Chlamydomonas reinhardtii under nitrogen deprivation is of special interest due to its resulting increment of triacylglycerols (TAGs), that can be applied in biotechnological applications. However, this same condition impairs cell growth, which may limit the microalgae’s large applications. Several studies have identified significant physiological and molecular changes that occur during the transition from an abundant to a low or absent nitrogen supply, explaining in detail the differences in the proteome, metabolome and transcriptome of the cells that may be responsible for and responsive to this condition. However, there are still some intriguing questions that reside in the core of the regulation of these cellular responses that make this process even more interesting and complex. In this scenario, we reviewed the main metabolic pathways that are involved in the response, mining and exploring, through a reanalysis of omics data from previously published datasets, the commonalities among the responses and unraveling unexplained or non-explored mechanisms of the possible regulatory aspects of the response. Proteomics, metabolomics and transcriptomics data were reanalysed using a common strategy, and an in silico gene promoter motif analysis was performed. Together, these results identified and suggested a strong association between the metabolism of amino acids, especially arginine, glutamate and ornithine pathways to the production of TAGs, via the de novo synthesis of lipids. Furthermore, our analysis and data mining indicate that signalling cascades orchestrated with the indirect participation of phosphorylation, nitrosylation and peroxidation events may be essential to the process. The amino acid pathways and the amount of arginine and ornithine available in the cells, at least transiently during nitrogen deprivation, may be in the core of the post-transcriptional, metabolic regulation of this complex phenomenon. Their further exploration is important to the discovery of novel advances in the understanding of microalgae lipids’ production.The metabolism of the model microalgae  Chlamydomonas reinhardtii (C. reinhardtii) has been an important model species for molecular characterisation studies using omics data, especially transcriptome, proteome and metabolome. Over the last decade, these studies revealed several nuances of the C. reinhardtii metabolism and aspects of importance for biotechnological applications, such as lipids’ accumulation and biomass production  was  project . Both ceurviving  and BlabC. reinhardtii were retrieved  proteins or metabolites at the time point 24 h -N relative to the time point 0 h, during which cells were still under N-replete conditions in each study  by Park C. reinhardtii DNA sequences 1500 bp upstream of each transcription starting site (TSS) for all annotated genes were retrieved from the genome sequence (annotation v.5.6)  and trimmed if a neighbouring gene was found within the vicinity of this region. The putative promoter regions (up to 1500 bp) were retrieved, and the promoter region of the differentially expressed genes was retrieved and common DNA motifs were queried using the MEME tool (EME tool . The maxThree groups of promoter sequences were organised: (1) gene promoters from Arg metabolism-related genes encoding upregulated proteins ; (2) genC. reinhardtii promoterome  and performed a gene promoter motif in silico analysis on target gene subsets. Our meta-analysis of omics data reinforced the knowledge of the effects of -N into the central metabolism  and suggested a significant role of amino acids’ synthesis and degradation and related metabolic pathways, Arg-related in particular, as possible regulatory nodes of the stress response leading to TAGs accumulation and cell growth arrest. Subsequently, we identified the conserved common promoter DNA sequence motifs from co-expressed genes, giving us a further understanding of the regulatory mechanisms of target pathways at the transcriptional level.Most amino acids had their relative abundance diminished in response to -N, while the enzymes of the Arg biosynthesis increased. Though arginine, asparagine, glutamine and ornithine , togetheSeveral amino acid catabolic enzymes were upregulated in one of the investigated datasets during the 24 h under the -N condition  Suppl. . The uprODC1 ,,43,34,43CHLAMYDOMONAS-SPECIFIC FAMILY U PROTEIN . This protein family may contain the PUA (PseudoUridine synthase and Archaeosine transglycosylase) domain and a highly conserved RNA-binding motif, likely involved in post-transcriptional modification and ribosome degradation [C. reinhardtii cells under -N, likely regulating protein degradation together with the proteasome machinery.Of note, our results showed five proteins containing all six M1–M6 conserved promoter motifs in their genes which belong to the protein family radation . This grC. reinhardtii. However, we recognise that our analysis is limited because it is mostly based on transcriptomics data. Moreover, the proteomic  brought insightful expression patterns. The upregulation of putrescine and GABA biosynthesis during early -N  suggestsnhardtii ,65. As pnhardtii , takes pnhardtii ,67), enenhardtii  and C:N nhardtii ,67,68,69and AOF6  in respon plants ,71,72, hn plants ,74 and etabolism , and to tabolism ,77.NO production in Chlamydomonas has been related to the degradation of cytochrome b6f in cells under -N, a process that has been shown to be regulated by the nitrate reductase (NIT1) enzyme, which requires the function of nitrite reductase (NAR1) to modulate the intracellular availability of NO and ammonium produced from the nitrite uptake. However, the nitrate reductase function seems to be dispensable for NO production in cells under -N conditions . In our C. reinhardtii stress responses , and itiewed in ,36,80. Tiewed in ,81,82,83C. reinhardtii.Therefore, the omics reanalysis we performed indicates that NO might be produced in the cells by another pathway and function as a second messenger of the TAGs biosynthesis in cells under mixotrophy and -N conditions, considering that cells have one or more nitrate reductases that are non-expressed or non-induced, with a reduced capacity to generate NO via Nit1 . FurtherGLB1) [GLB1 transcripts were upregulated in early -N datasets  , a well-GLB1) ), glutamGLB1)  and actiGLB1) ,87, whicdatasets  and the datasets  togetherdatasets . This copression ; howeverwn route . This reC. reinhardtii lipid metabolism (such as ACP1) are prone to nitrosylation [C. reinhardtii.NO is also recognised by indirectly repressing the function of the NIT2 transcription factor, the known master regulator of nitrate assimilation ,81, modusylation , suggestsylation ,81,83,90C. reinhardtii brought new insights into the role of specific amino acids in the metabolic regulation of the TAGs synthesis, indicating that the initial and early responses of the cell metabolism directly affect cell growth through the modulation of the Arg catabolic pathway and consequently polyamine synthesis, suggesting that the transient accumulation of Arg activates a sequence of signalling events that, in the second moment of the response, initiate a series of molecular events that induce TAGs accumulation. This is likely through the effect of NO as a signalling molecule.In conclusion, the careful reanalysis of omics data from the studies of -N in Moreover, our omics reanalyses and the identification of conserved promoter motifs suggest the existence of regulatory modules coordinating the transition to TAGs accumulation in late -N, indicating that Arg metabolism and N assimilation are transcriptionally co-regulated with different regulatory modules likely coordinating protein degradation, amino acids transport and catabolism and signalling events. These results suggest that TAGs accumulation can be controlled without interfering with the function of regulatory modules of cell growth, and this brings hope for improving the further biotechnological large-scale production of microalgae lipids and biomass. The metabolic engineering of the Arg catabolism pathway towards inducing TAGs accumulation without the need for -N or any stress-induced condition may be the next necessary breakthrough for expanding microalgae industrial applications.


