reproduce the performed analysis are freely available at https://genometric.github.io/MSPC/; MSPC is distributed as a command-line application and an R package available from Bioconductor (https://doi.org/doi:10.18129/B9.bioc.rmspc).We argue the biological relevance of weak binding sites and the information they add when The online version contains supplementary material available at 10.1186/s12859-023-05340-x. A peak is generally inferred when the ChIP-seq read distribution differs significantly from the background signal, and its corresponding In general, peak evaluation is intrinsically limited due to the lack of annotations for “true” binding sites, especially for experimental conditions in which biological signals might be shifted, changed, or depleted . Peak evA common consensus suggests that “true” binding sites are reproducible across replicated samples , whereas non-overlapping peaks are categorized as either of the following: (a) Characterizing “true” biological variability when studying biological replicates ; or (b) rescues weak peaks; in other words, it differentiates the weak binding sites which are reproducible across replicated samples from background signals , taking into consideration if the replicates are biological or technical ) echnical . In thisconsensus regions, and both MSPC and IDR can identify them. IDR ranks pairs of peaks in the two replicates based on their irreproducible discovery rate and combines those peaks with rates below a threshold. MSPC improves the sensitivity and specificity of each replicate and identifies their true-positive peaks using the Benjamini–Hochberg procedure; the extended version of MSPC then identifies consensus regions by merging the true-positive peaks and assigns each a combined stringency score (χ2 and right-tail probability).The binding sites that are highly reproducible across all the replicated samples are commonly referred to as The present study assesses the biological validity of the peaks MSPC (extended version) and IDR identify as “true binding sites” and the consensus regions they yield. Accordingly, we developed a novel feature enrichment test to prove the relevance of weak rescued peaks. Our results suggest that MSPC identifies more true binding sites and consensus regions than IDR, encompassing the IDR-identified regions in large. Additionally, our results show that the identified regions are enriched in biologically meaningful annotations and fully encompass essential information needed to understand genomic regulatory networks. For instance, using data from K562 cell line, we show the recovery of a large-scale enhancer regulatory network, depending on HDAC2 and GATA1 rescued peaks, whose components are involved in Chronic Myeloid Leukemia (CML) and several cancer-associated processes –16.Identifying “true” binding sites (in noisy samples in particular) and consensus regions have a significant impact on studying high-throughput sequencing data , 18 withIn the following, we first provide a brief literature review on the peak callers, we then discuss the characteristics of IDR and of the original version of MSPC, we describe the improvements implemented in the extended version of MSPC, and finally we define a novel functional enrichment test to assess the biological validity of the MSPC- and IDR-identified peaks.peak calling methods has been developed .Peak callers identify binding affinities by either scanning the entire genome using a sliding window and test for differential binding between ChIP and control samples at each window based on the Poisson model or its extensions . The second group jointly models binding affinities across replicated samples to identify combinatorial enrichment patterns e.g. , 38–40),–40,38–40MSPC , 9 and IIDR measures consistency between two replicates in high-throughput experiments. This quantitative irreproducibility score can then be used to rank pairs of peaks in the two replicates, determine a cutoff for irreproducibility and combine the two replicates. IDR uses a copula mixture model for estimating the expected irreproducible discovery rate of each pair of peaks in two replicates, yielding the expected rate of irreproducible discoveries .Calling consensus regions using IDR falls short in two areas. First, IDR is developed for conservative peak detection, where only highly reproducible peaks across samples are called. Hence, it fails to call peaks in samples with large variance such as biological replicates, where strong peaks on one replicate do not colocalize with peaks from other replicates with a low signal-to-noise ratio (SNR) , 7. Low rescues weak peaks, addressing the first aforementioned shortcoming of IDR. It processes biological and technical replicates differently  tly (see ), hence g., 1e−4 ;). Such p values using Fisher’s combined probability test, yielding a combined stringency, χ2, and the corresponding combined p value. MSPC confirms the overlapping peaks if the combined χ2 is larger than a user-defined threshold, and discards if otherwise. A peak might be tested multiple times if it overlaps with multiple peaks on another sample. Therefore, a peak might be confirmed based on one test and discarded based on another. When samples are biological replicates, MSPC confirms a peak if it passes at least one test , and with technical replicates, MSPC discards a peak if it does not pass all the tests (since more homogeneity is expected in this case). The confirmed peaks in each replicated sample are then corrected for false-discovery rate using the Benjamini–Hochberg procedure [For each peak on a sample, MSPC finds the peaks in the other samples overlapping with it. If the number of overlapping peaks is more than a user-defined threshold, it then combines their rocedure .p values of the overlapping peaks using the Fisher’s combined probability test  presented in this paper calls a consensus region where true-positive peaks on either of the replicates suggest binding loci. The coordinates of a consensus region are the union of overlapping true-positive peaks across all the samples, and its stringency is determined by combining the see Fig. . Finallysee Fig. . A perfoOther post-processing methods for identifying reproducible peaks across replicates exist. For example, ChIP-R  represen–mfold 5, –bw 300 –pvalue 0.0001. For each sample we used control samples as linked on ENCODE for each experiment; some experiments use a common control between multiple replicates , some experiments use different control samples for each replicate , or use two controls for each sample .ChIP-seq raw data on K562 cell line were downloaded from ENCODE , −s 1E−08 (stringent significance threshold), and −g 1E−06 (combined significance threshold), was the one yielding the best enrichment score for every TF. We then verified that this threshold choice leads to good results beyond the K562 cell line, by repeating the enrichment analysis for three TFs in MCF7 cell line  for the cell line K562, as listed in the Additional file The objective of the validation procedure is to assert if MSPC-rescued peaks are enriched in biologically meaningful loci. Accordingly, we defined three types of genomic regions: peaks, annotations, and regions not covered by any of them; where the first two may overlap to some extent. The higher the overlap between peaks and annotations, the higher the ability of the peak caller/rescuer to recall functional genomic regions. Since the number and coverage of a specific annotation is fixed for a given database, the only variables we need to consider are the number and position of called peaks. In particular, we define the conditional probability mentclass2pt{minimp and a and the resulting z-score are novel, in the sense that—to the best of our knowledge—they have never been used before for functional enrichment analysis. The scripts for the functional enrichment test are freely available from https://github.com/Genometric/MSPC/tree/dev/ValidationScripts.For both MSPC-rescued and IDR consensus peaks, we computed the enrichment score as the ratio mentclass2pt{minimhttps://web.mit.edu/meme_v4.11.4/share/doc/meme-chip.html. Motif enrichment was evaluated using the threshold E-value < 1E−10.TF binding motif enrichment was performed using MEME-ChIP with default settings  availablhttps://maayanlab.cloud/Enrichr.Overrepresentation analysis against the KEGG pathways  and ChEAFor each of the 48 TFs considered in this study, we obtained the list of transcription factor binding (TFB) motifs enriched at MSPC rescued enhancers  within 100 kb from these peaks, referred to as the HDAC2-GATA1 target gene set. We chose a 100 kb maximum distance to reduce enhancer-TSS false-positive associations, in accordance with recent literature , 54. To In addition, we generated a graph of the portion of the rescued HDAC2-GATA1 regulatory network that can be confirmed through experimental evidence, co-expression analysis, and/or curated databases. Protein–protein connections were fetched from the STRING interaction database and retained only if the confidence score was at least 0.4. These filters allowed us to highlight strength and density of the known regulatory network underlying MSPC-rescued peaks.https://github.com/nboley/idr) with a global IDR threshold of 0.05 from the output consensus regions. This assessment has two goals: (i) Verify the number of reference IDR peaks that are also detected by MSPC, and (ii) Assert if those MSPC-rescued peaks that are not in the IDR set are enriched in functionally important genomic regions . Notably, for every TF, the extended version of MSPC was able to find all the reproducible IDR peaks; we name them as the common set of peaks. Reproducible IDR peaks obtained with higher thresholds were also included in MSPC peaks  to the score of the common set of peaks , which has been recently described as a key factor in enhancer-dependent cell reprogramming  and T-ce0/48 TFs,For many transcription factors, the biological enrichment of the peaks rescued by MSPC is exceptionally higher than those considered as reproducible by IDR. This shows how peaks discarded by IDR could participate in biological processes that are actively involved in cell phenotype, and was confirmed by the over-representation analysis over Gene Ontology, KEGG, Reactrome, and ChEA. However, over-representation analysis cannot explicitly find which connections of the genome regulatory network are supported by MSPC-rescued elements. Therefore, we assessed the extent of the K562 regulatory network rescued by MSPC by directly reconstructing the interactions among annotated genomic elements.GATA1-HDAC2 are among the most enriched transcription factors in MSPC-specific dataset, and are both known to be involved in leukemic transformation in chronic myeloid leukemia . It is also known that TFs regulatory networks and their impact on gene expression modulation is highly dependent on the 3D structure of the DNA, which is in turn deeply influenced by enhancer activity. While a deep description of these mechanisms is out of the scope of this work, we anyhow assessed the presence of enhancer-related regulatory networks within the pool of MSPC-specific results, further highlighting the biological importance of MSPC-rescued peaks.common set. Over-representation analysis against the KEGG database , with 25 CML genes as targets of rescued enhancers. Other leukemia-related pathways were significantly enriched, including: VEGF signaling , Calcium reabsorption , Cell cycle , Rap1 signaling , Cellular senescence , Platelet activation , Leukocyte transendothelial migration . In addition, GATA1 and GATA2 binding in K562 cells were the top overrepresented terms , indicating a non-random set of interacting nodes. Edges have a minimum STRING confidence score of 0.4 . In addition, leukemic genes showed significant enrichment in the regulatory network, with respect to the whole genome .We identified 26,514 HDAC2 and 2,513 GATA1 enhancers . These percentages correspond roughly to those of GATA1 motif in HDAC2 peaks . The percentage increases to 40%  if we consider all enriched GATA1 motifs in HDAC2 peaks  on a reCollectively, these results show how MSPC may successfully recover