Independent lines of evidence suggested that a large fraction of human genes possess multiple promoters driving gene expression from distinct transcription start sites. Understanding which promoter is employed in which cellular context is required to unravel gene regulatory networks within the cell.We have developed a custom microarray platform that tiles roughly 35,000 alternative putative promoters from nearly 7,000 genes in the human genome. To demonstrate the utility of this array platform, we have analyzed the patterns of promoter usage in 17β-estradiol (E2)-treated and untreated MCF7 cells and show widespread usage of alternative promoters. Most intriguingly, we show that the downstream promoter in E2-sensitive multiple promoter genes tends to be very close to the 3'-terminus of the gene, suggesting exotic mechanisms of expression regulation in these genes.The usage of alternative promoters greatly multiplies the transcriptional complexity available within the human genome. The fact that many of these promoters are incapable of driving the synthesis of a meaningful protein-encoding transcript further complicates the story. The regulation of human gene expression is known to be an extraordinarily complex process, including transcription, mRNA processing, mRNA transport, mRNA stability, mRNA translation, protein modification and protein stability. Nevertheless, the picture that has emerged over the past two to three decades is one in which the process of transcription itself is a highly regulated process , and oneTP53 [MYC [CYP19A1 [BRCA1 [P73 [MID1 [CTSB [SRC [KLK6 [TGFB3 [CYP19A1 is a well-characterized example that has five known alternative promoters, many of which are separated by more than 10 kb and are therefore regulated by completely non-overlapping promoters [Alternative promoters are of particular interest because their aberrant expression has been linked to a number of diseases, particularly cancer. There are a number of experimentally well-characterized multiple promoters for known genes, for example TP53 , MYC [10[CYP19A1 , BRCA1 [1 [BRCA1 , P73 [1373 [MID1 , CTSB [1D1 [CTSB , SRC [16RC [KLK6  and TGFB6 [TGFB3 , to nameromoters .de novo computational prediction  to the genome . Databa FirstEF  and H-DB FirstEF  provide  FirstEF . Recentl FirstEF . Current FirstEF ,29. The  FirstEF . The thi FirstEF . The dat FirstEF , there d FirstEF ,34. Here FirstEF ,36. KnowIn order to design an alternative promoter array, we first used a bioinformatics approach to annotate all known and putative promoters in the human genome. Using evidence from three sources: UCSC Known Genes , FirstEFet al [In order to identify potential active promoters, we conducted ChIP-chip with antibody against RNA Pol II in MCF-7 cell lines with and without E2 treatment for 3 hours, as described in the Methods. The amplified immunoprecipitated DNA and input control, after labeling with Cy5 and Cy3 fluorescent dyes respectively, was used to probe the alternative promoter microarray Figure . Each exet al  found ~9et al . When weNCOA7 were shown to bind to RNA polymerase II with a "low" level of confidence, although in the absence of E2 the upstream promoter was predicted to be "strongly off"  of alternative promoters due to E2 treatment, we found that the downstream promoters are more often affected by E2 treatment than the upstream promoter. We found that there were a total of 127 downstream promoters affected by E2 treatment, while only 87 upstream promoters were affected – a significant bias (p = 0.00625 in a chi-squared test). These intriguing patterns provide some insight into the regulatory control of genes and their isoforms by E2. To investigate this phenomenon further, we examined the locations of active promoters within each gene. As shown in Figure Although genome tiling arrays are increasingly becoming a viable alternative to focused microarrays, they remain significantly more expensive than focused microarrays, and their signal-to-noise ratio is very high due to the large numbers of inactive probes and lack of probe design considerations . AnotherTraditionally, expression analysis was used to define promoter activity. However, one recent report has found that a number of genes experience transcription initiation but show not detectable full-length transcripts . NeverthHere, we have presented a novel 244 k microarray that is capable of measuring alternative promoter usage in over 34,000 putative promoters from nearly 7,000 genes. This platform is suitable for indirect expression analyses using RNA polymerase II ChIP-chip as we have shown in this paper, but it is also suitable for methylation based studies using DMH or meDIP experiments , or for ChIP-chip experiments using other proteins of interest, such as transcription factors or histone modification signatures. We have demonstrated clear evidence for alternative promoter activities within genes, including the verification of a number of putative promoters. These results suggest that a large fraction of genes in the human genome possess undiscovered promoters and transcription start sites, which agrees with findings based on the mapping of ESTs to the genome ,21, and DHFR gene in humans, for example, although in this case the interfering RNA is transcribed from a promoter that lies upstream of the primary promoter [Most intriguingly, we discovered that there is a distinct bias for the downstream promoter in E2-sensitive two-promoter genes to be very close to the 3' end of the gene, whereas no such bias exists in E2-insensitive genes. These promoters are very unlikely to produce a functional transcript of any sort, and we therefore speculate that its purpose is merely to regulate the expression of the transcript initiated at the upstream promoter by "blocking" the progression of the RNA polymerase II complex. This "stalling" mechanism has been observed in other contexts. For example, inhibiting DNA replication was recently found to cause RNA polymerase II to stall during the transcription of p21 . Similarpromoter ,52. MuchWe have demonstrated clear evidence of alternative promoter activity for approximately 7,000 human genes, using a 244 K custom microarray that span across 34,000 putative promoters. Our results suggest that a large fraction of genes in the human genome possess undiscovered alternative promoters, which agrees with findings based on the mapping of ESTs and CAGE tags to the human genome. We found that a significantly more number of downstream promoters were affected by E2 treatment than the upstream promoters. And, there is a distinct bias for the downstream promoter in E2-sensitive two-promoter genes to be very close to the 3' end of the gene, whereas no such bias exists in E2-insensitive genes. The custom microarray can also be used for epigenome analyses, such as methylation based studies using DMH or meDIP experiments. The present data will help discovery of novel promoters and ongoing annotation of alternative promoters of human genes in different experimental conditions.ab initio promoter predictions generated by the FirstEF program [We considered three sources of evidence for identifying promoter targets for our microarray. The first was the 5'-end of genes as identified in the UCSC Known Gene track, which is largely based on the alignment of RefSeq mRNAs to the human genome . A secon program .Each line of evidence identifies a transcription start site (TSS). We considered TSSs separated by > 500 bp to be distinct promoters – a commonly used criterion. Although there are undoubtedly transcription factor binding sites that extend beyond this region, this distance is great enough for the core promoters of each TSS to be distinct , and we Each promoter region was aligned to the genome using BLAT  in orderNot all genes could be put on the array, so to prioritize we assigned each gene a score. Three points were awarded for each promoter supported by "known gene" evidence, two points for those supported by CAGE tag evidence , and oneMCF-7 human breast cancer cells  were maintained in growth medium  as described by Fan et al . Prior tFive million MCF-7 cells with and without E2 treatment  were crosslinked with 1% formaldehyde for 10 min, at which point 0.125 M glycine was used to stop the cross-linking. Chromatin immunoprecipitation was performed using a ChIP assay kit  as described . The antChIP was conducted in the same manner as in the ChIP-chip experiments, described above. The pooled DNA from ChIP and input control were first measured by spectrophotometer . Quantitative PCR with SYBR green-based detection  was performed as described previously. In brief, primers were designed using Primer Express software . Quantitative ChIP-PCR values were normalized against values from a standard curve  constructed by input control with the same primer sets.Qiagen RNeasy kit  was used to extract total RNA from MCF-7 cells with or without E2 treatment according to the manufacture's manual. Two μg of RNA was first treated with DNase I  to remove potential DNA contamination and then was reverse transcribed with SuperScript II reverse transcriptase . Quantitative RT-PCR was performed by using SYBR green  as a marker for DNA amplification on a 7500 Real-Time PCR System apparatus . The relative mRNA level of a given locus was calculated by relative quantization of gene expression  with glucose phosphate isomerase mRNA as an internal control.The washed slides were scanned by a GenePix 4000A scanner  and the acquired microarray images were analyzed using GenePix 6.0 software. Briefly, the user-selectable laser power settings for Cy5  and Cy3  were adjusted