We developed a web application, available at cissector.nki.nl, that lets researchers easily perform this analysis as a starting point for their research into any promoter of interest.Classic promoter mutagenesis strategies can be used to study how proximal promoter regions regulate the expression of particular genes of interest. This is a laborious process, in which the smallest sub-region of the promoter still capable of recapitulating expression in an ectopic setting is first identified, followed by targeted mutation of putative transcription factor binding sites. Massively parallel reporter assays such as survey of regulatory elements (SuRE) provide an alternative way to study millions of promoter fragments in parallel. Here we show how a generalized linear model (GLM) can be used to transform genome-scale SuRE data into a high-resolution genomic track that quantifies the contribution of local sequence to promoter activity. This coefficient track helps identify regulatory elements and can be used to predict promoter activity of any sub-region in the genome. It thus allows  The level at which each gene in the human genome is expressed is dictated by a complex interplay between transcription factors and gene-specific regulatory elements encoded in the DNA. Since almost every gene requires a distinct pattern of expression across cell types, the architecture of both the promoter region proximal to the transcription start site (TSS) and the more distal enhancer regions varies widely in terms of the identity, strength, and spatial arrangement of the transcription factor (TF) binding sites they contain .Although enhancers are undoubtedly important to control the expression of most genes, it has been estimated that nearly 50% of the variance in genome-wide gene expression in a given cell type may be explained by proximal promoter sequences . Thus, iMore recently, massively parallel reporter assays (MPRAs) were introduced that can test large numbers of reporter constructs simultaneously ,10. MPRAhttps://cissector.nki.nl) that enables researchers to inspect these profiles, and to view predictions of intrinsic promoter activity for any region in the human genome. This visualization can be used to easily identify the minimal promoter region that retains most of the full promoter activity. Our tools provide a rational starting point for detailed functional dissection of individual promoters.We previously reported proof-of-principle  that genGenome-wide SuRE experimental data were taken from , to whicFor validation of our GLM models, a second SuRE dataset (GEO accession GSE206935) was generated in K562 cells from a focused SuRE library consisting of fragments from four bacterial artificial chromosomes  containing four genomic regions covering several housekeeping genes . Mapping errors cause a small number of BAC fragments to map to positions outside the original BAC regions; these elements were excluded from our analysis.We used a generalized linear model (GLM) based on the Poisson family, with a logarithmic link function guaranteeing that the expected value of the count of each element is positive, which is well-suited for modeling sequencing counts in a regime where the expected counts are low. Note that for the Poisson distribution the expected variance in the count for each element is equal to the expected count itself, i.e. all variation is due to the sampling error associated with the discrete count distribution, as opposed to other sources of variation.The GLM framework of CISSECTOR allows for the inclusion of both spatial and non-spatial covariates. Non-spatial covariates can include any other information about individual elements that may influence the observed counts. For the GLM used in this paper, the model took the following form:wherej, contributes to the predicted mean of element Here, j, for all elements i that contain bin j, and to zero otherwise. Finally, for the purpose of making predictions outside the training set, the predicted effect of including any additional single genomic base pair in the reporter construct was taken as Theoretically, at a high enough library complexity, the number of spatial covariates for a given strand could approach or equal the combined length of the areas with non-zero coverage, with a uniform bin length of one base-pair and Various non-spatial covariates can be included in a CISSECTOR fit. The ones used in this paper reflect the particular structure of the SuRE dataset. de count , possiblglmnet . Each of the eight SuRE library datasets contained three K562 biological replicates, two HepG2 biological replicates, and one iPCR replicate. One replicate was selected from each library to generate three distinct K562 datasets and two distinct HepG2 datasets, which were then fit separately. Correlations between the resulting tracks were then computed. Because only a single iPCR replicate is available, these fits were based on the same iPCR counts. As these input counts are shared across cell types, they cannot explain the difference between intra-cell-type and inter-cell-type correlations.We selected TFs for which we had both a motif and ChIP data from ENCODE available. As a source of TF motifs, we used a curated set of position weight matrices from Ref. . These wIn SuRE Figure , millioncis-regulatory influences on reporter fragment expression by modeling the expected expression count as the product of contributions from every base-pair position contained within a genomic fragment. CISSECTOR builds upon an earlier prototype model  map of hypothetical reporter fragment expression. This is illustrated in Figure P < 10−16) between measured and predicted activities , while others encode almost all their activity in the first 100 bp upstream (e.g. GBAP1). For other promoters, such as BIRC5, while a specific fragment of the promoter is predicted to maximize expression, either extending or reducing it is expected to reduce activity.Our genome-wide coefficient tracks and 2D visualization of expression across all possible promoter fragments can be used to explore the diversity of promoter architectures in the human genome. Examples are shown in Figure r = 0.39) suggests that there are substantial differences between the cell lines. To confirm that this correlation reflected cell type-specific differences, we split the dataset for each cell type by biological replicates  and generated separate fits with each replicate for the same subset of the genome. Correlations between replicates from the same cell type (mean r = 0.60) were consistently and significantly  higher than correlations between replicates from different cell types (mean r = 0.39); for details, see We also investigated differences in the coefficient profiles between K562 and HepG2 cells. While fit separately, the SuRE data for these two cell types were generated with exactly the same fragment libraries, and therefore the coefficient tracks could be compared directly. The modest genome-wide correlation between the respective coefficient tracks across both strands .Through GLM-based analysis of SuRE data we have derived fine-grained genome-wide coefficient tracks that can be used to analyze and predict promoter activity across the human genome. These coefficient tracks and the derived 2D plots highlight the subregions of each promoter that are most likely to be relevant for its functioning, and provide clues about what the effect might be of introducing mutations at a specific location. Our data-driven CISSECTOR improves interpretability of SuRE data and helps reduce intrinsic noise due to low read counts for individual unique SuRE fragments. At any given locus, SuRE data will include reporter counts for many overlapping fragments. This makes them a natural source of information for predicting the boundaries and activity of promoter-associated regulatory elements. However, we had to address some technical challenges associated with extracting accurate predictions from SuRE results: High-expression SuRE elements must contain functional promoter regions, but may be accompanied by flanking non-functional regions which could be removed without reducing overall expression. The expression of individual elements can also be highly variable due to transfection-related drop-out, natural cell heterogeneity, and other sources of experimental noise, making single-element counts fairly inaccurate predictors of reporter expression. Additionally, even high-coverage SuRE libraries cannot provide an exhaustive survey of all possible start/end combinations at a given locus, making it difficult to predict the expression of unobserved elements. CISSECTOR overcomes these challenges by summarizing information from all observed elements in terms of a single genome-wide predictive coefficient track.Although with some examples we tried to explain cell-type-specific differences in the GLM track between K562 and HepG2 cells, we stress that the model we used is purely based on sequence geometry in terms of start and end positions of fragments. It does not explicitly consider the base sequence of the DNA, and as such, its purpose is solely to locally deconvolve the SuRE data so as to achieve precision on a spatial scale much smaller than the size of a typical SuRE fragment or the width of a typical SuRE coverage peak Figure . The maiApproaches that explicitly try to explain functional genomics data such as ChIP-seq, ATAC-seq, DNA-seq or MPRA in terms of the underlying DNA sequence, with the goal of predicting activity for unseen or mutated sequences, such as deep learning algorithms  address The current SuRE-based GLM predictions also have limitations. Because the SuRE assay is based on transiently transfected plasmids, not all detected signals necessarily reflect promoter activity in the native genomic context. For example, some promoters are repressed by heterochromatin in their natural context, or substantially dependent on distal enhancers. Nevertheless, the genome-wide correlation of about 0.5 between SuRE activity and endogenous promoter activity  suggestsIn this study we focused on promoters. However, SuRE can also be used to study enhancers, because most enhancers drive low-level expression, and this activity correlates with other measures of enhancer activity . Furtherhttps://cissector.nki.nl) using which researchers interested in a particular promoter region can easily browse the coefficient tracks and inspect our predictions of transcriptional activity for many different truncations of the same promoter regions (Figure We discussed several examples of how our understanding of promoters can be gained by inspecting the CISSECTOR coefficient tracks in the context of other genomic features. However, we are aware that our analyses just skim the surface of what might be learned from our GLM tracks. Expert domain knowledge is required to properly interpret them in the right context. To facilitate other researchers, we have designed a webtool (s Figure .https://github.com/BussemakerLab/CISSECTOR and https://doi.org/10.5281/zenodo.7750893.The SuRE data that were used for validation are available under GEO accession GSE206935. Our code is available at gkad232_Supplemental_FileClick here for additional data file.


