This event is associated with dramatic changes in chromatin accessibility and transcriptional reprogramming. In contrast, the global loss of H3K27me3 in the haploid form shapes a chromatin accessibility landscape that is poised to re-initiate the transition back to diploid life after fertilisation. Hence, distinct epigenetic reprogramming events rewire transcription through major reorganisation of the regulatory epigenome to guide the alternation of generations in flowering plants.Alternation between morphologically distinct haploid and diploid life forms is a defining feature of most plant and algal life cycles, yet the underlying molecular mechanisms that govern these transitions remain unclear. Here, we explore the dynamic relationship between chromatin accessibility and epigenetic modifications during life form transitions in Arabidopsis. The diploid-to-haploid life form transition is governed by the loss of H3K9me2 and DNA demethylation of transposon-associated  Each pollen grain from a flowering plant houses sperm, which contain half of the genes needed to make a new plant, and a companion or vegetative cell (VC) that serves to deliver sperm to the egg. The genes in the vegetative cell and those in the sperm are identical to the genes of the plant they come from, so how can this set of identical genetic information produce such different cells?Both DNA and histones, the proteins that pack and order DNA, can be chemically modified locally through a process called methylation. The location of these modifications can affect how genetic information in the DNA is read to make different types of cells. The use of processes like methylation to regulate whether genes are switched on or off is called epigenetics. So what role does epigenetics play in plant pollen?Arabidopsis thaliana, a widely studied plant and common weed. In vegetative cells, DNA methylation is lost together with a different methylation mark (H3K9me2), which unlocks several genes needed for pollen to transport sperm. By contrast, sperm loses an entirely different methylation mark, called H3K27me3, which unlocks a different set of genes that help to prepare development of a new plant once sperm fertilizes the egg. Through these different set of epigenetic changes, activity increases at different groups of genes that are important for shaping the function of each pollen cell type.To answer this question, Borg et al. examined the epigenetics of pollen in These results reveal how the loss of DNA and histone methylation are important for plants to reproduce sexually via pollen. This offers insights into the evolution of plants and other related life forms. Learning about plant reproduction may also help to increase food production by improving crop yields. Epigenetic reprogramming refers to the global erasure and remodelling of epigenetic marks during development. DNA and histone methylation are reprogrammed during germline differentiation and after fertilisation to reconfigure transcription in the mammalian embryo . In planAncestral land plants represented by extant bryophytes typically have a dominant haploid gametophytic phase and require Polycomb Repressive Complex 2 (PRC2) activity to repress precocious sporophyte development . DNA metIn contrast to sperm, the events that govern differentiation of the VC remain largely unknown. VC differentiation involves acquiring the competency to grow a pollen tube for the delivery of sperm to the embryo sac, where fertilisation of the egg cell re-initiates diploid sporophyte development . Asymmetcis-regulatory activity is intricately linked with the differential reprogramming of repressive epigenetic marks after microspore division. In the VC, accessible chromatin is shaped by the loss of constitutive heterochromatin and active cytosine demethylation of cis-regulatory regions that are predicted to be bound by pollen-expressed transcription factors (TFs) involved in VC differentiation. In contrast, chromatin accessibility in sperm is shaped by the loss of H3K27me3, resulting in a chromatin state that forecasts the re-initiation of sporophyte development in the next generation. Our findings thus reveal how epigenetic reprogramming orchestrates the rewiring of transcription during haploid–diploid life form transitions in flowering plants.Here, we assess chromatin and transcriptional reprogramming during the sporophyte-to-gametophyte transition in developing Arabidopsis pollen. We reveal widespread reprogramming of accessible chromatin across this transition and map hundreds of loci that gain accessibility in each pollen cell type. We demonstrate how the rewiring of As sperm cells are embedded within the VC cytoplasm, existing Arabidopsis whole pollen transcriptomes represent a composite profile derived from both cell types . To circTo assess chromatin reprogramming in the male gametophyte, we profiled chromatin accessibility in the distinct cell types of the developing Arabidopsis pollen lineage using Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) . We isolWe first examined differences in chromatin accessibility between the microspore, sperm, and vegetative cell. We identified a total of 20,634 ATAC-seq peaks covering over 15 Mb of the Arabidopsis genome . We defiR = 0.61) with differential expression of the associated genes between sperm and the VN   . Strikinype (WT) , with juype (WT) . A smallype (WT) . Metaploype (WT) , which iype (WT) . Consistype (WT) , the levn the VN .dme-2/+ mutant pollen, these predicted binding sites were hyper-methylated to around half of the levels in WT sperm, consistent with the heterozygous nature of the dme-2 mutant   , which iE (RFK2) . The upsand ERF8 , three Dand ERF8 . Similarand ERF8 , containand ERF8 . Interesand ERF8 . These 3and ERF8 , around and ERF8 . Thus, Dlen tube . In concEARLY FLOWERING 6 (ELF6), RELATIVE OF ELF6 (REF6), and JUMONJI 13 (JMJ13)  . We thus (JMJ13) . Consist (JMJ13) . Almost  (JMJ13) . The rem (JMJ13) , suggestDUO1-ACTIVATED ZINC FINGER (DAZ3) and PLANT CADMIUM RESISTANCE 11 (PCR11), as well as the paternally transcribed embryonic regulator SHORT SUSPENSOR (SSP)  were associated with a protein-coding gene , around OR (SSP) . The genOR (SSP) . IncidenOR (SSP) , furtherBABY BOOM (BBM), LEAFY COTYLEDON 1 (LEC1), and WUSCHEL-RELATED HOMEOBOX 2 (WOX2), as well as the floral repressor FLOWERING LOCUS C (FLC)  were also associated with genes expressed specifically during the initiation of sporophytic development in the early embryo . Strikin C (FLC) . This tr C (FLC) . Thus, scis-regulatory regions at developmental genes required to initiate transition back to diploid life after fertilisation. While substantial differences in ACRs have been observed across sporophytic Arabidopsis tissues . For the ProVCK:NTF construct, the NTF reporter with a stop codon  was recoThe primer sequences were:GGGACAAGTTTGTACAAAAAAGCAGGCTCTATGGATCATTCAGCGAAAACCACNTF-attB1-F GGGGACCACTTTGTACAAGAAAGCTGGGTCTCAAGATCCACCAGTATCCTCNTF-attB2-R GGGGCAGCTTTCTTGTACAAAGTGGCGATGGATCATTCAGCGAAAACCNTF-attB2-F GGGGCAACTTTGTATAATAAAGTTGTTCAAGATCCACCAGTATCCTCNTF-attB3-R GGGACAAGTTTGTACAAAAAAGCAGGCTTCATGATGACTTCTCGTTCGATTGTTCMDB-attB1-F GGGGACCACTTTGTACAAGAAAGCTGGGTCCTTCTCTCGAGCAGCAACTAAACCMDB-attB2-R Dry pollen from the ProVCK:NTF and ProHTR10:HTR10-Clover marker lines was harvested using a method published previously . SN and 2, 2 mM MES, 1 mM KCl, 1% H3BO3, 10% Sucrose, pH 7.5). The crude spore preparation was then filtered through two layers of miracloth and the spore suspension centrifuged at 800 g for 5 min at 4°C to pellet the spores. The resulting pellet was resuspended in PEB, filtered through a 20 μm mesh, and immediately subjected to FACS on a FACS Aria III cell sorter. The sorter was run in standard configuration, using a 100 μm ceramic nozzle with 1× PBS running at a constant pressure of 20 psi. Microspores were gated for high angle scatter (SSC) and autofluorescence in the GFP channel using a 488 nm blue laser and 530/30 nm bandpass filter. Microspore purity and integrity were assessed by DAPI staining and microscopy immediately after FACS-based purification.Intact microspores were isolated by fluorescence-activated cell sorting (FACS) based on a method described previously . Unopeneg for 5 min at 4°C to pellet the spores. The resulting pellet was resuspended in ice-cold Galbraith Buffer (with 1% Triton X-100)   supplemedme-2/+ pollen, dry pollen was harvested from Col-gl and BASTA-sprayed dme-2/+ plants using a method published previously . Reads were aligned to the Arabidopsis genome (TAIR10) using the STAR aligner version 2.5.2a . Published RNA-seq data sets were analysed and described previously .Adapter trimming was performed using TrimGalore version 0.4.1 RRID:n 2.5.2a . Transcrn 2.5.2a  with an eviously  with theeviously . Principn 1.22.2  for trang for 5 min at 4°C and the spore pellet resuspended in 500 μl lysis buffer . The microspores were pelleted once more at 500 g for 10 min at 4°C and ~50,000,000 added to a 25 μl transposase reaction mix . The Tn5 reaction mixes were incubated at 37°C for 1 hr and tagmented DNA fragments recovered with a MinElute PCR Purification Kit (Qiagen). Sequencing-ready libraries were generated as described previously . For microspore ATAC-seq, FACS-purified spores were centrifuged at 800 eviously . The libCTGTCTCTTATACACATCTCCGAGCCCACGAGAC’ -A ‘CTGTCTCTTATACACATCTGACGCTGCCGACGA’--minimum-length 5 --overlap 1 –a ‘ . Reads were mapped to the Arabidopsis genome (TAIR10) with Bowtie2 version 2.1.0 using --end-to-end -X 2000 -x (MarkDuplicates version 1.141 (https://github.com/broadinstitute/picard), RRID:SCR_006525. Only reads with a mapQ >10 were used in our analysis. Biological replicates were subsequently merged for downstream analysis after confirming high correlation among replicates. Genome bigwig coverage files were generated with the deepTools version 2.2.4 utility bamCoverage using --normalizeTo1 × 119146348 --binSize=10 --smoothLength=100 . The region range to TSS was set to 900 bp . ATAC-seq heatmaps were generated using the R package EnrichedHeatmap  enrichment was performed using the gProfileR package in R (map version 2.26 (https://bedtools.readthedocs.io/en/latest/) using ChIP-seq coverage files analysed and described previously (https://gat.readthedocs.io/en/latest/).Chromosomal plots of ATAC-seq peak density based on the average number of peaks were calculated in 10 kb bins using bedtools dHeatmap . Averageage in R  using gSeviously . Histoneeviously . Peak ovhttps://github.com/marcelm/cutadapt/tree/v1.9.1). Trimmed sequences of 18–30 bp were aligned to the Arabidopsis genome (TAIR10) using STAR aligner version 2.7 . Bisulphite-converted reads were aligned against the TAIR10 genome using Bismark version 0.22.2 with --non_directional -q --score-min L,0,–0.4bismark  (https://bitbucket.org/schultzmattd/methylpy) to extract weighted methylation rates at each cytosine as described previously  with coverage ≥4 overlapping reads were identified by root mean square tests and a false discovery rate ≤0.05. Differentially methylated sites within 250 bp were collapsed into DMRs. DMRs were further filtered by discarding regions with <5 DMCs. Statistical analyses and associated figures were generated with a custom script in R. Sequence logo for DMCs was generated using the R package ggseqlogo . Overlap enrichment of genes associated with non-intergenic DME-targeted ACRs against gene lists of interest was determined with the R package GeneOverlap version 1.18.0 (https://github.com/shenlab-sinai/GeneOverlap). Motif enrichment was performed using motifs in the DAP-seq data