so that the overall Cy5 to Cy3 ratios were close to 1 and that the signal intensities spanned the entire spectrum with minimal signal saturation at the high intensity range. When these conditions were satisfied, the microarray was scanned and a grid file was loaded to mark the general location of the scanned image. The GenePix 6.0 software performed a spot finding function and captured intensity-related information in a GPR file.The complete array dataset can be viewed in the ArrayExpress microarray database (accession number E-MEXP-1644). GPR files were passed through a custom-built quality control filter which flagged all probes that didn't meet all of the following criteria in both the green and red channels: (1) % > B + 2SD greater than 30; (2) median – background > 0; (3) signal-to-noise ratio greater than 1.5. These filtered results were then normalized using the default parameters  in Agilent's Chip Analytics software version 1.3. A post-normalization MA plot is shown in Figure Since each promoter contains many probes, for each promoter region we chose the probe with the best p-value for inclusion into the "bound" distribution and compared these across the various experimental treatments and replicates. We found that between replicates these "best probes" were within 80 bases of each other (the resolution of the array) 90% of the time. We used the following criteria to classify promoters within individual experiments: strongly bound promoters had probes that were classified in the "bound" distribution with a p-value less than 0.05. Weakly bound promoters were those that did not significantly fall within the "unbound" distribution with a p-value of 0.05. Unbound promoters were those whose probes fell within the "unbound" distribution with a p-value less than 0.05. As Figure TSS: transcription start site; ChIP-chip: Chromatin Immunoprecipitation (ChIP) followed by microarray analysis; E2: 17β-estradiol; CAGE: cap analysis gene expression; RNA Pol II: RNA polymerase II.GACS designed the computational methods and performed the statistical analyses. JW designed the experimental methods and performed the ChIP-chip experiments. PY coordinated the microarray experiments. CP participated in the design of the study. RVD and THMH formulated and directed the design of the study. All authors read and approved the final manuscript.M-A plots for the four ChIP-chip experiments. MA plots for the two control and two E2 treated experiments, data after normalization ; A = log2(Red*Green)/2). The plots show two distinct clusters of points. The larger cluster of probes represents those that are not bound to RNA polymerase II, while the smaller cluster (higher on the M axis) represents bound probes.Click here for fileList of gene promoters active in both control (E2-) and E2 treated (E2+) conditions. Column 1 shows the Gene symbol, column2 gives the genomic location of the promoter (hg18 build); columns 3 and 4 show the activity of the promoter (1 – active and 0 – inactive) in E2- and E2+ conditions.Click here for fileList of genes with 2 promoters, both active and not affected by E2 treatment. Column 1 shows the Gene symbol, columns 2 and 3 give the genomic location of the alternative promoters – promoter 1 and promoter 2 (hg18 build); columns 4,5,6 and 7 show the activity of the two promoters (1 – active and 0 – inactive) in E2- and E2+ conditions.Click here for file


In the last several years, a number of studies have shown that spliceosome assembly and splicing catalysis can occur co-transcriptionally. However, it has been unclear which specific transcription factors play key roles in coupling splicing to transcription and the mechanisms through which they act. Here we report the discovery that Gcn5, which encodes the histone acetyltransferase (HAT) activity of the SAGA complex, has genetic interactions with the genes encoding the heterodimeric U2 snRNP proteins Msl1 and Lea1. These interactions are dependent upon the HAT activity of Gcn5, suggesting a functional relationship between Gcn5 HAT activity and Msl1/Lea1 function. To understand the relationship between Gcn5 and Msl1/Lea1, we carried out an analysis of Gcn5's role in co-transcriptional recruitment of Msl1 and Lea1 to pre-mRNA and found that Gcn5 HAT activity is required for co-transcriptional recruitment of the U2 snRNP (and subsequent snRNP) components to the branchpoint, while it is not required for U1 recruitment. Although previous studies suggest that transcription elongation can alter co-transcriptional pre-mRNA splicing, we do not observe evidence of defective transcription elongation for these genes in the absence of Gcn5, while Gcn5-dependent histone acetylation is enriched in the promoter regions. Unexpectedly, we also observe Msl1 enrichment in the promoter region for wild-type cells and cells lacking Gcn5, indicating that Msl1 recruitment during active transcription can occur independently of its association at the branchpoint region. These results demonstrate a novel role for acetylation by SAGA in co-transcriptional recruitment of the U2 snRNP and recognition of the intron branchpoint. Pre-messenger RNA splicing, the removal of non-coding RNA sequences (introns) that interrupt the protein-coding sequence of genes, is required for proper gene expression. While recent studies have revealed that intron recognition begins while the RNA is actively being synthesized by RNA polymerase II, little is known about how the proteins involved in gene transcription and RNA splicing interact to coordinate the two reactions. Here we show that the protein complex SAGA, which allows RNA polymerase II to navigate the three-dimensional structure of packaged DNA by acetylating histone proteins, has an additional role in pre-messenger RNA splicing. Our genetic analysis shows that the SAGA complex has functional interactions with specific components of the splicing machinery. Furthermore, SAGA's acetylation activity, which we find to be targeted toward promoter-bound histones of intron-containing genes, is required for proper recruitment of these components to RNA during active transcription. Our work supports a model whereby SAGA–dependent acetylation facilitates recruitment of the splicing machinery to the pre–mRNA for proper co-transcriptional splicing. Eukaryotic genes are interrupted by stretches of noncoding sequence (introns), which are removed from the newly-synthesized RNA by the spliceosome, a dynamic ribonucleoprotein complex made up of 5 highly structured snRNAs and over a hundred snRNA-associated proteins.In vivo, recognition of splice sites within the pre-mRNA by the spliceosome can occur while the polymerase is actively engaged with the DNA template stable association of snRNPs with the transcription complex, occurs in response to synthesis of specific signals in the pre-messenger RNA Although RNA synthesis and RNA splicing have been analyzed as biochemically separate reactions, recent studies demonstrate that these processes are spatially and temporally coordinated Transcription of DNA is strongly influenced by its packaging. The core histone proteins, H2A, H2B, H3, and H4 form an octameric complex that DNA is wrapped around to form the nucleosome, which is further compacted into chromatin—a general repressor of transcription. However, histones undergo extensive post-translational modifications on their N-terminal tails including acetylation, ubiquitination, methylation, and phosphorylation, which alter the chromatin and, in turn, affect transcription. One of the best-characterized histone modifications is the reversible acetylation of lysine residues on the N-terminal tails of histones H2B, H3, and H4. Histone acetylation, which is a positive mark of transcription, neutralizes the charge on the basic histone proteins leading to relaxation of the protein/DNA interactions, and the acetylated histone tails can serve as binding sites for proteins that regulate transcription.Spt/Ada/Gcn5/Ada) complex . Gcn5 primarily acetylates histones H3 and H2B, and these modifications are thought to loosen chromatin for specific transcription factor binding. Furthermore, association between the SAGA complex and general transcription factors, such as TBP, facilitate preinitiation complex formation Histone acetylation is carried out by several different acetyltransferases, the best characterized of which is the protein Gcn5, a component of the multi-subunit transcription co-activating SAGA  is synthetically lethal when combined with deletion of either gene encoding the U2 snRNP proteins Lea1 and Msl1 . A mutation in GCN5 that eliminates the protein's catalytic activity is sufficient to confer the synthetic lethality. Co-transcriptional recruitment of the U2 snRNP to the branchpoint and subsequent steps in spliceosome assembly are dependent on Gcn5 HAT activity. While previous studies indicate that transcription elongation can alter co-transcriptional spliceosome assembly, chromatin IP results reveal no obvious changes in elongation in the absence of Gcn5's HAT activity. Moreover, we observe a dramatic peak in Gcn5-dependent acetylation of histone H3 in the promoters of these intron-containing genes. Unexpectedly, we also find recruitment of Msl1 at the promoter region, indicating that Msl1 recruitment during active transcription can occur independently of its association at the branchpoint region. These results demonstrate a novel role for acetylation by SAGA in co-transcriptional recruitment of the U2 snRNP and recognition of the intron branchpoint.Based upon the spatial and temporal proximity of chromatin, chromatin-modifying enzymes (such as