Rho gene that codes for the visual pigment rhodopsin. The Rho gene is robustly expressed by rod photoreceptors of the retina, and essential for the subcellular structure and visual function of rod photoreceptors. Mutations in RHO cause severe vision loss in humans. We found that each Rho regulatory region can independently mediate local epigenomic changes, but only the promoter is absolutely required for establishing active Rho chromatin configuration and transcription and maintaining the cell integrity and function of rod photoreceptors. To our surprise, two Rho enhancers that enable strong promoter activation in reporter assays are largely dispensable for Rho expression in vivo. Only small and age-dependent impact is detectable when both enhancers are deleted. Our results demonstrate context-dependent roles of enhancers and highlight the importance of studying functions of cis-regulatory regions in the native genomic context.Enhancers function with a basal promoter to control the transcription of target genes. Enhancer regulatory activity is often studied using reporter-based transgene assays. However, unmatched results have been reported when selected enhancers are silenced in situ. In this study, using genomic deletion analysis in mice, we investigated the roles of two previously identified enhancers and the promoter of the  Both classes can be defined by the presence of active histone modifications; specifically, promoters with H3K4me3 and H3K27Ac marks6, and enhancers with H3K27Ac and H3K4me1 marks7. Studies have further classified the regulatory units based on the landscape and density of the enrichment of these histone modifications. Super enhancers and broad H3K4me3 domains13 are hence defined as genomic regions regulating the specific gene expression that is imperative to a cell’s state and function.Transcriptional regulation is a tenet of cellular development and homeostasis. Each cell type uses a unique set of regulatory sequences, namely promoters and enhancers, to enact expression of specific genes. A gene’s promoter is the DNA region immediately upstream of the transcription start site (TSS). Enhancers are often found distally upstream or downstream of a gene’s coding region and modulate the promoter activity. The cooperative regulation by promoter and enhancers is important for directing gene transcription within a specific cell type at a precise timing and an appropriate level. In particular, promoters and enhancers contain accessible regions of sequence-specific binding sites for cell-type specific and general transcription factors (TFs). Base-content analysis indicates the differences between the two classes: promoters display high-GC content and contain the requisite TATA box, initiator sequences and binding sites for a variety of TFs; enhancers are often AT-rich and contain binding sites for cell-type specific TFs16. Rod photoreceptors make synaptic connections at the outer plexiform layer (OPL) with horizontal and bipolar cells that reside in the inner nuclear layer (INL) to transfer light-evoked signals further to the brain via ganglion cell axons that comprise the optic nerve. The first step of phototransduction is accomplished within a unique cellular structure called the outer segment (OS) which consists of a stack of membrane discs packed with the light-sensing visual pigment, Rhodopsin19.Rod and cone photoreceptors are the cell types of the retina that mediate the conversion of light photons into electrical signals in a process called phototransduction. In mouse and human retinas, rod photoreceptors reside within the outer nuclear layer (ONL) and comprise 70% of all retinal cellscis-retinal that accounts for the absorption properties. Rhodopsin is also required for OS structural integrity21. Mutations in the human Rhodopsin gene (RHO) cause blinding diseases, such as retinitis pigmentosa (RP) and night blindness25. The Rho gene is tightly regulated and highly transcribed, with its mRNA representing the most abundant transcript in rod photoreceptors28. Irregular Rho expression, either at lower or higher than normal levels, can lead to photoreceptor degeneration in animal models31. Mechanisms of the precise regulation on Rho expression have been extensively investigated. Initial studies identified the Rho proximal promoter region (PPR), a conserved ~ 200 bp region immediately upstream of Rho TSS, which was sufficient to drive the expression of the LacZ reporter in mouse photoreceptors33. Addition of a conserved genomic region, the Rho enhancer region (RER) lying 2 kb upstream of PPR, greatly increased PPR-mediated LacZ expression33.Rhodopsin is a G-protein-coupled receptor consisting of an opsin apo-protein and its chromophore 11-cis-regulatory elements (CREs) within PPR are detailed as sequence motifs bound by photoreceptor-specific TFs, including Cone-Rod Homeobox (CRX)35 and Neuroretina Leucine Zipper (NRL)38. ChIP-seq targetome analysis has discovered that CRX and NRL cooperatively bind not only to PPR and RER, but also to a region 4 kb upstream of Rho TSS, which is designated as CRX-Bound Region 1 (CBR)40. Both CBR and RER could further activate a PPR-driven reporter by eight- to tenfold in retinal explant reporter assays39. CRX/NRL synergistically activated PPR activity in transient HEK293 cell transfection assays, and loss of CRX or NRL in mice caused reduction in Rho expression and defects to rod photoreceptor development and maintenance42. In Crx-knockout mice, photoreceptor cells failed to gain appropriate active chromatin configuration at cis-regulatory regions of CRX-dependent genes, including PPR, RER and CBR of the Rho locus43, implicating the role of these regions in epigenomic remodeling of Rho expression.Individual RER and Rho PPR/gene body44. These findings collectively suggest that CBR and RER are Rho enhancers, which potentially act by mediating local epigenomic changes to increase PPR activity. However, it is unknown if such enhancer-promoter interactions are necessary for Rho expression in vivo. More specifically, it remains to be determined if these two previously identified Rho enhancers are essential for the epigenomic modulation and transcriptional activation of the Rho gene, and what degree of regulation each contributes.In vivo chromosome conformation capture assays (3C) have illustrated CRX/NRL-dependent intrachromosomal loop interactions between Rho enhancers (CBR and RER) and promoter (PPR) using a loss-of-function approach in mice. By delivering CRISPR/Cas9 and specific guide RNAs (gRNAs) to C57BL/6J mouse embryos, we generated mouse lines carrying < 500 bp deletions at each genomic region individually and a line carrying a paired deletion of both CBR and RER. We performed gene expression analyses and cellular phenotype characterization for each mouse line at various ages. Our results indicate that, as expected, the Rho promoter PPR is absolutely required for Rho expression, rod photoreceptor integrity and survival. However, to our surprise, the two enhancers (CBR and RER) collectively play only minor roles in Rho expression, which is only applicable in developing and aging retinas. CBR/RER deficiency did not largely impact rod development and maintenance up to one year of age. Together, our results suggest that Rho proximal promoter is necessary and sufficient for Rho expression in rod photoreceptors, while Rho enhancers are largely dispensable. Our findings highlight the importance of investigating the function of cis-regulatory regions in the native genomic context.In order to fill this knowledge gap, we aimed to dissect and determine in vivo functional roles of the Rho promoter and enhancers in Rho transcription, 4 mouse lines carrying a knockout of each genomic region were generated using CRISPR-Cas9/gRNA mediated excision. Based on the locations of CRX ChIP-seq peaks39, a pair of gRNAs targeting each peak at Rho promoter and two enhancer regions were introduced together with Cas9 enzyme into C57BL/6J mouse embryos to generate a 200–400 bp deletion at each region. A mouse line lacking both enhancers (CBR−/−RER−/−) on the same allele was made using a sequential targeting approach, i.e., RER deletion on the CBR−/− allele by introducing RER gRNAs and Cas9 enzymes to the CBR−/− embryos. PCR combined with sanger sequencing confirmed the absence of each region with the indicated length of deletion: 217 for PPR−/− (retaining the TATA box and transcription start site), 234 bp for RER−/− and 491 bp for CBR−/−  using Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq). In wildtype (WT) retina, rod photoreceptor specification is completed by P14 and the Rho gene is fully expressed; this timing is consistent with epigenetic remodeling of the Rho locus measured by ATAC-seq and DNAse I hypersensitivity profiling49. Duplicate ATAC-seq experiments on P14 whole retinas of WT and each of the four knockout mouse lines, PPR−/−, CBR−/−, RER−/−, and CBR−/−RER−/− yielded highly reproducible results , including the peaks at CBR, RER, PPR, and two intragenic regions (INT and 3ʹUTR). The PPR−/− retina lost ATAC-seq signal not only at Rho promoter as expected, but also at INT and 3ʹUTR . Furthermore, PPR removal also caused a significant reduction of ATAC-seq signal upstream of the promoter, though the reduction was more profound at the proximal RER than at the more distal CBR . Similar to the case of PPR−/−, global changes in chromatin accessibility were not observed in single- or double-enhancer knockout samples  analysis was performed to report the high-resolution chromatin profiling on P14 retinal samples. Two validated antibodies specific to these histone marks were used in CUT&Tag experiments. In WT retina, H3K4me3 peaks showed a broad domain encompassing the gene body, starting at the PPR and extending to the final exon of Rho gene , but the signal was absent at CBR and RER regions. Analysis of mutants showed strikingly different landscapes: PPR−/− retina lost H3K4me3 deposition at the Rho locus while all enhancer-knockout samples showed comparable H3K4me3 marks to the WT control . Inspection of the 50 kb region surrounding Rho confirmed that no other H3K4me3 sites were affected in the mutants . In PPR−/− retina, H3K27ac signal was missing or greatly reduced at PPR, INT and 3ʹUTR, but retained WT levels at CBR and RER, implying CBR and RER are capable of loading H3K27ac marks in the absence of PPR . In RER−/− retina, H3K27ac signal