set . Peak overlap enrichment of ATAC-seq peak groups with different features was performed using GAT with 10,000 permutations of random Arabidopsis genomic regions (https://gat.readthedocs.io/en/latest/). DUO1 target genes were defined as all genes significantly induced by DUO1 overexpression in seedlings as described previously As the editors have judged that your manuscript is of interest, but that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Essential revisions:1) The study is incomplete as the most critical stage in pollen development – the bicellular microspore stage , has not been included in this analysis. This is surprising considering that this stage is critical for male gamete and pollen development , and that this experiment is technically feasible.2) Comparing chromatin dynamics between two epidermal root cells and two pollen cells is not a valid argument to support the view that "haploid development" is not a result of cell differentiation. To address this point, authors should conduct a detailed analysis of all cell types at the three stages of pollen development.3) The role of DME in poising accessible regulatory regions for the transcriptional events that regulate pollen tube growth should be validated experimentally. Authors should also perform chromatin accessibility assays in VN and SN using dme2/+ plants to confirm their hypothesis.Similarly, the impact of DNA methylation at transcription factor binding sites that overlap with DME targeted ACRs should also be experimentally tested. Some of the correlations are weak and without experimental validation, their hypotheses are largely speculative.4) The most significant comment is the conclusion that "epigenetic reprogramming" is deterministic instead of differentiation. The evidence to support this claim is that thousands of differential ACRs are identified in the VN, MN or the SN, but not between root hair and non-hair datasets. This is a strawman argument, that is built upon a negative result. Although it is true that the "hair" and "non-hair" datasets do not show differences in accessibility, it is likely a reflection of the lack of purity of these cells. Two recent single cell ATAC-seq studies using Arabidopsis somatic tissues were published on bioRxiv  and both show substantial variation of cell type specific differential accessibility. Furthermore, published studies comparing maize tissues from a couple of labs have shown thousands of regions of differential accessibility. Therefore, selecting to compare root hair and non-hair to support this major claim in the study is not advised. I believe focusing on what the data do show is actually significant enough for publication, without overinterpreting the results.5) The evidence that regions that become accessible in the VN lose H3K9me2, siRNAs and DNA methylation requires additional analysis. Currently, the data are averaged for all regions. How many VN ACRs have H3k9me2, siRNAs, DNA methylation in somatic cells? What proportion of these actually lose H3k9me2, siRNAs and/or DNA methylation. One way to show this result is to take Figure 2C and create a heatmap where all the VN ACRs are rows and the columns are data for H3K9me2, CG, CHG, CHH, 20, 21, 22, 23 and 24 nt siRNAs. This would allow the reader to evaluate how many actual regions are enriched for these data.6) Figure S2A does show high reproducibility, but correlations of 1.0 should not exist. These correlations show that read coverage per window is the same. If the windows are large enough the actual peaks do not have much influence over the result. For all replicates, please report the number of ACRs identified and their overlap of ACRs between each replicate. You could also consider using IDR.7) The y-axis for enrichment in 2C is very small indicating enrichment is not as prevalent at these loci as the authors have presented.8) The pollen data in 2G doesn't show enrichment of siRNAs. 20 nt siRNAs are not known to have a function, yet they are accumulating to greater levels than 22-24 nt siRNAs.9) Evaluate if the siRNAs are siRNAs and not mRNA degradation products. This can be done be showing the siRNAs from individual regions are aligning to both strands of DNA instead of being derived from a single strand. This would explain the lack of enrichment of expected siRNA sizes and the over enrichment of classes that are not known to have function (20 and 25 nt siRNAs).10) I really like the section of TF motif enrichment in these cell type specific ACRs. Given the purity and high-quality nature of the data, the authors should consider using DNA footprint analysis, which usually plagues bulk tissue data. It might provide more specificity that using the entire ACRs.11) What are the negative controls used to identify TF motif enrichment in ACRs?12) Figure 5A needs to control for gene length. Long genes will show more discrete H3K4me3, whereas short genes will short more enrichment over gene bodies.13) The idea that chromatin accessibility in sperm is poised for sporophyte development has been previously proposed by the authors  but the data presented in this manuscript does not provide direct evidence to support this hypothesis.Thank you for submitting the revised manuscript of your work entitled "Distinct epigenetic reprogramming events rewire transcription during life cycle transitions in Arabidopsis" that addresses most of the issues raised in the first submission. There are a few more items from the review of your revised manuscript, that we would like for you and your co-authors to consider and respond to.It would be a more useful contribution to literature if you would provide the Venn diagrams of ACRs between the individual samples prior to merging the replicate data. Your reason for not showing this was that the Spearman correlations were so high that it was unnecessary. However, adding the data for each replicate would permit the reader to appreciate the variability in the data. The addition of the FRIP scores in Figure S3 shows quite a bit of variation between samples. Although this doesn't invalidate the major conclusions of this study, the variability should be better acknowledged. Showing overlap of ACRs from individual replicates with and between samples is one way to do so.The addition of the heatmap to Figure 2 is much appreciated. However, it shows that the majority of ACRs do not possess H3K9me2 and/or siRNAs in leaf tissue. This result does not invalidate conclusions, but this is important to note in the main text. In particular, note how many ACRs overlap and H3K9me2 region and make it clear to the reader that only a minor number of ACRs in the VN are affected.The conclusion that there are thousands of differential ACRs during this developmental progression as compared to somatic tissues based on published ATAC-seq data is not likely to hold up over time. Published studies are limited in their data analyses preventing accurate identification of cell-type-specific ACRs. The limits of other published studies are in contrast to your work and perhaps you do not want to appear critical of other studies, but your work would be a better contribution to the field if it was made clear that the major rewiring of cis-regulatory elements that you have observed in the haploid phase of the life cycle may very well occur in other phases of development when cell-type-specific methods and more sophisticated data analyses are applied. Essential revisions:1) The study is incomplete as the most critical stage in pollen development – the bicellular microspore stage , has not been included in this analysis. This is surprising considering that this stage is critical for male gamete and pollen development , and that this experiment is technically feasible.We thank the reviewers for this suggestion. The bicellular pollen stage encapsulates the stage after microspore division, where the germ (or generative) cell becomes engulfed in the cytoplasm of its sister vegetative cell. The germ cell is present during a very narrow and transient window during pollen development, with no specific molecular markers available. Bicellular pollen remains embedded within unopened floral buds, making this a technically very challenging developmental stage to isolate, and it is not a routine procedure as suggested by the reviewers.To address this, we generated a tailored marker line that specifically and transiently marks germ cell nuclei using a ProDUO1:MDB-NTF construct. NTF is a nuclear envelope targeted GFP marker, while MDB denotes the CYCB1;1 destruction box. The construct is driven with the DUO1 promoter, which is active solely in germ cells and sperm cells . MDB-NTF is susceptible to degradation by APC/C outside of the G2/M-phase . See Figure 1—figure supplement 1H for details. In pollen at mid-bicellular stage, the GFP signal in the germ cell nucleus shows signal similar to autofluorescence background in the pollen cell wall. In late bicellular pollen, germ cells enter late G2 phase and MDB-NTF accumulate to high levels well-above background autofluorescence. As germ cells exit mitosis, APC/C is reactivated leading to progressive turnover of the NTF marker in the resulting two sperm, which only show a cytoplasmic signal barely above autofluorescence. We devised sorting parameters that allowed to isolate germ cell nuclei from pollen at late bicellular stage .We isolated germ cell nuclei from this marker line to perform ATAC-seq profiling . Like microspores, we observed no clear nucleosome laddering . Nonetheless, we observed strong overlap with other ATAC-seq libraries, suggesting it is still useable to support the major interpretations of this study. Our analysis did not reveal de novo gains in chromatin accessibility in germ cells compared with microspores but rather a gradual loss in accessibility among the class of sporophytic-specific peaks . Incidentally, H3K27me3 in germ cell nuclei also remains comparable to levels in microspores  but is specifically lost in divided sperm, further supporting our observation that major reorganization of chromatin accessibility in sperm coincides with H3K27me3 reprogramming.2) Comparing chromatin dynamics between two epidermal root cells and two pollen cells is not a valid argument to support the view that "haploid development" is not a result of cell differentiation. To address this point, authors should conduct a detailed analysis of all cell types at the three stages of pollen development.This was a nice suggestion from the reviewers and as requested, we have performed pairwise comparisons  between the four stages of pollen development we profiled. Because the SN and VN represent the culmination of chromatin accessibility reprogramming in pollen, we focused on the ACRs recovered from the SN vs VN comparison together with a set of sporophyte-specific ACRs. These 20,634 ACRs represent the master peak set in our pairwise analyses.These comparisons reveal the progressive accumulation of differential ACRs during pollen development, which remain modest in more closely related cell types  but reach a maximum at the latest stage of pollen development (i.e. between the SN and VN). These data are now presented in a new main figure panel .”