Gcn5), and pre-mRNA splicing complexes during gene expression, we undertook an analysis of genetic interactions between GCN5 and genes encoding factors involved in pre-mRNA splicing, a targeted genetic screen was performed to identify synthetic lethal interactions between null alleles of non-essential splicing factors and GCN5. In this analysis, we uncovered genetic interactions between GCN5 and the splicing factors MSL1 and LEA1  GCN5 which changes amino acids 126–128 (KQL) in domain I to alanines eliminates the histone acetyltransferase activity of Gcn5 gcn5Δ msl1Δ or gcn5Δ lea1Δ double mutant . While this does not rule out the possibility that Gcn5 acetylates some other splicing factor, these data do suggest that the genetic interactions between GCN5 and MSL1 and LEA1 are probably not due to acetylation of the U2 snRNP proteins by Gcn5, and indicate a novel functional interaction between the transcriptional co-activator complex, SAGA, and core components of the spliceosome.The best-characterized substrates of Gcn5 are lysine residues on histones, suggesting a model in which chromatin modification has some overlapping function with pre-mRNA splicing factors. Nonetheless, we considered the possibility that the genetic interactions we observed between in vivo spliceosome recruitment to pre-mRNA occurs while the nascent RNA is actively engaged with the transcription complex Recent studies in yeast demonstrate that GCN5, we analyzed the well-characterized intron-containing gene DBP2 with an extended exon 2 .To determine if co-transcriptional recruitment of either Msl1 or Lea1 is affected by deletion of d exon 2 . In strasequence , a resulith DBP2 . RNA polpe cells . To deteOur discovery of an essential requirement for Gcn5's HAT activity in its interaction with Lea1/Msl1 led to the prediction that its HAT activity would also be required for the co-transcriptional recruitment of Lea1, and this is what is observed. When Gcn5's HAT activity is abrogated by the KQL mutation, no co-transcriptional recruitment of Lea1 is observed, whereas Lea1's association is unaffected by the LKN mutation . Pol II GCN5 deletion or elimination of its HAT activity decreases the amount of Lea1, leading to a decrease in its association with the gene. However, total Lea1 protein levels are unchanged in the absence of Gcn5. Neither are levels of Msl1 protein altered  to a region further upstream of the promoter of DBP2 and find that neither protein is significantly recruited to these regions in the presence or absence of GCN5  have been shown to affect rapid/dynamic histone acetylation patterns we examined histone acetylation in the absence of the HDACs shown in The finding that Gcn5 HAT activity is required for co-transcriptional recruitment of the U2 snRNP to  of DBP2  with litGCN5 severely abrogates the co-transcriptional recruitment of the U2 snRNP. Combined with our genetic analysis, these results strongly suggest a specific role for Gcn5 activity in U2 snRNP function. Nonetheless, it is possible that deletion of GCN5 acts generally to disrupt co-transcriptional recruitment of all snRNPs. To address this, recruitment of a representative component of the U1 snRNP and triple snRNP was examined. Chromatin IP of Prp42 has been shown to be an indicator of U1 snRNP recruitment to intron-containing genes GCN5, Prp42 association with DBP2 was analyzed. The U1 snRNP associates with the DBP2 pre-mRNA shortly after synthesis of the 5′ splice site, consistent with reports by others  and long first exon (∼1 Kb) allow for resolution of protein association throughout the gene. We wanted to examine a second well-characterized, intron-containing gene to determine if Gcn5's role in co-transcriptional recruitment of Lea1 and Msl1 is more general. ECM33 has previously been described by others to be a gene to which splicing factors, including the U2 snRNP, are co-transcriptionally recruited ECM33 in the presence of Gcn5 revealed that Lea1 and Msl1 recruitment occurred after the formation of the branchpoint . As with DBP2, when recruitment of Msl1 and Lea1 to a region further upstream of the promoter of ECM33 was examined in the presence and absence of GCN5, we find that neither protein is significantly recruited to this region, reinforcing the transcription-dependence of their recruitment  (ruitment . Also si–5 fold) .ECM33-bound histones by ChIP. Consistent with what we observed with DBP2, a strong Gcn5-dependent peak in acetylation was observed at the promoter of ECM33,  of Gcn5 can affect co-transcriptional spliceosome assembly, which is something that we continue to explore. It would nonetheless be interesting if Gcn5's acetylation activity is targeted toward a non-histone substrate to abrogate co-transcriptional splicing.GCN5 deletion does not appear to significantly affect pol II levels throughout DBP2 or ECM33. Furthermore, the genetic interactions between MSL1 or LEA1 and GCN5 are not observed with the histone acetyltransferase that acts during elongation, ELP3. In light of these findings we favor a model in which Gcn5-dependent histone acetylation at the promoter facilitates co-transcriptional recruitment of splicing factors to the branchpoint. We think that it is likely that high promoter acetylation facilitates loading of a factor or factors onto elongating RNA polymerases, and these factors then recruit Msl1 and/or Lea1 to the branchpoint.Since previous studies have shown that Gcn5 can affect transcription elongation We cannot rule out that direct interactions between Gcn5 and the U2 snRNP may be important for recruitment, particularly since Gcn5 has been shown to associate both at the promoter and within the body of genes. Although our initial studies do not detect a direct association between Gcn5 and either Msl1 or Lea1, the interactions may be too weak or transient to detect biochemically.The analysis reported here indicates that deletion of Gcn5 leads to a reproducible increase in unspliced RNA relative to WT cells for both of the genes analyzed. While the amount of unspliced message that accumulates in the absence of Gcn5 is modest on a per gene basis, it is likely that the additive effect across the genome of decreased splicing efficiency when co-transcriptional splicing is abrogated is important. A number of studies of splicing in yeast have found, as we do, that some post-transcriptional splicing can occur even when co-transcriptional splicing is eliminated. Co-transcriptional recognition of splice signals is thought to be a means of increasing the efficiency and perhaps the rate of splicing. Hence, it is likely that conditions under which optimal splicing is necessary will be particularly sensitive to changes in co-transcriptional splicing, which we are currently exploring. We are also testing whether this Gcn5-dependence for optimal splicing increases under growth conditions in which the cell's transcription is particularly dependent on SAGA, which is reported to be the case under a variety of stress conditions Proper splicing is achieved by sequential recognition of the branchpoint by numerous factors, including the branchpoint binding protein (BBP) and the U2 snRNA (with its associated collection of snRNP-specific proteins). The exchange of BBP for the U2 snRNA is the first ATP-dependent step in splicing, and splice sites are committed to participate in this first ATP-dependent step when spliceosomal rearrangements lock the U2 snRNA into place Evidence that a transcriptional coactivator that functions at the 5′ end of the gene can influence U2 snRNP recruitment is particularly interesting in light of a recent proposal that the majority of second exons in yeast may be too short to support stable recruitment of the U2 snRNP and, as a consequence, most endogenous yeast gene splicing is completed post-transcriptionally. Our results suggest that the activity of Gcn5 facilitates co-transcriptional recruitment of the U2 snRNP to at least a subset of genes. Furthermore, co-transcriptional U2 snRNP recruitment may even involve recruitment of Msl1 before synthesis of the branchpoint since Msl1 appears to have unique interactions with the transcription machinery. Our data suggest that the commitment to splicing is likely made co-transcriptionally, and Gcn5 facilitates U2 snRNP association with the pre-mRNA to allow a fluid transition to a U2 snRNP poised to participate in post-transcriptional splicing catalysis.et al. reported that a U2 snRNP protein copurified with the human STAGA complex, although the functional significance of this interaction was not clear Studies of the mammalian counterpart of SAGA suggest that interactions between the complex and the U2 snRNP may be evolutionarily conserved. Martinez S. cerevisiae strains used in this study are listed in Methods in Yeast Genetics: A Cold Spring Harbor Laboratory Course Manual. The genotype of each viable spore was confirmed by PCR. Plasmids used in this study are listed in All GCN5 on a centromeric pRS316 (URA3) plasmid were selected for plasmid loss on 5-FOA. Strains were then grown overnight in YPD media at 30°C. Cells were diluted to an O.D.600 of 0.1 in 10 ml of YPD, and incubated at 30°C until all strains reached an O.D.600 of 0.35. A ten-fold serial dilution of each strain was spotted onto YPD plates and incubated 3–5 days at 30°C.For growth analysis, strains containing a wild-type copy of 600 of 1.0 and lysed using FA-1 Lysis buffer  and 0.5 mm glass beads with 5 minutes of vortexing at 4°C. The supernatant was cleared by centrifugation and protein concentration was determined by Bradford Assay (Bio-Rad). 