Phosphorylation of histone demethylase KDM3A in response to thermal stress enables its specific recruitment to target genes by Stat1. Histone lysine (K) residues, which are modified by methyl- and acetyl-transferases, diversely regulate RNA synthesis. Unlike the ubiquitously activating effect of histone K acetylation, the effects of histone K methylation vary with the number of methyl groups added and with the position of these groups in the histone tails. Histone K demethylases (KDMs) counteract the activity of methyl-transferases and remove methyl group(s) from specific K residues in histones. KDM3A  is an H3K9me2/1 demethylase. KDM3A performs diverse functions via the regulation of its associated genes, which are involved in spermatogenesis, metabolism, and cell differentiation. However, the mechanism by which the activity of KDM3A is regulated is largely unknown. Here, we demonstrated that mitogen- and stress-activated protein kinase 1 (MSK1) specifically phosphorylates KDM3A at Ser264 (p-KDM3A), which is enriched in the regulatory regions of gene loci in the human genome. p-KDM3A directly interacts with and is recruited by the transcription factor Stat1 to activate p-KDM3A target genes under heat shock conditions. The demethylation of H3K9me2 at the Stat1 binding site specifically depends on the co-expression of p-KDM3A in the heat-shocked cells. In contrast to heat shock, IFN-γ treatment does not phosphorylate KDM3A via MSK1, thereby abrogating its downstream effects. To our knowledge, this is the first evidence that a KDM can be modified via phosphorylation to determine its specific binding to target genes in response to thermal stress. Histone methylation regulates gene expression and can have drastic consequences for health if the process is defective. Histone lysine demethylases (KDMs) counteract the activity of methyl-transferases and remove methyl group(s) from histones. KDM3A is a H3K9me2/1 demethylase that performs diverse functions via the regulation of its target genes, which are involved in spermatogenesis, metabolism, and cell differentiation. However, the mechanisms underlying KDM3A regulation of specific genes at specific times are largely unknown. Here we found that a physiological stress—elevated temperature—induces KDM3A phosphorylation in human cells via the MSK1 kinase. This phosphorylated form of KDM3A directly interacts with the transcription factor Stat1, which enables Stat1 to recruit KDM3A to Stat1-binding sequences at the promoters of specific target genes. KDM3A then acts to demethylate H3K9me2/1 at these targets, thereby causing specific gene expression in response to the thermal stress. We conclude that heat shock can affect the expression of many genes in human cells via a novel activation mechanism that is centered around the phosphorylation of KDM3A. The phosphorylated KDM3A (p-KDM3A) is enriched at the regulatory regions of gene loci and co-localizes with Stat1 in the human genome. Extensive experiments indicate that p-KDM3A directly interacts with and is recruited by Stat1 to mediate chromatin remodeling and the expression of its target genes in response to heat shock.Histone modifications are recognized by specific proteins, including transcription factors (TFs), thereby mediating functional signaling to affect chromatin condensation or remodeling near target genes Drosophila ortholog of human MSK1, is activated in response to heat shock To explore the upstream kinase responsible for KDM3A phosphorylation under heat shock, mitogen- and stress-activated protein kinase 1 (MSK1) was considered as the most likely candidate because Jil1, the 32P-ATP to label the phosphorylated substrate. We demonstrated that only the GST-fused wild-type N-terminal KDM3A (1-394 aa), but not the S264A mutant (S/A), was phosphorylated by MSK1 based on 32P labeling . By screening the differential SICER intervals near gene promoters (from −5 kb to approximately +2 kb) (FDR threshold 10−20), KDM3A and the non-treated or heat-shocked p-KDM3A target genes were identified, as shown in the Venn diagrams  and reverse transcription quantitative PCR (RT-qPCR) were performed. The data demonstrated that the occupancy of p-KDM3A at all four gene loci examined  cell extracts for co-IP assays. We demonstrated that KDM3A and Stat1 interacted with one another only under HS . Based oThen, we determined which region of Stat1 is required for its interaction with KDM3A-S264D in these cells. Among the Stat1 fragments S1, S2, S4, and S5 that interacted with KDM3A-S/D . Based oData from hsp90α (hsp90aa1) under HS, which indicated the reads were enriched around the TSS of a cluster 1 gene. p-KDM3A under HS was markedly enriched at the TSS that is dominant over either non-heat shock p-KDM3A or non-phosphorylated KDM3A without HS  that is specific for the demethylation of H3K9me2/me1. This demethylase contains a JmjC domain at 1058-1281 aa and a zinc finger domain at 662-687 aa hsp90α  pathways to induce expression of heat-shock-related genes, we present a novel, generalized heat-shock-induced activation mechanism that is centered on the phosphorylation of KDM3A. (1) p-KDM3A-S264 is enriched genome-wide at the promoter region of several genes, including heat-shock-related genes, under heat shock; (2) p-KDM3A is guided by a TF to the binding element of TF in the genome; (3) the genomic occupancy of p-KDM3A at its target genes is a prerequisite for the demethylase activity of KDM3A in situ; and (4) the phosphorylation of KDM3A is specifically dependent on the upstream stimulus-dependent kinase activity of MSK1 in HS- but not IFN-γ-treated Jurkat cells.Antibodies against KDM3A, p-MSK1, GAPDH, H3K9me2, and H3K9me3 and recombinant activated MSK1 were purchased from Millipore Biotech . The FLAG and M2 antibodies were purchased from Sigma. The GST, MSK1, MSK2, HA, and Stat1 antibodies were purchased from Santa Cruz Biotechnologies . The anti-phosphorylated serine (p-Ser)  was purchased from Merck . A specific antibody against p-S264-KDM3A was produced by Beijing B&M Biotech  using the synthesized peptide VKRKSSENNG, corresponding to residues 260–269 of KDM3A, as an antigen.The FLAG-tagged MSK1 eukaryotic expression plasmid was constructed by cloning MSK1 into the pcDNA6-FLAG vector using a PCR product from a Jurkat cell cDNA library. We inserted point mutations at amino acids 165 (D to A) and 565 (D to A) in full-length FLAG-MSK1 to produce DN-MSK1 hsp90α) were normalized to those of GAPDH using the comparative CT method according to the manufacturer's instructions . The specific primers corresponding to the above genes are listed in RT-qPCR was performed as described previously hsp90α) are listed in The ChIP assays were performed as described previously For ChIP-reChIP analysis 7 Jurkat cells with or without HS treatment were immunoprecipitated using IgG or an antibody against KDM3A or p-KDM3A. The DNA fragments were end-repaired, adenylated, ligated to adaptors, and PCR-amplified for 18 cycles. The PCR products corresponding to bp 250-450 were gel-purified, quantified and stored at −80°C until use for sequencing. For high-throughput sequencing, the libraries were prepared according to the manufacturer's instructions, and to the samples were analyzed using an Illumina GAIIx system for 80-nt single-end sequencing .For ChIP-Seq, the chromatin fragments of 1×10The data were analyzed using Active Motif; the flow chart of analysis is shown in −10; window size: 200 bp; fragment size: 200 bp; gap size: 200 bp; hg19 genome database) and MACS 1.4.2  http://www.ncbi.nlm.nih.gov/geo/) accession number for the ChIP-seq data is GSE62309.For peak calling, the mapped BED files were inserted into SICER V1.1 p-value reflects the significance of the targeted genes enriched in a GO term.The GO and MSigDB Pathway analyses were conducted using GREAT 2.02 on the SICER intervals data limited to the regulator regions (from −5 kb to approximately +2 kb of the TSS). The pathway analysis database in GREAT is the MSigDB from the Gene Set Enrichment Analysis. The binomial To identify the genome sites with more p-KDM3A after heat shock, we used the p-KDM3A HS (+) MACS interval peaks in Active Regions  to perform a sample comparison with peak metrics against the p-KDM3A HS (−). The unique intervals were annotated into genes (between 10 kb upstream and 10 kb downstream). The GO analysis of these genes was described above.Transcription factor motifs were identified around p-KDM3A SICER islands (FA files) after heat shock using MEME (version 4.9.1) 7 cells were washed twice in PBS, and the nuclei were extracted as described above and digested with DNase I (ranging from 0 to 80 units/ml) on ice for 10 min. The DNase I digestion was terminated by incubating in stop buffer  at 65°C for 10 min. Then, the nuclei were digested with 50 µg/ml RNase A at 37°C for 60 min and 50 µg/ml proteinase K at 50°C overnight. The genomic DNA was purified via phenol/chloroform extraction and ethanol precipitation Jurkat cells were transiently transfected with shRNA-MSK1 or shRNA-KDM3A. A total of 1×10Escherichia coli (BL21 DE3) and purified using glutathione-sepharose. GST and GST-Stat1 were bound to glutathione-sepharose, and 10 µl packed beads containing 5 µg the GST or GST-Stat1 fusion protein were incubated in