genome-wide enrichments  that are part of the K562 CML regulatory networks, coherently with the sample cell line and phenotype.We argue the significant impact of improving the sensitivity and specificity while identifying binding affinities on high-throughput sequencing data by discussing the biological characteristics unveiled using weak but reproducible binding sites. Specifically, our main contribution is proving that these rescued peaks are enriched in biologically meaningful sites. This information emerges from the results provided by our novel functional enrichment test, which show overrepresentation of genomic elements, including promoters, CpG islands, enhancers, and DNase clusters (regions of open chromatin), suggesting that these weak but reproducible elements are part of large-scale active chromatin networks . We showed how one of these largest rescued regulatory networks is represented by the enhancers enriched in HDAC2-GATA1 peaks, which neighboring genes are involved in chronic myeloid leukemia-associated processes and K562-specific regulation.We discuss two methods for differentiating between weak and artifactual binding sites and calling consensus regions across replicated samples: MSPC and IDR. The MSPC method discussed here extends the first public release  in multiOur analysis over K562 and MCF7 ENCODE data showed that MSPC contains all IDR-identified reproducible regions, in addition to “rescuing” many other biologically relevant weak regions. Although MSPC generally rescues much more peaks than IDR, we demonstrated that these peaks are highly enriched for functional genomic elements and TF binding motifs, showing how IDR approach is often too stringent, severely hampering discovery.Additionally, MSPC consensus regions that are not common to IDR show a larger enrichment by annotation .Both MSPC and IDR operate on regions called using peak callers. Hence, their candidate sites are limited to the regions identified by the peak caller. To alleviate this limitation, a recommended practice for MSPC is to call peaks using a permissive Additional file 1. Supplementary Tables, Figures, Methods and Results.Additional file 2.Supplementary Table 4. This table shows the occurrence of transcription factor binding motifswithin MSPC-rescued enhancers associated with the 48 TFs. For each TF, a motif is considered to be enriched within enhancers and added to the table if the E-value reported by MEME-ChIPis below 1E-04.Additional file 3. Supplementary Table 5. KEGG pathways enrichment. KEGG overrepresentation analysis was done using the Enrichr suite, using the list of genes in the neighborhoodof HDAC2-GATA1 associated enhancers. For each KEGG pathway, the following information is shown: number of genes of the list belonging to the pathway, P-value, Benjamini-Hochberg adjusted P-value, enrichment odds ratio, Enrichr combined score, symbols of genes in the list that belong to the pathway.Additional file 4. Supplementary Table 6. IDR peak sets corresponding to different thresholds. The table reports the number of MSPC peaks with the chosen threshold setagainst several IDR thresholds for the TFs HDAC2, NRF1, and DDX20 in MCF7 cells. IDR peaks are always contained in the MSPC peak set.Additional file 5. Supplementary Table 7. Peak counts in each replicate, as well as for MSPC, IDR and ChIP-R results, for both K562 and MCF7 analyses.Additional file 6.Supplementary Table 8. ChEA transcription factor enrichment. ChEA overrepresentation analysis was done using the Enrichr suiteusing the list of genes in the neighborhoodof HDAC2-GATA1 associated enhancers. For each TF in ChEA, the following information is shown: number of genes of the list belonging to the pathway, P-value, Benjamini-Hochberg adjusted P-value, enrichment odds ratio, Enrichr combined score, symbols of genes in the list that belong to the pathway.Additional file 7. Supplementary Table 9. GATA1-HDAC2 regulatory network. Nodesare represented both as official gene names and taxon. Ensembl protein IDs. The table reports whether a node is known to be expressed in leukemia cellsor not. The combined score only considers the contribution of experimental evidence, coexpression analysis, and curated database informationto support the interactions, and it must be greater than or equal to 0.4. This ensures that only high-confidence interactions are included. Other scores are reported for completeness: homology relationship, literature textmining, closeness on chromosome, gene fusion partners, phylogenetic relationship. Note that the table is redundant.Additional file 8. Supplementary Table 10. Centrimo analysis results on HDAC2 enhancers. The table reports motif ID, motif name, consensus sequence, E-value and adjusted P-value for each detected motif. The strongest motif is GATA1.Additional file 9. Supplementary Table 11. Summary of Fimo results for GATA1 in K562 cells. The table contains three blocks of rows: GATA1 peaks, HDAC2 peaks, and HDAC2 enhancers. In all these cases the table reports the number of total peaks, the number of regions containing GATA1-associated motifs, Jaspar motifs ID, motif enrichment E-value and adjusted P-value.


The biological revolutions of computationally designed proteins, induced pluripotent stem cells (iPSCs), and the CRISPR-Cas9 system finally enables modifications that can spur deep understanding of spatial requirement of epigenetic information. This commentary describes the utility of a computationally designed protein, EED Binder (EB), when fused to dCas9 (EBdCas9) for identifying critical sites of PRC2 dependent histone H3K27me3 marks in the chromatin. By using EBdCas9 and gRNA, PRC2 function can be inhibited at specific loci, resulting in precise reduction of EZH2 and H3K27me3 marks, and in some  locations, activation of the gene and functional outcomes (such as regulation of cell cycle or trophoblast transdifferentiation). Interestingly, a functional TATA box located more than 500bp upstream of a TBX18 TSS was found to be repressed by PRC2, supporting the theory that epigenetic regulators control the repression of transcriptional elements on the promoter region. The EBdCas9 technology may provide a useful tool for controlling gene regulation through epigenomic control. While H3K27me3 marks are broadly decorating the promoter regions of developmental genes, and generally associate with gene repression , a centrPRC2 (Polycomb Repressive Complex 2) plays a crucial role in the process of determining the fate of cells during development by modifying chromatin through a specific histone modification called H3K27me3. Among the PRC2 proteins which facilitate new H3K27me3 modification on the chromatin are the reader EED which anchor the PRC2 complex to the chromatin and the writer/catalyzer, methyltransferase, EZH2 which methylates the lysine 27 sidechain in histone 3 (H3K27). To understand the requirement of the PRC2 in different developmental stages of pluripotency we generated a computer-designed protein that binds the EZH2 binding site in EED , thereby competing with EZH2 –19. HoweTargeting EBdCas9 and appropriate single guide RNA to specific chromatin locus on a promoter region of a gene, such as the transcription factor in pacemaker cells, TBX18 results in PRC2 repression and activation of TBX18 gene expression. Moreover, this derepression of TBX18 gene was inherited to the daughter cells since Tbx18 was expressed in subsequent cell divisions after removal of EBdCas9 and the guide RNA. While the repression of EZH2, H3K27me3, and JARID2 by EBdCas9/gRNA were initially limited to a single gRNA determined locus, in specific locations epigenomic changes readily spread onto the promoter region towards the transcription start site, TSS. Additionally, the previously facultative heterochromatin was replaced with histone marks associated with gene activation, such as H3K27ac. The enzyme which catalyzes the transfer of acetyl marks to histone, histone acetyltransferase, p300 was also recruited to the locus. The generated euchromatin was characterized by the presence of RNA polymerase 2, and the hydroxylation of DNA methyl marks at CpG island proximal to TSS. Therefore, the targeted PRC2 inhibition results in epigenomic remodeling, native activation of the transcriptional machinery, gene expression and inherited epigenomic memory.Interestingly, we identified two kinds of PRC2 responsive loci, the loci in which PRC2 function was essential for gene repression, and loci in which PRC2 did not function to repress transcription. We sought to understand what is/are the molecular mechanism/s responsible for such profoundly different effects.In the case of TBX18, one unique identifier was detected on the promoter region of the gene, in close proximity to the impactful single guide RNA that resulted in substantial epigenomic and transcriptional changes. The Element prediction tool  identifip16 is repressed due to hypermethylation at the promoter area, however, the capacity to activate p16 for cell cycle arrest could lead to a breakthrough in DMG therapeutics. Transient introduction of EBdCas9 and appropriate guide RNA to p16 gene promoter region of DMG patient cell line led to reduction of EZH2 and H3K27me3 epigenomic marks, p16 transcript and protein activation. Importantly, impactful reduction of over 50% in DMG cell replication and viability was observed in these experiments [Control of epigenomic regulation by AI-designed proteins holds immense therapeutic promise for human disease without using chemical drugs or manipulation of endogenous gene sequence. Diffuse Midline Glioma (DMG), is an aggressive form of brain cancer that develops in glial cells in the pons, primarily affecting children ,22. A lyFinally, regenerative medicine offers the potential to repair diseased cells and innovatively provide new gene and cell therapies for previously intractable conditions. One of the hallmarks of regenerative medicine is the ability to transdifferentiate one cell type to another beyond their natural bifurcation point. Likewise, the extraembryonic placental vs embryonic cellular fate decision (the first in eutherian development) is dependent on PRC2 . While aThe field of regenerative medicine is rapidly growing and offers a wide range of potential therapies for the treatment of various diseases. One promising area of research within regenerative medicine is the use of epigenetic modifiers towards epigenetic therapies. The absolute gem in epigenetic therapies is the ability to correct defects in gene regulation without altering the underlying genetic code. In this commentary we have discussed how a computer designed mini-protein fused to dCas9 can specifically activate genes through releasing epigenetic repression . In the