. Please see the response to major comment 4 for more clarification on these points.We realize that some confusion has arisen in regard to the differences we highlight between haploid and diploid development. We show that extensive chromatin accessibility changes in pollen are a result of unique and major waves of epigenetic reprogramming that only occur in the haploid gametophyte generation. Thus, 1000s of regulatory regions that are inactive and repressed in somatic tissues lose repressive marks in pollen, and in turn become active and gain chromatin accessibility. It thus stands to reason that these reprogramming events serve to establish and distinguish the haploid gametophytic program from the diploid program. Importantly, the genome-wide loss of repressive marks occurs after a single cell division, which in turn allows for extensive and rapid rewiring of transcriptional activity. Nonetheless, we agree that our discussion point might have led to the impression that "haploid development is not a result of cell differentiation3) The role of DME in poising accessible regulatory regions for the transcriptional events that regulate pollen tube growth should be validated experimentally. Authors should also perform chromatin accessibility assays in VN and SN using dme2/+ plants to confirm their hypothesis. Similarly, the impact of DNA methylation at transcription factor binding sites that overlap with DME targeted ACRs should also be experimentally tested. Some of the correlations are weak and without experimental validation, their hypotheses are largely speculative.The reviewers comment that the impact of DNA methylation at the TF binding sites should be tested experimentally. in vitro assays have already been performed in the DAP-seq dataset used in our analysis . Additional approaches would require ChIP-seq analysis of TF binding in pollen, or in vitro in gel shift assays with methylated and unmethylated DNA. We believe that such experiments extend far beyond the aims of this study, which focuses on delivering a genomic level perspective of how chromatin accessibility is reprogrammed during the alternation of generations.+/-dme-2 pollen and could confirm a direct association with at least 27 of the genes mis-regulated in dme-2 – this is highly significant and statistically supported  – even when working within the limitations of using +/-dme-2 plants. We doubt that in the context of our analysis presented in Figure 3 and Figure 4, ATAC-seq profiling of +/-dme-2 would be more insightful than the RNA-seq profiling we have already performed. Isolating dme mutant pollen from WT pollen produced by +/-dme-2 plants would also require tailored fluorescent marker lines that as-yet do not exist. Moreover, the use of SYBR-green dye to isolate the VN and SN from a mixed population (which is a common approach to separate the two cells) is not recommended for ATAC-seq analysis, since DNA intercalating dyes are known to strongly disrupt chromatin structure (please see https://kb.10xgenomics.com/hc/en-us/articles/360027640311-Can-I-sort-nuclei-for-Single-Cell-ATAC-sequencing-or-Single-Cell-Multiome-ATAC-GEX-).To validate the DME-targeted ACRs, we performed RNA-seq profiling of 4) The most significant comment is the conclusion that "epigenetic reprogramming" is deterministic instead of differentiation. The evidence to support this claim is that thousands of differential ACRs are identified in the VN, MN or the SN, but not between root hair and non-hair datasets. This is a strawman argument, that is built upon a negative result. Although it is true that the "hair" and "non-hair" datasets do not show differences in accessibility, it is likely a reflection of the lack of purity of these cells. Two recent single cell ATAC-seq studies using Arabidopsis somatic tissues were published on bioRxiv  and both show substantial variation of cell type specific differential accessibility. Furthermore, published studies comparing maize tissues from a couple of labs have shown thousands of regions of differential accessibility. Therefore, selecting to compare root hair and non-hair to support this major claim in the study is not advised. I believe focusing on what the data do show is actually significant enough for publication, without overinterpreting the results.instead of differentiation.We understand the concerns raised. As we clarified in our response to point 3, our reasoning is that epigenetic reprogramming is deterministic for the establishment of the haploid gametophyte programs, which in turn facilitates sperm and vegetative cell differentiation. These two processes are clearly inter-connected, and we do not imply that reprogramming is deterministic Arabidopsis root cells. We quote findings from Dorrity et al., 2020 below:Despite being separated by what is essentially a single cell division, we observe 1000s of unique ACRs between the sperm and vegetative cell. We have performed a similar comparison with sister cells of root epidermis to emphasize the scale of these changes. During the review process, we were referred to recent papers of scATAC-seq profiling of A. thaliana.7, 23 Although thousands of differentially accessible sites have been found across tissue types,7accessibility differences between more closely related cell types remains largely unexplored, with the exception of root hair vs non-hair, in which very few differences were found.7,11 ""For each cell type, the median number of genes with tissue-specific accessibility was 20 (range 5 to 53) . This small number of genes is consistent with earlier studies that show few open chromatin sites that define cell type identity in Thus, the findings from Dorrity et al., appear to support our reasoning since differentiation between closely-related sporophytic cell types involves less than 50 ACRs. This contrasts strongly with the 1000s of changes in the haploid generation between two closely-related cell types – which importantly are only separated by a single cell division. We have now revised this section by removing the pairwise comparison between the root epidermis cell types and toned down our conclusions on this point.5) The evidence that regions that become accessible in the VN lose H3K9me2, siRNAs and DNA methylation requires additional analysis. Currently, the data are averaged for all regions. How many VN ACRs have H3k9me2, siRNAs, DNA methylation in somatic cells? What proportion of these actually lose H3k9me2, siRNAs and/or DNA methylation. One way to show this result is to take Figure 2C and create a heatmap where all the VN ACRs are rows and the columns are data for H3K9me2, CG, CHG, CHH, 20, 21, 22, 23 and 24 nt siRNAs. This would allow the reader to evaluate how many actual regions are enriched for these data.We are very thankful for this useful suggestion from the reviewers. As requested, we have added a new heatmap to Figure 2 that summarizes the levels of H3K9me2, DNA methylation and siRNAs in sporophytic leaf tissue for all VN-specific ACRs. This heatmap has now replaced the bar chart previously occupying Figure 2G. The heatmap is grouped by whether the ACRs are targeted by DME or not. The heatmap nicely illustrates how VN-specific ACRs are highly enriched for H3K9me2 and DNA methylation in the sporophyte, as well as the stimulation of pollen siRNAs from regions that are not observed in sporophytic tissue. This is consistent with our peak overlap enrichment test shown in Figure 2C. This adds further evidence that the VN-specific gain of accessible chromatin in the gametophyte generation stimulates transcription of genomic regions normally silenced in sporophytic tissues.6) Figure S2A does show high reproducibility, but correlations of 1.0 should not exist. These correlations show that read coverage per window is the same. If the windows are large enough the actual peaks do not have much influence over the result.We thank the reviewer for pointing out this very valid point. After some consideration, we realize that a Spearman correlation is likely more appropriate here since ATAC-seq data by definition is not normally distributed. We have thus re-analysed the correlation between our datasets alongside our newly added germ cell nucleus profiles and report a Spearman correlation matrix .For all replicates, please report the number of ACRs identified and their overlap of ACRs between each replicate. You could also consider using IDR.Because our ATAC-seq replicates showed such high genome-wide correlation , we merged replicates for our differential analysis of ATAC-seq profiles. We reasoned that this would increase read complexity and improve peak detection, with which we then generated a master peak set for pairwise comparisons. This was then used in a DESeq2 comparison with the individual replicates to statistically assess chromatin accessibility within these regions – this has now been clarified with a new schematic diagram .7) The y-axis for enrichment in 2C is very small indicating enrichment is not as prevalent at these loci as the authors have presented.2 fold-change of observed overlaps for our ACR peaks groups against those expected from 10,000 permuted overlaps with random genomic regions. The positive enrichment values (in green), which are only observed for VN-specific ACRs, all range between a log2 fold-change of 0.6 to 2.1 – the inverse log of which equates to 1.6x to 5.5x the frequency of overlaps expected by chance. Importantly, all of the overlap tests we performed were statistically significant, as indicated by p < 0.001 below the heatmap legend. In contrast, common and SN-specific ACRs were all significantly depleted for these features, highlighting the association of VN-specific ACRs with heterochromatic features known to be altered during pollen development. We hope that our explanation clarifies this point.We agree and have improved the heatmap legend to present a more gradual gradient between the green and grey colors. These values represent the log8) The pollen data in 2G doesn't show enrichment of siRNAs. 20 nt siRNAs are not known to have a function, yet they are accumulating to greater levels than 22-24 nt siRNAs.9) Evaluate if the siRNAs are siRNAs and not mRNA degradation products. This can be done be showing the siRNAs from individual regions are aligning to both strands of DNA instead of being derived from a single strand. This would explain the lack of enrichment of expected siRNA sizes and the over enrichment of classes that are not known to have function (20 and 25 nt siRNAs).Arabidopsis . We have corrected our statement in the revised version to refer to 21-22nt siRNAs to avoid confusion and cited this important study.We thank reviewers for raising this point. We have replaced previous panel Figure 2G with a new heatmap in Figure 2C, which assesses the mean level of siRNAs over the ACRs including ±10bp upstream and downstream. As requested, we have also assessed the strand bias for 20nt, 21-22nt and 23-24nt siRNAs. As the plot in 10) I really like the section of TF motif enrichment in these cell type specific ACRs. Given the purity and high-quality nature of the data, the authors should consider using DNA footprint analysis, which usually plagues bulk tissue data. It might provide more specificity that using the entire ACRs.We thank the reviewer for their positive comment about our analysis of TF motif enrichment. As suggested, we have performed DNA footprinting analysis with HINT-ATAC, which assesses TF activity through bias corrected cleavage analysis. Increased HINT-ATAC signals over TF motifs implies increased TF activity in a cell , which was evident for several TFs enriched within DME-targeted ACRs, including the methylation-sensitive TFs . This suggests active TF occupancy at the predicted binding sites that become accessible through the epigenetic reprogramming of constitutive heterochromatin in the VN.11) What are the negative controls used to identify TF motif enrichment in ACRs?We thank the reviewer for pointing out this omission. We now mention that to perform motif