the product of the kinase assay for MSK1 and KDM3A. After overnight incubation at 4°C, the beads were washed three times, and the bound proteins were analyzed via western blot.The GST-Stat1 fusion protein was expressed in The Co-IP analyses were performed using approximately 500 µg protein samples that were incubated in a specific antibody for 2 hr at 4°C. In total, 20 µl Protein A (or G)-agarose were added, and the samples were incubated at 4°C overnight. Then, the pellets were washed with RIPA buffer, followed by the addition of 40 µl 1× Laemmli buffer. Then, the samples were resuspended and boiled. The samples were separated via SDS-PAGE and analyzed via sequential western blot using individual antibodies 32P]ATP in kinase buffer  for 30 min at 30°C. The reaction products were resolved via SDS–PAGE for western blot using specific antibodies; alternatively, the 32P-labeled proteins were visualized via autoradiography. Recombinant MSK1 was incubated in 1 µg of the synthesized peptide cVKRKSSENNG, corresponding to residues 260-269 of KDM3A, in the presence of 50 µM ATP in kinase buffer for 30 min at 30°C. The reaction products were purified for mass spectrometric analysis . Recombinant MSK1 was incubated in full-length GST-KDM3A for the kinase assay; then, 2 µg histone from HeLa cells was added to demethylation buffer 2(SO4)2) at 37°C for 2 hr, and the reaction was terminated by adding SDS-PAGE loading buffer. The results were analyzed via western blot using specific antibodies.Recombinant MSK1 (Millipore Biotech) was incubated in 1 µg purified wild-type or mutant KDM3A (1-394) in the presence of 50 µM ATP or 5 µCi [γ-The numerical data in all figures are included in S1 DataThe numerical data in all figures.(XLS)Click here for additional data file.S1 FigureKDM3A is recruited to the upstream of hsp90αin response to heat shock. The ChIP assay demonstrated the recruitment of KDM3A, KDM4A, and KDM4C upstream of human hsp90α upon HS treatment. The cells were transfected with FLAG-tagged KDM3A, KDM4A, or KDM4C. The chromatin fragments were pulled down using a specific antibody against FLAG. The duration of HS treatment is indicated at the bottom of each bar (0–60 min). The annotations are the same as those in p<0.05, **p<0.01). The data used to make this figure can be found in (TIF)Click here for additional data file.S2 FigureCharacterization of the antibody specific for p-KDM3A-S264. (A) Western blot indicating the antibody efficiency for p-KDM3A using KDM3A phosphorylated by MSK1 in vitro. The phosphorylated peptide cVKRK(p)SSENNG (p-peptide) was used as a specific competitor, and the non-phosphorylated peptide was used as a control. (B) The cells were treated with HS for 0, 30, or 60 min. The specificity of the anti-p-KDM3A antibody was determined via western blot, as described above.(TIF)Click here for additional data file.S3 Figurep-KDM3A interacts with MSK1 in heat-shocked cells. (A) The cells were transfected with FLAG-S/A-KDM3A. Co-IP assays were performed using an anti-FLAG antibody, followed by western blot using antibodies for p-MSK1 and FLAG. (B) The cells were transfected with FLAG-tagged wild-type or DN-MSK1. Co-IP was performed using an anti-FLAG antibody, followed by western blot using anti-KDM3A and anti-FLAG antibodies. The inputs and the IP using IgG are shown as controls.(TIF)Click here for additional data file.S4 FigureHistone H3K9me2 demethylation assay in vitro. The histone demethylation assay demonstrated that the phosphorylation of KDM3A at S264 did not affect the demethylase activity of KDM3A on H3K9me2. Recombinant MSK1 and GST-KDM3A were initially mixed for the kinase assay and were subsequently added to histones that were purified from HeLa cells for the demethylase activity assay. The reaction products were separated via SDS-PAGE for western blot using the H3K9me2 antibody. Other antibodies used included those used for the kinase assay control: H3K9me3 as a demethylase activity control and MSK1, GST, and H3 as input controls.(TIF)Click here for additional data file.S5 FigureGO and pathway analyses of the KDM3A HS (-) and p-KDM3A HS (-) binding genes.(TIF)Click here for additional data file.S6 FigureMotif analysis of the p-KDM3A-enriched regions using discriminative DNA motif discovery (DREME) (TIF)Click here for additional data file.S7 FigureInteraction between Stat1 and p-KDM3A. (A) Jurkat cells were transfected with FLAG-KDM3A(1-661), FLAG-KDM3A(661-1321) and FLAG-KDM3A(214-306) and treated with HS for 1 hr. Co-IP assays were performed using an anti-FLAG antibody, followed by western blot using antibodies for p-MSK1, MSK1, and FLAG. (B) The cells were treated with HS for the indicated time (min). Then, the cell lysates were immunoprecipitated using an anti-Stat1 antibody, followed by western blot using antibodies against Stat1, MSK1, and p-KDM3A. The inputs and IP using IgG are shown as controls.(TIF)Click here for additional data file.S8 FigureThe effects of Stat1 knockdown on the occupancy of phosphorylation mimic of KDM3A. (A) The cell extracts from Jurkat cells transfected with either the i-Stat1 or mock vector were used for western blot. Based on western blot for Stat1, only a minimal level of Stat1 was detected in the i-Stat1-transfected cells. GAPDH was used as a control. (B) The Jurkat cells were co-transfected with KDM3A-S/D and Mock or i-Stat1. A ChIP assay showed the effect of knockdown of Stat1 on the occupancy of KDM3A-S/D at the upstream of hsp90α. Data are mean ± SD (**p<0.01). The data used to make this figure can be found in (TIF)Click here for additional data file.S9 FigureThe effects of KDM3A knockdown on the occupancy of Stat1, phosphorylated Stat1, and Brg1 at the GAS of hsp90α. (A) Western blot of the cell extracts from Jurkat cells that were transfected with either the shKDM3A or mock vector using the antibodies shown on the right. GAPDH was used as a control. (B–F) ChIP assays. The cells were transfected with KDM3A (i-KDM3A) or GFP shRNA (Mock) and then subjected to ChIP using anti-KDM3A (B), anti-Stat1 (C), anti-pY-Stat1 (D), anti-pS-Stat1 (D), or anti-Brg1 (F). HS: filled bars; control: open bars. Data are mean ± SD (**p<0.01). The data used to make this figure can be found in (TIF)Click here for additional data file.S10 FigureThe effects of MSK1 knockdown on the phosphorylation of KDM3A and the occupancy of Stat1 at the GAS region of hsp90α. (A) The cell extracts from Jurkat cells transfected with either the shMSK1, shGFP or mock vector were used for western blot. Based on western blot for MSK1, only a minimal level of MSK1 was detected in the shMSK1-transfected cells. MSK2 and GAPDH were used as controls. (B) The phosphorylation of KDM3A was abolished in H89 (an inhibitor of MSK1)-treated-cells treated with HS (+) or not (−). (C) The phosphorylation of KDM3A was induced using anisomycin (+), an activator of MSK1, and was abolished via MSK1 shRNA (i-MSK1)-mediated knockdown. The duration of anisomycin treatment is indicated on top of each lane (min). (D) The cells were transfected with MSK1 (i-MSK1) or GFP shRNA (Mock) and then subjected to ChIP using anti-Stat1. HS: filled bars; control: open bars.(TIF)Click here for additional data file.S11 FigureThe effects of KDM3A mutants on the occupancy of Stat1 and phosphorylated Stat1 at the GAS region of hsp90α. (A) The Jurkat cells were transfected with western blot of the cell extracts from Jurkat cells that were transfected with either wild type KDM3A, S264A, or S264D mutant of KDM3A using an anti-FLAG antibody. GAPDH was used as a control. (B–D) ChIP assays showed the occupancy of Stat1 and phosphorylated Stat1 at the upstream of hsp90α.(TIF)Click here for additional data file.S12 FigureThe H3K9me2 levels on the promoter of hsp90α, CIITA, and BCL-6 genes. (A–D) The Jurkat (A and B) and Raji cells (C and D) were treated by heat shock or IFNγ. ChIP assays were performed by using an antibody against H3K9me2, the primers of qPCR were described in Ref p<0.05, **p<0.01). The data used to make this figure can be found in (TIF)Click here for additional data file.S13 FigureFlow chart of the ChIP-seq analysis.(TIF)Click here for additional data file.S1 TableThe ChIP-seq signal peak distributions across the genome. As controls, two different sets of 7,500 peaks of the same average length and with randomly sampled locations were run, which intersected with the genomic characteristics in the same manner.(XLSX)Click here for additional data file.S2 Table−20) that were subjected to ChIP for KDM3A or p-KDM3A.The list of genes with binging peaks (FDR <1×10 Only the peaks in the promoter region (from 4 kb upstream to 2 kb downstream of the TSS) were considered.(XLSX)Click here for additional data file.S3 TableDetailed information for the top statistically valid motifs and the TFs displaying similar motifs based on TOM-TOM.(XLS)Click here for additional data file.S4 TableThe list of p-KDM3A sites displaying the greatest significance in the differences between the HS and control treatments.(XLSX)Click here for additional data file.S5 TablePrimers used in plasmids constructed.(DOC)Click here for additional data file.S6 TablePrimers used in RT-qPCR.(DOC)Click here for additional data file.S7 TablePrimers used in ChIP-qPCR.(DOC)Click here for additional data file.