Many Mendelian developmental disorders caused by coding variants in epigenetic regulators have now been discovered. Epigenetic regulators are broadly expressed, and each of these disorders typically exhibits phenotypic manifestations from many different organ systems. An open question is whether the chromatin disruption – the root of the pathogenesis – is similar in the different disease-relevant cell types. This is possible in principle, since all these cell-types are subject to effects from the same causative gene, that has the same kind of function (e.g. methylates histones) and is disrupted by the same germline variant. We focus on mouse models for Kabuki syndrome types 1 and 2, and find that the chromatin accessibility abnormalities in neurons are mostly distinct from those in B or T cells. This is not because the neuronal abnormalities occur at regulatory elements that are only active in neurons. Neurons, but not B or T cells, show preferential chromatin disruption at CpG islands and at regulatory elements linked to aging. A sensitive analysis reveals that the regions disrupted in B/T cells do exhibit chromatin accessibility changes in neurons, but these are very subtle and of uncertain functional significance. Finally, we are able to identify a small set of regulatory elements disrupted in all three cell types. Our findings reveal the cellular-context-specific effect of variants in epigenetic regulators, and suggest that blood-derived “episignatures” may not be well-suited for understanding the mechanistic basis of neurodevelopment in Mendelian disorders of the epigenetic machinery. Reflegulators –8. A chagulators . This isgulators , and raiThere are two major reasons for why this question is important. First, from a basic biology standpoint, the answer would provide insights into whether the same variant, in the same epigenetic regulator, causes the same (or a highly similar) chromatin disruption in different cellular contexts. For example, is it the same genomic locations that exhibit changes in chromatin accessibility? The degree of such similarity would serve as an indicator of how much the intra- and extra-cellular environment dictate the specific effect that epigenetic regulators have on chromatin, and in what way. Second, from a practical standpoint, answering this question is important for understanding the potential of DNA-methylation-based ”episignatures” , 11 to sKMT2D , or the histone demethylase KDM6A  [One of the prototypical Mendelian disorders of the epigenetic machinery is Kabuki syndrome (KS). Approximately 70% of cases of KS are caused by variants in either the histone methyltransferase  2; KS2) , 16. Sev 2; KS2) –28. In a 2; KS2) –31. TherHere, we investigate this using mouse models of KS1 and KS2, and profiling genome-wide chromatin accessibility in sorted neurons as well as sorted B and T cells from peripheral blood.We performed ATAC-seq in order to characterize the genome-wide chromatin accessibility landscape in KS mice (both type 1 and type 2) and wild-type littermates . We examWe started with a differential accessibility analysis in neurons, after validating our detected ATAC peaks . CompariWe next asked if the same regulatory elements are disrupted in neurons and immune cells. We employed a previously developed method that uses conditional p-value distributions to evaluate the overlap between the results of genome-wide differential analyses, and has been shown to have increased power relative to more “naive” approaches ; MethodsMethods. We note that all pairwise comparisons were performed after restricting to ATAC peaks detected in both cell types . This enWe next looked at mouse orthologs of human genes whose promoters are differentially methylated in KS1 patients versus controls in whole blood ; these aWe set out to investigate the nature of the discrepancy between neurons and B/T cells. We focused on promoters, since their unambiguous association with the downstream gene can help connect that discrepancy to the underlying biology. A major distinction of mammalian promoters is between those that are associated with CpG islands, and those that are not . We thusRanking promoter ATAC peaks based on how strong the evidence for disruption of their accessibility is in KS1 neurons, we discovered a clear pattern: the stronger the evidence for accessibility disruption, the higher the probability of the promoter peak overlapping a CpG island . Of the Kdm6a , Kmt2a , Tet3 , and Chd1 . In KS2, epigenetic regulators with disrupted promoters range from Kmt2d  and Suz12  to Kat6a . These findings indicate broad epigenetic dysregulation in KS1 and KS2 that extends beyond what would be expected on the basis of the function of their respective causative genes, and could explain the disruption of CpG island chromatin. In addition, they may be relevant to the high degree of neurodevelopmental/neurological phenotypic overlap among the different Mendelian disorders of the Epigenetic Machinery [It is known that the chromatin accessibility of CpG islands is related to the absence of DNA methylation and the presence of specific histone modifications –39. We tOur findings yield insights into: a) the genomic distribution of the chromatin defects in KS1 and KS2; b) the cell-type selectivity of these defects. However, our findings thus far fall short of providing a link between the chromatin defects and the abnormal development known to be a component of KS. To obtain such a link, we first performed a pathway analysis of the genes with disrupted promoters in neurons . As a final analysis, we returned to our overarching question of whether there is overlap between the chromatin disruption in immune cells and neurons, this time adopting an alternative approach. We asked whether the accessibility of the regions disrupted in B cells is altered in the same direction (increased or decreased) in neurons, more than expected by chance. In other words, we examined the concordance between the sign of the effect (log-fold change) in the two cell types, without taking the p-value into account –47. ThisIn all comparisons performed, we found statistically significant concordance in the direction of accessibility disruption . This inp < 2.2 10−16, Fisher’s exact test). This echoes previous results in blood and immune cells [Encouraged by the above, we suspected that it would be possible to define a set of regulatory elements with disrupted accessibility in all three cell types. Our ultimate goal was to derive, in a principled manner, a set of regions that can potentially comprise a blood-based biomarker that also accurately reflects the neuronal chromatin state. We employed a step-wise procedure. Starting from regions disrupted in B cells, we then selected the subset that are disrupted in neurons as well (10% FDR level). We then further narrowed these down by examining the accessibility of these regions in T cells, and selecting only those that are disrupted (10% FDR level). This procedure yielded 381 regions for KS1, and 91 regions for KS2 , 13, 14.ne cells . MoreoveKmt2d and the histone demethylase Kdm6a in 3 different cell types: hippocampal neurons, and peripheral blood B and T cells. Our results paint a nuanced picture. On one hand, the most statistically significant chromatin alterations occur – mostly – at different genomic locations in neurons vs B/T cells . On the other hand, the locations harboring the most significant chromatin disruption in B/T cells are in fact also disrupted in neurons, but the degree of this disruption is very small.We have characterized, compared and contrasted the genome-wide chromatin alterations ensuing from genetic disruption of the histone methyltranferase Kmt2d and Kdm6a are genetically disrupted the chromatin alterations also occur at different locations. In the second scenario, KMT2D and KDM6A exert their function at the same locations both in neurons and immune cells. However, other epigenetic regulators act after KMT2D and KDM6A to further modify the chromatin landscape in a different fashion in neurons versus immune cells. The chromatin alterations in this case may initially occur at the same locations, but are subsequently molded differently. In the third scenario, the chromatin alterations occur at a common progenitor cell type early during development. Then, subsequent chromatin changes occurring as part of neuronal and immune cell developmental programs [Broadly speaking, the differences between the cell types are compatible with three different scenarios. In the first scenario, when KMT2D and KDM6A exert their catalytic effects, the relative abundances of their interacting partners –51 are dprograms , 53 ultiKmt2d as well as Kdm6a loss [Notwithstanding the underlying explanation, we consider it surprising that a major difference between neurons and B/T cells is the extent to which the chromatin alterations are concentrated at promoter CpG islands of housekeeping genes; regulatory elements that have broad, non-cell-type-specific activity. Assuming that these chromatin alterations ultimately lead to abnormal levels of housekeeping gene protein products, this might also mean that the neuronal cellular phenotype is more severe, with fundamental processes affected. Also of potential mechanistic relevance is our finding that the chromatin disruption in neurons occurs preferentially at regulatory elements containing aging clock CpGs and at promoters of lifespan-associated genes. In other words, at locations that somehow track – and in some cases potentially regulate – the passage of biological time. This may represent a new link between the chromatin disruption in KS and the disruption of normal neural development/maturation. Our observation that, based on some of the accessibility changes, KS neurons may be epigenetically ”older” than wild-type neurons fits with a previous report of precocious neuronal differentiation in KS1  and warrm6a loss , 55. We m6a loss . The ream6a loss . In addim6a loss , 22, 26.m6a loss ). In thiWith respect to the very subtle neuronal chromatin alterations at the locations where the immune cells are most saliently disrupted, what our study does not answer is whether these subtle alterations are biologically significant in neurons. In other words, do alterations of such small magnitude have any effect on neuronal cellular function? Future work is needed to address this. It is also worth noting here that even the top significant neuronal alterations are of relatively small magnitude, with the majority of changes being much smaller than 2-fold. However, we anticipate that, collectively, these individually small changes can lead to the emergence of disease phenotypes , 57. HowOne limitation pertinent to all our results and interpretations is that the sorted cell populations we analyzed were defined by the expression of specific surface markers. However, it is now well-appreciated that cells expressing these markers represent a mixed population of cell sub-types –60. HencFinally, we emphasize that our goal when defining the sets of locations disrupted in all three cell types is not for these locations to replace those currently comprising existing KS episignatures; the latter have demonstrated their diagnostic utility , 11, 29.Kmt2dβGeo+/) and KS2 (Kdm6a+/−) mouse models and genotyping protocols have been previously described [All experiments were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. All experiments received approval by the Animal Care and Use Committee of the Johns Hopkins University (protocol number: MO18M112). Both the KS1 (B cells (CD19+) from peripheral blood were described in Luperchio et al. . T cellsHippocampal neurons were isolated using a protocol adapted from the INTACT  and OmniRaw ATAC-seq data were processed as follows. Sequencing reads from the fastq files were mapped to the mouse genome (assembly mm10) using bowtie2 , duplicahttps://hgdownload.soe.ucsc.edu/gbdb/mm10/encode3/atac/. They consist of a set of genomic regions, with each region having a corresponding ATAC signal score. We found that 98% of KS1, 98.5% of KS2, and 97.8% of the wild-type neuronal peaks overlap ENCODE regions with score greater than 0. This supports the validity of our peak locations. To then assess the validity of the ATAC signal intensity at these locations, we restricted to ENCODE regions with a score greater than 0, and stratified them according to whether they: a) overlap at least one of our ATAC peaks at promoters; b) overlap at least one of our ATAC peaks outside promoters; c) do not overlap any of our peaks. Examining the scores of the stratified regions, we found that, in each of the genotypes, promoter-overlapping regions have strong signal, and non-promoter overlapping regions have weaker (as expected), but still pronounced signal  mouse forebrain (since the hippocampus is part of the forebrain). These data were generated by Gorkin et al.  as part d signal . By contd signal .As a final validation, we compared the ATAC peaks we detected in our samples to the peaks detected by Su et al.  GSE8201.Together, these results provide strong evidence that our neuronal ATAC-seq peaks represent true neuronal regulatory elements, in all three of the genotypes.https://github.com/Boyle-Lab/Blacklist/archive/v2.0.zip; Amemiya et al. [For neurons and T cells, the differential accessibility analyses were performed separately, as follows. First, we unionized the 3 sets of peaks called from the 3 meta-samples  and excluded peaks which had at least 1 base-pair overlap with ENCODE blacklisted regions . This ya et al. . Using ta et al.  . This iπ0) corresponds to what we often refer to in the main text as the overlap between the two cell types. We computed π0 using the pi0est function from the “qvalue” R package, with the ”pi0.method” argument set to ”bootstrap”. To test whether the resulting overlap is statistically significant, we derived a null distribution by repeatedly  sampling random sets of B cell peaks, and estimating the neuronal or T cell π0 from these peaks in an identical manner as for the B cell differential peaks. The number of peaks contained in each randomly sampled set was equal to the number of differential B cell peaks.Using the results of the differential accessibility analyses, we compared the chromatin disruption between cell types using the approach developed in Luperchio et al.  , and T cells . We subsequently examined the p-value distribution of these peaks in B cells, and estimated π0 as before. Our results unambiguously recapitulate our original findings: there is much greater overlap between the chromatin disruption in B and T cells, than between B cells and neurons  sampled random sets of B cell peaks. The sampling was done so that, within each random set: a) the balance of peaks with increased vs decreased accessibility in the mutant vs wild-type mice was the same as in the set of B cell differential peaks ; b) the total number of peaks sampled was equal to the number of B cell differential peaks. For each random set of B cell peaks, we obtained overlapping neuronal or T cell peaks and examined the proportion of these peaks whose accessibility changes in the same direction as in the B cell differential peaks. These random proportions formed the null distribution.We defined promoters as regions +/− 2kb from the transcriptional start site. We obtained coordinates of such promoter regions in mm10 using the promoters function from the EnsDb.Mmusculus.v79 R package, with the ”upstream” and ”downstream” parameters set to 2000.We obtained the coordinates of differentially methylated positions (DMPs) in KS1 patients vs controls from Aref-Eshghi, Schenkel, et al. . We thenWe obtained coordinates of CpG islands in the mouse genome (mm10 assembly) using the ucscTableQuery function from the ”rtracklayer” R package. We defined ATAC peaks that overlap CpG islands as peaks with at least 1 overlapping base pair.−5 in B cells and 1.57·10−5 in T cells before downsampling vs 7·10−5 and 10−3 after downsampling) and KS2 (1 ratio of deviance to null deviance = 0.003 in B and T cells before downsampling vs 8·10−4 in B cells and 2·10−4 in T cells after downsampling).As expected, peaks overlapping CpG islands have higher read counts compared to peaks that do not. Since the number of read counts can be inversely related to the uncertainty around log fold-change estimates, we sought to verify that the strong quantitative relationship between probability of CpG island overlap and differential accessibility p-value  is not dTo identify mouse CpG islands bound by the Polycomb Repressive Complex 2 (PRC2), we first identified locations bound by EZH2 – the catalytic subunit of the PRC2 complex – in the human genome. To do this, we used the ucscTableQuery function from the ”rtracklayer” R package to query the UCSC Table Browser. Specifically, from the “Txn Factor ChIP” track (which belongs to the “Regulation” group) we obtained the “wgEncodeRegTfbsClusteredV3” table. This provided us with a set of genomic locations and their coordinates, with each location having an associated score. The maximum possible score was 14. That score represents the number of EZH2 ChIP-seq peaks detected in that location, after uniform processing of ENCODE ChIP-seq experiments from 14 cell lines . To minimize the inclusion of potential false positives in our analysis, we restricted to locations having at least 5 EZH2 peaks . We subsequently identified human genes whose promoters overlap these locations  using the promoters function from the ”EnsDb.Hsapiens.v75” R package. Then, we obtained mouse orthologs of these human genes using the ”biomaRt” R package. We only selected high-confidence ortholog pairs, by setting the “mmusculus homolog orthology confidence” parameter equal to This yielded a total of 1,515 mouse orthologs. Finally, we identified mouse CpG islands that overlap the promoters (+/−2 2kb from the TSS) of these mouse orthologs.https://deeptools.readthedocs.io/en/develop/). Reads were CPM normalized also using deeptools . Mouse Transcript annotations as well as CpG island annotations (both from mm10) were obtained from UCSC. All plots were then generated using the karyoploteR R package. We plotted regions between 5000bp upstream and 8000bp downstream of the selected promoters using the plotKaryotype function. The normalized ATAC signal tracks were plotted with the kpPlot-BigWig function, from the bigwig files. Gene tracks were added by addGeneNames and mergeTranscripts and added to the plot by kpPlotGenes function. CpG islands overlapping the promoter regions were added to the plot by using kpPlotRegions. Labels and text positions were determined by ”r0” and ”r1” parameters in karyoploteR.The BAM files corresponding to the genotype-specific “meta-samples” were indexed (using samtools) and converted to bigwig files using deeptools  (see httEpigenetic machinery (EM) genes and their co-expression status (highly co-expressed vs non-co-expressed) were obtained from . ThereinThe differential accessibility analyses provide results at the peak level. To perform a pathway analysis, we obtained a single p-value for each gene by selecting the smallest p-value out of the p-values corresponding to the peaks that overlap the gene’s promoter . We defined differential genes as those with p-values in the bottom 5%. We subsequently used these differential genes and the Reactome pathway annotations  to identWe obtained mouse epigenetic clock CpGs from Thompson et al. . SpecifiTo examine the chromatin disruption at promoters of genes associated with lifespan regulation in mammals, we used the KEGGREST R package to obtain genes in the ”Longevity regulating pathway” (pathway: mmu04213). We then analyzed peaks within promoters (+/− 2kb) of these genes. For In both KS1 and KS2, we adopted the following stepwise strategy. We started by obtaining peaks disrupted in B cells . Among these genes, we then seelcted peaks disrupted in neurons as well (at 10% FDR level) using the qvalue function from the ”qvalue” R package . FinallySupplement 1Supplement 2Supplement 3Supplement 4Supplement 5Supplement 6Supplement 7Supplement 8Supplement 9Supplement 10Supplement 11Supplement 12Supplement 13Supplement 14Supplement 15


ZMYM2 is a zinc finger transcriptional regulator that plays a key role in promoting and maintaining cell identity. It has been implicated in several diseases such as congenital anomalies of the kidney where its activity is diminished and cancer where it participates in oncogenic fusion protein events. ZMYM2 is thought to function through promoting transcriptional repression and here we provide more evidence to support this designation. Here we studied ZMYM2 function in human cells and demonstrate that ZMYM2 is part of distinct chromatin-bound complexes including the established LSD1-CoREST-HDAC1 corepressor complex. We also identify new functional and physical interactions with ADNP and TRIM28/KAP1. The ZMYM2-TRIM28 complex forms in a SUMO-dependent manner and is associated with repressive chromatin. ZMYM2 and TRIM28 show strong functional similarity and co-regulate a large number of genes. However, there are no strong links between ZMYM2-TRIM28 binding events and nearby individual gene regulation. Instead, ZMYM2-TRIM28 appears to regulate genes in a more regionally defined manner within TADs where it can directly regulate co-associated retrotransposon expression. We find that different types of ZMYM2 binding complex associate with and regulate distinct subclasses of retrotransposons, with ZMYM2-ADNP complexes at SINEs and ZMYM2-TRIM28 complexes at LTR elements. We propose a model whereby ZMYM2 acts directly through retrotransposon regulation, which may then potentially affect the local chromatin environment and associated coding gene expression. The zinc finger protein ZMYM2 (otherwise known as ZNF198) was originally identified as part of an oncogenic fusion protein with the receptor tyrosine kinase FGFR1 in myeloproliferative disease . In thisMolecularly, ZMYM2 has been shown to act as a transcriptional repressor and can bind to the LSD1-CoREST-HDAC1 corepressor complex (LCH) via its zinc fingers . It is tWe identified ZMYM2 in a screen for multi-SUMO binding proteins  which buIn this study, we further investigated the molecular activities of ZMYM2 and demonstrated that it is associated with two distinct chromatin-bound complexes containing either ADNP or TRIM28. Both types of ZMYM2 chromatin binding regions are associated with different subclasses of retrotransposons. We focussed on TRIM28 and demonstrated widespread functional cooperativity with ZMYM2 in gene regulation. However, ZMYM2-TRIM28 binding regions are directly associated with ERV retrotransposable elements rather than with protein coding genes, suggesting an indirect mechanism for transcriptional control of the coding genome.To investigate the molecular function of ZMYM2, we used the mass spectrometry approach RIME  to identHaving established a physical interaction between ZMYM2 and ADNP, we next studied their functional interplay. First, we performed two independent ChIP-seq experiments for ADNP in U2OS cells and uncovered 5108 peaks found in both datasets. These ADNP peaks showed enrichment of binding motifs for several transcription factors with the top two motifs for HBP1 and IRF both found in over 35% of target regions . The topGiven this co-occupancy on chromatin we asked whether ZMYM2 and ADNP shared gene regulatory activity. We depleted each protein individually  and perfTogether these results demonstrate that ZMYM2 can function through a transcriptional regulatory complex containing ADNP to co-ordinately control gene activity.Next, we turned to the potential interplay between ZMYM2 and TRIM28. Previous studies demonstrated that many zinc finger transcription factors can recruit TRIM28 to chromatin to repress transcription  are the direct binding target of ZMYM2, although ZMYM2 is itself SUMOylated , as is TPrevious studies have focussed on the role of ZMYM2 in the context of its association with the LSD1-CoREST-HDAC corepressor complex . Indeed,The identification of TRIM28 as a chromatin-associated interaction partner further supports a role for ZMYM2 in transcriptional repression, and suggests that it can do so via a variety of different means. Indeed a recent study identified ZMYM2 as an important regulator of silencing of imprinting control regions which are under the control of the zinc finger protein ZFP57 . In termThe association of ZMYM2 with transposons and its ability to regulate their expression suggests several possible modes of subsequent effects on broader gene expression programmes. Retrotransposons have been shown to exhibit a multitude of different molecular mechanisms to control gene regulation . Stable U2OS-derived cell lines, flag-ZMYM2(WT) and SUMO3(K11R/Q90P) were created using U2OS-Flp-in cells , 20 mM NEM and 30 mM IAA) incubated with gently agitation for 5 min at 4 °C. Samples were spun down at 2000 g for 5 min at 4 °C. Pellets were resuspended in LB3 , 20 mM NEM and 30 mM IAA), aliquoted and sonicated; 15 cycles of 30 s sonication, 30 s paused, in a water bath at 4 °C. After sonication, triton X-100 was added to the samples. Samples were spun down at 13,000 rpm for 10 min at 4 °C. The supernatant was removed and incubated with antibody coupled beads. Samples were incubated with gentle agitation over night at 4 °C. Beads were washed 10 times with 1 ml of RIPA buffer and twice with freshly made 100 mM ammonium bicarbonate. Beads were spun down at 1000 rpm for 1 min to remove residual ammonium bicarbonate. To release the proteins bound to the beads, 100 ng of trypsin was added and incubated overnight at 37 °C. The supernatant was carefully removed and analysed by an Orbitrap velos mass spectrometer as described previously (U2OS-derived cells were grown in 15 cm