enrichment, we used the complete set of pollen ACRs recovered in our study as negative control for the Analysis of Motif Enrichment (AME) tool in MEME.12) Figure 5A needs to control for gene length. Long genes will show more discrete H3K4me3, whereas short genes will short more enrichment over gene bodies.We appreciate this as a highly valid point since the averaging we performed had been scaled to a consistent length over the whole gene body. The metaplots in Figure 5A have been replotted and are now averaged upstream and downstream of the transcription start site to control for gene body length. This has been simplified into two clusters – one with and without H3K4me3 (i.e primed and un-primed), with the primed cluster specifically characterized by increased promoter accessibility that is followed by H3K4me3 accumulation.13) The idea that chromatin accessibility in sperm is poised for sporophyte development has been previously proposed by the authors  but the data presented in this manuscript does not provide direct evidence to support this hypothesis.cis-regulatory regions, is largely regulated through epigenetic reprogramming of H3K27me3 in sperm.We use the term “poised” to refer to the transcriptionally-primed states that we report in sperm chromatin, which specifically targets genes expressed during the earliest phases of embryogenesis and which is corroborated with both chromatin accessibility (this study) and active H3K4me3 marks . We have revised our use of the term “poised” and replaced this with “primed” or “forecast” throughout the revised manuscript. How sperm chromatin state influences transcriptional events after fertilization are indeed very interesting questions. Building on these important observations with further studies would allow one to explore the direct transgenerational impacts of reprogramming and any potential paternal effects. These would be such novel findings that they necessitate further investigations in the framework of a new and in-depth study. Such studies would require highly technical isolation of very early zygotes for gene expression profiling, which is by no means a straightforward experiment and would require new collaborations. We hope that the reviewers realize and agree that addressing these points go far beyond the scope of the present study, which reports the first example of how transcription is reprogrammed during the alternation of generations, and for which we show compelling evidence that chromatin accessibility, and by extension the binding of TFs to active 1) It would be a more useful contribution to literature if you would provide the Venn diagrams of ACRs between the individual samples prior to merging the replicate data. Your reason for not showing this was that the Spearman correlations were so high that it was unnecessary. However, adding the data for each replicate would permit the reader to appreciate the variability in the data. The addition of the FRIP scores in Figure S3 shows quite a bit of variation between samples. Although this doesn't invalidate the major conclusions of this study, the variability should be better acknowledged. Showing overlap of ACRs from individual replicates with and between samples is one way to do so.As requested, we have now added pie charts of ACR overlaps between among the replicates for each sample. This is indeed a useful addition to help the reader identify any variability, which we acknowledge is present in our data. As clarified in our revised manuscript, we control for this by merging replicates prior to peak calling, then using the replicates later to statistically assess differential chromatin accessibility within these regions using DESeq2.2) The addition of the heatmap to Figure 2 is much appreciated. However, it shows that the majority of ACRs do not possess H3K9me2 and/or siRNAs in leaf tissue. This result does not invalidate conclusions, but this is important to note in the main text. In particular, note how many ACRs overlap and H3K9me2 region and make it clear to the reader that only a minor number of ACRs in the VN are affected.As requested, we now explicitly state the number of VNsp ACRs that overlap with H3K9me2 as follows: “just over one-third  are marked by leaf H3K9me2”. This proportion is not minor, but we acknowledge that selection of VC expressed genes is not only the product of removal of marks of constitutive heterochromatin in the discussion “Once microspore division is complete, the programmed removal of H3K9 and DNA methylation in the VC facilitates the expression of a fraction of the genes required for somatic haploid development.”3) The conclusion that there are thousands of differential ACRs during this developmental progression as compared to somatic tissues based on published ATAC-seq data is not likely to hold up over time. Published studies are limited in their data analyses preventing accurate identification of cell-type-specific ACRs. The limits of other published studies are in contrast to your work and perhaps you do not want to appear critical of other studies, but your work would be a better contribution to the field if it was made clear that the major rewiring of cis-regulatory elements that you have observed in the haploid phase of the life cycle may very well occur in other phases of development when cell-type-specific methods and more sophisticated data analyses are applied.We have moved our small discussion points from the Results section to the beginning paragraph of the Discussion section and added the following sentence: “Future cell type-specific analysis of the sporophyte and female gametophyte promises to reveal whether the extent of transcriptional rewiring in the male haploid phase is shared during other major developmental transitions in the plant life cycle.” Still, we maintain that our data shows that transitions between life forms are the product of epigenetic mechanisms distinct in scale and identity from cell differentiation


Cryptococcus neoformans. Qsp1 functions to control cell wall integrity in vegetatively growing cells and also functions in mating. Rather than acting on a cell surface receptor, Qsp1 is imported to act intracellularly via the predicted oligopeptide transporter Opt1. Here, we identify a transcription factor network as a target of Qsp1. Using whole-genome chromatin immunoprecipitation, we find Qsp1 controls the genomic associations of three transcription factors to genes whose outputs are regulated by Qsp1. One of these transcription factors, Cqs2, is also required for the action of Qsp1 during mating, indicating that it might be a shared proximal target of Qsp1. Consistent with this hypothesis, deletion of CQS2 impacts the binding of other network transcription factors specifically to Qsp1-regulated genes. These genetic and genomic studies illuminate mechanisms by which an imported peptide acts to modulate eukaryotic gene expression.Qsp1 is a secreted quorum sensing peptide required for virulence of the fungal meningitis pathogen  Cryptococcus neoformans, we find that an imported eukaryotic quorum sensing peptide that promotes virulence, Qsp1, controls the binding of three different transcription factors to promoters, thereby modulating the expression of Qsp1-regulated genes. This discovery reveals the mechanism for how an imported peptide affects gene expression.For many fungal pathogens, the ability to adapt to changing and diverse environments forms the basis for their ability to infect and survive inside macrophages and other niches in the human body. These changes are accomplished by transcription factors. Many pathogenic microbes coordinate their gene expression as a function of cell density in a process known as quorum sensing. Here, in the human fungal meningitis pathogen  Cryptococcus neoformans is the most common cause of fungal meningitis, causing over 200,000 deaths annually [Cryptococcus neoformans, the first described in a eukaryote [C. neoformans lacking a transcriptional co-repressor, Tup1 [qsp1Δ knockout phenotype (a dry colony phenotype), we infer that Qsp1 acts intracellularly after import [The opportunistic basidiomycete yeast annually . The uniannually . In prevukaryote . This syor, Tup1 . Our anaor, Tup1 . The actor, Tup1 . As cytor import .C. albicans Wor1 and H. capsulatum Ryp1, acts downstream of Qsp1 [C. neoformans as well as mating-induced transcription [In this prior work, we demonstrated that a WOPR domain transcription factor, Liv3, which is related to key regulatory proteins  of Qsp1 ,6–10. Li of Qsp1 ,11. Othecription . This fucription ,13. The qsp1Δ mutant. These experiments illuminate the mechanism by which an imported quorum-sensing peptide impacts gene expression.In this paper, we demonstrate that mutants of two transcription factors in addition to Liv3 display a rough colony phenotype when deleted, Nrg1 and Cqs2. By performing a series of transcriptomics experiments, we show that these transcription factors and Qsp1 regulate a common set of target genes. Whole-genome chromatin immunoprecipitation demonstrates that these transcription factors generally bind together to a common set of target genes, forming a highly connected transcription factor network. Significantly, the presence of Qsp1 impacts the binding of all three transcription factors to a subset of target genes which are highly enriched for genes whose expression is controlled by Qsp1. Cqs2 is particularly sensitive to the presence of Qsp1 for its genomic binding. Cqs2 is strongly required for the binding of Nrg1 and Liv3 to Qsp1-regulated genes, suggesting it may be an upstream factor in the pathways. Furthermore, while Qsp1 seems to negatively regulate protein levels of Nrg1 and Liv3, the association of these factors with promoters is still greatly decreased in the QSP1 gene (qsp1Δ) exhibit a wrinkled colony morphology phenotype at either 25°C or 30°C [liv3Δ knockout strain forms dry, wrinkled colonies at 30°C. We hypothesized that the deletion of genes encoding factors involved in the response to Qsp1 signaling would also exhibit a wrinkled colony morphology. Therefore, we screened strains in a C. neoformans knockout collection generated in our laboratory for genetic candidates. We discovered two additional strains that exhibited a qsp1Δ-like colony morphology that is also temperature-dependent, corresponding to genes encoding the transcription factors Cqs2 and Nrg1. Cqs2 was recently reported as a regulator of the Qsp1 response for unisexual filamentation [Wild-type yeast form glossy colonies, whereas cells lacking the  or 30°C . We preventation . Nrg1 isentation .qsp1Δ mutant, each transcription factor deletion strain exhibits this phenotype at a more restricted range of temperatures , and the qsp1Δcqs2Δ double mutant is unable to respond to peptide. The SDS sensitivity of the qsp1Δliv3Δ mutant could be rescued by prior growth in synthetic Qsp1, indicating that Liv3 is not required for the ability of Qsp1 peptide to promote SDS resistance (as expected from the lack of SDS sensitivity in the liv3Δ strain). These data are consistent with a model in which Cqs2 and Nrg1 function downstream of Qsp1 to promote resistance to a cell wall stress.To test whether Nrg1, Cqs2, or Liv3 were downstream of Qsp1, we created double knockouts of each transcription factor gene and 48 hours . The qspqsp1Δ mutant cultures grown in either rich media (YPAD) or minimal media (YNB) at an optical density at 600 nm (OD600) of 1, 5 and 10  or OD600 of 5 (OD5), more genes than in rich media at any culture density . Additionally, we confirmed that Qsp1 biology was not affected in these strains by verifying that qsp1Δ mutant strains harboring tagged C-terminally tagged Cqs2 and Liv3 and N-terminally tagged Nrg1 were sensitive to SDS in a manner that could be rescued by growing these strains in an excess of synthetic Qsp1 peptide  overlaps between promoters that displayed lower levels of binding by any two transcription factors in the qsp1Δ mutant, more so than between groups of promoters that are more bound in the qsp1Δ mutant  grown to OD1 in minimal media. Immunoblotting demonstrated that no significant difference in the levels of each of these tagged transcription factors in the mutant background compared to wild type   , which enfection ,43. In oIn conclusion, it seems that these three transcription factors are at the core of a gene regulatory network that integrates Qsp1 signaling with starvation or other unknown signaling inputs to determine which genes to express in different contexts (such as in the host or in different media), ultimately influencing the mating and virulence of this organism.Gene deletions were generated using nourseothricin (NAT) resistance, neomycin (NEO) resistance, or hygromycin (HYG) resistance cassettes. Proteins were tagged with 2x-FLAG or 3x-FLAG epitope tags using one of these three resistance cassettes as previously described . ConstruAll strains are derived from the KN99alpha (CM26) parent.Part of a colony was cultured in 5 ml of YPAD  for 48 hours, when all cultures were fully saturated. 