Spi-B and PU.1 are highly related members of the E26-transformation-specific (ETS) family of transcription factors that have similar, but not identical, roles in B cell development. PU.1 and Spi-B are both expressed in B cells, and have been demonstrated to redundantly activate transcription of genes required for B cell differentiation and function. It was hypothesized that Spi-B and PU.1 occupy a similar set of regions within the genome of a B lymphoma cell line.To compare binding regions of Spi-B and PU.1, murine WEHI-279 lymphoma cells were infected with retroviral vectors encoding 3XFLAG-tagged PU.1 or Spi-B. Anti-FLAG chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) was performed. Analysis for high-stringency enriched genomic regions demonstrated that PU.1 occupied 4528 regions and Spi-B occupied 3360 regions. The majority of regions occupied by Spi-B were also occupied by PU.1. Regions bound by Spi-B and PU.1 were frequently located immediately upstream of genes associated with immune response and activation of B cells. Motif-finding revealed that both transcription factors were predominantly located at the ETS core domain (GGAA), however, other unique motifs were identified when examining regions associated with only one of the two factors. Motifs associated with unique PU.1 binding included POU2F2, while unique motifs in the Spi-B regions contained a combined ETS-IRF motif.Our results suggest that complementary biological functions of PU.1 and Spi-B may be explained by their interaction with a similar set of regions in the genome of B cells. However, sites uniquely occupied by PU.1 or Spi-B provide insight into their unique functions.The online version of this article (doi:10.1186/s12864-015-1303-0) contains supplementary material, which is available to authorized users. Spi1, also known as Sfpi1) and Spi-B (encoded by Spib). PU.1 is expressed in hematopoietic stem cells and common lymphoid progenitors, and throughout B cell development [-/- B cells still express PU.1 at wild-type levels [Development and survival of the B cell lineage is regulated by a number of important transcription factors, and dysregulation of the expression of these proteins can lead to impaired B cell function and disease such as leukemia . Two higelopment . PU.1-nuelopment ,4. Develelopment . In compelopment . Therefoe levels .Fcgr2b (encoding FcγRIIb) [Blnk (encoding B cell linker protein) [c-fes promoter in some cell lines, which is predicted to be through the function of distinct activation domains outside of the ETS-binding domain, particularly at the N terminus where there is low homology between the two proteins [Several lines of evidence suggest that PU.1 and Spi-B have at least some complementary function in the B cell lineage. First, PU.1 and Spi-B have 43% overall amino acid identity and share 67% amino acid identity within their ETS DNA binding domain [FcγRIIb)  and Blnkprotein) . At otheprotein) . PU.1 anproteins ,12.in vivo. Heterozygosity for Spi1 encoding PU.1 on a Spib-/- mutant background leads to defects in BCR signalling and reduced frequencies of splenic follicular B cells that are more severe compared to Spib-/- mice, demonstrating a degree functional overlap between PU.1 and Spi-B [Spi1 gene after B cell commitment under the control of the B cell-specific CD19-Cre leads to mild defects in B-cell development and function [Spi1 under the control of CD19-Cre on a Spib-/- (CD19-CreΔPB) background leads to severely impaired B cell development and B cell acute lymphoblastic leukemia, which is partially attributed to loss of Blnk [Btk) is also directly regulated by Spi-B and PU.1, and induction of PU.1 in cultured CD19-CreΔPB Pro-B cells restores Btk expression and induces apoptosis [SPIB is amplified in Activated B Cell Diffuse Large B Cell Lymphomas (ABC-DLBCL) compared with other B cell lymphoma subtypes, and is translocated in the OCI-Ly3 ABC-DLBCL cell line leading to over-expression of SPIB mRNA compared with other lines [in vitro [PU.1 and Spi-B appear to have complementary function in the B cell lineage nd Spi-B ,14. Condfunction ,16. Howe of Blnk . Bruton poptosis . While dpoptosis . SPIB iser lines ,21. Spi-in vitro . It is pin vitro .Next-generation sequencing (NGS) technologies allow for high-resolution analysis and detection of transcription factors across the entire genome. By combining chromatin-immunoprecipitation with high-throughput sequencing, all regions within the genome bound by PU.1 and Spi-B can be identified. Based on the demonstrated complementary function of PU.1 and Spi-B, we hypothesize that PU.1 and Spi-B can interact with the same set of binding sites in the genome of B cells. In this study, we report a genome-wide comparison of genomic regions of interaction by PU.1 and Spi-B in the murine lymphoma cell line WEHI-279. To remove bias introduced by different antibodies, expression levels, or gene regulation we expressed 3XFLAG-tagged PU.1 and Spi-B at similar levels in WEHI-279 lymphoma cells. Chromatin immunoprecipitation was performed using anti-FLAG antibodies. Our results support the hypothesis that PU.1 and Spi-B occupy similar regions within the genome, but also identified a unique subset of regions only occupied by one factor. Additionally, motif analysis has suggested that these regions contain binding regions for different co-activator partner proteins. In summary, these experiments provide biochemical insight into both the similarities and differences between the biological functions of PU.1 and Spi-B.Spi1 and Spib mRNAs at a ratio of 1:1.3, relative to the normalizer gene Gapdh. 3XFLAG-PU.1-infected WEHI-279 cells expressed Spi1 mRNA and Spib mRNA at ratio of 1.2:1. 3XFLAG-Spi-B-infected WEHI-279 cells expressed Spi1 mRNA and Spib mRNA at a ratio of 1:2.6. These results suggested that Spib mRNA was modestly overexpressed relative to Spi1 mRNA. However, protein levels of the FLAG-tagged products were similar between 3XFLAG-tagged PU.1 and 3XFLAG-tagged Spi-B, as described in Xu et al. [To determine if the transcription factors PU.1 and Spi-B occupied identical regions within the mouse genome ChIP-seq was performed. To ensure a fair comparison, WEHI-279 B lymphoma clones expressing 3XFLAG-tagged full-length PU.1 or Spi-B protein were selected to ensure equivalent levels of protein expression [PU.1 or Spi-B binding peaks were determined using replicate data from two experiments, sequenced by two independent sequencing core facilities with over 20 million reads per sample. Replicate ChIP-seq experiments were merged in MM-ChIP, which estimates sample-specific shift-size of ChIP-seq tags with MACS modeling and then pools shifted tags from different samples to identify ChIP-peaks with a dynamic Poisson model . This me2 correlation coefficient of 0.8196  . Gene ontology analysis of predicted Spi-B targets revealed significant (FDR ≤ 0.05) enrichment of antigen processing functions  immune response (GO:0006955), intracellular signaling cascade (GO:0035556) and regulation of lymphocyte activation (GO:0051249). Histocompatibility 2 class II loci, several Fc receptors , as well as B cell markers CD40 and CD74 were included in multiple ontologies.Potential target genes of PU.1 and Spi-B were identified using BETA-minus, which annotates binding data to predict target genes based on proximity to the transcription start site . To idenBak1, Bcl6, Hdac7, Pou2f2, Spi1, and Vav1. The region upstream of Spi1 demonstrated a single peak at the -14 kb position (not shown). The region at -14 kb of Spi1 has been demonstrated to be occupied by PU.1 in both B lymphocytes and myeloid cells, as opposed to a site at -12 kb that is occupied by PU.1 only in myeloid cells [These gene lists suggest that Spi-B has functions in lymphoid development, antigen processing, and B cell receptor-mediated signalling. Top processes for PU.1 were highly similar to Spi-B, consistent with previous studies that presumptive targets for PU.1 are involved with B cell and myeloid lineages. The gene ontology of processes for PU.1 target genes included cell activation (GO:0001775), leukocyte activation GO:0045321), and hematopoiesis (GO:0030097). Top genes involved in all three pathways included id cells . These gid cells . Peak to1, and heTlr4, Tlr6, Tlr9, and Tlr13 in addition to well-documented targets such as Btk and Blnk. Top processes were immune response (GO:0006955), multiple terms for antigen processing and presentation  and regulation of lymphocyte activation (GO:0051249). These pathways were considered most significant due to high ranking of MHC class II genes in the associated gene list, including H2-Aa, H2-Q4, H2-Q7, H2-Q9 and H2-Q6  of DNase I hypersensitivity in mouse B cells from ENCODE/University of Washington (GSM1003813) was used to predict regions of the genome containing e Figure D. Regione Figure E.To further characterize binding locations of Spi-B and PU.1 in relation to the gene body, peak intervals were clustered against histone modifications by ChIP-seq from ENCODE/LICR (GEO series GSE31039). Whole genome coverage of H3K36me3 (GSM1000148) was inversely associated with regions of the genome bound by either PU.1 or Spi-B Figure F. This mPU.1 and Spi-B demonstrated a consistent pattern of high ChIP signal at transcriptional start sites. Interestingly, binding sites unique to either PU.1 or Spi-B demonstrated distinct profiles. Unlike common peaks, which tended to be associated with TSSs, unique PU.1 or Spi-B sites were frequently found in intergenic regions 10Kb beyond the nearest gene promoter Figure A. AnalysDifferentially bound regions were analyzed with MEME-ChIP for unique conserved binding motifs to identify potential differential associated binding partners. Regions uniquely bound by PU.1 contained the ETS motif. A POU2F2  motif was identified by MEME chip, with an E-value of 2.5e-007. This motif was observed within 100 bp of 19.7% of the ETS motifs seen in PU.1-bound regions, but was overrepresented to 51.9% of ETS motifs in unique PU.1 peaks. There were 267 POU2F2 motif occurrences in unique PU.1 peaks with a p-value less than 0.0001. Combined POU2F2-PU.1 motifs were observed upon re-analysis of PU.1 ChIP in primary splenic B-cells (GSE21512) , where aRemarkably, de-novo motif finding with Spi-B unique regions revealed that 100% of unique Spi-B regions contained an ETS–IRF composite element  with an E-value of 1.7e-119  and with a number of