50 µg of total protein was fractionated by SDS-PAGE electrophoresis and transferred to a nitrocellulose membrane for immunoblotting with 1∶2000 dilution of anti-PGK1 (Molecular Probes) and 1∶1000 dilution of anti-HA 12CA5 (Roche), followed by chemiluminescent detection (Pierce).Cells were grown to an O.D.600 0.5–0.7 and then crosslinked for 15 minutes with formaldehyde to a final concentration of 1%. Cells were disrupted with glass beads (0.5 mm) for 40 minutes at 4°C and lysates were cleared by centrifugation. To shear chromatin, lysates were sonicated for a total of six minutes at 30% intensity . After sonication, samples were precleared with CL4B Sepharose beads (Sigma). The precleared samples were then used for immunoprecipitation with either 12CA5 (Roche) antibody against the HA epitope or 8WG16 (Covance) antibody against RNA pol II. After immunoprecipitation, samples were washed and incubated overnight at 65°C to reverse crosslinking, followed by incubation with Proteinase K (Sigma). DNA was purified using a PCR product purification kit (Qiagen) and analyzed by real-time PCR. Input DNA was diluted 1∶20 and 1 µl of this was used in a 25 µl reaction volume. For ChIP DNA, samples were diluted 1∶5 and 1 µl of this was used in a 25 µl reaction volume. Reactions consisted of 12.5 µl SYBR GREEN Master Mix (Applied Biosystems) and 0.5 µM Primers. Real time PCR was performed using an ABI7700 (Applied Biosystems). All samples were run in triplicate for each independent experiment.Cells were grown in YPD to an O.D.For quantification, standard curves were generated for each primer set, and DNA concentration for each INPUT and ChIP sample was calculated. ChIP values were divided by the INPUT, and these values were divided by the non-transcribed control and expressed as fold accumulation over the non-transcribed control. Reported values are averages of at least three independent experiments, and error bars represent the standard deviation.For ChIP experiments in For quantification, standard curves were generated for each primer set. DNA concentration for each INPUT and ChIP sample was calculated using these standard curves and normalized to the non-transcribed control VI_R1. The normalized IP values calculated for acetylated H3 were divided by the normalized IP values calculated for total H3. These values are expressed as diacetylated H3 over total Histone H3. Reported values are averages of three independent experiments, and error bars represent the standard deviation.The data in Figure S1DBP2 and ECM33 is dependent on transcription. (A) Schematic of chromosome XIV and relative location of DBP2. Underlined numbers represent amplicons from each primer set used in this study. (B) Graph represents occupancy of Lea1 and Msl1 at each region of DBP2 relative to the non-transcribed region in the presence and absence of GCN5. Dark grey bars represent Lea1/Msl1 recruitment in the presence of GCN5 and light grey bars represent recruitment of Lea1/Msl1 in the absence of GCN5. (C) Schematic of chromosome II and the relative location of ECM33. Underlined numbers represent amplicons from each primer set used in this study. (D) Occupancy of Lea1 and Msl1 at ECM33 relative to the non-transcribed region. Dark Grey bars represent Lea1/Msl1 recruitment in the presence of GCN5 and light grey bars represent Lea1/Msl1 recruitment in its absence. Graphs depict the average of three independent experiments, and error bars represent the standard deviation.Recruitment of Msl1 and Lea1 to (1.19 MB TIF)Click here for additional data file.Figure S2GCN5 alters splicing of DBP2 and ECM33 transcripts. Quantitative RT-PCR of DBP2 and ECM33 in the absence of GCN5, MSL1, or LEA1. (A) Graph represents the ratio of precursor DBP2 or ECM33 transcript relative to mature message in wild type and GCN5 deleted cells. Data is represented as a ratio of precursor (unspliced) RNA to total message. (B) Graph represents the ratio of precursor (unspliced) RNA to total DBP2 or ECM33 message in wild type, MSL1 deleted, and LEA1 deleted cells. Error bars represent the standard deviation.Deletion of (0.25 MB TIF)Click here for additional data file.Table S1List of yeast strains used in this study.(0.06 MB PDF)Click here for additional data file.Table S2List of plasmids used in this study.(0.03 MB DOC)Click here for additional data file.Table S3DBP2 primers used for ChIP (for ChIP .(0.03 MB DOC)Click here for additional data file.Table S4DBP2 and ECM33 primers used for ChIP real time PCR analysis.(0.04 MB DOC)Click here for additional data file.Table S5DBP2 and ECM33 primers used for quantitative RT-PCR (e RT-PCR .(0.03 MB DOC)Click here for additional data file.Text S1Supplemental (0.04 MB DOC)Click here for additional data file.


The transcriptional regulator c-Myc is the most frequently deregulated oncogene in human tumors. Targeted overexpression of this gene in mice results in distinct types of lung adenocarcinomas. By using microarray technology, alterations in the expression of genes were captured based on a female transgenic mouse model in which, indeed, c-Myc overexpression in alveolar epithelium results in the development of bronchiolo-alveolar carcinoma (BAC) and papillary adenocarcinoma (PLAC). In this study, we analyzed exclusively the promoters of induced genes by different in silico methods in order to elucidate the c-Myc transcriptional regulatory network.We analyzed the promoters of 361 transcriptionally induced genes with respect to c-Myc binding sites and found 110 putative binding sites in 94 promoters. Furthermore, we analyzed the flanking sequences (+/- 100 bp) around the 110 c-Myc binding sites and found Ap2, Zf5, Zic3, and E2f binding sites to be overrepresented in these regions. Then, we analyzed the promoters of 361 induced genes with respect to binding sites of other transcription factors (TFs) which were upregulated by c-Myc overexpression. We identified at least one binding site of at least one of these TFs in 220 promoters, thus elucidating a potential transcription factor network. The analysis correlated well with the significant overexpression of the TFs Atf2, Foxf1a, Smad4, Sox4, Sp3 and Stat5a. Finally, we analyzed promoters of regulated genes which where apparently not regulated by c-Myc or other c-Myc targeted TFs and identified overrepresented Oct1, Mzf1, Ppargamma, Plzf, Ets, and HmgIY binding sites when compared against control promoter background.Our in silico data suggest a model of a transcriptional regulatory network in which different TFs act in concert upon c-Myc overexpression. We determined molecular rules for transcriptional regulation to explain, in part, the carcinogenic effect seen in mice overexpressing the c-Myc oncogene. The proto-oncogene c-Myc is highly expressed in many cancer types -3 and plThe molecular mechanisms by which c-Myc functions to effect tumorigenesis have been the subject of extensive research in the past several decades. c-Myc is a transcription factor, a basic helix-loop-helix leucine zipper protein that dimerizes with Max to bind the DNA sequence 5'-CACGTG-3', known as an E box, and activates transcription . Myc alsThe pleiotropic effects of c-Myc on tumorigenesis are thought to be mediated by its target genes, because transcriptionally defective Myc alleles have diminished transforming potential . FurtherDespite extensive research, the specific mechanisms by which tumorigenesis are achieved are not well understood. This is largely because a comprehensive list of biologically relevant Myc target genes has not yet been defined and such "transformation" associated genes remain elusive . In ordeIn this study, we report a genetic and bioinformatic approach designed to identify regulatory gene networks induced by overexpression of c-Myc in alveolar epithelium of our female transgenic mouse model, resulting in the development of bronchiolo-alveolar carcinoma (BAC) and papillary adenocarcinoma (PLAC). Because the mechanisms of transcriptional repression by c-Myc do not occur by direct binding of c-Myc to E boxes, we restricted our analysis to promoter sequences of induced genes in which the potential c-Myc binding sites can be identified in silico. Thus, we have identified potential direct target genes of c-Myc and propose different transcription factors which either cooperate with or compete against c-Myc. Furthermore, we in silico describe different indirect mechanisms possibly participating in the Myc tumorigenic phenotype. Taken together, we suggest a model of a regulatory gene network in which different TFs act in concert upon overexpression of c-Myc in our transgenic mouse model.Global gene expression studies were done with lung tissue stemming from a female mouse transgenic line overexpressing the c-Myc proto-oncogene. The complete data have been deposited in NCBIs Gene Expression Omnibus (GEO)  and are ®. Comparison of fold changes determined by microarray analysis and real time PCR are shown in Figure For the validation of microarray data, five different genes were selected: Met (met proto-oncogene), Myct1 (myc target 1), Myc (myelocytomatosis oncogene), Pnliprp1 (pancreatic lipase related protein 1) and Pbk (PDZ binding kinase). Expression of these genes was alternatively investigated with real time quantitative PCR using the LightCyclerA flowchart of our in silico strategy used to elucidate the c-Myc regulatory network is depicted in Figure ® Professional rel. 8.3 integrated tool MATCH® by using the matrices V$EBOX_Q6_01 , V$MYC_Q2 , and V$MYCMAX_B . The results of these analyses including positions and sequences of the corresponding binding sites are given in Additional file By using their RefSeq annotation, 361 promoter sequences