was similar to the WT control, besides a drop at the deleted RER region . However, in CBR−/− retina, a compensatory increase in H3K27ac signal was observed at RER, INT and 3ʹUTR, as well as at a few regions outside the Rho locus , possibly suggesting coordination or compensation between enhancers. In CBR−/−RER−/− retina, no change in H3K27ac signal was found at PPR, suggesting that the epigenetic activation state of the Rho promoter is unaltered by CBR and RER knockouts . Collectively, H3K27ac CUT&Tag results suggest that Rho promoter and enhancers can independently modify their histone tails to establish an active epigenetic environment and there is little crosstalk between these three regulatory regions in establishing the local epigenetic state. Altogether, these CUT&Tag data indicate that PPR is absolutely required for establishing the chromatin configuration and active histone marks at the accessible regions of the Rho locus, especially at the promoter and intragenic regions, but Rho enhancers are largely dispensable for this remodeling.H3K27ac CUT&Tag analysis displayed 5 distinct peaks at cus Fig. C, CBR−/−Rho expression, we performed next-generation RNA sequencing (RNA-seq) on P14 retinal samples of mutant and WT mice. Principal component analysis (PCA) showed that PPR samples were transcriptionally distinct from all other samples, evidenced by their distinct clustering , suggesting that no compensatory regulation or feedback from the intact allele when one PPR allele is lost. In contrast, RER−/− retina had comparable Rho expression to the WT control, while CBR−/− retina reduced Rho expression to roughly 70–80% of the WT level , suggesting that CBR, not RER, makes a small contribution to Rho expression at P14. CBR−/−RER−/− retina showed reduced Rho expression to approximately 70–80% of the WT level, similar to, or slightly lower than that of CBR−/− retina . These results suggest that the regulatory capacity of Rho enhancers, specifically CBR, contributes 20–30% of Rho expression. When a copy of RER was present in Rho enhancer regions (CBR−/−RER+/−), Rho expression generally matched with those in CBR−/− and CBR−/−RER−/− retinas, collectively suggesting that RER is not required for Rho expression . When a copy of CBR was present in Rho enhancer regions (CBR+/−RER−/−), Rho expression reached to an indistinguishable level as the WT control , suggesting that at least one copy of CBR is needed to achieve maximum-level Rho expression in the presence of two PPR copies. Lastly, Rho expression in a retina heterozygous for PPR and Rho enhancers was comparable to that of PPR+/− retina, i.e., about 50% to the WT control . These results together indicate that PPR is absolutely required for Rho expression and the regulatory functions of Rho enhancers are detectable and secondary to PPR in retinal development.We further investigated the degree by which promoter and enhancer knockouts impacted Rho enhancers functioned to maintain Rho expression in adult retina. qRT-PCR experiments were performed with enhancer-knockout retinas up to 1 year-old (1YO). Rho expression in RER−/− retina always remained indistinguishable from that in the WT control . Rho expression in CBR−/− retina initially reached about 70–80% of the WT level at P14 but increased steadily to a comparable level after the completion of retinal development and throughout adulthood . Interestingly, although Rho expression in CBR−/−RER−/− retina was as low as 70–80% to the WT control at P14, the relative Rho expression profile became comparable from P21 to 4-month-old (MO). However, at 6MO, the relative Rho expression dropped back to 70–80% of WT expression and persisted to 1YO . Overall, these results suggest that Rho enhancers possess age-dependent regulatory activity to control Rho expression during rod differentiation and aging, but this regulation is only responsible for 20–30% of the overall expression.Furthermore, we tested if rol Fig. C, RER−/−Rho expression in P7 retinas prior to eye opening. The immunohistochemistry (IHC) staining , while CBR−/−RER−/− and PPR+/− retinas maintained well-laminated structures . No mislocalized cells were observed in the mutants. OS length of CBR−/−RER−/− and PPR+/− retinas appeared shorter than that of the WT control . Therefore, the slightly shorter OS of CBR−/−RER−/− retina is associated with reduced Rho expression. On the other hand, the decreased ONL thickness of PPR+/− retina indicates loss of rod photoreceptors. Furthermore, visual function of mutant retinas was assayed by dark-adapted (rod response) electroretinography (ERG). As expected, PPR−/− mice had no ERG responses, while PPR+/− mice had decreased dark-adapted A-wave and B-wave amplitudes at high-light intensities as compared to the WT control  showed similar results as PPR mutants: ONL thickness of PPR−/− retina degenerated dramatically after 6WK . PPR+/− retina began to show ONL degeneration at 6MO but reduced OS length at 3MO, similar to that of Rho+/− retina .Rod morphological and functional changes in mutant retinas were also assessed at early adult ages. At 6 weeks (6WK) and 3MO, OS length of the Rho enhancers, namely CBR and RER, and of Rho promoter in regulating Rho expression. Previous reporter-based assays showed that both the RER and CBR were capable of enhancing Rho promoter activity by eight- to tenfold in postnatal mouse retina39. However, this study shows that knockout of each or both enhancers surprisingly produce null or only moderate impact (20–30% loss) on endogenous Rho expression. Notably, RER, that was previously identified by a study with transgenic mice33 and known to interact with PPR by 3C assay44, fails to display any necessity for endogenous Rho expression in developmental and adult ages. Consistent with their minor roles, single Rho enhancer knockout also shows limited impact on rod photoreceptor identity, health, and function. These results are in contrast to the essential function of the Locus Control Region (LCR) in its regulation of red/green (R/G) cone opsin expression54. The conserved LCR is absolutely required for mutually exclusive expression of red vs green opsin in the respective cone subtype in humans, mouse, and zebrafish models. RER and R/G LCR share a conserved core region of similar TF binding motifs, including CRX binding sites33. Several models might explain the minimal relevance of Rho enhancers in situ, but high activity in reporter assays: firstly, the strength of a promoter might determine its cooperation with enhancers. Rho promoter (PPR) is highly robust and sufficient to drive cell type-specific expression of target genes both in episomal vectors39 and at ectopic genomic locations33. Future studies are needed to determine the relative strength of Rho promoter vs other photoreceptor gene promoters and how the promoter strength influences functional interactions with specific enhancers. Secondly, dynamic expression and precision might also be a determinant. Genes coding for cell fate-determining transcription factors with complex spatial and temporal expression patterns often harbor strong distal enhancers or enhancer clusters, such as those found in Pax655, Otx258, Vsx259, Atoh760 and Blimp161. Rho expression, despite being one of the highest expressed genes in the retina, follows a simple induction curve during rod photoreceptor development62, which may not require a prominent enhancer but a strong self-functioning promoter. Finally, results of reporter assays may have exaggerated Rho enhancer activities, as reporter gene constructs do not contain the correct genomic context, including native gene structure and epigenomic landscape. Overall, the findings of this study highlight the importance to validate functions of reporter-identified enhancers in a native genomic context.Using CRISPR-Cas9/gRNA mediated excision, this study examined the specificity and strength of two Rho enhancers. During postnatal development, CBR clearly plays a dominant role, as it is responsible for 20–30% Rho expression, while RER’s activity was dispensable. Thus, CBR, but not RER, is required for appropriate Rho transcription during development. However, this minor contribution of CBR does not affect the development of rod subcellular structure and function. In adult retinas, deletion of Rho enhancers results in no detectable effects in young and mid ages, but after 6 MO, RER/CBR knockout causes a loss of 20–30% of Rho transcription. These suggest that CBR and RER play a redundant role in maintaining high-level Rho transcription in aged retinas. Similar “shadow” or redundant enhancers have been reported elsewhere, such as individual enhancers in β-globin LCR, Pax6 lens enhancers and Atoh7 distal enhancer66. These enhancers mostly act during tissue/organ genesis and are required for regulating the expression timing of specific genes. Our discovery of redundant enhancers in aged retina provides a new insight into enhancers’ roles in aging and tissue-specific maintenance. Consistently, CBR/RER knockout mutants affected Rho expression as well as morphological and functional integrity at 6MO. Since PPR+/− (50% Rho reduction) produced a more severe phenotype than CBR/RER knockout mutants (20–30% Rho reduction) in aged adults, our results confirm that rod homeostasis requires precise control of Rho expression. Aged rod photoreceptors with disrupted Rho enhancer activity may be more susceptible to genetic and/or environmental insults. Since RHO coding mutations are linked to congenital stationary night blindness and retinitis pigmentosa24, sequence variants in the non-coding regions could potentially affect the expression of the mutant (or WT) allele and thus contribute to phenotype severity or age of onset. Future studies in mouse models and phenotype-genotype correlations in humans are required to determine if RHO enhancer deficiency complicates (or modifies) defective phenotypes under disease/stress conditions, particularly in progressive rod photoreceptor degeneration.This study shows age-dependent activities of Rho upstream regulatory sequence. Changes of these characteristics in mutants, in general, agreed with the impact of each region on Rho transcription. In particular, loss of ATAC signals at Rho gene body was only seen in PPR−/−, but not in enhancer knockout mutants, highlighting the interdependency and necessity of PPR in epigenomic modulation during Rho transcription activation. It is notable that