miRNAs; mir-155/AY096003, mir-3108/zfhx3, mir-1938/Ifrd1, mir-3102/Arhgef17, and mir-5130/Kctd12. Of these, mir-155 has a known oncogenic role in lymphomas [Nod2, a NOD-like receptor involved in B cell proliferation and activation [In order to determine what genes were regulated at differentially bound regions, gene function for differential gene targets was determined using gene ontology. Peak to gene associations were determined using CEAS and annotated using DAVID. Genes uniquely bound by PU.1 were predominantly associated with leukocyte activation during immune response GO:0002366) and cell activation during immune response (GO:0002263)  were also identified due to the inclusion of Fgf23, Myc and Pou2f2  (Linker For Activation Of T Cells 2) demonstrated significant Spi-B binding directly at the promoter, which was not observed in the PU.1 ChIP  and B cells (CD19+) [Gene targets of differentially bound Spi-B peaks were associated with immune effector process GO:0002252) and B cell activation GO:0042113) that included 2 and B c that inc in mice . Lat2  . These aTo investigate genome-wide binding of Spi-B and PU.1, we conducted next generation sequencing of chromatin immunoprecipitated with anti-3XFLAG-tagged Spi-B or PU.1. Previous studies have reported ChIP-seq analysis of PU.1 binding sites in erythroid, myeloid, and lymphoid cells but none have compared PU.1 to Spi-B sites in mouse cells due to technical constraints ,40,41. DAn important finding of this study is that of the genomic regions occupied by PU.1, a majority were also occupied by Spi-B. This fraction was 1900/4528 regions using MM-ChIP, and 5378/5974 regions (90%) using DiffBind. This result supports our hypothesis that PU.1 and Spi-B can interact with the same regulatory regions in the genome of B cells. This result is consistent with previous reports that PU.1 and Spi-B have similar DNA binding specificities ,24. We sFewer regions of the genome were found to be differentially occupied by PU.1 or Spi-B in the genome of WEHI-279 B cells. One important aspect of next-generation sequencing demonstrated in this study is the importance of mathematical modeling and input sequencing in identifying differential peaks. While direct subtraction of regions called by peak callers revealed several peak regions that did not overlap between samples, affinity (quantitative) data analysis using DiffBind determined that only several hundred regions demonstrated significant differences between factors. Using DiffBind to identify differential peaks in combined data most likely under-predicted gene targets, but provided high-stringency lists. Therefore, while there remains a possibility that other differential regions exist within the genome, this method provides the most accurate and specific regions. Therefore, subsequent analysis of these high-stringency peaks highlights the most significant differences between Spi-B and PU.1 binding within the genome.Regions identified that were occupied uniquely by PU.1 or Spi-B may reveal insights into biological functions unique to each factor. Genes associated with unique PU.1 bound regions were related to immune development using Gene Ontology analysis. Regions unique to PU.1 contained a number of microRNA genes, while no microRNA genes were observed at unique Spi-B peaks. The unique binding of PU.1 to the sequence of MiR-155 may have a role in regulation of PU.1 itself, as expression this micro-RNA has been shown by others to down-regulate PU.1 . BindingUnique Spi-B associated genes were associated with roles in B-cell activation and immune function. All unique Spi-B peaks had an associated IRF sites in WEHI-279 lymphoma cells, while ETS-IRF sites were not overrepresented in unique PU.1 peaks. This suggests that in cases where genes are under the regulation of an ETS-IRF composite element (EICE), Spi-B and not PU.1 is the preferential IRF partner. Our results are consistent with a recent study showing that combined EICEs were dominantly occupied by Spi-B in B lymphoma cell lines, and that PU.1 could not fully compensate for Spi-B in recruiting IRF4 to target regions . RNAi-meOur model of PU.1 and Spi-B binding is based on lymphoma cells over-expressing the proteins, which bears similarity to other B cell studies in terms of binding near target genes and associated binding motifs. While the changes in the expression level of PU.1 can enhance binding of other factors to influence local chromatin structure and dictate eventual cell fate ,30,45,46In conclusion, we report genome-wide binding of the ETS transcription factors Spi-B and PU.1 to similar regions of the genome, with high affinity for transcriptional start sites containing ETS motifs. These regions are frequently located near the regulatory domains of genes involved in B cell development and function. In addition to these common regions, there exists a small set of unique regions where PU.1 and Spi-B demonstrate differential preference for associated factors based on sequence identity, target gene function, and potential co-activators. Our findings provide novel insight into differences between the two factors, and offers novel biochemical pathways involving Spi-B as a molecular targeting therapy in B cell malignancies.Gapdh transcripts using the comparative threshold cycle method [Relative frequencies of Spi-B and PU.1 mRNA transcripts were measured by reverse transcription–quantitative PCR (RT-qPCR) and normalized as a percent of e method .-5 β-mercaptoethanol and 5 mM HEPES buffer. Cells were stably transfected to express 3x FLAG-tagged Spi-B or PU.1 as previously described [Mouse WEHI-279 lymphoma cells were cultured in complete DMEM medium  supplemented with 10% fetal bovine serum, 1x penicillin/streptomycin/L-glutamine, 5 × 10ChIP-seq experiments were conducted in duplicate for each transcription factor. Peak finding and data analysis was performed using Galaxy Suite ,49. FASTPeak to gene associations were determined using BETA-minus in Cistrome . BED filEnriched motifs within the ChIP-seq peaks were determined using MEME-ChIP . Peak re2ratio) of the ChIP-seq data was clustered against the intervals of all Mouse RefSeq genes  using computeMatrix v. 1.5.8. RefGenes were plotted using reference-point mode, with the beginning of each gene region designated as the transcription start site. Heatmapper images were generated, with regions sorted in descending order. K means clustering was set to 5, and individual profiles generated from each cluster. To profile GEO datasets against declared peak regions, computeMatrix was run using signal from published datasets and significant ChIP peaks from this study as the regions of interest in scale-regions mode. All peak regions were scaled to 1500 bp for signal profiling. Signal was calculated across the region, plus 4 kb upstream from the peak start site and 4 kb downstream of the peak end site. Scores were averaged over 50 base pair bins. Indicate missing data as zero: False, Skip zeros: False. Signal files of ChIP-seq in B cells were downloaded from GEO: DNaseI Hypersensitivity by Digital DNaseI from ENCODE/University of Washington (GEO Series GSE37074). Whole genome coverage of DNaseSeq in CD43- B cells (GSM1014170). ChIP-seq from ENCODE/LICR (GEO series GSE31039). Whole genome coverage of H3K36me3 in CD43- B cells (GSM1000148). ChIP-seq for serine 5-phosphorylated RNA Polymerase II in Wild-type B cells (GSM1038232). For GEO datasets, all files were downloaded as pre-analyzed wig/bigwig files and used directly for profiling. Profiles were generated using the Matrix file from computeMatrix in profiler, combining both regions plotted on the same plot.Binding profiles of Spi-B and PU.1 relative to features in the mouse genome were depicted using DeepTools . Normalihttp://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58128. Additional data files supporting the results of this article are included as Additional file The data sets supporting the results of this article are available in the GEO repository,


Twenty-four-nucleotide small interfering (si)RNAs are central players in RNA-directed DNA methylation (RdDM), a process that establishes and maintains DNA methylation at transposable elements to ensure genome stability in plants. The plant-specific RNA polymerase IV (Pol IV) is required for siRNA biogenesis and is believed to transcribe RdDM loci to produce primary transcripts that are converted to double-stranded RNAs (dsRNAs) by RDR2 to serve as siRNA precursors. Yet, no such siRNA precursor transcripts have ever been reported. Here, through genome-wide profiling of RNAs in genotypes that compromise the processing of siRNA precursors, we were able to identify Pol IV/RDR2-dependent transcripts from tens of thousands of loci. We show that Pol IV/RDR2-dependent transcripts correspond to both DNA strands, whereas the RNA polymerase II (Pol II)-dependent transcripts produced upon derepression of the loci are derived primarily from one strand. We also show that Pol IV/RDR2-dependent transcripts have a 5′ monophosphate, lack a poly(A) tail at the 3′ end, and contain no introns; these features distinguish them from Pol II-dependent transcripts. Like Pol II-transcribed genic regions, Pol IV-transcribed regions are flanked by A/T-rich sequences depleted in nucleosomes, which highlights similarities in Pol II- and Pol IV-mediated transcription. Computational analysis of siRNA abundance from various mutants reveals differences in the regulation of siRNA biogenesis at two types of loci that undergo CHH methylation via two different DNA methyltransferases. These findings begin to reveal features of Pol IV/RDR2-mediated transcription at the heart of genome stability in plants. Arabidopsis, the methyltransferases DRM2 and CMT2 establish DNA methylation in all sequence contexts and maintain asymmetric CHH methylation  to ensure genome stability. DNA methylation occurs at CG, CHG, and CHH  sequence contexts in plants . In Arabhylation . The maihylation .Arabidopsis, RNA-dependent DNA Methylation (RdDM) mediated by DRM2 deposits DNA methylation at TEs to cause their transcriptional silencing  and Pol IV/RDR2-dependent transcripts by dsRNA-seq in dcl2 dcl3 dcl4 and dcl2 dcl3 dcl4 nrpd1. This effort led to the identification of Pol IV/RDR2-dependent transcripts from tens of thousands of genomic loci. Further molecular and bioinformatics analyses revealed features of Pol IV/RDR2-dependent transcripts as well as the genetic and epigenetic