could be extracted from the UCSC Genome Browser for the 469 upregulated genes. Furthermore, promoters of 100 genes which were not regulated at all were extracted  of the occurrence values are listed in Table For this analysis, we extracted the 110 c-Myc binding sites including the flanking sequences (100 bp flanking the 5 bp core sequence to both sides (= 205 bp)). We further randomly extracted the same number of 205 bp sequences from the control promoters which were not regulated at all , V$HFH8_01 , V$SMAD-4 , V$SOX_Q6 , V$STAT5A_03 , V$STAT5A_04 , V$STAT5A_01 , V$STAT5A_02 , and V$SP3_Q3 . The results of this analysis including positions and sequences of the corresponding binding sites are given in Additional file Next, we analyzed the 361 promoter sequences out of 469 upregulated genes which are available in the UCSC Genome Browser, using the TRANSFAC® Professional rel. 8.3 integrated tool MATCH® by applying the matrix profile "vertebrates_minSUM_highQual". We further performed the same analysis using control promoters which were not regulated at all , which is the most widely used method for recognition of transcription factor binding sites ,35, we a® Mouse Genome 430 2.0. This list contains 15 targets stored in the Myc Target Database, whereas 79 genes are putative novel targets. Whether they are real targets remains to be elucidated. The functional categories of these 94 putative c-Myc targets revealed that various cellular processes like transcriptional regulation, protein modification and transport, cell cycle control/proliferation, metabolism, and signal transduction are putatively directly regulated by c-Myc. These findings correlate well with expectations based on the biology of c-Myc.By applying three different weight matrices for recognition of c-Myc binding sites, we predicted 94 putative c-Myc targets among the genes presented on Affymetrix platform GeneChipIn higher organisms transcription factors usually operate in combination with other transcription factors bound in direct neighborhood in promoter sequences to influence gene transcription. Up to now, less is known about transcription factors (TFs) collaborating with c-Myc. Previously, Elkon et al.  reportedIn the present study, we analyzed exclusively the flanking sequences around the in silico identified c-Myc binding sites by use of all available positional weight matrices in the TRANSFAC database. Especially binding sites of the transcription factors E2F, AP2, ZF5, and ZIC3 were found to be significantly enriched from 2.2- to 10-fold over control promoter background. The poor concordance of our results and those of Elkon et al.  might beNotably, both studies identified E2F to be a transcriptional regulator associated with c-Myc. Like c-Myc, E2F also controls cell cycle progression and DNA replication . Thus, dBy using four different matrices for AP2, we also found AP2 binding sites in the direct neighborhood of c-Myc binding sites in 32 sequences out of 110 sequences possessing a c-Myc binding site. Depending on the matrix applied, they are 2.4- to 10.5-fold enriched over the control promoter background. In 2006, Zeller et al. already identified AP2 to be significantly enriched in cis-regulatory modules with c-Myc . The AP2We also found ZF5 binding sites in the direct neighborhood of c-Myc binding sites in 41 sequences out of 110 sequences possessing a c-Myc binding site. They are 2.3-fold enriched over the control promoter background. ZF5 is a ubiquitously expressed protein originally identified by its ability to bind and repress the murine c-Myc promoter . It contFurthermore, we found ZIC3 binding sites in the direct neighborhood of c-Myc binding sites in 19 sequences out of 110 sequences possessing a c-Myc binding site. They are 2.2-fold enriched over the control promoter background. ZIC3 is a developmental specific zinc finger transcription factor defining early embryo patterning . AlthougAs mentioned above, gene expression studies alone cannot discriminate between direct and indirect targets of c-Myc action. Nevertheless, with overexpression of c-Myc in alveolar epithelium of our female transgenic mouse model resulting in the development of bronchiolo-alveolar carcinoma (BAC) and papillary adenocarcinoma (PLAC) 36 of 477 deregulated genes possess transcription factor activity or transcription regulator activity. These transcription factors mediate the indirect actions of c-Myc. By using the corresponding available Positional Weight Matrices from TRANSFAC  for the analysis of the 477 deregulated genes, many putative indirect targets of c-Myc action could be identified. In 73 promoters at least one binding site for ATF2 has been identified. ATF2 belongs to the basic region leucine zipper (bZIP) family of transcription factors and is an important member of activating protein 1 (AP-1) . ATF2 fuThe members of the forkhead box (Fox) family of transcription factors play important roles in regulating transcription of genes involved in cellular proliferation, differentiation, and metabolic homeostasis . Foxf1 R1 arrest and inhibition of cell proliferation in epithelial cells. Three studies identified the TIE sequence in the c-Myc promoter as mediating the TGF-β effect on c-Myc expression. A complex of Smad3-Smad4, E2F4/5, DP1, and p107 binds to the TIE in response to TGF-β to inhibit transcription of c-Myc  and abundance  of an individual transcript. The detection (absolute information) and the signal  are calculated independently.As detailed by the manufacturer, expression of a gene is corroborated by a set of 11 pairs of 25-oligomer. Next to perfect sequence matches, deliberate mismatches which differ by one base only in the middle of the oligomer are introduced to confirm hybridization products. A statistical expression algorithm within the GeneChip® Data Mining Tool Software (version 3.1) and Microsoft® Excel 2003. Additionally, the number of "present" calls (P-count) for each gene in four replicates was determined.To determine whether a gene is "significantly present", the average signal value  and the standard deviation [Stdev and Stdev(%)] were calculated using AffymetrixCriteria applied for a "significantly present" gene were, for example: average signal value ≥ 100, and all four "detection calls" must be "present" (P-count).® GeneChip® Operating Software (GCOS) and Data Mining Tool (DMT). The average and standard deviation statistics within Affymetrix® DMT were used to summarize the expression level  for each transcript across the replicates. The unpaired t-test and comparison ranking were used to determine the direction and significance of change in a transcript's expression level between sets of replicates. Fold change values were calculated as the ratio of the average expression levels for each gene between c-Myc-transgenic animals and the correlating control experiment.Multiple data from replicates were evaluated and compared using statistical analysis with the Affymetrix® Operating Software (GCOS). A comparison analysis was conducted for the female group within the c-Myc-transgenic line: transgenic versus non-transgenic strains.To extract genes with significantly altered expression, a comparison between groups of animals was conducted using the GeneChip2ratio).For the comparison analysis, it was ensured that the scale factors for the compared chips did not differ by a factor larger than 3. The result of a single analysis between two different arrays was reported for each gene as "increase" (I) or "decrease" (D), and the change in signal intensity was determined as the signal logarithm ratio  were conducted. Comparison ranking analysis was additionally done to study concordance between "increase calls" (I) or "decrease calls" (D) for replicates .The unpaired one-sided t-test converting the p-value to a two-sided p-value was used to determine the direction and significance of change in a transcript's expression level . Signal values of each group were used as basis for calculation, with the original p-value cutoff determined to be 0.05.Comparing different groups, a "fold change" (FC) was calculated, which is the ratio between the average signal values of groups to be compared. Ratios ≤ 1 were recalculated to give negative numbers whose magnitude resembles the extent of repression (for example: ratio of 0.5 was changed to -2).To select genes in the c-Myc experiments, the following criteria were applied for the comparison conducted:○ the average signal value of the "treatment" had to be higher than 100○ 4 P-calls had to be in the 4 "treatment" arrays○ the fold change had to be 2.0 or higher ○ the result of the t-test had to be an "Up"-change call  ○ there had to be more than 13 (out of 16) induced calls in the comparison ranking○ the average signal value of the "control" had to be higher than 100○ 4 P-calls had to be in the 4 "control" arrays○ the fold change had to be -2.0 or less ○ the result of the t-test had to be a "Down"-change call  ○ there had to be more than 13 (out of 16) decrease calls in the comparison rankingApplying these criteria as detailed above, probe sets significantly altered in expression were selected. In a few cases, two or more of these "probe sets" were targeting the same gene.To prevent reiterations, the following criteria were applied and only one "probe set" per gene was selected:x_at).1. Primarily, "probe sets" not specific for one transcript were eliminated  or all were not specific, those with higher signal values were selected.