only loss of PPR caused dramatic depletion of H3K4me3 from the locus, suggesting a specific reliance on the PPR and/or transcription for this histone modification. Deleting CBR and/or RER enhancers had limited impact on the epigenomic landscape of the Rho locus, despite the 20–30% contribution to transcription, suggesting that the two enhancers do not act in a “trans” manner on DNA accessibility, histone modifications, or chromatin configuration. Most likely, they promote RNA polymerase II activity at the promoter and gene body via CRX/NRL-dependent looping interactions44. A similar interactive pattern of predominant promoter versus subsidiary enhancers has been previously reported67. Although CBR and RER deletions are insufficient to elicit large changes in Rho expression and DNA accessibility of Rho PPR, this study cannot exclude the existence or importance of more distal Rho-specific CREs in regulating chromatin dynamics at the Rho PPR region. One potential candidate is a region(s) located in the distal intron of the neighboring lft122 gene . Importantly, Rho enhancers at the Rho locus also share remarkably conserved topography between the two species in terms of CRE number and relative locations71. The profile of transcription factors bound to Rho enhancers and promoter is also conserved72. While numerous non-synonymous and nonsense variants within the Rho coding region have been found to cause human retinal disease, no regulatory variants have yet been discovered. This study suggests that future efforts should be primarily focused on the proximal promoter region, particularly at critical TF binding motifs. Distal regulatory variants within the RER and CBR are unlikely to cause pathogenic transcriptional deficits for severe developmental defects. These variants however could be genetic modifiers of other pathogenic mechanisms or independently yield minor but detectable effects on visual function and alter long-term photoreceptor health.The mouse Rho enhancers on in vivo Rho transcription, and their redundant activity in rod functional maintenance in aging retinas. These findings contribute to our understanding of enhancer functions and mechanism of action in vivo and highlight the importance of dissecting individual enhancer functions and interactions in the native genomic context at various ages.In conclusion, this study has described unexpectedly minor roles of two previously identified C57BL/6J background. Both male and female mice were used in experiments. All animal procedures were conducted according to the ARRIVE guidelines (https://arriveguidelines.org), and were approved by the Washington University in St. Louis Institutional Animal Care and Use Committee .All mice in this study were on the CBR, RER, PPR in order to delete these elements  flanking each element of see Fig. . The gRNEyes were enucleated at tested ages and fixed in 4% paraformaldehyde at 4 °C overnight for paraffin embedded sections. Each retinal cross-section was cut 5 microns thick on a microtome. Hematoxylin and Eosin (H&E) staining was used to examine retinal morphology.For IHC staining, sections firstly went through antigen retrieval with citrate buffer, and blocked with a blocking buffer of 5% donkey serum, 1% BSA, 0.1% Triton-x-100 in 1× PBS (pH-7.4) for 2 h. Sections were then incubated with primary antibodies at 4 °C overnight, then washed, followed by 2-h incubation of specific secondary antibodies. Primary antibody to Rho  or Gnat1  and secondary antibodies were applied with optimal dilution ratios. All slides were mounted with hard-set mounting medium with DAPI .2). The statistical analysis was done by two-way ANOVA with multiple pairwise comparisons (Tukey’s).ERGs were performed on adult mice at different ages using UTAS-E3000 Visual Electrodiagnostic System . Mice were dark-adapted overnight prior to the tests. The experimental procedures were previously established in our lab. ERG responses of biological replicates were recorded, averaged, and analyzed using Graphpad Prism 8 . The mean peak amplitudes of dark-adapted A and B waves and light-adapted B waves were plotted against log values of light intensities , Crx , Gnat1 . Data were analyzed with QBase software . The statistical analysis was done by Student’s t-test with p < 0.05, CI 95%.Each RNA sample was extracted from 2 retinae of a mouse using the NucleoSpin RNA Plus kit . RNA concentrations were measured using a NanoDrop One spectrophotometer (ThermoFisher Scientific). 1 μg of RNA was used to synthesize cDNA with First Strand cDNA Synthesis kit . The reaction master mix contained EvaGreen polymerase , 1 μM primer mix, and diluted cDNA samples. Samples were run using a two-step 40-cycle protocol on a Bio-Rad CFX96 Thermal Cycler . At least 3 technical triplicates were run for each gene. Primers (5ʹ–3ʹ) used in this study were 73. Mapped reads were cleaned using Samtools (v1.9.4), gene counts generated by HTseq (v0.11.2)74, and visualizations generated in R (v3.6.1).RNA was extracted from retinal samples using the NucleoSpin® RNA kit (Macherey–Nagel) using the manufacturer’s protocol. Total RNA integrity was determined using Agilent Bioanalyzer or 4200 Tapestation. Library preparation and sequencing experiments were performed at Genome Technology Access Center, Washington University in St. Louis. A brief description of the experimental procedures was outline as follows. Library preparation was performed with 5–10 μg of total RNA with a RIN score greater than 8.0. Ribosomal RNA was removed by poly-A selection using Oligo-dT beads . mRNA was then fragmented in reverse transcriptase buffer and heating to 94° for 8 min. mRNA was reverse transcribed to yield cDNA using SuperScript III RT enzyme  and random hexamers. A second strand reaction was performed to yield ds-cDNA. cDNA was blunt ended, had an A base added to the 3ʹ ends, and then had Illumina sequencing adapters ligated to the ends. Ligated fragments were then amplified for 12–15 cycles using primers incorporating unique dual index tags. Fragments were sequenced on an Illumina NovaSeq-6000 using paired end reads extending 150 bases. Reads were trimmed using Trim-Galore! (v0.6.7) a wrapper for Cutadapt (v3.4) and FastQC (v0.11.9). Trimmed reads were mapped to mm10 using STAR (v2.7.0)49, a slightly modified protocol to that published by Buenrostro et al.75. Briefly, retinas were dissected from P14 mice of each genotype and washed in PBS. Retinal cells were dissociated using 2% collagenase in TESCA buffer for 13 min at 37C. DNase I  was added for the final 3 min to minimize clumping. DMEM + 10% FBS was added to stop the reaction. Nuclei were stained with SYBR Gold (ThermoFisher Scientific) diluted 1:100 in trypan blue and counted using a fluorescent microscope. 50,000 cells were resuspended in TD buffer for a 1 h incubation at 37 °C with TDE1 . Remaining steps of library prep were performed as published by Buenrostro et al. Final libraries were pooled and sequenced using the Illumina 2500.ATAC-seq libraries were generated as published in Ruzycki et al.Retinal cells were dissociated and counted in same manner as described above for ATAC-seq. After quantification, cells were diluted, aliquoted and cryopreserved by adding DMSO to a final volume of 10% and slowly freezing to – 80 °C in a Mr. Frosty container. CUT&Tag reactions were performed using Epicypher reagents . Briefly, cells were thawed on ice and 100,000 bound to ConA beads for each sample. Primary antibody incubation was left at 4 °C overnight on a nutator. 0.5 μg of antibody was used for each reaction; H3K4me3  and H3K27Ac . 0.5ug secondary antibody  was incubated for 0.5 h at room temperature, and Tn5 binding and transposition done exactly as specified in CUTANA protocol. After transposition, custom 10 bp unique-dual-indexed primers were used to amplify transposed fragments  for 20 cycles. Libraries were cleaned and size-selected using Ampure XP beads. Libraries were pooled and sequenced using the Illumina NovaSeq 6000.77. Mapped reads were cleaned using Samtools (v1.9.4), Picard (v2.25.7), and Bedtools (v2.27.1). For visualization, reads were converted to bigwig format and compared using Deeptools (v3.5.1). ATAC-seq peak calling was accomplished using MACS2 (v2.2.7.1).Reads were trimmed using Trim-Galore! (v0.6.7) a wrapper for Cutadapt (v3.4) and FastQC (v0.11.9). Trimmed reads were mapped to the mm10 genome build using bowtie2 (v2.4.1)All animal procedures were conducted according to the Guide for the Care and Use of Laboratory Animals of the National Institute of Health and were approved by the Washington University in St. Louis Institutional Animal Care and Use Committee.Supplementary Figures.


KMT2C) histone methyltransferase occur in a range of solid tumors, and heterozygous deletions encompassing KMT2C occur in a subset of aggressive leukemias. Although MLL3 loss can promote tumorigenesis in mice, the molecular targets and biological processes by which MLL3 suppresses tumorigenesis remain poorly characterized. Here, we combined genetic, epigenomic, and animal modeling approaches to demonstrate that one of the mechanisms by which MLL3 links chromatin remodeling to tumor suppression is by co-activating the Cdkn2a tumor suppressor locus. Disruption of Kmt2c cooperates with Myc overexpression in the development of murine hepatocellular carcinoma (HCC), in which MLL3 binding to the Cdkn2a locus is blunted, resulting in reduced H3K4 methylation and low expression levels of the locus-encoded tumor suppressors p16/Ink4a and p19/Arf. Conversely, elevated KMT2C expression increases its binding to the CDKN2A locus and co-activates gene transcription. Endogenous Kmt2c restoration reverses these chromatin and transcriptional effects and triggers Ink4a/Arf-dependent apoptosis. Underscoring the human relevance of this epistasis, we found that genomic alterations in KMT2C and CDKN2A were associated with similar transcriptional profiles in human HCC samples. These results collectively point to a new mechanism for disrupting CDKN2A activity during cancer development and, in doing so, link MLL3 to an established tumor suppressor network.Mutations in genes encoding components of chromatin modifying and remodeling complexes are among the most frequently observed somatic events in human cancers. For example, missense and nonsense mutations targeting the mixed lineage leukemia family member 3 (MLL3, encoded by  TERT (telomerase reverse