requirements for Pol IV transcription.We reasoned that comparing RNAs between epressed  such thadcl2-1 dcl3-1 dcl4-2 nrpd1-3 quadruple mutant with that of the dcl2-1 dcl3-1 dcl4-2 triple mutant (hereafter referred to as dcl234) through RNA-seq. Three biological replicates were conducted for each genotype using inflorescences containing unopened flower buds. To derive Pol IV-dependent siRNA loci from the same tissue types, small RNA sequencing (sRNA-seq) was performed with wild-type (WT) and nrpd1-3 inflorescences. RNA-seq revealed 698 regions showing statistically significant reduction in transcript levels in dcl234 nrpd1 relative to dcl234 . Enrichment for P4RNAs in the total RNA population was necessary for the discovery of more P4RNAs.The 635 regions shown to produce P4RNAs above only constituted 1.3% of the 47,442 P4siRNA regions. We found that 98% of the P4siRNA regions had little read coverage in the RNA-seq libraries. In dcl234 background. We sought to confirm the dsRNA nature of P4RNAs that were detected through RNA-seq above. We performed strand-specific RT-PCR using region- and strand-specific primers for reverse transcription. Indeed, transcripts corresponding to both DNA strands were detected in dcl234, and the abundance of transcripts was greatly reduced in dcl234 nrpd1 . Therefore, P4RNAs could be potentially enriched by separation of dsRNAs from ssRNAs.P4RNAs are thought to be converted to dsRNAs by RDR2 before being processed to P4siRNAs, so the P4RNAs should exist as dsRNAs in the dcl234 and dcl234 nrpd1 to enrich for P4RNAs , suggesting that there was differential expression at P4siRNA loci between the two genotypes. Indeed, 24,035 regions were found to have a statistically significant reduction in transcript abundance in dcl234 nrpd1 . Therefore, these regions were also likely to produce P4siRNAs. This suggests that most  P4RNAs serve as P4siRNA precursors.Having detected P4RNA-generating regions, we next asked whether all these regions produce P4siRNAs. Our sRNA-seq detected P4siRNAs at 22,990 of the 24,035 regions where P4RNAs were detected by dsRNA-seq. For the 1045 regions from which P4siRNAs were not detected, 946 showed a reduction in small RNA read abundance in A previous study identified 982 genomic loci bound by Pol IV, among which 787 had detectable siRNA production . The P4RRDR2 is required for the accumulation of P4RNAs. We evaluated the effects of loss of function in RDR2 on P4RNA levels by performing RT-PCR on dcl234, dcl234 nrpd1, and dcl234 rdr2 at five P4siRNA loci. No transcripts were detected in dcl234 rdr2 or dcl234 nrpd1 at these loci , which resulted in a moderate increase in read coverage at P4siRNA loci  in RNA-seq-DSN as compared to 698 from RNA-seq (described previously). With the same criteria , 968 regions were found to produce transcripts in dcl234 relative to dcl234 rdr2. Eight hundred fifty regions were common . A total of 17,606 P4RNAs were assembled , with most being in the range of 100–500 nt .A profound A/T enrichment was found for regions surrounding P4RNAs. We aligned all P4RNAs at their 5′ or 3′ ends and determined the proportion of A/T at each nucleotide position in the 1000 nucleotide window upstream of the 5′ end or downstream from the 3′ end. Since the P4RNAs were double-stranded and the actual orientation of P4RNAs was unknown, the 5′ ends of transcripts were defined as the beginning nucleotides on the Watson strand of the TAIR10 reference sequence. The A/T composition was obviously much lower in the P4RNA bodies than the surrounding regions , which cNucleosomes, units of chromatin that influence the access of protein factors to the DNA, are known to be enriched on exons and A/T poor regions . We deteThe genomic distribution of P4RNAs was also examined. P4RNAs were mainly present at intergenic regions—65% of them overlapped with annotated TEs or repeats, and only 9% of them overlapped with genes . We performed GO analysis on the set of genes overlapping with P4RNA loci. Intriguingly, the GO term “endomembrane system” was highly enriched for the gene set . To determine whether this unexpected association was due to the concentration of “endomembrane system” genes at pericentromeric regions, we examined the chromosomal distributions of the set of genes overlapping with P4RNAs. We found that the gene set resembled the set of all annotated genes in that the genes were dispersed at euchromatic regions and depleted at pericentromeric regions .drm1 drm2 and cmt2 relative to WT, respectively, in a published methylome study  with reduced methylation in me study . Similarme study . Althougme study . Pol IV-me study . We founme study . This suBesides siRNAs and DNA methylation, H3 lysine 27 monomethylation (H3K27me1) and H3 lysine 9 dimethylation (H3K9me2) are two other common heterochromatic marks, for which the genomic distributions were profiled through ChIP-chip . We founThe 5′ initiating nucleotides of Pol I and Pol III transcripts have triphosphate groups, and those of Pol II transcripts contain 7-methylguanosine caps. To determine the 5′ end structure of P4RNAs, we performed enzymatic treatments of total RNAs followed by the detection of P4RNAs by RT-PCR. First, we treated total RNAs with no enzyme (control), tobacco acid pyrophosphatase (TAP), which converts 5′ triphosphate or 5′ 7-methylguanylate cap to 5′ monophosphate, or T4 polynucleotide kinase (PNK), which adds a 5′ phosphate group to 5′ hydroxyl RNAs. Next, we digested the RNAs with Terminator, a 5′ to 3′ exonuclease that acts on RNAs with a 5′ monophosphate. Finally, RT-PCR was conducted on these treated RNA samples to detect various P4RNAs. The RNAs treated with Terminator alone showed a dramatic reduction in the abundance of P4RNAs , suggestdcl234 dsRNA-seq libraries with TopHat2 − fractions followed by the detection of P4RNAs by RT-PCR. P4RNAs were detectable from total RNAs and poly(A)− RNAs, but not from poly(A)+ RNAs, suggesting that P4RNAs do not have poly(A) tails (Polyadenylation is part of the maturation process of Pol II-dependent transcripts. To determine whether P4RNAs have poly(A) tails, total RNAs were separated into poly(A)A) tails .− and poly(A)+ RNAs were first isolated from two biological replicates of dcl234 and dcl234 nrpd1 and subjected to an RNA-seq library construction procedure that preserved the strandedness of the transcripts. In poly(A)+ libraries, a total of 1639 P4siRNA loci were found to have read coverage >1 reads per million (RPM), indicating that they were expressed. Transcript abundance at these loci was similar in dcl234 nrpd1 and dcl234  in the poly(A)− RNA-seq libraries. At 98% of the regions where expression was detected from dcl234, decreased expression in dcl234 nrpd1 was also observed + RNA fraction.Given that P4RNAs lack poly(A) tails and Pol II-dependent transcripts are expected to have poly(A) tails, we sought to distinguish the two types of transcripts at RdDM loci through the presence or absence of poly(A) tails. Poly(A)d dcl234 , suggestobserved . In addiobserved . This codcl234. At P4siRNA loci, P4RNAs in poly(A)− RNA-seq were derived from two strands as expected + RNAs at these loci had a ratio of 9:1 or larger between reads derived from the two strands . This suggests that Pol II-transcribed RNAs were not converted to dsRNAs. We next examined the strand distribution of P4siRNAs at these loci. P4siRNAs were present at some of the loci in dcl234, probably because DCL1 was able to produce P4siRNAs. The reads for P4siRNAs were derived from two strands, whereas the Pol II-transcribed poly(A)+ RNAs were from one strand . In dcl234 nrpd1, the P4siRNAs were depleted, suggesting that the P4siRNAs were derived from Pol IV. The fact that Pol II-dependent RNAs from loci that generate P4siRNAs are only from one strand  and that no P4siRNAs are present in dcl234 nrpd1 suggests that Pol II-dependent transcripts are not channeled to P4siRNA production.Our previous studies showed that a partial loss-of-function mutation in a Pol II subunit gene compromised P4siRNA biogenesis at some RdDM loci. This raised the question of whether Pol II-dependent transcripts at RdDM loci are directly channeled to P4siRNA biogenesis or Pol II promotes P4siRNA biogenesis indirectly, such as by recruiting Pol IV . The abidcl234 and dcl234 nrpd1 dsRNA-seq libraries, which made it impossible to make any comparisons between the two genotypes . Therefore, low levels of the P4RNAs were the major reason prohibiting their discovery.Our dsRNA-seq effort uncovered 22,990 regions producing P4RNAs, less than half the 47,442 regions that produce P4siRNAs. Thus, we interrogated why P4RNAs were not detected from half the P4siRNA-generating loci. We examined the 24,452 P4siRNA regions from which P4RNAs were not detected and found that 72% of the regions had low read coverage of <0.9 RPM in both dcl234 were attributable to the fact that these regions have low Pol IV activity in WT. The output of Pol IV activity is P4siRNAs. We divided all regions producing P4siRNAs in WT into four quartiles according to the abundance of P4siRNAs. The percentage of P4RNAs discovered was calculated for each quartile. As expected, with the decrease in P4siRNA abundance, the percentage of P4RNAs discovered also decreased. However, even in the first quartile that contained regions with the most abundant P4siRNAs in WT, still 30% of the regions lacked detectable P4RNAs in dcl234 , but different in dcl234; the type without P4RNAs detected had much lower levels of CHH methylation than the type with P4RNAs detected . These trends were not found for C2 sites  and WT . The abundance of D2 siRNAs but not C2 siRNAs decreased with decreasing CHH DNA methylation. In summary, Pol IV transcription appeared to depend on CHH DNA methylation to a greater extent at D2 sites than at C2 sites.We examined whether P4RNAs were affected differently by CHH methylation at DRM2- and CMT2-targeted sites, which will be referred to as D2 and C2 loci for simplicity. The P4siRNAs produced from these two types of loci will be referred to as D2 and C2 siRNAs. First, the relative abundance of D2 and C2 siRNAs was determined by sRNA-seq in WT. Although the number of C2 loci was larger than that of D2 loci, the total small RNA read number of C2 siRNAs was much smaller than that of D2 siRNAs even when total P4siRNAs or only 21-nt, 22-nt, 23-nt, or 24-nt P4siRNAs were separately considered . Next, we calculated the percentage of P4RNA