® . RNA was treated with Dnase and purified with RNeasy Mini Kit. Quality of purified RNA was analyzed in a denaturating Agarose gel. Reverse transcription (RT) was performed with 2 μg of RNA using Omniscript (Qiagen), RNase inhibitor and hexamers (Promega) in a final volume of 20 μl. RT reactions were diluted 1:4 and 2 μl was used for Real-time PCR. SYBR® Green I was used as a fluorescent dye to determine the amplified PCR products after each cycle. The lengths of PCR products were checked in gel electrophoresis. PCR primers were synthesized by Invitrogen . At the end of each extension phase fluorescence was observed and used for quantitative measurements within the linear range of amplification yielding calculated concentrations as relative units. Exact quantification was achieved by serial dilution with cDNA produced from total RNA extracts using 1:5 or 1:3 dilution steps, depending on the expression level of the gene. Six runs were necessary to measure expression of the genes in all samples. For comparability of the six independent runs, standards were used, which were identical sample pools for all six runs. The standardized sample values for each gene of interest were divided through the standardized values of the housekeeping gene. As housekeeping gene, Ppib  was used.Real-time PCR measurement was done with the LightCyclerThe UCSC Genome Browser  was used® Professional rel. 10.1 is the largest collection of weight matrices for eukaryotic transcription factors [®-integrated MATCH™ algorithm was employed, calculating scores for the matches by applying the so-called information vector [The most widely used method for recognition of transcription factor binding sites is the application of positional weight matrices (PWMs)  TRANSFAC factors ,84 ○ the result of the t-test had to be a "None"-change call○ there had to be less than 5 (out of 16) induced calls in the comparison rankingApplying these criteria for c-Myc as detailed above, 164 probe sets for genes with almost no change in expression were selected. In addition, 100 genes with a Transcript RefSeq number were selected to be used to extract promoter sequences for controls , and the corresponding recognized sequence are included in the right column.Click here for file110 randomly extracted 205-bp sequences from the control promoters which were not regulated at all.Click here for fileComplete results: analysis of flanking sequences (+/- 100 bp) around c-Myc binding sites. This table shows the TRANSFAC identifier of the applied matrices, the number of hits identified in the sequences of the induced gene promoters, the number of hits identified in the sequences of the control gene promoters, and the corresponding fold occurrences of hits.Click here for file36 deregulated genes possessing transcription factor or transcription regulator activity.Click here for fileAnalysis of promoters of 361 induced genes with respect to binding sites of TFs which were transcriptionally induced by c-Myc overexpression. RefSeq accession numbers, TRANSFAC identifier of the respective matrix, the position of the hit  and the corresponding recognized sequence are given in this table.Click here for filePutative target genes identified in silico by using positional weight matrices. This table shows the RefSeq accession numbers, gene titles, and gene symbols of the putative target genes identified for the respective promoter analysis.Click here for filePutative target genes of c-Myc and of transcription factors the expression of which was induced by c-Myc. This table shows the RefSeq accession numbers of genes in the promoters of which the matrix hits were found, the TRANSFAC identifier of the respective matrices, and the location and corresponding sequence of the matrix hits.Click here for file


DNA topoisomerase II (topo II) catalyzes a strand passage reaction in that one duplex is passed through a transient brake or gate in another. Completion of late stages of neuronal development depends on the presence of active β isoform (topo IIβ). The enzyme appears to aid the transcriptional induction of a limited number of genes essential for neuronal maturation. However, this selectivity and underlying molecular mechanism remains unknown. Here we show a strong correlation between the genomic location of topo IIβ action sites and the genes it regulates. These genes, termed group A1, are functionally biased towards membrane proteins with ion channel, transporter, or receptor activities. Significant proportions of them encode long transcripts and are juxtaposed to a long AT-rich intergenic region (termed LAIR). We mapped genomic sites directly targeted by topo IIβ using a functional immunoprecipitation strategy. These sites can be classified into two distinct classes with discrete local GC contents. One of the classes, termed c2, appears to involve a strand passage event between distant segments of genomic DNA. The c2 sites are concentrated both in A1 gene boundaries and the adjacent LAIR, suggesting a direct link between the action sites and the transcriptional activation. A higher-order chromatin structure associated with AT richness and gene poorness is likely to serve as a silencer of gene expression, which is abrogated by topo IIβ releasing nearby genes from repression. Positioning of these genes and their control machinery may have developed recently in vertebrate evolution to support higher functions of central nervous system. The process is catalyzed by the α isoform of topo II that evolved from a single eukaryotic gene through a gene duplication event, which occurred around the time of vertebrate diversification. Topo IIα and its counterpart β (topo IIβ) exhibit similar enzymatic properties on naked DNA in vitro experiments with naked DNA and purified enzymes in vivo was also mapped with various cell systems in vivo cleavage sites are highly restricted by the local chromatin structure, being located mostly in the linker segment between phased nucleosomes in vivo do not always coincide with strong naked DNA sites but often correspond to only minor in vitro sites. It is clear then that the topo II cleavage consensus in vitro is inadequate to predict its in vivo sites. It has been generally accepted that topo II cleavage sites are within an AT-rich region often in the vicinity of the matrix attachment region (MAR/SAR) in vivo topo II action sites has not been attempted.A group of topo II inhibitors such as etoposide, often called topoisomerase poisons, have been used to map the topo II action sites on DNA. The drug binds specifically to the enzyme and stabilizes the topo II-DNA covalent complex (cleavable complex) by preventing the ligation of the transiently cleaved DNA strands The mapping technique used in the present study also utilizes etoposide to enrich the reaction intermediate, but the enzyme-DNA complex was recovered by lysing the cells in milder conditions without strong denaturants Because of complicated terminology used in the study, we provide an explanatory document in Supporting Information to avoid possible confusions .in vitroIn the primary culture of newborn cerebellar tissue that we used in this study, only precursor granule neurons can survive after the final division We first categorized the genes expressed in differentiating cerebellar neurons by analyzing the expression array data on the basis of combination of two factors: induction rate during differentiation and susceptibility to ICRF-193. The inhibitor not only inhibits the activity but also depletes the enzyme by selective degradation To see whether these expression groups reflect some functional characteristics, the gene ontology (GO) database was used to find over- or under-represented terms in each group . GO termWe next explored how A1 genes are physically different from the other gene groups. When locating A1 genes on genome browser displays, we noticed that they frequently reside close to large gene-poor regions that are mostly AT-rich. For this type of analysis, we first constructed a rat genome compilation (exRefSeq) by combining the data from rat, mouse, and human RefSeq genes . The ratTherefore, typical A1 genes can be characterized by three properties: LAIR-proximal, long, and AT-rich. The category of genes with all these characters will be referred to as “LA genes” hereafter. We adopt the average gene length (51 kb) and overall GC content (0.44) to define the thresholds . A two dBased on these results, it should be possible to predict LAIR-proximal A1 genes without knowing their actual response to topo II inhibitors. To test this, from the genes without any assigned array probes (group N), we selected five LA genes that are up-regulated during the cerebellar differentiation and sharing similar GO terms with A1 genes. Expression profiles analyzed by RT-qPCR showed that 4 out of 5 candidate genes indeed behave like A1 genes , corroboAs a next step, we intended to locate direct action sites of topo IIβ on DNA, not just binding sites. This can be done by using etoposide, a topoisomerase poison, which stabilizes the reaction intermediate to cross-link the enzyme to DNA at the site of action. The enzyme-DNA complex was recovered by immunoprecipitation with specific antibody. We name this technique eTIP for etoposide-mediated topoisomerase immunoprecipitation . No form II or form Ir relaxed DNA was detected in P2 fraction indicating that the enzyme-DNA complex is not dissociated from the beads and reversal of the cleavage does not take place in the stringent washing conditions , the eTIP DNA fractions were prepared at the second day of granule cell differentiation. We chose this timing because mRNA expression is maximally induced by day 3 in many A1 genes see , indicatTo avoid possible confusions, the relationship among the terms