1 uM pure synthetic Qsp1 peptide (LifeTein) was added to indicated cultures at the time of inoculation. 50 ul of saturated culture was mixed with 150ul of either water or SDS in a series of 1:2 dilutions (starting at 10%) and incubated for 3 hours at room temperature without shaking. The supernatant of settled incubations was partially replaced with water following SDS incubation and prior to cell resuspension to minimize the amount of SDS transferred to the recipient plate, and 3 ul were spotted on YPAD plates containing no SDS to assay survival. Cells incubated in water were then serially diluted 1:6 to provide a measure of the titer of the input culture. Plates were allowed to grow for 4 days at room temperature.600 units of cells per sample were fixed with 10% TCA, then 100% acetone, then lysed by two 1.5 minute rounds of bead-beating in sample buffer. Samples were then boiled for 5 min and cell debris was spun down. For supernatant analysis, 2 mL of conditioned media were snap frozen and lyophilized overnight, then resuspended in 150 μl of 1x Laemmli Sample Buffer. 5–10 μl of each sample was loaded on 4–12% Bis-Tris gels (Thermofisher).Two biological replicates from each strain were cultured for immunoblot. 2 OD600 units of cells were harvested sequentially from the same culture at the indicated ODs.Each strain of C. neoformans was inoculated in YPAD or YNB at 30°C. The next day, larger cultures were started from the starter cultures at an OD < 0.01. On the following day, as each culture grew in density 50 OD600’s of cells at each optical density  for each replicate for each strain were harvested sequentially as the cultures grew. For ChIP-seq samples, 50 OD600’s of cells were crosslinked in a 50 mL volume of conditioned media from the same culture, harvested at the same time as the cells.50 OD600’s of cells as previously described [Total RNA was isolated from 50 ODescribed  and librescribed . In brieescribed  and DNasescribed . 0.5 μg Expression analysis for each transcription factor mutant was performed by counting the number of reads aligned by STAR for each transcript . DEseq2 600 units of cells were crosslinked in 50 mL total of conditioned media. Lyophilized pellets were resuspended in 600 ul ChIP lysis buffer with protease inhibitors for bead beating until >95% of cells were lysed. The chromatin pellet was resuspended in 350 ul ChIP lysis buffer for sonication. After sonication and removal of cell debris via centrifugation, the supernatant was brought to 3 ml in ChIP lysis buffer. Immunoprecipitation was performed at 4°C overnight with nutation in 1 ml chromatin aliquots with 3 ul of anti-FLAG M2 antibody  and 20 ul of Protein G Dynabeads (Invitrogen). See GEO accession #GSE147378 for raw data. ChIP-qPCR primers are shown in ChIP was performed as described previously , with thChIP-seq library construction was performed as described previously  with theChIP-seq reads were trimmed with CutAdapt and aligned using Bowtie1 . Up to tt-test. P-values <0.05 were considered significant. All other P-values were adjusted (P-adj) for multiple testing using the Bonferroni correction  from the time of inoculation, or not (-). Water dilutions of each culture are shown to the right as a measure of cell input. Plates were allowed to grow up at room temperature for 4 days.(TIF)Click here for additional data file.S2 FigChIP-seq data visualized using the Integrative Genomics Viewer software. Scale bars measuring 1kb are shown at the top of each screenshot, along with chromosomal location. Gene transcripts are shown in black boxes with white arrows showing directionality.(TIF)Click here for additional data file.S3 Figqsp1Δ mutant highlighted in orange or light blue, respectively. The number of promoters in either of these groups is labeled with the corresponding color.ChIP score for each gene was calculated as the read depth in the 1 kb region upstream of the transcription start site, normalized to the untagged control. Only promoters that are called as bound in either genotype by k-means analysis are shown, with promoters that are more or less bound (>1.5-fold changed) by each factor in the (TIF)Click here for additional data file.S4 Figqsp1Δ knockout using the primers in ChIP was performed on tagged strains followed by qPCR to quantify binding of CNAG_00758 and CNAG_03465 by tagged Nrg1, Liv3, and Cqs2 in wild type or (TIF)Click here for additional data file.S5 Figqsp1Δ over wild type are shown in yellow.Non-significant differences are colored in white, significant decreases in mutant are shown in blue, and significant increases in (TIF)Click here for additional data file.S6 FigThe ChIP score for each gene was calculated as the read depth in the 1 kb region upstream of the transcription start site, normalized to the untagged control. Only genes that are called as bound in either genotype by k-means analysis are shown, with genes that are differentially bound by each factor in mutant compared to wild type (greater than 1.5-fold changed) highlighted in light blue.(TIF)Click here for additional data file.S7 Figqsp1Δ mutant highlighted (light blue and yellow). Promoters that are differentially bound by each transcription factor in the transcription factor mutant are highlighted in yellow.The ChIP score for each gene was calculated as the read depth in the 1 kb region upstream of the transcription start site, normalized to the untagged control. Only genes that are called as bound in either genotype by k-means analysis are shown, with genes that are differentially bound (>1.5-fold changed) by each transcription factor in the (TIF)Click here for additional data file.S1 Tableqsp1Δ mutant conditions.Quantification of bands in fluorescent immunoblots was performed using the ImageJ gel analysis tool. A student's T-test was performed to determine significance of the difference in averages of transcription factor expression between wild type and (XLSX)Click here for additional data file.S2 TableThe Fig generated from the data is indicated. Analysis of differentially bound (diff), decreased in binding level (less bound), or increased in binding levels (more bound) of these transcription factors to sets of genes in different mutants compared to wild type is included.(XLSX)Click here for additional data file.S3 Tableqsp1Δ over wild type are shown in yellow.Set of genes that are differentially bound by either Cqs2, Liv3, or Nrg1, and their respective log2-fold expression difference in qsp1Δ compared to wild type. Non-significant differences are given a value of "NA", significant decreases in mutant are shown in blue, and significant increases in (XLSX)Click here for additional data file.S4 Tableqsp1Δ mutants vs wild type, and the gene's respective log2fold expression change in qsp1Δ mutant vs wild type.Genes that are differentially bound by all three transcription factors are shown, with their fold change in Cqs2, Nrg1, or Liv3 binding in (XLSX)Click here for additional data file.S5 TableQuantification of bands in fluorescent immunoblots was performed using the ImageJ gel analysis tool. A student's T-test was performed to determine significance of the difference in averages of transcription factor expression between wild type and mutant conditions.(XLSX)Click here for additional data file.S6 Tableqsp1Δ vs. wild type by RNA-seq followed by DE-seq analysis.Values of ChIP scores for binding of each transcription factor in each genotype for each promoter, whether the promoter was called as bound by k-means analysis (“1”) or not (“0”), values for fold change in binding levels in differential binding analysis, and log2fold-change for expression of the gene downstream of the promoter in (XLSX)Click here for additional data file.S7 TableBound genes in each sample were determined by k-means analysis.(XLSX)Click here for additional data file.


Nicotiana benthamiana rRNA (NbrRNA) gene promoter. The system utilizes the cap-independent translation strategy of viral genomic mRNA and uses the virus-expressed green fluorescent protein (GFP) as an indicator of the rRNA gene promoter activity in virus-infected plants. Based on the reporter system, some characteristics of the N. benthamiana rRNA gene promoter were revealed. The results showed that the strength of the NbrRNA gene promoter was lower than that of the cauliflower mosaic virus (CaMV) 35S promoter, a well-characterized polymerase II promoter. The sequences between −77 and +42 are sufficient for the NbrRNA gene promoter-mediated transcription and the NbrRNA gene promoter may lack the functional upstream control element (UCE). Interestingly, NbrRNA gene promoter activity was increased when the 35S enhancer was introduced. An intron-excision mediated assay revealed that the NbrRNA gene promoter can be inefficiently used by RNA polymerase II in N. benthamiana cells. This virus-based reporter system is easier to operate and more convenient when compared with the previously Pol I promoter assays. And it offers a promising solution to analyzing the functional architecture of plant rRNA gene promoter.Reporter gene-based expression systems have been intensively used in plants for monitoring the activity of gene promoters. However, rRNA transcripts are unable to efficiently express a reporter gene due to a lack of a 5' cap. Because of this obstacle, plant rRNA gene promoters are less well characterized to this day. We developed a virus-based reporter system to characterize the  Transcription of nuclear DNA to RNA in eukaryotic cells is carried out by at least three distinct RNA polymerases . Each ofArabidopsis thaliana  that separate the numerous rRNA gene transcription units . In animthaliana . In commthaliana . Howeverthaliana .in vivo or in vitro  belongs to the genus Tombusvirus within the family Tombusviridae  in tobacco plants (sviridae . The gensviridae . We prevo plants . TheoretNicotiana benthamiana. Agroinfiltration (Agrobacterium tumefaciens-mediated approach) was employed to deliver the viral vectors into N. benthamiana leaves. The promoter activity was estimated by measuring the accumulation of recombinant GFP in the agroinfiltrated leaves. Through this virus-mediated reporter system, the characteristic