discovery in nsidered . We obsehylation . SimilarPrevious studies demonstrated that Pol IV, RDR2, and DCL3 are responsible for the biogenesis of P4siRNAs and that CHH DNA methylation and H3K9me2 affect P4siRNA accumulation. By utilizing published sRNA-seq, ChIP-seq, and methylome data  but had almost no effect on CHH methylation at C2 loci . P4siRNA levels were also reduced in these mutants at both D2 and C2 loci, but D2 loci were affected to a greater extent; the remaining P4siRNAs were at 20% and 60% of wild-type levels for D2 and C2 loci, respectively , P4siRNA accumulation was not affected at D2 loci or C2 loci (drm1 drm2 cmt2 cmt3 (drm12cmt23), in which all non-CG methylation is lost and H3K9me2 cannot be maintained because of the loss of non-CG methylation  as the distinguishing feature, we found that P4RNAs are derived from both DNA strands, whereas the derepression of RdDM loci results in Pol II transcription from a single strand. The single-stranded nature of Pol II-dependent transcripts from RdDM loci in I mutant . TogetheI mutant , suggestThe lack of introns in P4RNAs also has implications. Several splicing related proteins were reported to affect both P4siRNA abundance and CHH methylation, although their effects are less prominent than that of Pol IV . The absA surprising finding was that the 5′ ends of P4RNAs bear a monophosphate. The 5′ end of a primary transcript is expected to bear a 5′ triphosphate, or a cap as in Pol II-derived transcripts. It is possible that the P4RNAs that we detected represent processed transcripts. Alternatively, Pol IV or RDR2 may use 5′ monophosphate-containing RNAs as primers to initiate transcription. Regardless, the predominant form of P4RNAs in vivo is the form with a 5′ monophosphate. In this respect, P4RNAs resemble rRNAs, which are present in vivo as processed forms with a 5′ monophosphate . It is oA striking finding was the higher A/T composition and lower nucleosome occupancy of the flanking sequences of P4RNAs. This raises the possibility that high A/T composition and absence of nucleosomes promote the initiation and termination of Pol IV transcription. Nucleosome depletion in the 5′ flanking region is immediately followed by nucleosome enrichment 3′ to the transcription start site for P4RNAs. Such a pattern of nucleosome distribution is also found around the transcription start sites of protein-coding genes in diverse eukaryotes , and thusuvh456 mutant that lacks H3K9me2 or in drm12cmt23 that lacks both H3K9me2 and CHH methylation  sites that are more dispersed within chromosomal arms, P4siRNAs and CHH methylation levels are high, and loss of CHH methylation impedes Pol IV transcription to result in reduced P4siRNA abundance. Therefore, CHH methylation and P4siRNA biogenesis are engaged in a positive feedback loop at D2 sites . CMT2-tahylation . We founhylation .Arabidopsis strains are in the Columbia ecotype. The dcl2-1 dcl3-1 dcl4-2 (dcl234), nrpd1-3 (nrpd1), and rdr2-1 (rdr2) lines were previously described  and treated with DNase I . cDNA was synthesized using random primers with RevertAid Reverse Transcriptase (Fermentas EP0442). To determine the strandedness of the transcripts, reverse transcription was performed with gene-specific primers from each of the two strands. Sequences of primers are in Supplemental Table S4.dcl234 were divided into each of four tubes and were treated as follows. First, the RNAs were incubated for 2 h at 37°C with or without enzymes: tube 1 and tube 2 with buffer only; tube 3 with tobacco acid pyrophosphatase ; and tube 4 with T4 Polynucleotide Kinase . After phenol-chloroform extraction and ethanol precipitation, RNAs in tube 1 were incubated for 1 h at 30°C with buffer only, whereas RNAs in the other three tubes were incubated with Terminator Exonuclease  for 1 h at 30°C. The RNAs were extracted with phenol-chloroform and precipitated with ethanol before being subjected to RT-PCR.To determine the nature of the 5′ ends of P4RNAs, 5 μg total RNAs from dcl234. Then neighboring reads no more than 60 nt apart were joined together to form transcripts. The transcripts that passed the following three filters were retained. First, the transcripts must be longer than 60 nt. Second, the normalized read count from the combined three libraries of dcl234 was >1 RPM. Third, the levels of the transcripts in dcl234 were at least fourfold higher than those in dcl234 nrpd1. Finally, the transcripts were overlapped with P4siRNA loci to filter out the transcripts without corresponding P4siRNA expression.In-house R scripts provided in the Supplemental Material under Supplemental Codes were used to assemble P4RNAs. The first step was to collect and combine all the reads located at P4RNA regions from the three replicates of dsRNA-seq libraries from Exons and genes were according to TAIR10 annotation; P4RNAs were determined in this study as described above. Only P4RNAs longer than 200 bp were included in this analysis. The start and end sites of P4RNA regions were arbitrarily defined as the 5′ and 3′ ends of P4RNAs on the Watson strand. Within each category , sequences were aligned at the start site of transcription (for P4RNAs and genes) or the beginning of exons, or at the end site of transcription/end of exons. Up to 1 kb of sequences flanking these sites were interrogated. The number of A, T, C, or G at each position for all the sequences in each category was counted. The A/T composition was calculated as the proportion of A and T nucleotides in the total. The Perl script is provided in the Supplemental Material under Supplemental Codes.Nucleosome occupancy was examined at the same exons, genes, and P4RNAs interrogated for their A/T composition (described above). The positions of nucleosomes  in the genome were obtained by analysis of the data set  using thTo identify reads that represent potential splicing events, the first step was to filter out reads that mapped perfectly to the genome. The unmapped reads were mapped to the genome again using blastall with a minimum mapped length of 15 nt . The readrm2 and wild type versus cmt2, named D2 and C2 DMRs, respectively, were derived from published methylome data sets  in wild type versus ata sets  with accThe regions with H3K9me2 modifications were defined through analysis of published ChIP-chip data set  using BLDetailed methods included in the Supplemental Material describe the construction of RNA-seq, RNA-seq-DSN, dsRNA-seq, and sRNA-seq libraries; the processing and mapping of RNA-seq, RNA-seq-DSN, dsRNA-seq; and sRNA-seq reads.http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE57215.The genome-wide data sets generated in this study are available at the NCBI Gene Expression Omnibus (GEO;


Comprehensive analysis of the mammalian transcriptome has revealed that long non-coding RNAs (lncRNAs) may make up a large fraction of cellular transcripts. Recent years have seen a surge of studies aimed at functionally characterizing the role of lncRNAs in development and disease. In this review, we discuss new findings implicating lncRNAs in controlling development of the central nervous system (CNS). The evolution of the higher vertebrate brain has been accompanied by an increase in the levels and complexities of lncRNAs expressed within the developing nervous system. Although a limited number of CNS-expressed lncRNAs are now known to modulate the activity of proteins important for neuronal differentiation, the function of the vast majority of neuronal-expressed lncRNAs is still unknown. Topics of intense current interest include the mechanism by which CNS-expressed lncRNAs might function in epigenetic and transcriptional regulation during neuronal development, and how gain and loss of function of individual lncRNAs contribute to neurological diseases. Drosophila  as they are typically transcribed by RNA polymerase II (RNA Pol II), as opposed to RNA Pol III. Moreover, lncRNAs share many features with protein-coding messenger RNAs (mRNAs) – they are capped and polyadenylated. Many lncRNAs also contain multiple exons and are subjected to alternative splicing. However, in comparison to protein-coding transcripts, lncRNAs are roughly one-third as long, contain fewer exons (~2.8 exons in lncRNAs compared to 11 exons for protein coding genes), and are expressed at 10-fold lower levels on average . In addiFigure 1A), intronic lncRNAs , large/long intergenic or intervening non-coding RNAs , promoter associated lncRNAs , and natural antisense transcripts . LincRNAs have been identified through examination of sequencing reads that map expressed transcripts without clearly defined ORFs to intergenic regions. These lincRNAs usually also possess signatures of active transcription including H3K4me3, polyadenylation signals, and RNA polymerase II occupancy  or transcribed ultra-conserved region lncRNAs  by binding to PRC2 component histone-lysine N-methyltransferase Ezh2, leading to a local increase in H3K27me3 content and subsequent transcriptional repression  from the locus  of protein-coding genes, and therefore, share sequence complementarity. The degree of complementarity of NATs with corresponding sense transcripts varies greatly, however, genome-wide analysis suggests that localization of antisense transcription is generally confined to 250 bp upstream of the sense transcript’s transcription start site and 1.5 kb downstream of the sense gene . As prevAir interacts with HMT G9a while Kcnq1ot1 interacts with PRC2 components and HMT G9a  recruits the TLS protein to the promoter of CCND1 during DNA damage. The recruitment of TLS reduces transcription of CCND1 by inhibiting the histone acetyltransferase activity of CBP/p300 at the gene’s promoter  define a more unconventional and less well understood class of functional ncRNAs. These unique transcripts were originally identified in plants where they function to encode subviral components . In animcircRNAs . The idecircRNAs .cis to regulate expression of nearby genes (trans to directly or indirectly regulate the activity of co-expressed protein coding genes through RNA–protein interactions.Other lncRNAs do not overlap with either protein coding genes or promoter or enhancer sequences. These are collectively termed long intergenic ncRNAs (lincRNAs). Analyses of the correlated expression patterns of lincRNA and transcripts of neighboring protein-coding genes imply that lincRNAs participate in similar biological processes to neighboring