used in the study is represented schematically in Ndst4, an A1 gene predicted from the positional information  or at the termination site (TTS) . A discr+ and Na+, respectively.Type II DNA topoisomerases catalyze the strand passage events between the two segments of DNA, termed G- and T-segments in vivo. A working hypothesis implied by the present study is summarized in The characteristic partitioning of DNA fragments into eTIP fractions (P1 and P2) provided an important clue to understanding the mechanism of topo IIβ action Transcriptional profiling of topo IIβ knockout mouse embryos showed that the enzyme regulates only a small fraction of genes (1–4%) in the late embryonic brain cells The c2 toposite identified in this study is unique in that it involves two segments of DNA from distant genomic locations . AccordiAll experiments were carried out under the control of the Animal Research Control Committee in accordance with the Regulation for Animal Experiments of Okayama University Medical School. All efforts were made to minimize the number of animals used and their suffering.7 cells per 100 mm plastic dish pre-coated with poly-L-lysine. The cells were incubated at 37°C in DMEM containing 10% FCS, 25 mM KCl, and 60 µg/ml kanamycin sulfate. After 15–16 h, medium was replaced with fresh medium supplemented with 10 µM cytosine arabinoside to suppress the glial growth. The medium change was repeated at 24 h and at every 48 h afterwards. When it is required, expression of topo IIβ was inhibited specifically throughout the culture period by daily addition of 10 µM ICRF-193.Cerebellar tissue was isolated from the Wistar rat 8 days after birth. Minced tissue was treated briefly with trypsin-EDTA and DNase I in PBS. Dispersed cells were plated at 2.5×10Cells grown on culture dishes were lysed directly in SDS-PAGE sample buffer  and boiled for 5 min. To normalize the loading amounts, concentration of nucleic acids in sample was determined by a fluorometric procedure with Quant-iT RiboGreen RNA Assay Kit . GENios multi-detection microplate reader (Tecan) was used for detection. Samples equivalent to 0.5–2.0 µg of nucleic acids were subjected to SDS-PAGE and then transferred to a PVDF membrane by electroblotting. The membrane was incubated with blocking buffer  for at least 2 h at room temperature. Immunodetection with first antibodies and horseradish peroxidase-conjugated second antibodies (listed in 6 cells per reaction). The cell suspension was transferred to the Nucleofector cuvette and the transfection sequence was started using program G-13. Immediately after transfection, 0.5 ml of pre-warmed culture medium was added to the cuvette. Cells were then transferred to a 6-well plate containing 1.5 ml of culture medium and incubated in a humidified incubator . Three hours later, the medium was replaced with fresh medium. During the time before the RNAi effect becomes significant, topo IIβ was suppressed by 10 µM ICRF-193 between 16 h and 2.5 days, and then the medium was replaced with fresh medium without inhibitor. Our previous study suggested that the effect of ICRF-193 is fully reversible and after removing the inhibitor the normal gene induction sequence proceeds with delays To knockdown topo IIβ in cultured granule cells, transfection of rat topo IIβ siRNA was carried out using an electroporation equipment, Nucleofector, and Rat Neuron Nucleofector Kit (Amaxa). The cDNA sequence of rat topo IIβ (GenBank Accession AB262979) was used to design siRNAs as siGenome SMARTpool siRNA . The control siRNA was Negative Control siRNA (Qiagen). Freshly isolated cerebellar cells were resuspended in 100 µl of Nucleofector solution and mixed with 3 µg of siRNA  according to supplier's instructions. Three micrograms of total RNA was reverse-transcribed in 60-µl reactions as recommended by manufacturer, using 5 µM random hexamer primer, 0.5 mM deoxyribonucleoside triphosphates (dNTPs), 1 unit/µl RNase inhibitors (Takara Bio) and 10 units/µl M-MLV reverse transcriptase, RNase H minus (Takara Bio). Real time qPCR was performed on the GeneAmp 5700 Sequence Detection System (Applied Biosystems) using SYBR Green I chemistry in triplicate reactions. Primer sets for qPCR were based on the rat gene sequences retrieved from the UCSC genome browser site (listed in http://hgdownload.cse.ucsc.edu). The annotation date for these files was 29-Jul-2007. Available programs  were used to process the data. All the data sets for refSeq and xenoRefSeq were first deposited in a MySQL database and then rat, mouse, and human data were extracted. Items with uncertain genomic position (designated Un or random) were excluded. Non-rat olfactory and vomeronasal receptor genes were also eliminated. Furthermore, the following items were filtered out: RefSeq of low transcript-genome matching (<60%), mouse RefSeq of low homology (<85%), and human RefSeq of low homology (<80%). All the gene names in the remaining items were converted to the mouse format using the programs g:Convert and g:Orth at the g:Profiler website (http://biit.cs.ut.ee/gprofiler/) Using the rat genome database , refSeq  and xenoRefSeq  coordinates were downloaded from UCSC genome bioinformatics (http://www.affymetrix.com). One microgram of total RNAs from culture day 1 (D1), day 5 (D5), and day 5 with inhibitor (D5+) were reverse-transcribed to cDNAs by GeneChip One-Cycle cDNA Synthesis Kit. The cDNAs were transcribed to cRNAs in vitro with biotinylated nucleotides using GeneChip IVT Labeling Kit. The Test3 array was used for quality control. Biotinylated cRNAs were hybridized to GeneChip Rat Genome 230 2.0 for 16 h at 45°C in GeneChip Hybridization oven 640. After hybridization, arrays were washed and stained with Streptavidin-Phycoerythrin in GeneChip Fluidics Station 450, and scanned on GeneChip Scanner 3000. GeneChip Operating Software  was used for image processing and data acquisition. The expression array data were deposited in GEO (GSE13112).Expression microarray experiments were performed mostly at Bio Matrix Research, Inc. . The labeling procedure was carried out as described by the manufacturer  and excluded from further classification. Expressed probes were classified into 9 groups  by applying 2-fold and 0.5-fold thresholds to the combination of two parameters, induction (D5/D1) and inhibition (D5+/D5) (illustrated in http://gostat.wehi.edu.au/cgi-bin/goStat2.pl) http://www.ncbi.nlm.nih.gov/sites/entrez?dbGene) with search terms: neuron* OR neural* OR neurite OR axon AND human[ORGN]. Out of 2,844 human neuronal genes, 1,429 genes that possess rat exRefSeq counterparts were used for GO analysis.To analyze the gene ontology (GO) annotation of selected rat genes, gene names of exRefSeq were converted to corresponding human gene names using the programs g:Convert and g:Orth at the g:Profiler site. GO statistics were examined by the program GOstat2 to identify the GO terms that are enriched or depleted between two sets of genes (Primary cultures of rat cerebellar granule cells at day 2 were treated with 0.5 mM etoposide (VP-16) in serum-free medium for 15 min. The treated cells on 100 mm dishes were lysed with 750 µl of a buffer containing 1% Sarkosyl, 10 mM Tris-HCl (pH 7.5), 10 mM EDTA, and protease inhibitor mixture  by passing through a 23G needle 10 times. Concentrated CsCl (7 M) was added to a final concentration of 0.5 M. To fragment DNA, the lysates were sonicated with VP-5S sonicater (Taitec) for 30 sec at power setting 3. Under these conditions, length of DNA fragments, as determined by agarose gel electrophoresis, varied from 0.5 kb to 5 kb. Before immunoprecipitation (IP), 3 volumes of a buffer containing 10 mM Tris-HCl (pH 7.5), 10 mM EDTA, 100 mM NaCl, 0.1% Triton X-100, and protease inhibitor mixture was added to 1 volume of lysates and the diluted lysates were centrifuged for 15 min at 15,000 rpm at 4°C. The supernatant was pre-cleared with 10 µl of Dynabeads Protein G  by rotating the tubes for 2 h at 4°C. Unbound fraction recovered by magnetic separation was used as an input for IP reactions. The reaction was initiated by the addition of 10 µl of Dynabeads protein G, which had been pre-incubated with 5 µg of specific antibody (3B6) or control antibody (mouse IgG). The beads suspension was incubated overnight at 4°C and the IP fraction was recovered by magnetic separation followed by washing 3 times with TEST-150 . The IP-beads were then eluted 3 times with 500 µl of TEST-500  by magnetic separation each time and the eluates were pooled (designated as P2 fraction). DNA fragments still bound on the beads (designated as P1 fraction) were treated with 50 µg/ml RNase A at 55°C for 30 min, 200 µg/ml Proteinase K at 55°C overnight, and purified by phenol/chloroform extractions followed by ethanol-precipitation with 10 µg of glycogen. The DNA in IP input and P2 fraction was also purified and DNA amounts in each fraction were determined by Quant-iT PicoGreen dsDNA Quantitation Kit  according to the manufacture's protocol.2, 0.5 mM DTT, 0.5 mM EDTA, 0.5 mM ATP, 30 µg/ml BSA). In some reactions, 0.5 mM etoposide was added. After 15 min, the reaction was terminated either by Sarkosyl-CsCl  or SDS . Samples were digested with SDS/Proteinase K and subjected to electrophoresis in 1% agarose gel in the presence of 0.5 µg/ml ethidium bromide.Nuclei were prepared from 2-week-old rat brain (without cerebellum) by the high-density sucrose method. By removing the cerebellar tissue, it was possible to eliminate topo IIα from the preparation For immunoprecipitation, supercoiled pUC18 DNA (7.5 µg) was incubated with the nuclear extract at 37°C in the presence of 0.5 mM