dishes (6 confluent dishes per sample) and fixed using DMA for 20 min at room temperature, followed by 1% formaldehyde (ultra pure) addition for 5 min at room temperature. Cells were washed twice with cold 1 X PBS. Cells were harvested in 1 X PBS supplemented with protease inhibitors . Cells were centrifuged at 3000 rpm for 10 min at 4 °C. The supernatant was removed, the cell pellet was resuspended in 1 X PBS with protease inhibitors and spun down again. The pellets were resuspended in LB1 buffer , 20 mM N-ethylmaleimide (NEM) and 30 mM iodoacetate (IAA)), and incubated with gently agitation for 10 min at 4 °C. Cells were spun down at 2000 eviously .Homo sapiens) database. Data was validated using Scaffold (5.0.2). Proteins found in more than 50% of CRAPome samples, ribosomal proteins and proteins with zero spectral counts in any experiment were removed from the list. Proteins showing an average of >4 spectral counts across three experiments were included in the final list and were then compared to proteins bound to ZMYM2 on mouse ESCs , against the SwissProt (version 2018_01) with a taxonomic break of the Human (use ESCs  and BioIuse ESCs .Lipofectamine RNAiMAX (Invitrogen) according to the reverse transfection method in the manufacturer’s instructions. Forty-eight hr after transfection, cells were transfected again with the same siRNA using forward transfection method for another 48 hr.ON-TARGETplus SMART pool siRNAs from Dharmacon were used to knock-down ZMYM2, TRIM28 or ADNP . Non-targeting siRNA pool  was used as a control. To carry out RNA interference (RNAi), cells were transfected with siRNA using Total RNA was extracted using the RNeasy plus kit (Qiagen) following the manufacturer’s protocol. To detect LTR expression, additional DNase digestion was performed using RNase-Free DNase Set (Qiagen) to completely remove genomic DNA.For RNA-seq analysis, U2OS cells in 6-well plates were transfected twice, second transfection was done 48 hr after the first one. A final concentration of 100 nM of each siRNA using Lipofectamine RNAiMAX (ThermoFisher Scientific), were used following the manufacturer’s instructions. Total RNA was extracted 48 hr after the second transfection using an RNeasy plus kit with on-column DNase treatment, following the manufacturer’s instructions (Qiagen). Immuno-blot and RT-qPCR for ZMYM2, TRIM28 or ADNP expression were carried out to verify knock-down of the mRNAs and proteins.GAPDH (primers ADS2184 ACAGTCAGCCGCATCTTCTT and ADS2185 TTGATTTTGGAGGGATCTCG) or RPLP0 . RNA samples were run in duplicate from at least three independent experiments. The primer-pairs used for RT-qPCR experiments were ZMYM2 , TRIM28 , ADNP , MER11A , LTR10A , THE1C-int , AluSx3 , AluJr , MIRb .Quantitative RT-PCR was performed using 30 ng of total RNA and QuantiTecT SYBR Green RT-PCR kit  according to the supplier’s protocol. Data were analysed by Qiagen Rotor-Gene Series software. Data were normalised against the control gene For immuno-blotting, the antibodies used are listed in Co-immunoprecipitation was performed as previously described . The ant+ . Cells were incubated with PLA probes, ligase and polymerase following Duolink-II fluorescence instructions. After the final wash, nuclei were stained by incubating with Hoechst solution (1 μg/ml) for 10 minutes at room temperature. Cells were washed twice with 1 X PBS. Preparations were mounted in vectashield antifade medium (Vector) and sealed with nail varnish. Z-stack images covering the entire thickness of the samples were acquired on a Delta Vision RT (Applied Precision) restoration microscope using a 100 X/1.40 Plan Apo objective. The images were collected using a Coolsnap HQ (Photometrics) camera with a Z optical spacing of 0.2 μm. Raw images were then deconvolved using the Softworx software. Deconvolved images were used to quantify interactions per cell using the single cell analysis function of Blobfinder software.U2OS cells grown on glass cover-slips were washed once in 1 X PBS and then fixed and permeabilised with 3.7% para-formaldehyde plus 0.8% Triton X-100 for 15 min at room temperature. Cells were washed three times with 1 X PBS. During all of the incubation steps cells were kept in a humidified chamber. The cells were first incubated for one hour with 1% BSA dissolved in 1 X PBS, followed by one hour incubation with both primary antibodies (anti-c-myc Santa Cruz (9e10):sc-40 1:500, and anti-KAP1 Abcam, ab10483 1:500, diluted in 1% BSA-1X PBS). Cells were washed three times with 1 X PBS and once with PBSP-value <0.01 and fold change >1.6. The online tool Metascape (RNA-seq was performed in triplicate. Libraries were generated using TruSeq stranded mRNA library kit (Illumina) and sequenced on HiSeq 4000 platform (Illumina). Reads were mapped to the human genome hg19 by the aligining tool STAR as described previously . 14.6 M-etascape  was used2 and 0.1% Igepal). Nuclei were concentrated by centrifugation at 500 g for 10 min at 4 °C. The nuclei pellet was resuspended in nuclease free water, diluted 1:10 and quantified. The equivalent volume for 50,000 nuclei was made up to 22.5 μl with nuclease free water. Nuclei were incubated for 30 min at 37 °C, 300 rpm, with 25 μl of 2 x TD buffer and 2.5 μl tagmentation enzyme (Illumina transposase FC-121–1030). DNA was purified using a Qiagen mini elute kit following the manufacturer’s instructions. Library fragments were amplified using standard PCR and Nextera primers (Illumina) by adding 25 μl of 2 x NEBnext master mix, 2.5 μl forward primer, 2.5 μl reverse primer and 20 μl transposased DNA. PCR was run for 5 cycles, 72 °C, 5 min; 98 °C 30 s; cycle of 98 °C 10 s, 63 °C 30 s, 72 °C 1 min, paused at 4 °C. To determine the total number of cycles needed for the amplified library, 5 μl of the PCR mix were taken and added to a new mix containing 5 μl 2 X master mix, 1 ml of each primer, 0.6 μl of 10 X SYBR green and 2.4 μl H2O. In a qPCR machine, 20 cycles were run, the total number of cycles for the ATAC library was chosen according to qPCR cycle that gave 1/3 of saturation. DNA was purified using Ampure XP beads following the manufacturer’s instructions.The ATAC-seq data in U2OS cells were generated by using the protocol essentially as described previously . ATAC-seThe reads in the ATAC-seq data were mapped to the human genome hg19 using Bowtie2 with the same settings as those described below for the ChIP-seq data, and only the uniquely mapped reads were used in the analysis.TXNL4A, EMX2, and FAM109A, as described previously , and THE1C-int .ChIP-qPCR was performed using primers to detect eviously , MSTB  and approximately 10 ng of DNA were processed for sequencing.kmeans with the option ‘centers = 3’ was used to cluster the 1180 ZMYM2 peaks into three clusters.The data analysis of both ZMYM2(WT) and ZMYM2(SIM2mut) ChIP-seq data were described previously , includihttp://broadinstitute.github.io/picard/) and the reads mapped to the mitochondrial chromosome were also removed. The correlation coefficients between the two replicates of the read counts was between 0.85 and 0.95 for the two replicates in our ChIP-seq data. The reads in the two replicates were then pooled, and with the pooled reads from the two input replicates as control, peaks were called from the pooled ChIP-seq using MACS2  and the distance of the ZMYM2 peaks in different clusters to the nearest TAD domains were also performed using the Student t test. Fisher Exact test was used for testing the significance of the overlaps between different sets of DE genes. The software PEGS  was usedOur ChIP-seq and RNA-seq data from U2OS cells have been deposited with ArrayExpress. Accession numbers: E-MTAB-12292 (ADNP and TRIM28 ChIP-seq), E-MTAB-12293 (SUMO ChIP-seq), E-MTAB-12294 (ATAC-seq), and E-MTAB-12291.https://egg2.wustl.edu/roadmap/data/byFileType/alignments/consolidated/; The following existing datasets were used: H3K18ac ChIP-seq in U2OS cells (E-MTAB-3695), ZMYM2 ChIP-seq (WT and SIM2mut) in U2OS cells  that are important to understand the molecular functions of ZMYM2. These datasets were generated with replicates and will be very useful for the scientific community. This study provides important novel insights into the molecular roles of ZMYM2 in human U2OS cells. In this study the authors investigate functional associations made by transcription factor ZMYM2 with chromatin regulators, and the impact of perturbing these complexes on the transcriptome of the U2OS cell line. They focus on validating two novel chromatin-templated interactions: with TRIM28/KAP1 and with ADNP, concluding that via these distinct chromatin regulators, ZMYM2 contributes to transcriptional control of LTR and SINE retrotransposons, respectively.Strengths of the study:-The co-localization of ZMYM2 with ADNP and TRIM28 is validated through RIME, ChIP-seq and co-IP. Since TRIM28 is a highly abundant nuclear protein, the use of multiple methods is important to add confidence in particular for the novel SUMO-dependent interaction identified between ZMYM2 and TRIM28. That TRIM28 pulls down less of the ZMYM2-SIM mutant is reassuring.-It is good that uniquely-mapped reads are used in the ChIP-seq analysis given the interest in repetitive elements. Likewise, though the RT-qPCR data in Fig 6 should be complemented by analysis of the RNA-seq data that the authors already have, it seems that the primers are carefully designed such that a single retrotransposon copy is amplified.-The paper is generally written very clearly, the experiments well done and the different datasets appear to be robust.Weaknesses of the study:-The transcriptional response using bulk RNA-seq in ZMYM2-depleted cells remains gene-centric despite the title of the paper being about TE transcription. In fact, the only panels about TE transcription are the RT-qPCR data in Fig 6D, F. During the revision the authors said that their RNA-seq data is unfortunately too shallow to retrieve TEs. Fair enough - however, it remains the case that the central claim is control of TE transcription by ZMYM2. Thus, without additional transcriptomic analysis we are left with only a few qPCRs, even if they are nicely done! Perhaps the title could be modified a bit in that case?-The mechanism by which ZMYM2 and TRIM28 work together does remain a mystery. Following review the authors performed TRIM28 ChIP on ZMYM2-depleted cells, but identified no changes over three transposons. It remains unclear if H3K9me3 levels are altered. ZMYM2 is a transcriptional repressor known to bind to the post-translational modification SUMO2/3. It has been implicated in the silencing of genes and transposons in a variety of contexts, but lacking sequence-specific DNA binding, little is known about how it is targeted to specific regions. At least two reports indicate association with TRIM28 targets  but no physical association with TRIM28 targets had been demonstrated. Tsusaka 2020 theorizes an indirect, potentially SUMO-independent, interaction via ATF7IP and SETDB1.Here, Owen and colleagues show that a subset of ZMYM2-binding sites in U2OS cells are clearly TRIM28 sites, and further find that hundreds of genes are silenced by both ZMYM2 and TRIM28. They next demonstrate that ZMYM2 homes to chromatin, and interacts with TRIM28, in a SUMOylation-dependent manner, suggesting that ZMYM2 is recognizing SUMOylation on TRIM28 or a protein associated with TRIM28. ZMYM2 separately homes to SINE elements bound by the ChAHP complex in an apparently SUMOylation independent manner. Although this is not the first report to show physical interaction between ZMYM2 and ChAHP, it is the first to show that ZMYM2 homes to ChAHP-binding sites and functions as a corepressor at these sites. Finally the authors demonstrate that ZMYM2 and TRIM28 coregulate genic targets by inducing repression at LTRs within the same TADs as the genes in question.Overall, the manuscript is well-written, convincing, and fills a significant hole in our understanding of ZMYM2's mechanistic function. The revised version of this manuscript addresses all of my previous concerns well. The following is the authors’ response to the original reviews.Thank you for your recent editorial decision on our manuscript. I have