transcriptase), amplifications, or chromosomal gains encompassing the MYC oncogene, activating hotspot mutations in CTNNB1 (β-catenin), and inactivating mutations and deletions in the TP53 and CDKN2A tumor suppressor genes  is a deadly primary liver cancer with a 5 year survival rate of only 18% . HCC is es 2017; .MYC and inactivation of tumor suppressor p53 are known to cooperate to drive tumorigenesis in HCC  typically catalyze H3K4 monomethylation (H3K4me1) at enhancers  developed liver tumors, with a slightly later onset and slightly longer survival than mice receiving the Myc transposon combined with an sgRNA targeting Trp53  did not succumb to disease over the observation period  is a frequently mutated oncogene in human HCC  and Myc; sgTrp53 liver cancer cell lines. Compared to the sgTrp53 cells, sgKmt2c cells had a marked reduction in MLL3 chromatin binding at a subset of genomic loci  showed the most pronounced differences in chromatin modifications between the two liver tumor genotypes. In contrast, the loci in cluster 2 showed increased H3K4me1 and H3K27ac marks, most of which mapped to intergenic regions. The loci in cluster 3 showed increased H3K4me3 and included some p53 target genes such as Cdkn1a and Eda2r.Similar to the  complex , the mam complex . To dete sgTrp53 . Clusterfication . Loci inMyc; sgTrp53 cells compared to the Myc; sgKmt2c genotype. These data support a model whereby MLL3 binding to these loci facilitates the acquisition of a chromatin environment conducive for active gene transcription.To determine whether these drastic changes in the chromatin landscape were associated with changes in MLL3 binding, we integrated the chromatin modifications results with our MLL3 ChIP-Seq results . InteresMyc; sgTrp53 and Myc; sgKmt2c liver cancer cell lines described above. Despite the broad binding of MLL3 across the genome, we found only 248 differentially expressed genes (DEGs): 132 significantly upregulated  and 116 significantly downregulated  in Myc; sgKmt2c liver tumor cells compared to Myc; sgTrp53 controls.Transcriptional profiling helped hone in on potentially critical targets of MLL3. Specifically, we determined the output of these chromatin landscape changes by transcriptional profiling of the same set of Ccng1, Cdkn1a, and Zmat3 , Utx (Kdm6a), Mll1 (Kmt2a), and Mll2  tumor suppressors  expression, (2) H3K4me1/3 and H3K27ac levels, and (3) MLL3 binding at the Cdkn2a promoter compared with Myc; sgTrp53 cells  for cDNA transduction, we turned to the CRISPR activation (CRISPRa) system  system  in a hum or TP53 , which w2A locus  and trannscripts . TherefoCdkn2a products, INK4A and ARF, may contribute to the tumor suppressive activity of MLL3. In this regard, Myc overexpression in primary cells  often triggers apoptosis  with retroviral vectors encoding Myc linked to a reverse tetracycline transactivator (rtTA3), together with doxycycline (dox)-inducible Kmt2c shRNAs to enable reversible Kmt2c silencing (Myc in the presence of MLL3 (i.e. cells infected with Myc-rtTA3 and a dox-inducible shRNA targeting Renilla luciferase) acutely activated INK4A and ARF expression  significantly rescued colony-forming capacity and prevented cell death following Kmt2c restoration as determined by time-lapse microscopy of cells cultured with a fluorescent dye that stains dead cells , MLL3 (KMT2C), and MLL4 (KMT2D), the core catalytic components of the COMPASS-like complex, are all considered tumor suppressors, with frequent loss-of-function genomic alterations found in a broad spectrum of human cancers . While eactivity . Additioactivity . Our stuCdkn2a—showed MLL3/4 chromatin enrichment at gene promoters, and indeed, transcription of this class of genes was most affected by Kmt2c disruption. Interestingly, Kmt2c suppression preferentially limited the MLL3/4 enrichment at promoters and shifted residual complex binding toward intergenic regions. Such dynamic regulation of distinct cis-acting elements by the MLL3/4 complex has also been observed in other contexts  broadly repress gene expression in different cell types through the coordinated action of PRC1 and PRC2 complexes that deposit and maintain repressive H3K27me3 marks on the enhancers of target genes, including CDKN2A . Sequences of sgRNAs, shRNAs, and primers used in this manuscript are included in the Myc or 10 µg pT3-Ctnnb1 N90) with either 20 µg of pX330 expressing the indicated sgRNAs or 20 µg of pT3-EF1a-GFP-miRE plasmid together with CMV-SB13 Transposase (1:5 ratio). Mice were randomly assigned to experimental groups and injected with the 0.9% NaCl solution/plasmid mix into the lateral tail vein with a total volume corresponding to 10% of body weight in 5–7 s as described before . Each experiment was performed in mice from the same order. Arf-null animals (C57BL/6 background), originally provided by Dr. Charles Sherr, St. Jude Children’s Research Hospital, were maintained in our breeding colony. For HTVI, a sterile 0.9% NaCl solution/plasmid mix was prepared containing oncogene transposons (5 µg DNA of pT3-d before . InjecteMyc vector Addgene (#92046) and pT3-EF1a-GFP-miRE plasmid were described before ) with gentle shaking, washed with PBS, incubated for 5 min in 0.05% (w/v) trypsin, and washed and plated in complete DMEM , 1× penicillin/streptomycin) on collagen-coated plates . Primary cultures were passaged until visibly free from fibroblasts. Cell lines were authenticated on a routine basis using short tandem repeat profiling, as well as tested for mycoplasma contamination and immediately discarded upon a positive test.CRISPR-mediated insertions and deletions were detected by surveyor assay as directed by the manufacturer (Transgenomic/IDT). Briefly, after overnight lysis of primary tumors and cell lines at 37°C in buffer containing 0.4 mg/mL proteinase K, 10 mM Tris, 100 mM NaCl, 10 mM EDTA, and 0.5% SDS, pH 8.0, genomic DNA was extracted by isopropanol precipitation. ~250–500 bp regions flanking predicted CRISPR cleavage sites were PCR amplified with Herculase II taq polymerase, column purified (Qiagen), heated to 95°C, and slowly cooled to promote annealing of heteroduplexes. Following treatment with Surveyor nuclease, products were analyzed by electrophoresis on a 2% polyacrylamide gel. Primers used for surveyor assay are listed in KMT2C , was transduced by the lentivirus expressing nuclease-dead Cas9 (dCas9) fused with VPR  and sgRNMyc and conditionally suppress Kmt2c, LPCs were co-infected with a retroviral construct constitutively expressing both Myc and a reverse tet-transactivator (rtTA) (MSCV-Myc-IRES-rtTA) along with retroviral TRMPV vectors (MSCV-TRE-dsRed-miR30/shRNA-PGK-Venus-IRES-NeoR)  and dox (1 µg/mL) starting 2 days after infection. To introduce constitutively expressed shRNAs in the setting of inducible shKmt2c, retroviral MLPe vectors  were co-infected with MSCV-Myc-IRES-rtTa and TRMPVe-shKmt2c. Triple-infected cells were maintained in media with neomycin, puromycin (2 µg/mL), and dox 2 days post infection. Infected continuously proliferating cells were transitioned to growth in complete DMEM and maintained on collagen-coated plates.LPCs from E13.5–15.5 C57BL/6 embryos were isolated and grown in hepatocyte growth media (HGM) as previously described . To simuES-NeoR)  expressiKmt2c–expressing LPCs were grown in the presence or absence of dox, and after 5 days, cells were fixed with formalin and methanol and stained with 0.05% crystal violet. MEFs were fixed after 6 days with formalin and methanol and stained with 0.05% crystal violet.For measurement of cell proliferation, 5000 transduced and selected LPCs or MEFs were plated in triplicate in 6-well plates. Tetracycline-inducible shApoptosis was measured in LPCs via Annexin V staining according to the manufacturer’s instructions . 25,000 cells were grown with and without dox for 3 days, trypsinized, washed with Annexin-V binding buffer, and ~100,000 cells were incubated with Annexin-V APC and analyzed on an LSRII flow cytometer (BD).Myc and two independent, inducible Kmt2c shRNAs constitutively expressing shRNAs targeting Renilla luciferase or Cdkn2a (generated as detailed above). 1000 cells were plated on collagen-coated, 96 well, clear bottom imaging plates in media supplemented with 300 nM Draq7 (Invitrogen) with and without dox, in triplicate by genotype. 18 hr after plating cells, Venus  and Draq7 fluorescence was collected in two, 10× fields of each well every 15 min for 41 hr using an automated, high content microscope .Imaging was performed on LPCs immortalized by linked overexpression of Cdkn2a promoter (Histone ChIP was performed as previously described . Brieflypromoter  are listThe complete dataset is available at NCBI Gene Expression Omnibus (GSE85055), as well as the Dryad digital repository (doi:10.5061/dryad.7pvmcvdwm).7 cells per immunoprecipitation. Cells were collected, washed once with ice-cold PBS, and flash-frozen. Cells were resuspended in ice-cold PBS and cross-linked using 1% paraformaldehyde  for 5 min at room temperature with gentle rotation. Unreacted PFA was quenched with glycine  for 5 min at room temperature with gentle rotation. Cells were washed once with ice-cold PBS and pelleted by centrifugation (800 g for 5 min). To obtain a soluble chromatin extract, cells were resuspended in 1 mL of LB1  and incubated at 4°C for 10 min while rotating. Samples were centrifuged (1400 g for 5 min), resuspended in 1 mL of LB2 , and incubated at 4°C for 10 min while rotating. Finally, samples were centrifuged (1400 g for 5 min) and resuspended in 1 mL of LB3 . Samples were homogenized by passing seven to eight times through a 28-gauge needle, then Triton X-100 was added to a final concentration of 1%. Chromatin extracts were sonicated for 14 min using a Covaris E220 focused ultrasonicator. Lysates were centrifuged at 20,000 g for 10 min at 4°C, and 5% of the supernatant was saved as input DNA. Beads were prepared by incubating them in 0.5% BSA in PBS and antibodies overnight (100 μL of Dynabeads Protein A or Protein G [Invitrogen] plus 20 μL of antibody). The antibody was anti-MLL3/4, which was kindly provided by the Wysocka laboratory .Cell pellets were lysed in Laemmli buffer . Equal amounts of protein were separated on 12% SDS–polyacrylamide gels and transferred to PVDF(polyvinylidene difluoride) membranes . β-actin was used as a control to ensure equal loading, and images were analyzed using the AlphaView software (ProteinSimple). Immunoblotting was performed using antibodies for MYC , p53 , p19 , p16 , Axin1 , and β-actin . Source files of all western blots were provided for Actb mRNA expression and calculated using the ΔΔCt method. Each qRT-PCR was performed in triplicate using gene-specific primers (sequences listed in Table S1).Total RNA was isolated using RNeasy Mini Kit, QIAshredder Columns, and RNase-Free DNase Set (Qiagen). cDNA synthesis was performed using TaqMan Reverse Transcription Reagents (Thermo Fisher Scientific). Real-time PCR was carried out using Power SYBR Green Master Mix (Thermo Fisher Scientific) and the Life Technologies ViiA 7 machine. Transcript levels were normalized to the levels of mouse or human Myc; sgTrp53 and Myc; sgKmt2c) was isolated using RNeasy Mini Kit, QIAshredder Columns and RNase-Free DNase Set (Qiagen). RNA-Seq library construction and sequencing were performed according to protocols used by the integrated genomics operation Core at MSK. 5–10 million reads were acquired per replicate sample. After removing adaptor sequences with Trimmomatic, RNA-seq reads were aligned to GRCm38.91(mm10) with STAR  in MLL3 ChIP-seq and RNA-seq data were selected for the integrated analysis. Enriched pathways were scored using the enrichGO function with ‘biological process’ in the clusterProfiler R package. Redundant pathways were collapsed using the ‘simplify’ function with a cutoff of 0.7 with the p.adjust metric. Network analysis was performed using differential peaks and genes by running enrichplot::cnetplot in R with default parameters.KMT2C, CDKN2A, TP53, or RB1 in the TCGA HCC dataset were downloaded from Broad Institute TCGA Genome Data Analysis Center. To obtain transcriptional signatures of HCC with genomic mutations and deletions of either KMT2C, CDKN2A, and RB1, differential gene expression analyses were performed by DESeq2 (with TP53-mutated HCCs as controls). The oncoprints of homozygous deletions and somatic mutations of KMT2C, CDKN2A, and TP53, as well as MYC gains and amplifications from human HCC datasets , and in human HCCs with mutations in KMT2C vs TP53 . These signatures were compared to the transcriptional comparison of TCGA human HCCs with genomic inactivation of CDKN2A vs TP53.GSEA was performed using the GSEAPreranked tool for conducting GSEA of data derived from RNA-seq experiments (version 2.07) against other signatures. The metric scores  were calculated using the sign of the fold change multiplied by the inverse of the p-value . SpecifiData are presented as mean ± D or SEM as specified. The statistical comparison between two groups was accomplished with the two-tailed student’s t-test or one-way ANOVA followed by post hoc t-tests among three or more groups. The analyses for co-occurrence or mutual exclusivity of mutations were performed using Fisher Exact test. Comparisons of survival curves were performed by log-rank tests. All statistical tests were performed using the Prism 8 software. All data presented in the manuscript have been replicated in independent cohorts of mice or in at least three biological replicates for in vitro experiments. On the basis of predicted effects of oncogene-tumor suppressor interaction introduced by HTVI in mice, with a power of 0.8 and p<0.05, we calculated a minimum sample size of 5 mice per group. Animals within the same cage were randomly allocated into control and experimental groups, with the group assignment recorded in a master spreadsheet and unmasked only when all samples of the respective experiments were analyzed. Data collection of each experiment was detailed in the respective figures, figure legends, and methods. No data were excluded from studies in this manuscript. This paper convincingly shows that MLL3 regulates the CDKN2A tumor suppressor in MYC-driven liver cancers. The use of in vivo models and epigenomic analysis made the findings particularly robust. This work significantly advances our understanding of the function of MLL3 in cancer. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:CDKN2A tumor suppressor locus in liver cancer" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Hao Zhu as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Erica Golemis as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Jeffrey A Magee (Reviewer #2).Thank you for submitting your article "MLL3 regulates the The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:There are several suggestions or clarifications from each reviewer that need to be addressed; Please address all of the comments from the reviewers.Reviewer #1 (Recommendations for the authors):1. How can we tell that the ChIP-seq signal is not coming entirely from MLL4 in the MYC; sgMLL3 tumors? if the signal is partially or entirely from MLL4, then it is hard to know what amount of binding is contributed by MLL3. Moreover, there could be concomitant up-regulation of MLL4 when MLL3 is deleted.2. Figure 1C should have some statistical analysis. The numbers of mice used here are low, so the stats should be included.3. Figure 4 D, F, and G would benefit from an increased number of replicates.4. Figure S1D is missing "T" for transposons.Reviewer #2 (Recommendations for the authors):1. There are no statistical arguments to support the claim that MLL3 and CDKN2A mutations are mutually exclusive in HCC or any of the other cancers that they analyzed. When each mutation is relatively rare, is it that surprising that they do not co-occur? The authors should support this argument with a more rigorous analysis or remove it.2. The authors argue that MLL3 binds the CDKN2A promoter, but the ChIP-seq data also show peaks within the gene body. Can they clarify how they distinguish direct interactions between MLL3 and promoter elements from indirect interactions that might occur when enhancers loop and engage the promoter?3. The authors should show a Western blot demonstrating specific loss of MLL3 expression in the sgMll3 lines, given that the antibody used for the ChIP-seq studies recognizes both MLL3 and MLL4.4. Figures 2C and 3B, C both need clarification. For Figure 2C, what do the values in the heatmap reflect? For Figure 3C, how did they generate the gene set for the GSEA, and what are these comparisons meant to prove? This point did not come through clearly in the manuscript.5. The study focuses entirely on MYC-driven HCC, with the exception of the CRISPRa experiments performed in p53/Tert mutant HLE cells. Can the authors comment on whether the MLL3/CDKN2A interaction generalizes to non-MYC-driven HCC?Reviewer #3 (Recommendations for the authors):The authors of the manuscript describe an interesting and novel mechanism by which MLL3 regulates the CDKN2A tumor suppressor locus in MYC amplification-dependent liver cancer.eLife journal.Overall, the manuscript is well written, organized, and focused on an interesting topic, and with data presented supports the authors' claims. Nevertheless, there are a few concerns that remain in relating to the study, and therefore, need to be addressed further for publication in the Specific comments:1. While the mouse genetic model manipulating the overexpression (OE) of oncogene and knockout of tumor suppressor gene (TSG) is an elegant design to show the combined effects of MYC-OE and MLL3-loss in liver tumorigenesis, it might not be a straightforward idea to use Myc; sgp53 as a control, especially in the ChIP-seq study and the downstream analyses. Because p53 has its own distinct set of targets that may cooperate with MYC and p53 negatively regulates Ink4a and Arf expression, the authors should explain more clearly the rationale for choosing Myc; sgp53, rather than Myc; sgChr8 (only overexpress Myc but does not silence any targets), as a control in the comparison with Myc; sgMll3.2. Although the authors provide a potential mechanism for MLL3's tumor suppressive role in MYC-driven tumorigenesis, the clinical relevance is shown in Figure 1A may only fit their hypothesis into a small fraction of human HCC patients due to the relatively low mutation rate of MLL3 (4%) compared to the high MYC amplification/gain mutation rate (41%). In addition, it would be interesting to assess the clinical relevance between amplification/gain mutations of MYC and truncating mutations/deep deletions of MLL3 or CDKN2A in human cancers shown in figures S4C and S4D. This will strengthen their hypothesis that loss of MLL3 or CDKN2A impairs MYC-induced apoptosis, but instead, contributes to MYC-induced tumorigenesis in vivo.3. The supplementary experiments described in lines 179 – 188 to "demonstrate a direct transcriptional effect of MLL3 on the Cdkn2a locus" were not very clear and somehow confusing to the reviewer the first time reading and it was unclear why the authors involved p53 and Axin1 targeting in this set of experiments.4. The novel findings of this manuscript would be: (i) MLL3 regulates tumor suppressor gene expression via modulating their promoter chromatin landscapes (i.e. H3K4me3), and (ii) loss of normal function of MLL3 or the downstream effector CDKN2A may impair MYC-induced apoptosis, and in turn, lead to MYC-induced tumorigenesis. Thus, it would be better to introduce more background regarding the current knowledge of (i) MLL3's roles in the regulation of enhancer-associated H3K4me1 modulation and somehow unclear but detectable function in promoter-associated H3K4me3 regulation, and (ii) the cellular content-dependent regulation of MYC-induced apoptosis or tumorigenesis. The current introduction is too short. Essential revisions:There are several suggestions or clarifications from each reviewer that need to be addressed; Please address all of the comments from the reviewers.Reviewer #1 (Recommendations for the authors):1. How can we tell that the ChIP-seq signal is not coming entirely from MLL4 in the MYC; sgMLL3 tumors? if the signal is partially or entirely from MLL4, then it is hard to know what amount of binding is contributed by MLL3. Moreover, there could be concomitant up-regulation of MLL4 when MLL3 is deleted.Myc; sgKmt2c tumors, with Myc; sgTrp53 tumors, the unchanged peak signals in some areas of the genome are most likely due to MLL4 binding. It is important to note that this overlapping specificity highlights an exclusive relationship between MLL3 and targets such as Cdkn2a, as our RNA-seq data demonstrate that there was no compensatory change in Kmt2d (Mll4) expression in Kmt2c targeted cells , where we