of N. benthamiana rRNA gene promoter was analyzed. The results suggest that the established system is suitable for a detailed exploration of the structural architecture of plant rRNA gene promoters.To test the potential of the virus-based reporter system for functional analysis of plant rRNA gene promoters, a GFP-expressing TBSV-based viral vector was fused downstream of the rRNA gene promoter of the model plant N. benthamiana were planted in 12.5 cm (in diameter) plastic pots filled with the sterilized soils. After germination, the young seedlings were maintained under conditions at 22–25°C with 65% relative humidity and 16 h of light per day. All plants were well-watered when required and fed with standard Hoagland’s culture solution once a week.The seeds of in vitro DNA synthesis and conventional restriction enzyme-mediated cloning method.All TBSV viral cDNA constructs were created by NbrRNA gene promoter (GenBank accession no. KC352713.1), the various 5' deleted promoter fragments were synthesized in vitro with ApaI at 5' end and PvuI at 3'end . The fragments were then subcloned into the corresponding restriction enzyme sites of the Pol II-G vector, which is a non-fusion GFP-expressing TBSV construct under the control of the 35S promoter , hammerhead ribozyme, the 230 nts of 5'-UTR of TBSV genomic RNA (having a PvuI restriction enzymes site at its 3' end), were synthesized and substituted for the ApaI-PvuI digested fragment of Pol II-G as described above.To obtain −77+42 construct, the 407 bp long fragment, which successively contained EcoRI-HpaI fragment) and an enhancer sequence of 35S promoter (−343 to −90) were placed at 5' end of 407 bp long fragment of −77+42, respectively. The designed sequences were then synthesized and were separately cloned into the ApaI and PvuI sites of the Pol II-G vector to generate E1−77+42 and E2−77+42.To create two constructs with enhancer sequences, the sequence of Pol I enhancer from yeast  were selected that met the canonical consensus sequences of AG/GT. Nine positions were chosen  for intron insertion: 1, nt 581; 2, nt 784; 3, nt 939; 4, nt 1,135; 5, nt 1,372; 6, nt 1,581; 7, nt 1,880; 8, nt 2,137; and 9, nt 2,370. The designed sequences were then synthesized and were separately cloned into the PvuI and NheI sites of the previously constructed vectors to generate corresponding intron-containing viral vectors.For the generation of constructs with introns, introns ranging in size from 109 to 159 nt were selected from the A. tumefaciens cells of strain GV3101 according to procedures of the freeze-thaw method and then transformed cells were plated on LB agar plate with appropriate antibiotics  for plasmid selection. Colony PCR was used for identifying the positive recombinant colony. Selected A. tumefaciens colony was transferred into 4 ml of LB liquid medium containing appropriate antibiotics  and then incubated (28°C and 250 rpm shaking) for 24 h. After adding another 100 ml of LB medium (containing 200 μM acetosyringone), the cultured cells were keep growing overnight under the same conditions. Cells harvested by centrifugation at 4,000 g were suspended and adjusted to an OD600  of 1.0 by using MES buffer . The cell suspensions were put into the dark chamber and allowed to stand at room temperature for 2 h just before infiltration. After treatment for 2-h standing, the suspensions were infiltrated into N. benthamiana leaves via a needleless syringe.The recombinant plasmids were transformed into competent In one experiment, 15–20 seedlings and three leaves of each seedling were infiltrated. Three independent experiments were performed. The infiltrated leaves were then sampled at 4, 6, and 8 days post-inoculation (dpi). Every sample consisted of about 15 leaf disks that were cut out from inoculated leaves. The harvested leaves were stored in a −80°C refrigerator for later analysis. All samples were analyzed in triplicate.Green fluorescent protein signals were observed in infiltrated leaves under irradiation by a hand-held UV-lamp. And all of the pictures of images were taken in the darkened room by the use of a digital camera.g for a total of 15 min at 4°C. The supernatant containing total soluble proteins was transferred to a new tube and then was used in Western Blot or ELISA analysis immediately.Leaf tissue samples were frozen in liquid nitrogen and ground to a fine powder. A total of 0.1 g of leaf powder was then mixed with 400 μl of extraction buffer . The tissue homogenates were centrifuged at 12,000 Protein samples were heated at 95°C for about 5 min and then loaded on a 12% polyacrylamide gel  for protein separation. After electrophoresis (1 h at 180 V), the gel was stained overnight in the solution of Coomassie Brilliant Blue G-250 with gently shaking. For Western blot assays, a semi-dry electrophoresis transfer unit (Bio-Rad) was used to transfer the proteins from SDS-gel to a 0.45 μm NC membrane (Sigma-Aldrich). Following an overnight-block with phosphate-buffered saline (PBS) made 5% non-fat dry milk, strips from the blot were developed by rabbit polyclonal anti-GFP antibody (Abcam) with 1:1,000 dilutions, next by 1:5,000 goat anti-rabbit HRP conjugated secondary antibody (Sigma-Aldrich). Specific protein bands were visualized after the treatment of the strips with ECL-solution .Escherichia coli-produced GFP (BioVision) was used to generate an ELISA standard curve, and then the standard curve was utilized for calculating the GFP accumulation in the collected leaf samples. The protein samples from the empty vector infiltrated leaves were used as a negative control. The observed values from three independent experiments were performed with Student’s t-test and a value of p < 0.05 was considered significant.ELISA assay was applied to measure the accumulation of recombinant GFP in the inoculated leaves. ELISA plate filled with 100 μl of protein samples per well was incubated overnight at 4°C for antigen coating. Following washing procedures (three times and 5 min for each with PBS), 100 μl of 1:10,000 rabbit polyclonal anti-GFP antibody (in PBS) was added to each well of the plate, and then the plate was put into an incubator at 37°C for 2 h. Next, 100 μl of 1: 5,000 secondary antibody  was added to the carefully washed plate. After a 2-h incubation and a washing step, each plate-well was re-filled with 100 μl of tetramethylbenzidine (TMB) substrate solution (Solarbio) and incubated for 15–30 min. After that, 100 μl of phosphoric acid (1.0 M) was pipetted into wells to stop the reaction. A microplate reader (Bio-Rad) was employed to read optical density (OD) values at a wavelength of 450 nm. Meanwhile, N. benthamiana were extracted from 0.1 g of fresh tissue using the RNAprep Pure Plant Kit  according to the manufacturer’s instructions. Prepared total RNA (10 ng) was used to synthesize cDNAs by using a first-strand cDNA synthesis kit . The primer pair was 46intr-F (5'-GTAACACAGATCAATCGAG-3') and 46intr-R (5'-TGGATTCCATATGCCGTAG-3'). The predicted size of the PCR product with or without introns was 919 and 541 bp, respectively.Total RNAs of NbrRNA gene promoter, successive 5'-promoter deletions were prepared and designated as −498, −251, −125, and −77 . EcoRI-HpaI fragment of the Saccharomyces cerevisiae ribosomal gene spacer can enhance transcription from an adjacent RNA polymerase I promoter in vitro. To explore whether the heterologous enhancer could increase the activity of the NbrRNA gene promoter, the EcoRI-HpaI fragment and the enhancer of 35S promoter (−343 to −90) were used to generate two enhancer-containing constructs, E1−77+42 and E2−77+42.N. benthamiana leaves  construct with introns still can express GFP proteins and the ratio of GFP expression between the intronless and intron-containing construct is similar in all tests, indicating that the non-specificity of NbrRNA gene promoter transcription is not due to the modification and it is the intrinsic characteristic for NbrRNA gene promoter.To examine if the modification of the 7+42-int , were gea leaves , GFP flua leaves , and thea leaves . The datNbrRNA gene promoter regions show that both promoters share a consensus TATA-like sequence and the relatively conservative downstream flanking sequences  element. The transcriptional Inr overlaps the TIS and locates about 25–30 bp downstream of the TATA-box . Transcrequences , indicatNbrRNA gene promoter sequence, which is located 15–42 bp downstream of the TATA-like sequence, was substituted by A–T and G–C to generate construct E2−77+42 mD  and/or in the nucleolus, instead, rDNAs are located in NORs and are transcribed in the nucleolus. The nucleolus does not have a known membrane and/or other physical barriers  and all osophila , indicatN. benthamiana rRNA gene promoter was defined at least as far as −77 without losing promoter activity. However, we did not further define the 5' boundary of N. benthamiana rRNA gene promoter. It has been shown that diploid wheat rRNA gene promoter sequences localized between −113 and +15 are sufficient to guide RNA Pol I-mediated transcription  and the NbrRNA gene promoter is highly conserved (In art site . Howeveramanitin . Unfortuamanitin . Like whonserved . Second,onserved , 5. Thironserved . Doellinonserved .Xenopus spp. (S. cerevisiae (Arabidopsis enhancer can increase the transcription level of the frog rRNA gene in injected Xenopus oocytes (Enhancer elements were well characterized for RNA polymerase II genes . Howeverpus spp. , S. cererevisiae , mouse (revisiae , and so revisiae . Likewis oocytes . In our  oocytes . However oocytes . The Pol oocytes , convers oocytes , it is tOne might expect that the high efficiency of virus replication may obscure the differences in Pol I promoter activity. Very low levels of an initial transcript may be amplified to a level that reduces the differences in initial transcript levels from the promoter. So, it is possible that the 15-fold decrease in GFP caused by intron insertion, or the 4–5-fold increase due to the 35S enhancer, might be more than suggested by GFP levels. Even so, the TBSV replicon offers more sensitivity, and indeed shows some quantitative response that makes it a useful system.N. benthamiana. The developed assay system provides a novel solution to rRNA gene promoter analysis by employing a cap-independent translation strategy of viral RNAs. Besides, rRNA gene promoters of other plant species can be investigated by this methodology, because of the availability of the diverse viral RNA-based vectors. Furthermore, the system can also be used for the protoplast system by directly transferring virus plasmid vectors. Taken together, this RNA virus-based assay system could open up new possibilities for analyzing the characteristic of plant rRNA gene promoters.Plant rRNA gene promoters are less well characterized, largely due to the lack of a convenient and robust assay system to measure the promoter activity. In this study, we established a viral RNA-based reporter assay to characterize the rRNA gene promoter of The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.SW conceived, designed, and supervised the study, discussed the results, and wrote the manuscript. LX and ZL performed the experiments, analyzed the data, and wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Hepatocyte is a hub for cholesterol metabolism. Augmented synthesis of cholesterol in the liver is associated with hypercholesterolemia and contributes to the pathogenesis of a host of cardiovascular and metabolic diseases. Sterol response element binding protein 2 (SREBP2) regulates hepatic cholesterol metabolism by activating the