protein-coding genes . This haby genes . HoweverHOTAIR. HOTAIR is transcribed from an intergenic region in the HOXC locus and is involved in recruitment of chromatin modifiers to hundreds of genomic loci . Aptamer selection experiments reveal that it is relatively easy to evolve RNAs that show moderate binding affinity to a broad range of substrates, including proteins and small molecules, and demonstrate that aptamer–protein interactions show far less constraint at the level of primary sequence than do protein–protein interactions  differentiation of mouse embryonic stem cells (ES) revealed 174 lncRNAs that displayed differential expression patterns  transcription and PRC2-mediated repressive signals at the utNgn1 locus are associated with both decreases in utNgn1 and Neurog1 transcript abundance  prognosis, and inhibition of TUNA in zebrafish results in locomotor defects  through perturbation of cell cycle progression in neural progenitors . Gomafu is one of the most abundant polyadenylated RNAs found in the neonatal retina  located between the convergently transcribed Dlx5 and Dlx6 genes  of the dorsal telencephalon, reduced production of upper layer cortical neurons and a reduction in total size of the barrel cortex . Genome-wide association studies have associated numerous polymorphisms on human chromosome 9p21 that segregate with diseases including cardiovascular disease, Type-2 diabetes, Alzheimer’s disease (AD), primary open angle glaucoma, endometriosis, periodontitis, and several cancers  and was expressed at higher levels in ganglia exhibiting lower levels of Kcna2 protein expression, or after spinal nerve injury  that encodes a conserved ~2 kb lncRNA with a 104 bp overlap with the human BACE1 transcript  is cleaved in the initial and rate-limiting step by β-secretase enzyme (BACE1) to form the amyloid β precursor proteins Aβ 1–40 and Aβ 1–42. In pathological conditions, the Aβ 1–42 proteins oligomerize and contribute to the plaques that participate in AD . As a reanscript . Both ovbundance . Mechani regions . Importaoduction . The impcontrols . Furtherproteins , suggestBDNF-AS and GDNF-OS), however, one of the three GDNF-OS transcripts is likely protein coding  correlate with the onset of neurodegenerative disorders such as Parkinson’s disease, AD, and HD -derived neurons  for genetic analysis of patients with neurological diseases, our understanding of the importance of lncRNAs in neurodevelopment will only be further increased. For example, one patient that displayed a cognitive developmental delay possessed a chromosomal translocation that affected inc00299 . Further99 locus , suggestSix3os binding partners (trans. While not the focus of this review, it is also essential to consider the function of lncRNAs that display cytoplasmic expression, which represent a large fraction of lncRNAs and whose function is poorly understood (reviewed in Advances in sequencing technologies and the appreciation of functional non-coding elements have resulted in the rapid identification of a plethora of lncRNAs expressed in both vertebrate and invertebrates, alike. Systematic characterization of temporally and spatially restricted expression patterns in the developing nervous system has provided the groundwork for hypotheses regarding lncRNA function. As we understand more about the mechanism by which lncRNAs are regulating transcription, we are beginning to understand the biological significance of what once was labeled as “junk DNA.” Many lncRNAs regulate transcription through regulation of epigenetics and interactions with chromatin-modifying complexes, although the mechanism by which lncRNAs are recruited to specific genomic loci is still unclear. Recently developed technologies have the potential to greatly expand our understanding of the mechanism by which lncRNAs function. The advent of techniques such as ChIRP and CHART allow for systematic characterization of DNA binding sites of lncRNAs throughout the genome . Additiopartners , allow fin vitro, homogeneous cell populations and begin to examine the consequence within individual cell types. Neuronal diversification has exhibited a multitude of examples in which transcriptional regulation and cell-fate decisions are very context and cell-type specific. Therefore, it is plausible that individual lncRNAs may display diverse functions that are dependent on their spatial and temporal expression pattern. Inherent to the examination of specific cell types is that epigenetic marks may display vast temporal and/or cell-type specific signatures. In vivo experiments continue to shed light on the importance of lncRNA function throughout neuronal development. As mouse models for genetic loss of lncRNAs such as Evf2, Dlx1AS, Malat1, and Neat1 produce modest phenotypes or fail to recapitulate phenotypes observed in knockdown experiments (As we continue to understand the molecular basis of lncRNA function, it is imperative that studies move from eriments , it is iThe 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.


TP53. The sites spanned a 200-kb distance, which included the promoter, transcript sequence, and regions flanking the endogenous human TP53 gene. We showed that repression up to 86% can be achieved with dCas9 alone  by targeting the complex to sites near the TP53 transcriptional start site. This work demonstrates that efficient transcriptional repression of endogenous human genes can be achieved by the targeted delivery of dCas9. Yet, the efficiency of repression strongly depends on the choice of the sgRNA target site.The CRISPR  platform has been developed as a general method to direct proteins of interest to gene targets. While the native CRISPR system delivers a nuclease that cleaves and potentially mutates target genes, researchers have recently employed catalytically inactive CRISPR-associated 9 nuclease (dCas9) in order to target and repress genes without DNA cleavage or mutagenesis. With the intent of improving repression efficiency in mammalian cells, researchers have also fused dCas9 with a KRAB repressor domain. Here, we evaluated different genomic sgRNA targeting sites for repression of  Streptococcus pyogenes type II CRISPR system, CRISPR-associated 9 nuclease (Cas9) binds to an RNA complex of trans-acting RNA (tracrRNA) and CRISPR RNA (crRNA), which guides the complex to DNA sequences complementary to the crRNA. This Cas9-RNA complex can then recognize and cleave the DNA of pathogens or other foreign elements CRISPR  serves as an adaptive immune system for many bacteria and archaea More recently, researchers have adapted the CRISPR technology to deliver proteins of interest to targeted DNA sequences for applications beyond genome editing. By inactivating the two endonucleolytic domains of Cas9 to create a catalytically “dead” Cas9 (dCas9) protein et al. fused a Krüppel-associated box (KRAB) transcription repressor domain from Kox1 to the C-terminus of dCas9 and multiple sgRNA target sites against the same gene locus were evaluated In contrast to repression of genes in prokaryotes, recent reports of dCas9-mediated repression in mammalian cells have been more modest, with repression reported to be approximately 50%. To improve on the repression reported in mammalian cells, Gilbert The rules for achieving CRISPR-mediated repression in mammalian systems are still not clearly established. When targeting the transcribed regions of a gene, in bacteria it has been reported that targeting the non-template strand leads to greater repression than when targeting the template strand. Based on this finding, it has been previously recommended that only the non-template strand should be targeted for repression in mammalian cells TP53 gene locus on both the template and non-template DNA strands. p53, the gene product of TP53, is a transcription factor whose activation by DNA damage or other cellular stresses results in responses that promote cell cycle arrest, apoptosis, or other tumor suppressing processes TP53 gene locus. Finally, we evaluated whether repression could be achieved by targeting sites surrounding the transcriptional start site in other genes.A better understanding of how the choice of dCas9 target sites affects repression of mammalian gene expression would help those who seek to control gene expression for research or synthetic applications et al.TP53 promoter as well as to both the template and non-template strands of TP53 exons 4, 7, and 10. Because the U6 promoter was used for sgRNA expression, all target sequences without a “G” nucleotide in the leading position had one appended to the 5′ end of the target sequence to facilitate expression. Additionally, sgRNA sequences were designed to target sites approximately every 10 kilobases away from the transcriptional start site of TP53 in both the upstream and downstream directions. GFP targets against the non-template strand were created based on the sequences published in Gilbert et al. (GFP1) et al. (GFP2) Target sequences were designed using the protocol recommended in Larson Streptococcus pyogenes dCas9 sequence with nuclease-inactivating D10A and H840A substitutions Annealed oligonucleotides containing the sgRNA sequences were ligated into the plasmid pRSET B-U6 sgRNA-term that was digested with BpiI . This step results in a RNA polymerase III U6 promoter-driven sgRNA expression cassette consisting of a 20–25 nucleotide domain complementary to a target DNA region, a Cas9-binding hairpin, and a transcription terminator. The U6-sgRNA expression construct was then digested with PspOMI and NotI-HF  and ligated into pCru5-1.2.1-BFP-IRES-Blast that was digested with NotI-HF to create pCru5-BFP-IRES-Blast-U6 sgRNA. Mammalian codon-optimized HEK 293 HEK 293 and HEK 293T cells were transfected using polyethylenimine (PEI) transfection. Cells plated in 6-well plates were transfected with 2 µg of the dCas9 expression plasmid and 1.2 µg of the sgRNA expression plasmid per well which can be scaled by surface area for larger or smaller plates. Cells transfected with pSuper or pSuper-p53 plasmids were co-transfected with 0.5 µg pCru5-1.2-GFP-IRES-mCherry-F2A-Puro or pEGFP-N3 for selection or sorting purposes. Multiplexed transfection mixes contained equal amounts of all sgRNA expression plasmids used. 1 day post-transfection, cells were split 50% and selected with 2 µg/mL puromycin.HEK 293 cells were transduced with retrovirus for stable expression of destabilized GFP. Retrovirus