included a revised version of our manuscript in which we have addressed all of the required editorial and referees’ comments as requested. In summary, we have added substantial amounts of new data and analysis , amended several figures , added a new supplementary Table (Table S2) and we have changed the text and figure labelling/presentation in appropriate places to clarify or correct the issues raised by the reviewers.In summary, we firmly believe that we have addressed all the outstanding issues in a positive manner and that the manuscript is now suitable for publication in eLife. I look forward to receiving your final editorial decision on this manuscript.eLife assessment:ZMYM2 is a transcriptional corepressor but little was known about how it is recruited to chromatin. This study reveals that ZMYM2 homes to distinct classes of retrotransposons bound by the TRIM28 and ChAHP complexes in human cells, an important finding in the field of transcriptional regulation. The evidence supporting the claims of the authors is solid, although inclusion of more functional data would have strengthened the original model proposed.We have taken all the comments on board and provided additional new experimental data where requested and more data analysis to substantiate our claims.Reviewer #1 (Public Review):Owen D et al. investigated the protein partners and molecular functions of ZMYM2, a transcriptional repressor with key roles in cell identity and mutated in several human diseases, in human U2OS cells using mass spectrometry, siRNA knockdown, ChIP-seq and RNA-seq. They tried to identify chromatin bound complexes containing ZMYM2 and identified known and novel protein partners, including ADNP and the newly described partner TRIM28. Focusing mainly on these two proteins, they show that ZMYM2 physically interacts with ADNP or TRIM28, and co-occupies an overlapping set of genomic regions with ADNP and TRIM28. By generating a large set of knockdown and RNA-seq experiments, they show that ZMYM2 co-regulates a large number of genes with ADNP and TRIM28 in U2OS cells. Interestingly, ZMYM2-TRIM28 do not appear to repress genes directly at promoters, but the authors find that ZMYM2/TRIM28 repress LTR elements and suggest that this leads to gene deregulation at distance by affecting the chromatin environment within TADs.A strength of the study is that, compared to previous studies of ZMYM2 protein partners, it investigates binding partners of ZMYM2 using the RIME method on chromatin. The RIME method makes it possible to identify low-affinity protein-protein interactions and proteins interactions occurring at chromatin, therefore revealing partners most relevant for gene regulation at chromatin. This allowed the identification of novel ZMYM2 partners not identified before, such as TRIM28.The authors present solid interaction data with appropriate controls and generated an impressive amount of datasets  that are important to understand the molecular functions of ZMYM2. These datasets were generated with replicates and will be very useful for the scientific community. This study provides important novel insights into the molecular roles of ZMYM2 in human U2OS cells.The authors could have been more precise in the manuscript title and abstract to emphasize that these findings apply to human cells, as indeed there is no demonstration yet that the findings presented here can be transposed to mouse cells.We have slightly changed the title and abstract to emphasise that the findings are in human cells.The manuscript's main conceptual advance is that the authors propose a novel model of gene regulation whereby transcriptional repressors of transposable elements could regulate genes at distance by modulating the local chromatin environment within TADs. Additional experiments would be needed to strengthen this model. For example the authors could have performed TRIM28 ChIP in ZMYM2-kd cells to test if ZMYM2 favors the recruitment of TRIM28 to its genomic targets, as well as ChIP-seq of repressive chromatin marks (such as H3K9me3) in ZMYM2-kd cells to investigate if the loss of ZMYM2 leads to reduced H3K9me3 in ERVs and over large regions surrounding the ERVs.We have tested whether ZMYM2 is required for TRIM28 binding at several loci and find no evidence for this . We now discuss this in the results text and discussion where we already suggested that TRIM28 is likely recruited by KRAB-zinc finger proteins and ZMYM2 is subsequently recruited to this complex. Future extensive work is required to understand the mechanistic functions of ZMYM2 in these regions.Reviewer #2 (Public Review):In this study the authors investigate functional associations made by transcription factor ZMYM2 with chromatin regulators, and the impact of perturbing these complexes on the transcriptome of the U2OS cell line. They focus on validating two novel chromatin-templated interactions: with TRIM28/KAP1 and with ADNP, concluding that via these distinct chromatin regulators, ZMYM2 contributes to transcriptional control of LTR and SINE retrotransposons, respectively.Strengths and weakness of the study:The co-localization of ZMYM2 with ADNP and TRIM28 is validated through RIME, ChIP-seq and co-IP. .This is not correct as the co-IP experiments between endogenous ZMYM2 and TRIM28 were not performed in the presence of cross linkers. They did have NEM added, but this was to inactivate SUMO proteases rather than to cross link proteins.It is good that uniquely-mapped reads are used in the ChIP-seq analysis given the interest in repetitive elements. Likewise, though the RT-qPCR data in Fig5 should be complemented by analysis of the RNA-seq data that the authors already have, it seems that the primers are carefully designed such that a single retrotransposon copy is amplified.We re-analysed our RNA-seq data using the TEtranscripts tool and looked at TE transcription genome-wide. However very few TEs were expressed at high enough levels to get any statistically significant additional data beyond a few additional transposable elements. This likely results from the relatively low read depth we used and the lack of specific protocols being followed to preserve TE transcripts. We will return to the genome-wide effects in future studies where we plan to switch cell types and will generate more bespoke datasets . We added additional text to the results section to indicate that we could not see widespread deregulation of subclasses of TEs but that this needs further work.https://reprint-apms.org/) show that TRIM28 is identified in 466 / 716 AP-MS experiments with a mean spectral count of 16. While this does not indicate that the ZMYM2-TRIM28 interaction is not 'true', it would have been helpful to further dissect the interaction to strengthen this conclusion. For example, it would be nice to see the co-IP (fig 3A) repeated from the cells expressing the ZMYM2 mutant that is no longer competent to bind SUMO . Alternatively - if the model is that ZMYM2 recruits SUMOylated TRIM28 with well-characterized TRIM28 mutants that lack SUMOylation.The top-scoring interactors are highly-abundant nuclear proteins: for example, data from the contaminant repository for affinity purification mass-spec data ; new Supplementary Fig. S3C) shows reduced binding to TRIM28 in co-IP assays. This further strengthens the (SUMO-dependent) association between ZMYM2 and TRIM28.The transcriptional response using bulk RNA-seq in ZMYM2-depleted cells is rather gene-centric despite the title of the paper being about TE transcription. In fact the only panels about TE transcription are the RT-qPCR data in Fig 5D,F. I may be missing something (and there aren't many details given about the RNA-seq experiments) but why not look at TE transcription in an unbiased way with the transcriptomic data at hand? I appreciate potential hazards of multi-mapping etc but it would be interesting to see at least some subfamily analysis (e.g. using the TEtranscripts tool). On a similar point, why not show some RNA-seq in the genome browser snapshots of the epigenomics - together with a RepeatMasker annotation track of TEs...See response to the same point above.While the results broadly support the authors' conclusions, I have the overall impression that the central claim of TE transcriptional regulation by ZMYM2 could be strengthened a lot with some fairly straightforward additional experiments and analyses.Reviewer #3 (Public Review):ZMYM2 is a transcriptional repressor known to bind to the post-translational modification SUMO2/3. It has been implicated in the silencing of genes and transposons in a variety of contexts, but lacking sequence-specific DNA binding, little is known about how it is targeted to specific regions. At least two reports indicate association with TRIM28 targets  but no physical association with TRIM28 targets had been observed. Tsusaka 2020 theorizes an indirect, potentially SUMO-independent, interaction via ATF7IP and SETDB1.Here, Owen and colleagues show that a subset of ZMYM2-binding sites in U2OS cells are clearly TRIM28 sites, and further find that hundreds of genes are silenced by both ZMYM2 and TRIM28. They next demonstrate that ZMYM2 homes to chromatin, and interacts with TRIM28, in a SUMOylation-dependent manner, suggesting that ZMYM2 is recognizing SUMOylation on TRIM28 itself. ZMYM2 separately homes to SINE elements bound by the ChAHP complex, in an apparently SUMOylation independent manner. Although this is not the first report to show physical interaction between ZMYM2 and ChAHP, it is the first to show that ZMYM2 homes to ChAHP-binding sites and functions as a corepressor at these sites.The mode by which ZMYM2 and TRIM28 coregulate genic targets remains somewhat unclear. TRIM28/ZMYM2 bind to LTR elements, loss of these proteins results in upregulation of genes distal to (but in the same TAD as) these binding sites.Overall, the manuscript is well-written, convincing, and fills a significant hole in our understanding of ZMYM2's mechanistic function.We thank the referee for his/her positive evaluation of the mechanistic insights we provide. We have further added to these through addressing the specific issues raised in their “recommendations for authors”.Recommendations for the authors:The reviewers appreciated the novelty of the findings, and in particular, the use of the RIME method to identify the protein partners of ZMYM2 while bound on chromatin, and multiple validation steps of these novel ZMYM2 interactors. However, they also felt that the model presented at the end of the manuscript seems preliminary and would deserve additional experiments to be really supported, the essential ones being listed below:1 - Despite the claimed scope of the manuscript on TE regulation, their expression analysis is limited to RT-qPCR and targeted to a few families or copies. Please use the RNA-seq data generated in U2OS cells depleted for ZMYM2 to assess retrotransposon expression genome-wide, performing both family-level and copy-level analyses, and compare with TRIM28-depleted U2OS cells.We re-analysed our RNA-seq data using the TEtranscripts tool and looked at TE transcription genome-wide. However very few TEs were expressed at high enough levels to get any statistically significant additional data beyond a few additional transposable elements. This likely results from the relatively low read depth we used and the lack of specific protocols being followed to preserve TE transcripts. We will return to the genome-wide effects in future studies where we plan to switch cell types and will generate more bespoke datasets . We added additional text to the results section to indicate that we could not see widespread deregulation of subclasses of TEs but that this needs further work.2 - Clarify the relationship between dysregulated genes and TAD boundaries, as this seems important to support the model of distant gene regulation by the action of ZMYM2 on local chromatin environment within TADs (see comment of Reviewer #1 and #3).We have now provided further support for the idea that ZMYM2 functions within TADs as detailed below in response to the reviewers comments. New bioinformatics analysis has been done which is incorporated into the paper