transcription of rate-limiting enzymes in the cholesterol biosynthesis pathway. The underlying epigenetic mechanism is not well understood. We report here that mice with hepatocyte-specific knockout (CKO) of Brg1, a chromatin remodeling protein, exhibit reduced levels of hepatic cholesterol compared to the wild type (WT) littermates when placed on a high-fact diet (HFD) or a methionine-and-choline-deficient diet (MCD). Down-regulation of cholesterol levels as a result of BRG1 deficiency was accompanied by attenuation of cholesterogenic gene transcription. Likewise, BRG1 knockdown in hepatocytes markedly suppressed the induction of cholesterogenic genes by lipid depletion formulas. Brg1 interacted with SREBP2 and was recruited by SREBP2 to the cholesterogenic gene promoters. Reciprocally, Brg1 deficiency dampened the occupancies of SREBP2 on target promoters likely through modulating H3K9 methylation on the cholesterogenic gene promoters. Mechanistically, Brg1 recruited the H3K9 methyltransferase KDM3A to co-regulate pro-cholesterogenic transcription. KDM3A silencing dampened the cholesterogenic response in hepatocytes equivalent to Brg1 deficiency. In conclusion, our data demonstrate a novel epigenetic pathway that contributes to SREBP2-dependent cholesterol synthesis in hepatocytes. Cholesterol is an important bioactive lipid that plays a wide range of physiological and pathophysiological roles. As a major component of biomembranes, cholesterol regulates trans-membrane signaling events by modulating membrane fluidity . Cholestin vivo whereas SREBP2 mainly orchestrates cholesterogenesis , 3-hydroxy-3-methylglutaryl-CoA synthase 1 (HMGCS1), and squalene monooxygenase (SQLE)  .Transcriptional regulation in mammalian cells is dictated by the epigenetic machinery that includes various histone and nucleotide modifying enzymes, histone variants, non-coding regulatory RNAs, and ATPase-dependent chromatin remodeling complexes . Brahma Smarca4-Flox mice  to obtain hepatocyte conditional Brg1 knockout (HepcKO) mice. 6–8 week-old male HepcKO mice and their wild type (WT) littermates were fed on a high-fat high-carbohydrate (HFHC) diet  for 16 weeks to induce steatosis as previously described  supplemented with 10% FBS . To induce SREBP2 activity, cells were cultured in the LDM1 media containing lipid-depleted fetal bovine serum . Alternatively, cells were cultured in LDM2 media containing 0.1% bovine serum albumin  and 1% insulin-transferin-selenium . The sequences for siRNAs are: siBRG1#1, AACATGCACCAGATGCACAAG; siBRG1#2, GCCCATGGAGTCCATGCAT; siSREBP2, CCCAUAAUAUC AUUGAGAA; siKDM3A#1, GACATGTGGTAATTCTGCAAGA ATT; siKDM3A#2, TAAATGCTTCACAATCAAAGC. Transient transfection was performed with Lipofectamine 2000 . Cells were harvested 48 h after transfection.BRG1, 5′-GAGGAGGTCCGGCAGAAGAAATC-3′ and 5′-TTCTTCTGCTTCTTGCTCTC-3′; human KDM3A, 5′-GAGTTCAAGGCTGGGCTATTGT-3′ and 5′-TTCAGCCAC TTTGATGCAGCTA-3′; human HMGCR, 5′-GGGAACCT CGGCCTAATGAA-3′ and 5′-CACCACGCTCATGAGTTT CCA-3′; human LDLR, 5′-GTGCTCCTCGTCTTCCTTTG-3′ and 5′-GCAAATGTGGACCTCATCCT-3′; human HMGCS1, 5′-CTCCCTGACGTGGAATGTCT-3′ and 5′-GAACTGTCTGC CCAGGTGAT-3′; human SQLE, 5′-GCTGTGCTTTCCAGAG ATGG-3′ and 5′-GGCATCAAGACCTTCCACTG-3′; mouse Hmgcr, 5′-CTTGTGGAATGCCTTGTGATTG-3′ and 5′-AGC CGAAGCAGCACATGAT-3′; mouse Ldlr, 5′-AGGCTGTGGGC TCCATAGG-3′ and 5′-TGCGGTCCAGGGTCATCT-3′; mouse Hmgcs, 5′-GCCGTGAACTGGGTCGAA-3′ and 5′-GCATATAT AGCAATGTCTCCTGCAA-3′; mouse Sqle, 5′-AAATCAGAG CCGTGGGCTAC-3′ and 5′-GGAAGTGACACAGTTCTATG-3′. Ct values of target genes were normalized to the Ct values of a housekeekping control gene  using the ΔΔCt method  as previously described , 2019b. t method  and exprWhole cell lysates were obtained by re-suspending cell pellets in RIPA buffer  with freshly added protease inhibitor tablet . Immunprecipitation was performed essentially as previously described . Briefly7/∼10 reactions) were cross-linked with 1% freshly prepared formaldehyde at room temperature for 10 min. Cells were washed with PBS and re-suspended in cell lysis buffer and then nuclear lysis buffer to extract chromatin per vendor’s instruction. The resulting material was then sonicated to create appropriately sized (200–500 bp) chromatin fragments using a Bioruptor (Diagenode). For liver tissue ChIP, we used a Magna ChIP G Tissue kit . Briefly, chop tissue into small pieces (1–2 mm2) with a razor blade or scalpel. Transfer tissue into a tube with a screw cap lid and add formaldehyde to a final concentration of 1% and rotate tube at room temperature for 10 min. Chromatin was prepared by re-suspending fixed tissue pellet in tissue lysis buffer supplied by the vendor and sonicated to 200–500 bp. Aliquots of lysates containing 200 μg of protein were used for each immunoprecipitation reaction with the following antibodies: anti-Brg1 , anti-acetyl histone H3 , anti-acetyl histone H4 , anti-trimethyl histone H3K4 , anti-KDM3A , and anti-SREBP2 . For Re-ChIP, immune complexes were eluted with the elution buffer , diluted with the re-ChIP buffer , and subjected to immunoprecipitation with a second antibody of interest. Precipitated DNA was amplified with the following primers: human HMGCR promoter, 5′-GACCAATAGGCAGGCCCTAGTGC-3′ and 5′-CTCTGCAG GGCCAAGAACAGG-3′; human LDLR promoter, 5′-TCCTC TTGCAGTGAGGTGAA-3′ and 5′-TTTCTAGCAGGGGGA GGAGT-3′; human HMGCS1 promoter, 5′-TGGCCCGC ATCTCCTCTCAC-3′ and 5′-GCTAGGATTTTCCCTCGTG-3′; human GAPDH promoter, 5′-GGGTTCCTATAAATACGGA CTGC-3′ and 5′-CTGGCACTGCACAAGAAGA-3′; mouse Hmgcr promoter, 5′-TCGTGACGTAGGCCGTCAG-3′ and 5′-CCAATAAGGAAGGATCGTCCG-3′; mouse Ldlr promoter, 5′-AGCTTCAGGGGTTAAAAGAG-3′ and 5′-CGGTGCTCA TCCTTAGCTT-3′; mouse Hmgcs promoter, 5′-ATTGGTC GGAGAACCTCTC-3′ and 5′-AGGGGTGGGAACAAAGTCC-3′; mouse Gpadh promoter, 5′-ATCACTGCCACCCAGA AGACTGTGGA-3′ and 5′-CTCATACCAGGAAATGAGCTTGA CAAA-3′. 10% of the starting material was included as the input. Data are normalized to the input and expressed as % of recovery.ChIP assays were performed essentially as described before  using anpost hoc Scheffe analyses were performed to evaluate the differences using an SPSS package . The differences between two  groups were determined by two-sided, unpaired Student’s t-test.Data are presented as mean ± SD. For experiments concerning multiple groups, one-way ANOVA with in vivo in two classical models of steatosis. BRG1 was specifically deleted from hepatocytes by Alb-Cre driven removal of the floxed Smarca4 allele (Hmgcr), low-density lipoprotein receptor (ldlr), 3-hydroxy-3-methylglutaryl-CoA synthase 1 (Hmgcs1), and squalene monooxygenase (Sqle), were all down-regulated in the livers of CKO mice than WT mice . Exposure to LDM1 significantly up-regulated the transcription of cholesterogenic genes; BRG1 knockdown by two separate pairs of siRNAs attenuated the induction of cholesterogenic genes . AlternaGapdh promoter, in the steatotic livers than in the normal livers. Similarly, stronger BRG1-SREBP2 interaction was detected on the cholesterogenic gene promoters when HepG2 cells were stimulated with LDM1 or LDM2 . Previously Briggs and colleagues have reported that the lysine methyltransferase Set1, which is responsible for catalyzing the active trimethyl H3K4 marker, regulates the synthesis of ergosterol, the yeast equivalent of mammalian cholesterol . In addiIn summary, building on previous findings that BRG1 plays pivotal roles in regulating free fatty acid synthesis we present data to show that BRG1 functions as a co-factor for SREBP2 to regulate cellular cholesterol synthesis. Continuing this line of investigation with a focus on the molecular mechanism whereby BRG1 regulates SREBP2 activity will hopefully solidify the role of BRG1 leading to a rationalized decision to target BRG1 in the intervention of NASH.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.The animal study was reviewed and approved by the Committee on Ethical Conduct of Animal Studies of Nanjing Medical University.YX and XF conceived the project and supervised the project. ZF, MK, ML, and WH performed the experiments and collected the data, and analyzed the data. YX wrote the manuscript. ZF, XF, and YX secured the funding.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


S100A8/S100A9, a secreted cytokine, also acts in the nucleus as a transcriptional coactivator to promote cellular transformation. Cytokines are extracellular proteins that convey messages between cells by interacting with cognate receptors at the cell surface and triggering signaling pathways that alter gene expression and other phenotypes in an autocrine or paracrine manner. Here, we show that the calcium-dependent cytokines S100A8 and S100A9 are recruited to numerous promoters and enhancers in a model of breast cellular transformation. This recruitment is associated with multiple DNA sequence motifs recognized by DNA binding transcription factors that are linked to transcriptional activation and are important for transformation. The cytokines interact with these transcription factors in nuclear extracts, and they activate transcription when artificially recruited to a target promoter. Nuclear-specific expression of S100A8/A9 promotes oncogenic transcription and leads to enhanced breast transformation phenotype. These results suggest that, in addition to its classical cytokine function, S100A8/A9 can act as a transcriptional coactivator. Cytokines are a large and diverse group of small proteins that are important in many biological processes, such as immune responses, inflammation, and cancer. By conveying messages between cells by interacting with their cognate receptors at the cell surface, cytokines are a critical aspect of autocrine, paracrine, and endocrine signaling. Upon cytokine binding, some surface receptors activate the Janus kinase–signal transducer and activator of transcription (JAK-STAT) signaling pathway to regulate gene transcription  are calcium-binding proteins that form homodimeric, heterodimeric, and higher-order complexes that are essential for their biological activities  . These hybrid proteins localize to the nucleus , and theTo investigate the role of nuclear S100A8/A9, we performed ChIP-sequencing (ChIP-seq) using stable cell lines expressing Flag-tagged proteins at comparable levels of endogenous proteins in the absence of tamoxifen . The nucR = 0.88) between S100A8 and S100A9 binding levels . Polymerase II (Pol II) occupancy at all these three groups of genes is higher in the transformed condition as compared with the nontransformed condition . Genes wP = 4.4 × 10−11 by Fisher’s exact test; We analyzed the relationship between S100A8/A9 binding and gene expression upon transformation by comparing RNA expression levels of genes whose regulatory regions are bound by S100A8/A9 with those that are unbound. In both nontransformed and transformed conditions, the S100A8/A9 bound genes are expressed at higher levels than the unbound genes . In addiP < 2.2 × 10−16; Fisher’s exact test) with contributions from ONG (P = 2 × 10−8), TSG (P = 2 × 10−14), and OncoTSG enrichment (P = 6 × 10−6). The distinction between cancer and noncancer genes is also observed when considering S100A8/A9 target sites showing the highest level of inducible binding upon transformation , protein binding, mRNA processing, cytokine secretion, cell adhesion, and cell cycle (fig. S4). In addition, we addressed the relationship between S100A8/A9 target genes and human cancer gene annotations from UniProt . Notablyormation . At targormation .To examine the function of nuclear S100A8/A9, we measured the expression of ONG, TSG, and OncoTSG genes (six genes for each class), whose