observe a dramatic reduction of peaks associated with Cdkn2a locus by ChIP-seq. From these results, we conclude that the Cdkn2a locus is a tumor suppressive genomic target of MLL3. We acknowledge the limitations of the antibody and how this effects our interpretations in the revised text.We agree that the interpretation may be limited due to the fact that the antibody used for ChIP recognizes both MLL3 and MLL4. Unfortunately, this was the only antibody we found effective for the murine systems after years of trying. Thus, in comparing 2. Figure 1C should have some statistical analysis. The numbers of mice used here are low, so the stats should be included.P-values) in all relevant figures.We now include the statistics . Importantly, we also tested an additional sg4. Figure S1D is missing "T" for transposons.We re-made the figure to correct this omission.Reviewer #2 (Recommendations for the authors):1. There are no statistical arguments to support the claim that MLL3 and CDKN2A mutations are mutually exclusive in HCC or any of the other cancers that they analyzed. When each mutation is relatively rare, is it that surprising that they do not co-occur? The authors should support this argument with a more rigorous analysis or remove it.KMT2C and CDKN2A in human cancers are not statistically supported, mainly due to the low mutation frequencies of one or both genes. Thus, we removed the majority of the mutation analyses. The updated oncoprint in HCC is modified to include MYC and all the P-values of the comparisons of interest.We agree with the reviewer that the mutually exclusive mutation patterns between 2. The authors argue that MLL3 binds the CDKN2A promoter, but the ChIP-seq data also show peaks within the gene body. Can they clarify how they distinguish direct interactions between MLL3 and promoter elements from indirect interactions that might occur when enhancers loop and engage the promoter?We agree with the reviewer that we cannot rule out other possible promoter-engaging mechanisms, such as via MLL3 binding within the gene body and/or looping between promoters and enhancers. To address some of these concerns, we also performed ChIP-seq for several histone modifications that have been widely used to identify different cis-acting elements in the genome and their transcriptional state . We also modified the relevant text in the Results and in the Discussion to incorporate the reviewer’s point.3. The authors should show a Western blot demonstrating specific loss of MLL3 expression in the sgMll3 lines, given that the antibody used for the ChIP-seq studies recognizes both MLL3 and MLL4.We agree with the reviewer that a Western blot result would further support our conclusion. To address this, we tried several anti-mouse MLL3 antibodies, including one from Cell Signaling Technology (#53641) and another non-commercially available one that worked for ChIP-seq. Unfortunately, none of these attempts yielded specific bands with correct molecular weight in the immunoblots.4. Figures 2C and 3B, C both need clarification. For Figure 2C, what do the values in the heatmap reflect? For Figure 3C, how did they generate the gene set for the GSEA, and what are these comparisons meant to prove? This point did not come through clearly in the manuscript.We thank the reviewer for pointing out the lack of clarity, and we have updated the text in the Results and the Methods to provide the information requested.P <0.05; absolute fold change >2) in RNA-seq on mouse HCC cell lines , and in human HCCs with mutations in KMT2C vs. TP53 . Those signatures were then compared to the transcriptional results on human HCCs with genomic inactivation of CDKN2A vs TP53 in Figure 3B and 3C. We updated Figure 3 to add more relevant details.For Figure 2C, the values in the heatmap are the Z-scores of the normalized counts for the peak signals of histone modifications in ChIP-seq data. For GSEA in Figure 3, transcriptional signatures were derived based on significantly changed genes ; second, in human HCCs, KMT2C mutations and deletions co-occur with MYC gains and amplification.Our study primarily used Ctnnb1. However, we did not observe oncogenic cooperation between Ctnnb1 activation and MLL3 loss; no mice developed liver tumors by the experimental endpoint . Additionally, analysis of human HCCs showed no significant co-occurrence between CTNNB1 and KMT2C alterations . These results suggests that, similar to other epigenetic regulators, the tumor-suppressive function of MLL3 is likely oncogene- and context dependent. Recent studies support the notion that chromatin context can favor certain oncogenic alterations and we speculate in the discussion that this may also be true for MLL3. We have updated the text to reflect the context-specificity of MLL3 as a tumor suppressor.We agree with the reviewer that it is of interest to know whether the results are oncogene independent. Thus, we performed the analogous experiment by HTVI of transposon containing the constitutively active Reviewer #3 (Recommendations for the authors):The authors of the manuscript describe an interesting and novel mechanism by which MLL3 regulates the CDKN2A tumor suppressor locus in MYC amplification-dependent liver cancer.Overall, the manuscript is well written, organized, and focused on an interesting topic, and with data presented supports the authors' claims. Nevertheless, there are a few concerns that remain in relating to the study, and therefore, need to be addressed further for publication in the eLife journal.Specific comments:1. While the mouse genetic model manipulating the overexpression (OE) of oncogene and knockout of tumor suppressor gene (TSG) is an elegant design to show the combined effects of MYC-OE and MLL3-loss in liver tumorigenesis, it might not be a straightforward idea to use Myc; sgp53 as a control, especially in the ChIP-seq study and the downstream analyses. Because p53 has its own distinct set of targets that may cooperate with MYC and p53 negatively regulates Ink4a and Arf expression, the authors should explain more clearly the rationale for choosing Myc; sgp53, rather than Myc; sgChr8 (only overexpress Myc but does not silence any targets), as a control in the comparison with Myc; sgMll3.Myc; sgTrp53 cells as controls has downsides, due to p53specific tumor suppressive functions. In theory, HCC cell lines with only Myc activation would have been ideal. However, as shown in Figure 1, Myc activation alone  does not yield liver tumors from which we could eventually derive cell lines and, even had they occurred, there would likely be selected secondary events.We agree with the reviewer that using Cdkn2a in p53 null and WT cells . We found that targeting Kmt2c with 2 sgRNAs reduces p16Ink4a and p19Arf levels in Myc; shTrp53 cells , and Kmt2c shRNA knockdown reduces Ink4a and Arf transcripts in Myc; sgAxin1 cells where p53 was not altered . All of these observations indicate that the regulation of the Cdkn2a locus is MLL3-dependent rather than the result of p53 loss. Lastly, new Figure 4—figure supplement 1G shows that MLL3 binding peaks at Cdkn2a locus also exist in Myc; sgAxin1 cells, confirming that our results and conclusions are not exclusive to p53 inactivated cells. We acknowledge these potential confounding issues with compensatory activation of Cdkn2a and these confirmatory experiments in the revised text.We also agree that silencing p53 may result in compensatory upregulation of Ink4a and Arf. However, we performed a number of experiments to validate the ability of MLL3 to directly regulate 2. Although the authors provide a potential mechanism for MLL3's tumor suppressive role in MYC-driven tumorigenesis, the clinical relevance is shown in Figure 1A may only fit their hypothesis into a small fraction of human HCC patients due to the relatively low mutation rate of MLL3 (4%) compared to the high MYC amplification/gain mutation rate (41%). In addition, it would be interesting to assess the clinical relevance between amplification/gain mutations of MYC and truncating mutations/deep deletions of MLL3 or CDKN2A in human cancers shown in figures S4C and S4D. This will strengthen their hypothesis that loss of MLL3 or CDKN2A impairs MYC-induced apoptosis, but instead, contributes to MYC-induced tumorigenesis in vivo.KMT2C and CDKN2A mutations in different cancer types. Because MYC is a recurrent oncogene in some but not all cancer types, we focused the analysis to show statistically significant cooccurrences between MYC and KMT2C/CDKN2A mutations in HCC, and removed the less relevant oncoprints.We thank the reviewer for these constructive suggestions. We recognize the limitations of our previous analyses of 3. The supplementary experiments described in lines 179 – 188 to "demonstrate a direct transcriptional effect of MLL3 on the Cdkn2a locus" were not very clear and somehow confusing to the reviewer the first time reading and it was unclear why the authors involved p53 and Axin1 targeting in this set of experiments.Kmt2c by CRISPR in Myc; shTrp53 cells to demonstrate that MLL3 controls p16Ink4a and p19Arf expression in p53-suppressed tumor cells. Moreover, both shRNA knockdown of Kmt2c and ChIP-seq on MLL3 in Myc; sgAxin1 cells further show that the regulation of Cdkn2a by MLL3 is independent of p53 status. We have modified the text to more accurately and clearly reflect our results.We apologize for the confusion, and thank the reviewer for pointing it out. As referred to in our response to the first point, it is documented that p53 loss could result in activation of Ink4a and Arf. To rule out such a possibility, we targeted 4. The novel findings of this manuscript would be: (i) MLL3 regulates tumor suppressor gene expression via modulating their promoter chromatin landscapes (i.e. H3K4me3), and (ii) loss of normal function of MLL3 or the downstream effector CDKN2A may impair MYC-induced apoptosis, and in turn, lead to MYC-induced tumorigenesis. Thus, it would be better to introduce more background regarding the current knowledge of (i) MLL3's roles in the regulation of enhancer-associated H3K4me1 modulation and somehow unclear but detectable function in promoter-associated H3K4me3 regulation, and (ii) the cellular content-dependent regulation of MYC-induced apoptosis or tumorigenesis. The current introduction is too short.We thank the reviewer for this suggestion. We have revised the Introduction to weave in these two points.