was produced by co-transfecting a pCru5 plasmid and pCL-Ampho into HEK 293T cells using calcium phosphate precipitation. One pCru5 plasmid had a long terminal repeat (LTR) containing a promoter that drives transcription of the neomycin resistance gene and an additional mutated EF-1α promoter (EF-T05) Three days after antibiotic selection, cells were trypsinized, washed once with cold PBS, and resuspended in 1% FBS in PBS. For measurement of GFP knockdown, cells were analyzed on a LSRII  and gated for mCherry- and TagBFP-positive viable singlet cells. GFP fluorescence on the FITC-A filter was recorded and GFP expression relative to the Scramble1 control was analyzed using FlowJo software . For indicated qRT-PCR experiments, samples were sorted on a FACSAria II (BD Biosciences) for TagBFP-positive or GFP-positive viable singlet cells on the Pacific Blue-A filter using the 85 µM nozzle. At least 400,000 cells were sorted per sample.TP53 (GenBank Accession No. NM_000546), WRAP53α isoform (GenBank Accession No. NM_001143991), WRAP53 all isoforms (GenBank Accession No. NM_018081), GAPDH (GenBank Accession No. NM_001256799), MAPK1 (GenBank Accession No. NM_138957), MAPK14 (GenBank Accession No. NM_001315), PPIB (GenBank Accession No. NM_000942), and RB1 (GenBank Accession No. NM_000321).Cell samples were trypsinized, washed once with cold PBS, and resuspended in RLT buffer supplemented with β-mercaptoethanol before freezing at −80°C for up to a week. Total RNA was isolated using the RNeasy Mini Kit  using the manufacturer's directions. RNA was converted to cDNA using the High-Capacity cDNA Reverse Transcription Kit with random primers using the manufacturer's directions . All quantitative PCR reactions were done at 15 µL volume using SYBR Green PCR Master Mix (Applied Biosystems). Each independent biological replicate was plated in triplicate and run on the StepOnePlus thermal cycler (Applied Biosystems). TIMM17B (GenBank Accession No. NM_001167947) were used for normalization of human gene expression. Relative quantity was calculated by the comparative CT method (ΔΔCt) using cells transfected with the dCas9 construct and a Scramble sgRNA construct as the control. All reported levels of repression are relative to a Scramble control. Standardization between three independent biological replicates was performed via log transformation, mean centering, and autoscaling described by Willems et al.Expression levels of housekeeping gene To quantify p53 abundance, immunoblotting was performed using standard protocols. Endogenous p53 level was detected in lysate of transfected HEK 293T cells using a mouse anti-p53 primary antibody  and a goat anti-mouse IgG antibody conjugated to horseradish peroxidase (HRP). Loading control GAPDH was detected using a rabbit primary antibody conjugated to HRP . HRP activity was measured with the ECL Plus Western Blotting Detection Kit .Our goal was to investigate how the location of the sgRNA target site within a gene locus affects dCas9-mediated repression in human cells. We constructed a vector that expressed a human codon-optimized dCas9 protein with three nuclear localization signals (3xNLS). The vector also utilized an internal ribosome entry site (IRES) that allowed bicistronic expression of the dCas9 with mCherry-2A-Puro, which encoded both the mCherry red fluorescent protein and a puromycin resistance gene . To evalTP53 mRNA expression. To evaluate the effect of target site position on repression, we tested sgRNAs that targeted different sites at or near the TP53 gene and measured TP53 mRNA levels by quantitative real-time PCR (qRT-PCR). We hypothesized that the KRAB fusion may have long-distance repressive effects on gene expression since KRAB-mediated promoter silencing has been reported over distances of several tens of kilobases away from the KRAB binding site TP53 transcriptional start site (TSS) in approximately 10,000 bp increments , three sites spanning 30,000 bp upstream of TP53 (−T30K to −T10K), 10 sites spanning 100,000 bp downstream of TP53 (+T10K to +T100K), three sites spanning 30,000 bp downstream of TP53 (+T10K to +T30K), and three sites flanking the TP53 TSS (−T36 +T27 +T110). Again, there was no added transcriptional repression with dCas9-KRAB directed binding as compared to dCas9 (TP53 with the simultaneous targeting of three sites (−T36 +T27 +T100) as with the targeting of the single −T36 site . In addition, it appears that, depending on the targeted gene, similar levels of transcriptional repression can be achieved without the use of a KRAB effector domain, which, for A levels  when tarTP53 mRNA levels without use of a KRAB effector domain by targeting the TSS. Modest repression was also observed in four of five genes targeted at the TSS. Our study demonstrates that target site efficiencies can vary greatly. When one desires to utilize CRISPRi to repress genes in mammalian cells, one should evaluate a range of sgRNA target sites, including those near or at the transcriptional start site. However, the targeting of the transcriptional start site is not guaranteed to be effective for every gene and thus will need to be evaluated on a gene-by-gene basis. We propose that CRISPRi with dCas9 alone may prove useful for functional mapping of transcription factor motifs and enhancer elements via transcription initiation blocking without the potential additional repressive effect of a KRAB domain.To summarize, our results demonstrate significant CRISPRi repression of Figure S1Evaluation of CRISPR-mediated repression using dCas9 and dCas9 fused to a KRAB repressor domain. (A) HEK 293T cells were co-transfected with dCas9 or dCas9 fused to KRAB domain (left) and sgRNA (right) expression plasmids. Both codon-optimized dCas9 and dCas9-KRAB were fused to three copies of nuclear localization signal (NLS) and were co-expressed with mCherry fluorescent protein. sgRNA plasmid expresses mBFP and sgRNA off separate promoters. PCMV, CMV promoter; 2A, ribosomal slippage site; PuroR, puromycin resistance gene; IRES, internal ribosome entry site; mBFP, TagBFP fluorescent protein; BlastR, blasticidin resistance gene; PU6, U6 promoter. (B) Locations of sgRNA binding sites in the endogenous TP53 locus. Each sgRNA is numbered by the distance (bp) from the transcriptional start site. “−”, upstream of +1; “+”, downstream of +1; “K”, one thousand bp; +1, transcriptional start site. Labeled sites above and below the transcribed region indicate sgRNAs targeting the template or non-template DNA strands, respectively. (C–E) Relative expression of TP53 mRNA in cells co-transfected with dCas9 and sgRNA constructs targeting (C) upstream, (D) downstream of the +1 site or (E) in combinations of multiple sgRNA. After three days, cells co-transfected with indicated dCas9 and sgRNA constructs were analyzed by qRT-PCR. Data in  are fold change relative to Scramble1 or Scramble2 negative control sgRNA ± s.e. of three technical replicates. Data in (E) represents sorted cells and were normalized, linearly rescaled, and calculated for mean fold change (n = 3)±95% confidence interval, relative to Scramble1 negative control sgRNA. *P<0.01 compared to non-targeted sgRNA control by paired, one-sided t-test. See also TP53 sites.(TIF)Click here for additional data file.Figure S2CRISPR-mediated repression of GFP. Destabilized GFP (ds1GFP) expression cassette expressed using an (A) LTR (PLTR) or (B) mutant EF-1α promoter (PEF-T05) was retrovirally transduced into HEK 293 cells and selected with neomycin. LTR, retroviral long-terminal repeat; NeoR, neomycin resistance gene; IRES, internal ribosome entry site. Cells were analyzed three days post-co-transfection with dCas9-KRAB and either GFP1 or GFP2 sgRNAs targeting the template strand. GFP fluorescence was measured via flow cytometry after gating for BFP positive cells. Values are arithmetic means of GFP fluorescence ± s.d. (n = 3) calculated from geometric means of each sample population and were normalized to dCas9:Scramble1 negative control.(TIF)Click here for additional data file.Figure S3Target sequence −T36 also leads to a reduction in WRAP53α isoform mRNA. (A) Locations of sgRNA binding sites in the endogenous TP53 locus. Each sgRNA is numbered by the distance (bp) from the transcriptional start site. “−”, upstream of +1; “+”, downstream of +1; “K”, one thousand bp; +1, transcriptional start site. Labeled sites above and below the transcribed region indicate sgRNAs targeting the template or non-template DNA strands, respectively.  Relative expression of TP53, WRAP53α, and all isoforms of WRAP53 mRNA in cells co-transfected with (B) dCas9 and sgRNA constructs or (C) shRNA constructs targeting TP53. After three days, cells co-transfected with indicated constructs were sorted, analyzed by qRT-PCR. Data are fold change relative to (B) Scramble1 negative control sgRNA or (C) pSuper Control plasmid ± s.e. of three technical replicates.(TIF)Click here for additional data file.Figure S4Annealing temperature of sgRNA target sequence has minimal to no correlation with reduction in transcriptional expression. Minimal to no correlation was observed between transcriptional repression relative to Scramble1 control of all individual dCas9 knockdown experiments done in triplicate and the annealing temperature of the sgRNA target. Line represents linear regression of data.(TIF)Click here for additional data file.Figure S5Reduction of p53 protein in transfected HEK 293T cells. Uncropped immunoblot (see blot see  containi(TIF)Click here for additional data file.Table S1Target sequences for sites within and flanking human TP53. The leading G nucleotide required for U6 promoter expression is in bold. The underlined following 19 to 24 nucleotides comprise the target sequence. The PAM site is in gray.(TIF)Click here for additional data file.Table S2Control target sequences. The leading G nucleotide required for U6 promoter expression is in bold. The underlined following 19 to 24 nucleotides comprise the target sequence. The PAM site is in gray.(TIF)Click here for additional data file.Table S3Target sequences within the promoter of various human genes of interest. The leading G nucleotide required for U6 promoter expression is in bold. The underlined following 19 to 24 nucleotides comprise the target sequence. The PAM site is in gray.(TIF)Click here for additional data file.Table S4Primer sequences used for quantitative RT-PCR.(TIF)Click here for additional data file.