promoters and enhancers are bound by S100A8/A9 in cells expressing S100A8 or S100A9 derivatives with a nuclear localization signal (NLS) (fig. S5). When considered as a class, the ONGs show increased expression upon ectopic expression of either S100A8 or S100A9, either in the presence or absence of tamoxifen . In contAs S100A8/A9 does not have intrinsic DNA binding activity, they are presumably recruited to genomic sites by association with specific DNA binding transcription factors. To identify these transcription factors, we performed de novo motif searches in S100A8/A9 binding sites in promoter, enhancer, and repressed regions. We identified ETS (Erythroblast Transformation Specific), KLF (Kruppel-Like Factor)/SP1 (Specificity Protein 1), THAP11 (THAP domain Containing 11), AP-1, CREB (cyclic adenosine monophosphate response element–binding protein), and ZBTB33 (Zinc finger and BTB domain containing 33), motifs enriched in promoter peaks; AP-1, CTCF (CCCTC-binding Factor), CEBP (CCAAT/Enhancer-Binding Protein), and STAT3 motifs enriched in enhancer peaks; and CTCF motif enriched in repressed peaks . Using sFor each motif or selected combination of motifs located within 50 base pairs (bp) of the S100A8/A9 peak summits, we determined the fold change of S100A8/A9 binding at that site during transformation (tamoxifen:ethanol ratio). At promoters, fold increases in S100A8/A9 binding observed at sites with ETS, KLF/SP1, THAP11, CREB, or ZBTB33 motif alone or in combination are subtle, although statistically significant when compared against target sites lacking any of the listed motifs . HoweverTo examine more directly whether S100A8/A9 behaves like a coactivator, we first performed coimmunoprecipitation experiments in nuclear extracts from which nucleic acids were degraded. As expected from the motifs enriched among S100A8/A9 target sites, S100A9-Flag immunoprecipitates FOS, STAT3, C/EBPβ, and CTCF, but not SNAI1 . In accoTo examine whether S100A8/A9 has an autonomous transcriptional activation function, we fused the individual full-length proteins to the DNA binding domain of the yeast Gal4 proteins. When compared with the Gal4 DNA binding domain alone, the resulting fusion proteins confer about threefold higher levels of transcription from a Gal4-based reporter construct . These oCytokines are secreted molecules that bind and activate cell surface receptors, thereby transducing extracellular signals to effector proteins that regulate gene expression and other biological events. Many cytokines are also found in the nucleus and can be associated with chromatin, but the nuclear functions of cytokines are poorly understood. In two cases, S100A9 and interleukin-33, ChIP analysis of an individual gene has suggested that these cytokines could associate with a specific genomic region. Here, we show that S100A8/A9 associates specifically with numerous genomic regions even though the cognate receptors are absent from the nucleus. We provide evidence that S100A8/A9 functions as a transcriptional coactivator and show that nuclear-localized S100A8/A9 increases transformation efficiency.Multiple lines of evidence support the conclusion that S100A8/A9 can act as a transcriptional coactivator. First, S100A8/A9 binding sites occur primarily in promoters and enhancers, and binding is associated with active chromatin regions and Pol II transcription. Second, most DNA sequence motifs associated with S100A8/A9 binding are recognized by transcriptional activator proteins, with the level of binding being increased at motifs of activators and decreased at motifs of CTCF, an insulator protein associated with repression. Third, in nuclear extracts, S100A9 coimmunoprecipitates and, hence, interacts (directly or indirectly) with proteins that recognize the motifs enriched among genomic sites bound by S100A8/A9. Fourth, nuclear-specific expression of S100A8/A9 increases transcription of many genes tested, with a preference for ONGs. Fifth, S100A8 and S100A9 can stimulate transcription from a reporter construct when artificially recruited by a heterologous DNA binding domain.There are several possible mechanisms, not mutually exclusive, by which S100A8/A9 could function as a transcriptional coactivator upon recruitment to specific genomic sites. The artificial recruitment experiment  suggestsS100A8/A9 is up-regulated and functionally important in many forms of human cancer  were washed with phosphate-buffered saline (PBS) and treated with trypsin. The trypsinized single cells were resuspended in the upper agar phase , which was layered onto the lower agar phase (growth medium containing 1.0% agar). The solidified agar was covered with growth medium, which was changed every 3 days. Colonies (>0.01 mm2) were counted with ImageJ after 30 days.MCF-10A cells expressing ER-Src (MCF-10A–ER-Src cells) were grown in DMEM/F-12 (Dulbecco’s Modified Eagle Medium/Nutrient Mixture F-12) with 5% charcoal-stripped fetal bovine serum (Sigma-Aldrich) and supplements as previously described . For NLS-GFP, GFP with triplicated NLS (sequence copied from Invitrogen’s pCMV/myc/nuc) at both the N- and C-terminal sides was cloned from pEGFP-N1 (provided by R. Pomerantz from Temple University School of Medicine) using ultramer primers and then was subcloned into pLHCX. For NLS-GFP-A8 or NLS-GFP-A9, S100A8 or S100A9 open reading frame was subcloned to the C-terminal side of NLS-GFP in pLHCX.Retrovirus was produced by transfection of corresponding retroviral vector in HEK293T cells carrying pCL-Ampho (provided by V. Sukhatme). Retroviral supernatant from HEK293T cells was collected at 48 and 72 hours after transfection, filtered through 0.45-μm filter, and used to infect exponentially growing target cells in the presence of polybrene . Cells were passaged after 72 hours of infection, after which drug selection was performed with hygromycin B . For nuclear-specific expression of S100A8 or S100A9, cells expressing NLS-GFP, NLS-GFP-A8, or NLS-GFP-A9 were further selected by a cell sorting flow cytometer  based on GFP signals, and GFP levels for all groups of cells were kept similar on average during the selection.Firefly and Renilla luciferase activities were measured with the Dual-Glo Luciferase Assay System (Promega). Luciferase activity is calculated as Firefly normalized by Renilla.Gal4 DNA binding domain (DBD)–based constructs were generated by replacing the NLS-GFP region of NLS-GFP, NLS-GFP-S100A8, or NLS-GFP-S100A9 with Gal4DBD cloned from pCAG-GBP1-10gly-Gal4DBD . For the Gal4-based reporter assays, Gal4-based constructs (1 μg), pUAS-luc2 , and pCMV-RL  were electroporated together into human breast MCF7 cells by the 4D-Nucleofector System with the SE Cell Line 4D-Nucleofector X Kit (Lonza). Forty-eight hours after the electroporation, g for 10 min. For cytosol/nuclear fractionation, cells were incubated in buffer A  for 15 min, followed by mixture with Triton X-100  for 1 min, and the cytosol fraction was cleared by centrifugation at 16,000g for 5 min. The nuclear pellet was briefly washed in buffer B , dissolved in 1× RIPA buffer, and briefly sonicated, and the nuclear fraction was cleared by centrifugation at 20,000g for 10 min.Cells were lysed in 1× radioimmunoprecipitation assay (RIPA) buffer  supplemented with protease inhibitor cocktail (Sigma-Aldrich), followed by brief sonication, and the lysate was cleared by centrifugation at 20,0002, 0.4% NP-40, 25% glycerol, and protease inhibitor cocktail) for 1 hour, diluted to 100 mM NaCl strength with buffer D , and treated overnight with Benzonase (Millipore), and the nuclear extract was cleared by centrifugation at 20,000g for 10 min. Coimmunoprecipitation and Western blot analyses were performed as previously described –fluorescein isothiocyanate and anti-mouse IgG–Texas Red. Clinical immunohistology data of S100A9 and S100A8 were obtained from The Human Protein Atlas database , and S100A9 (HSC.RNAI.N002965.12.1) were obtained from Integrated DNA Technologies. siRNA-mediated knockdown was performed as previously described (20 and SuperScript III (Invitrogen). Analysis of relative RNA expression was performed by quantitative polymerase chain reaction in real time. The relative RNA levels of indicated genes were normalized to the average RNA level of two control genes rRNA18S and FAM50A in the same samples. Data presented were obtained from four independent experiments.RNA was extracted with QIAzol (QIAGEN) and precipitated with isopropanol. cDNA was generated from 3.5 μg of total RNA template using oligo(dT)Ribosome profiling and RNA sequencing (RNA-seq) experiments were described previously  for sonication and subsequent immunoprecipitation . The sheared chromatin fragments were enriched around 200 to 300 bp. For ChIP and corresponding chromatin input samples, DNA was isolated and used to prepare libraries, which were sequenced on Illumina NextSeq 500 or HiSeq 2000. Other ChIP-seq  and DNase sequencing (DNase-seq) experiments mentioned in this study were performed in transformed or nontransformed cells as previously described , 1.5 mM disuccinimidyl glutarate (DSG), and 1.5 mM disuccinimidyl suberate (DSS) in PBS] for 45 min at room temperature. Then, protein-DNA cross-linking with 1% formaldehyde (methanol free) was performed for 15 min at room temperature, and the reaction was quenched by 125 mM glycine. For Pol II ChIP, standard formaldehyde cross-linking was performed. After fixation, nuclei were isolated and dissolved in sonication buffer  peaks called by MACS2 were identified in at least two biological replicates; (iii) at least five raw reads were aligned at the peak region. Depending on the comparisons indicated, peak regions from different datasets were merged if overlapping at least 1 bp. Confident Pol II ChIP-seq peaks were defined as MACS2 paired-end peak-calling P value ≤1 × 10−8 against genomic background of 1-Mb genomic region around. In ChIP-seq data of histone modifications and DNase-seq data, confident sites were defined as previously described  in hg19 refGene collection were downloaded from UCSC  Genome Browser. Classification of S100A8/A9 peaks was determined by patterns of histone modifications. For promoter-bound S100A8/A9 peaks, genes were assigned by mapping the nearest annotated TSS to the peak summit if the peak summit was within 1500 bp upstream and 250 bp downstream of the TSS. If the peak summit was located downstream of the TSS even though within the 250-bp range, we further required that the distance of the peak summit to TSS be <5% of the gene length because the promoter region of a given gene should be around TSS but not largely extended to gene body region. For enhancer-bound S100A8/A9 peaks, genes were assigned either if their TSSs were within 50 kb the of peak summit or if the gene regions overlapped with the peak summit.Promoter proximal was defined as ±250 bp around TSS, and gene body was defined as the remaining length of gene. Calculation of Pol II density as reads per million per kilobase (RPM/kb) was carried out in promoter proximal and gene body of each gene longer than 251 bp. The Pol II density ratio of promoter proximal and gene body was defined as pausing index or traveling ratio  were compared with JASPAR CORE collection of vertebrate motifs (De novo motifs were searched with HOMER (We collected and consolidated annotations of human cancer genes from the following sources: (i) 420 human cancer genes from UniProt . Molecular subtypes of breast cancer were defined by PAM50 classification  online server with default knowledgebase mapping  and are accessible through GEO accession number GSE155421.