in Fig. 4D and Supplementary Fig. S4C.3 - Perform TRIM28 ChIP-seq in ZMYM2-kd cells, to prove that ZMYM2 indeed participates to TRIM28 recruitment to TE loci. This could be complemented by H3K9me3 ChIP-seq, to see if ZMYM2 depletion reduces H3K9me3 at retroytransposons, and over the regions surrounding ERVs. This last experiment seems also important for reinforcing the distant regulation model of nearby genes through ZMYM2-mediated repression of retrotransposons.As suggested by the referees below, we have tested whether ZMYM2 is required for TRIM28 binding at several loci and find no evidence for this . We now discuss this in the results text and discussion where we already suggested that TRIM28 is likely recruited by KRAB-zinc finger proteins and ZMYM2 is subsequently recruited to this complex. Future extensive work is required to understand the mechanistic functions of ZMYM2 in these regions.Reviewer #1 (Recommendations For The Authors):Figure S1D is not clear. The authors want to investigate if ADNP and ZMYM2 regulate gene expression in the same directionality. They compare the genes down in siADNP and up in siZMYM2 (or vice versa) and show very small overlaps. If I understand correctly, this shows that very few genes are regulated in opposite directions by ADNP and ZMYM2 and consequently that they tend to regulate genes in the same directionality. This is not what is said in the text page 19 ("with no clear common roles as either an activator or repressor") and should be clarified. Furthermore, to compare if ADNP and ZMYM2 regulate genes in the same directionality, there are better ways to represent this, for example scatter plots of log2 FC in ADNP kd vs ZMYM2 kd. Similar criticisms apply to Fig S3F.We agree that the text could be clearer and have rewritten it as “….although the large numbers of genes directionally co-regulated by these two proteins (ie either positively or negatively) indicates no clear common role as either an activator or repressor”. We have also added a scatter plot to the supplementary data  to further emphasise the common directionality of effect as suggested by the reviewer. Similarly, we changed the text and have added a scatter plot to support the conclusions on ZMYM2 and TRIM28 functional interactions .The authors suggest an indirect control of genes by ZMYM2 within TADs . Yet Fig 4C does not seem to address this point. Fig 4C shows that TADs with a ZMYM2/cluster 1 peak contain more upregulated than downregulated genes, but the key question should be: are upregulated genes significantly enriched in TADs containing a ZMYM2/cluster 1 peak compared to other TADs or other genomic regions?We have taken this suggestion on board and determined the frequency distribution of the number of TADs containing a gene upregulated  following ZMYM2 depletion. 10,000 iterations were performed by randomly selecting 216 TADs across all 3062 TADs. The observed number of TADs containing an upregulated gene (42) from 216 TADs containing a cluster 1 ZMYM2 peak is a clear outlier in this distribution  .A key question not addressed in the manuscript is whether ZMYM2 participates in the recruitment of TRIM28 to ERVs. I recommend performing TRIM28 ChIP in ZMYM2-kd cells.We have tested whether ZMYM2 is required for TRIM28 binding at several loci and find no evidence for this . We now discuss this in the results text and discussion where we already suggested that TRIM28 is likely recruited by KRAB-zinc finger proteins and ZMYM2 is subsequently recruited to this complex. Future extensive work is required to understand the mechanistic functions of ZMYM2 in these regions.Reviewer #2 (Recommendations For The Authors):Please give more details of RNA-seq analyses in the experimental section .We have now expanded on the description of the RNA-seq analysis including adding in the mapping statistics to a new Supplementary table. We followed the referee’s useful suggestion of looking at TE transcription genome-wide. However very few TEs were expressed at high enough levels to get any statistically significant additional data. This likely results from the relatively low read depth we used and the lack of specific protocols being followed to preserve TE transcripts. We will return to the genome-wide effects in future studies where we plan to switch cell types and will generate more bespoke datasets .Reviewer #3 (Recommendations For The Authors):Major Comments:The relationship of TRIM28/ZMYM2 repression of LTRs and silencing within/between TADs is interesting but underdeveloped. Upon ZMYM2 depletion, the authors observe simultaneous upregulation of genes within TADs more often than would be expected by chance, but this analysis does not distinguish "proximal to" from "in the same TAD". If a ZMYM2 binding site is X bases from a gene TSS, is it more likely to regulate that gene if it is in the same TAD? This can and should be tested bioinformatically.The basic question the referee is asking is whether ZMYM2 affects gene expression at a certain distance irrespective of whether the TSS of the gene is in the same TAD. We have now tested this and added text to the results section. Basically we took all of the ZMYM2 regions associated with genes upregulated by ZMYM2 depletion that resided in the same TAD and calculated the peak to TSS distance. Then we searched in the opposite direction for the TSS of genes at a similar distance (+/-25%) that resided in an adjacent TAD. We then asked whether these genes were upregulated by ZMYM2 depletion. 102 ZMYM2 peaks were positioned within these distance constraints with at least one gene in an adjacent TAD . Of these genes, only 11 were upregulated following ZMYM2 depletion. There is therefore not a general spreading of deregulation around ZMYM2 peaks in a distance-dependent manner.Furthermore, the authors note in the text and discussion that LTRs can demarkate TAD boundaries, but this is a distinct concept from the idea that they regulate genes within a TAD. Is there evidence that ZMYM2 binding sites are found at TAD boundaries?We have provided more evidence to support the associations of ZMYM2 peaks with TADs and now show that they are closer than randomly expected to TAD boundaries . However they are clearly not all located very close to the boundaries.The analysis of transposons expression was limited to qPCR of a handful of elements. Since the authors have conducted RNA-seq of U2OS cells depleted for both TRIM28 and ZMYM2, they can determine if certain classes of transposons are globally upregulated.We re-analysed our RNA-seq data using the TEtranscripts tool and looked at TE transcription genome-wide. However very few TEs were expressed at high enough levels to get any statistically significant additional data. This likely results from the relatively low read depth we used and the lack of specific protocols being followed to preserve TE transcripts. We will return to the genome-wide effects in future studies where we plan to switch cell types and will generate more bespoke datasets . We added additional text to the results section to indicate that we could not see widespread deregulation of subclasses of TEs but that this needs further work.Minor Comments:Typo: "human HEK393 cells". They are HEK293 cells.We have corrected this error."These ADNP peaks showed enrichment of binding motifs for several transcription factors with the top two motifs for HBP1 and IRF both found in over 35% of target regions ." According to Ostapcuz 2018, ADNP has its own motif (CGCCCYCTNSTG). It is intriguing that this does not appear enriched in ADNP sites in U2OS cells, this seems worthy of comment.This is a good point, so we did an additional search using the motif found in Ostapcuk 2018 and found this in 15% of ADNP binding regions. This value is substantially lower than the 63% seen previously. It therefore is present but is not the dominant motif. This new data and its implication regarding chromatin targeting mechanisms is now discussed in the Results section around Fig. 1D.Figures S2F and S2G are central to the paper and belong in the main text.We have now added these to the main figures as requested .A supplementary table including libraries generated and mapping statistics should be included.We have now added this (new Supplementary Table S2)


Chlamydomonas reinhardtii has shown great potential as a host for biotechnological exploitation. However, low expression of nuclear transgenes is still a problem and needs to be optimized. In many model organisms, viral promoters are used to drive transgene expression at high levels. However, no viruses are known to infect Chlamydomonas, and known viral promoters are not functional. Recently, two different lineages of giant viruses were identified in the genomes of Chlamydomonas reinhardtii field isolates. In this work, we tested six potentially strong promoters from these viral genomes for their ability to drive transgene expression in Chlamydomonas. We used ble, NanoLUC, and mCherry as reporter genes, and three native benchmark promoters as controls. None of the viral promoters drove expression of any reporter gene beyond background. During our study, we found that mCherry variants are produced by alternative in-frame translational start sites in Chlamydomonas. We show that this problem can be overcome by mutating the responsible methionine codons to codons for leucine and by using the 5′-UTR of βTUB2 instead of the 5′-UTRs of PSAD or RBCS2. Apparently, the βTUB2 5′-UTR promotes the use of the first start codon. This could be mediated by the formation of a stem-loop between sequences of the βTUB2 5′-UTR and sequences downstream of the first AUG in the mCherry reporter, potentially increasing the dwell time of the scanning 40S subunit on the first AUG and thus decreasing the probability of leaky scanning.Microalgae biotechnology has the potential to produce high quality bioproducts in a sustainable manner. Here,  Chlamydomonas reinhardtii (Chlamydomonas) is gaining increasing interest as a eukaryotic host to produce high value products and biopharmaceuticals. Chlamydomonas produces recombinant proteins well, folds them correctly, modifies them post-translationally and can secrete them [Chlamydomonas grows both photoautotrophically and heterotrophically in simple and inexpensive media, which can be scaled up in photobioreactors, making this organism particularly attractive for commercial applications [Chlamydomonas is the low expression of nuclear transgenes. Several approaches have helped to overcome this problem, such as the use of optimal codon usage, the use of promoters capable of counteracting gene silencing, the integration of introns into the coding sequence and the use of expression strains with defects in transgene silencing [Chlamydomonas, native promoters are commonly used to drive transgene expression [PSAD promoter and the chimeric HSP70A-RBCS2 promoter (AR) [AβSAP(i) promoter. This promoter is derived from a fusion of the HSP70A and βTUB2 promoters and harbors synthetic cis motifs as well as the βTUB2 5′-UTR, into which the first RBCS2 intron was integrated [Chlamydomonas and therefore promoters of viruses infecting other organisms were tested. These promoters showed at most weak activity in Chlamydomonas [Chlamydomonas were identified [Algavirales and Imitervirales, which belong to the so-called giant viruses of the phylum Nucleocytoviricota. The Nucleocytoviricota have ds-DNA genomes with sizes that can exceed 500 kb and contain 400 to 2500 genes [Chlamydomonas are called Giant Endogenous Viral Elements (GEVE) and are several hundred kilobases in length, harboring most of the hallmark genes and thus indicating that near-complete genomes are present [Chlamydomonas at 64% [Imitervirales contains about 910 genes, including those encoding enzymes of various DNA repair mechanisms, replication and transcription, and metabolic processes as well as genes encoding proteins of the virion. Unlike the Imitervirales, the Algavirales do not encode an endogenous RNA polymerase and use that of the host. Some of the most common proteins are the major capsid proteins (MCPs), suggesting a high expression level of the genes encoding them and thus genes with strong promoters [The unicellular green alga ete them ,3,4,5,6.ications . One limilencing ,9,10,11.pression . The moster (AR) ,14. Receydomonas ,18,19,20entified . These o00 genes ,23. The  present ,24,25. Ts at 64% ,26. The romoters .Chlamydomonas is the host of at least two distinct lineages of giant viruses. In this study, we tested six promoters of genes encoding capsomeres and RNA polymerases from these GEVEs for their capability to drive transgene expression in Chlamydomonas and compared them with three native benchmark promoters. Unfortunately, none of the viral promoters was able to drive the expression of any of the three tested reporter genes. During our studies, we could show that shorter isoforms of mCherry are produced from alternative translational start sites and that this can be overcome by mutating the methionine codons or by using different 5′-UTRs.E. coli TOP10 cells were used for heat-shock-mediated transformation of plasmid DNA. Selection was done on LB agar plates containing either 100 µg/mL spectinomycin (Level 0) or 50 µg/mL kanamycin (Level 2). The Chlamydomonas UVM4 strain [−2 s−1.Chemically competent 4 strain  was grow4 strain  at a con8 cells were mixed with 1 µg of linearized plasmid DNA, vortexed for 15 s and carefully spread on agar plates containing either 2 µg/mL zeocin (ble modules) or 100 µg/mL spectinomycin (NanoLUC and mCherry devices). After overnight incubation in the dark, plates were transferred to ~40 µmol photons m−2 s−1 and zeocin resistant colonies were counted after 8 days.Transformation was done using glass beads . For thiAlgavirales Capsid-Protein genes (AVCP1 and AVCP2) and from the large subunit of the DNA-Polymerase B gene (AVDPol) as well as two promoters from Imitervirales Capsid-Protein genes (IVCP1 and IVCP2) and the gene encoding the large subunit of the RNA-Polymerase (IVRPol) were selected from Giant Endogenous Viral Elements [BbsI recognition sites creating GGAG and AATG fusion sites according to the MoClo syntax for plant genes [BbsI and T4 DNA ligase (level 0) giving rise to pMBS978 (AVCP1), pMBS979 (AVCP2), pMBS980 (AVDPol), pMBS981 (IVCP1), pMBS982 (IVCP2) and pMBS983 (IVRPol). The level 1 ble modules were assembled into destination vector pICH47732 [PSAD promoter+5′-UTR), A1-B2_pMBS96 (AR promoter+5′-UTR), A1-B2 AβSAP(i) promoter+5′-UTR(i) [ble(i) CDS) and B6-C1_pCM0-119 (RPL23 3′-UTR). Level 2 devices for mCherry and NanoLUC reporters were generated by assembling the same parts as well as B3-B4 pCM0-063 (NanoLUC CDS), B3-B4_pCM0-067 (mCherry(i) CDS), and B5_pCM0-100 (3xHA CDS) into destination vector pMBS807 [Chlamydomonas.Promoter sequences from two Elements . Promotent genes ,31. EachmCherry(i) reporters were generated by site-directed mutagenesis using B3-B4_pCM0-067 (mCherry(i) CDS) as a template. For substituting the methionine codon with a leucine codon, the following oligonucleotides with flanking BbsI sites (bold) were used: M10L-for: TTGAAGACAAcTcGCCATCATCAAGGAGTTCATGCG and M10L-rev: TTGAAGACAACgAgGTTATCCTCCTCGCCCTTGCT, M17L-for: TTGAAGACAAcTcCGCTTCAAGGTGCACATG and M17L-rev: TTGAAGACAAGgAgGAACTCCTTGATGATGGCCATG, M23L-for: TTGAAGACAAcTcGAGGGCTCCGTGAACG and M23L-rev: TTGAAGACAACgAgGTGCACCTTGAAGCGCAT. PCR products were purified using the Machery–Nagel clean up kit following the manufacturer’s instructions. Purified PCR products were digested with BbsI and ligated to create level 0 parts. The sequences for B3-B4_pMBS1033 (mCherry(i)-M10L CDS), B3-B4_pMBS1034 (mCherry(i)-M17L CDS) and B3-B4_pMBS1035 (mCherry(i)-M23L CDS) were verified by Sanger sequencing. To exchange the 5′-UTRs of the PSAD and AR promoters with the βTUB2 5′-UTR, level 0 parts A1-A3_pCM0-001 (PSAD promoter), A1-A3_pCM0-002 (AR promoter) and B1-B2_pCM0-033 (βTUB2 5′-UTR) were used.Mutated NanoLUC reporter of one transformation event were pooled and grown to mid-logarithmic phase. A total of 1.5 × 107 cells were harvested and resuspended in 3 mL fresh TAP medium yielding a suspension with 5 × 106 cells/mL. From this suspension, 50 µL (2.5 × 105 cells) were transferred into a white 96-well plate. Finally, 50 µL Nano-Glo in its buffer (Promega) were added and NanoLUC activity was measured with a FLUOstar Omega plate reader (BMG Labtech) at 460 nm. Data were analyzed by subtracting the background signal from the recipient strain (UVM4) and normalizing to the bioluminescence signal from the PSAD-NanoLUC reporter.All spectinomycin-resistant colonies containing the mCherry reporter of one transformation event were pooled and grown to mid-logarithmic phase. Then, 107 cells were harvested and resuspended in 200 µL of a 100 mM MES/Tris buffer, pH 7.0. The 107 cells were transferred into a black/clear bottom 96-well plate. Cells were centrifuged for 2 min at 30× g and 25 °C, and fluorescence signals were measured with a FLUOstar Omega plate reader (BMG Labtech) using 584 nm excitation and 620 nm emission filters. Data were analyzed by subtracting the background signal from the recipient strain (UVM4) and normalizing to the fluorescence signal from the PSAD-mCherry(i) reporter.All spectinomycin-resistant colonies containing the Whole-cell protein extraction, SDS-PAGE, semi-dry blotting and immunodetections were performed as described previously . Total cImitervirales GEVE in our study. Since Algavirales do not encode an RNA polymerase, we included the promoter of the large subunit of the DNA polymerase B (AVDPol) for comparison. The promoter sequences comprised sequences from 485 to 519 bp upstream of the translational start codon   for theirt codon .Chlamydomonas MoClo kit [PSAD, AR and AβSAP(i) were combined with one of the following three reporter genes: (1) The antibiotic resistance marker ble, which encodes the ShBle protein. The latter binds and inactivates Bleomycin which causes DNA double strand breaks. The resistance level to Bleomycin correlates with the expression level of the ShBle protein, thus allowing a fast and simple read-out of promoter activity [ble and mCherry contain the first RBCS2 intron (i), whereas NanoLUC contains no intron. We added a sequence encoding a triple-HA tag to the NanoLUC and mCherry(i) reporter genes for detection on Western blots. The RPL23 terminator was used in all constructs, as it confers constant expression levels with different transgene combinations [NanoLUC and mCherry(i) reporters additionally contained the aadA cassette conferring resistance to spectinomycin. In total, 27 reporter constructs were assembled  activity ,36. (3) activity   reporter constructs was transformed six times, and zeocin-resistant colony-forming units (CFU) were counted and normalized to the numbers obtained with the PSAD-ble(i) reporter. Normalized counts revealed that, in contrast to the benchmark promoters, none of the viral promoters drove ble expression to levels high enough to confer resistance to zeocin above background (AβSAP(i) promoter performed significantly better than the PSAD promoter, while differences between AR and the other promoters were not significant.Each of the ckground A. Of theNanoLUC reporter constructs resulted in approximately 1200 spectinomycin-resistant colonies each, since expression of the aadA marker was independent of the promoter used to drive the NanoLUC reporter. To average out position effects for the NanoLUC reporter, all colonies of an individual transformation event were pooled [PSAD-NanoLUC reporter. As was observed for the ble reporter, none of the viral promoters drove NanoLUC expression to levels high enough to produce luminescence above background (AβSAP(i) promoter performed significantly better than the AR promoter, which again performed significantly better than the PSAD promoter.Transformation of the e pooled ,38. Threckground B. Of theNanoLUC reporter constructs, approximately 1200 spectinomycin-resistant transformants generated with each mCherry(i) reporter construct were pooled. Pools from three independent transformation events were further divided into three replicates before mCherry fluorescence was measured. Signals were normalized to those obtained for the PSAD-mCherry(i) reporter. As we observed for the ble and NanoLUC reporters, none of the viral promoters drove mCherry expression to levels high enough to produce fluorescence above background (AβSAP(i) promoter performed significantly better than the AR and PSAD promoters, between which no significant differences were observed.As for the ckground C. Of thePSAD- and AR-mCherry(i) reporters, but not in pools generated with the AβSAP(i)-mCherry(i) reporter  reporters, but not in M17L or M23L mutants. However, another protein band with an even smaller protein occurred in M10L mutants (arrowhead b). This protein band was more prominent in transformant pools harboring the PSAD-mCherry(i) reporter than in transformant pools harboring the AR-mCherry(i) reporter. We assume that it results from alternative translation initiation at M17 and would therefore not give rise to functional mCherry [The mCherry CDS contains three additional in-frame codons for methionine that are located 10, 17 and 23 codons downstream of the regular start codon A. While revisiae ,40. To t mCherry .PSAD- and AR-mCherry(i) reporters but not with the AβSAP(i)-mCherry(i) reporter? We hypothesized that the βTUB2 5′-UTR in the AβSAP(i)-mCherry(i) reporter construct disfavors the use of translational start codons downstream of the first AUG, while the use of alternative start codons is favored by the PSAD and RBCS2 5′-UTRs. To test this idea, we replaced the PSAD and RBCS2 5′-UTRs in the PSAD- and AR-mCherry(i) reporters with the βTUB2 5′-UTR. As shown in So far, our data indicate that the smaller proteins derive from internal translational start sites. However, why do they occur only with the Finally, we measured mCherry fluorescence in transformant pools generated with the engineered reporter constructs to reveal adverse effects of the engineering steps on reporter performance. Most strikingly, we found strongly reduced mCherry fluorescence in transformant pools producing the M17L mCherry variant C, suggesChlamydomonas promoters, none of the six viral promoters tested was able to drive expression of three different reporter genes  promoter drove the expression of three and two reporter genes to significantly higher levels than the PSAD and AR promoter, respectively (AβSAP(i) promoter contains the first RBSC2 intron in its 5′-UTR [AβSAP(i) promoter is due to the additional intron. In any case, the AβSAP(i) promoter represents a functional unit with superior performance on various reporter genes containing  or lacking (NanoLUC) introns.When comparing the activity of the three native benchmark promoters, the ectively . Notice,s 5′-UTR ,12,41,42PSAD and RBCS2 5′-UTRs, but not in the context of the βTUB2 5′-UTR, where only the first AUG was used (A/C)(C/A)AUGG-3′ [RBCS2 and βTUB2 5′-UTRs, leaky scanning by an unfavorable “Kozak consensus” appears unlikely. Second, leaky scanning is promoted if the length of the 5′-UTR is <~20 nt [PSAD, RBCS2 and βTUB2 comprise 43 nt, 23 nt, and 120 nt, respectively. Hence, the short length of the RBCS2 5′-UTRs could promote leaky scanning. Since RBCS2 is one of the most abundantly produced proteins in Chlamydomonas, inefficient translation initiation by a too short 5′-UTR of RBCS2 also seems unlikely. Third, initiation efficiency can be influenced by secondary structures in the 5′-UTR. A strong stem-loop structure just downstream of the first AUG will stall the scanning 40S subunit, increasing its “dwell time” on the first AUG, and thus decreasing the probability of leaky scanning [βTUB2 5′-UTR, but not with sequences from the PSAD and RBCS2 5′-UTRs AUGG-3′ . Since t <~20 nt . The 5′-scanning . Indeed,scanning  predicte 5′-UTRs .PSAD and RBCS2 5′-UTRs (where the M10 variant occurs) are not different from the signals produced by the corresponding constructs with βTUB2 5′-UTRs (where the M10 variant does not occur) , thus preserving its function as a reporter for promoter activity. The fluorescence signals produced by the constructs with t occur) C. Hence,PSAD and RBCS2 5′-UTRs by the βTUB2 5′-UTR solved the leaky scanning problem for the mCherry reporter (While replacing the reporter B, other