etoposide (300 µl reaction volume). After 15 min, equal volume of 2× stop solution  was added and incubated on ice for 15 min, then 7 M CsCl was added to a final concentration of 0.5 M. Before immunoprecipitation, 3 volumes of dilution buffer  were added. Immunoprecipitation procedures were the same as in eTIP. Purified DNA fractions  were analyzed by agarose gel electrophoresis as described above.2. After incubation at 30°C for 45 min, 0.5 M EDTA was added to a final concentration of 15 mM to stop the reaction. Proteins in these fractions were recovered by acetone precipitation with 2 µg of BSA as a carrier and subjected to 6.5% SDS-PAGE and immunoblotting with topo IIβ-specific antibody (3B6).Cultured granule neurons were treated with etoposide and eTIP fractions  were prepared as described in the eTIP procedure. Protein samples were subjected to Western blotting to examine the presence of topo IIβ. Before electrophoresis, DNAs cross-linked to topo IIβ were digested with RNase-free DNase I in a buffer supplemented with 10 mM MgClMboI and amplified by ligation-mediated PCR (LM-PCR) performed as described http://genome.ucsc.edu). The arrays were tiled with isothermal 50 to 75-mer oligos that were placed at 1 probe/100 bp on repeat-masked genomic sequence. Hybridization and scanning of the fluorescence intensities were performed at NimbleGen (http://www.nimblegen.com/). Briefly, amplified DNA preparations were labeled with Cy5 (P1 and P2) or Cy3 (IP input) and were hybridized competitively to the array with Cot-1 DNA. Scanned data were processed by the supplier's standard protocol for ChIP-chip analysis to obtain the scaled log2 (Cy5/Cy3 ratio).Purified DNA fractions  were digested with 2 (P1/input) and log2 (P2/input) values were used for the computational analysis named ACME (Algorithm for Capturing Microarray Enrichment) −4.5 were adopted as toposites. To classify the toposites, significant P1 signals were subjected to cluster analysis (Ward's method) between two factors, log2 (P2/input) and local GC content around toposites. Toposites were clearly separated into two clusters of comparable size: low P2 signal-high GC content (class 1) and high P2 signal-low GC content (class 2). The tiling array data were deposited in GEO (GSE13145).The logWe scanned the distribution of class 1 (c1) and class 2 (c2) toposites along the genome by using a sliding-window algorithm. With respect to the probes that were recognized as a toposite by ACME, the likelihood of being toposite was split and distributed evenly to every nucleotide across the probe. Namely, the inverse of probe length in number of nucleotides was defined as a “positional toposite potential” assigned to every nucleotide position within the toposite probe. To assess the toposite density around the transcription start sites (TSS) and termination sites (TTS) of 318 transcription units located in the tiled region, toposite potentials within ±10 kb from these sites were counted. The potential scores were then added up in every gene groups of interest that were aligned at TSS or TTS. Resulting figures were divided by the number of genes constituting the group in order to enable the comparison between different gene groups. Entire region of the 20 kb-potential map was scanned with a moving window of 1-bp step and 1,000-bp width. Sum of the potential scores within the window was plotted at the centre position as a toposite density. The highest density score would be 10 by definition. For a control, the same numbers of toposites were picked up randomly as Monte Carlo simulation of 10-times repetition and the toposite density was calculated as stated above.EcoRV site of a zero-background cloning vector pZErO-2.1 (Invitrogen) and transformed into Escherichia coli TOP10F' competent cells (Invitrogen). The transformants were screened by colony PCR using AmpliTaq Gold DNA polymerase (Applied Biosystems) with M13RV and M13(-21) vector primers. The nucleotide sequences of eTIP clones were determined by direct sequencing of the amplified fragments on the DNA sequencer 310 according to standard methods (Applied Biosystems). Genomic positions of resulting sequences were determined on the rat genome (rn3) by BLAT search performed at the UCSC genome browser website (http://genome.ucsc.edu). The complete list of eTIP clones is given in The eTIP DNA fractions (P1 and P2) amplified by LM-PCR were blunt-ended with T4 DNA polymerase (New England Biolabs), ligated into the Text S1Glossary of unconventional words and phrases(0.03 MB DOC)Click here for additional data file.Data S1Supplementary data containing (2.81 MB PDF)Click here for additional data file.Figure S1Confirmation of the array-based gene grouping by RT-qPCR and immunoblotting(2.58 MB TIF)Click here for additional data file.Figure S2Suppression of transcriptional induction of A1 genes by topo ΙΙβ siRNA(1.51 MB TIF)Click here for additional data file.Figure S3Schematic representation of the procedure for construction of exRefSeq(1.36 MB TIF)Click here for additional data file.Figure S4Classification of rat subgenomic regions by length and GC content(3.02 MB TIF)Click here for additional data file.Figure S5Comparison of expression groups in terms of gene's position, length, and GC content(1.12 MB TIF)Click here for additional data file.Figure S6Functional similarity between A1 genes and LA genes as revealed by a GO matrix(1.70 MB TIF)Click here for additional data file.Figure S7Prediction of A1 genes from positional and functional information(1.65 MB TIF)Click here for additional data file.Figure S8Characterization of eTIP DNA fractions by shotgun cloning and sequencing(1.57 MB TIF)Click here for additional data file.Figure S9Overview of the topography of toposites and genes in the seven chromosomal regions analyzed by tiling arrays(7.31 MB TIF)Click here for additional data file.Figure S10Similarity of expression patterns and genomic locations of relevant gene groups between embryonic brain and cultured granule cells(1.45 MB TIF)Click here for additional data file.Figure S11A high incidence of LA genes in monoallelically expressed autosomal genes(1.10 MB TIF)Click here for additional data file.Table S1Genomic location of eTIP DNA clones(0.10 MB XLS)Click here for additional data file.Table S2eTIP PCR primers(0.03 MB XLS)Click here for additional data file.Table S3RT-qPCR primers(0.03 MB XLS)Click here for additional data file.Table S4Antibodies used for Western blotting(0.02 MB XLS)Click here for additional data file.Table S5Gene Ontology (GO) analysis(5.98 MB XLS)Click here for additional data file.


A new approach for genome-wide transcription factor binding site prediction is presented that integrates sequence and chromatin modification data. We present an integrated method called Chromia for the genome-wide identification of functional target loci of transcription factors. Designed to capture the characteristic patterns of transcription factor binding motif occurrences and the histone profiles associated with regulatory elements such as promoters and enhancers, Chromia significantly outperforms other methods in the identification of 13 transcription factor binding sites in mouse embryonic stem cells, evaluated by both binding (ChIP-seq) and functional (RNA interference knockdown) experiments. Transcription factors (TFs) play a central role in regulating gene expression. Binding of TFs to their target loci is a key step of activating or repressing a gene. Determination of transcription factor binding sites (TFBSs) is an important but challenging problem because the DNA segments recognized by TFs are often short and dispersed in the genome . In addiBoth experimental and computational technologies have been developed to identify TFBSs. Chromatin immunoprecipitation (ChIP)-chip ,3 and, mde novo motif finding algorithms, and search for clusters of TFBSs. Methods like Stubb [de novo motif finding and cis-regulatory module (CRM) identification simultaneously in an iterative fashion. Recently developed methods like GibbsModule [Many computational methods -15  and discrete data (DNA sequence) in its model. It converts the discrete sequence data to continuous PSSM score signals. The binned histone modification and PSSM score are used as an input to the HMMs. Chromia uses three HMMs with a left-right structure and mixture of Gaussians to model promoter, enhancer, and background regions, respectively Figure . RefSeq The availability of the ChIP-seq experiments for the 13 TFs  in the mWe compared the performance of our method with several TFBS identification methods with publicly accessible software, including EEL , ClusterChromia uses HMMs to capture the special pattern of all eight histone marks and the PSSM scores in an integrated manner. To show the advantage of this approach, we also evaluated the performance of a baseline method that used the product of the PSSM score and a single histone mark read count (H3K4me3 for promoters and H3K4me1 for enhancer) to predict TFBSs. In order to make a rigorous comparison, we used binding sites of other TFs as negatives instead of using random sequences. Again, Chromia showed far better performance on all the TFs, except for CTCF, illustrating the advantage of using HMMs for TFBS prediction Figure . For CTCIn reality, what one cares about most is to predict TFBSs in the entire genome and reduce false positives among a given number of predictions. To evaluate how useful Chromia is to identify TFBSs at a genomic scale, we predicted the TFBSs for the 13 TFs in the mouse genome. We selected the top 2,000 predicted sites in promoters or enhancers and evaluated the prediction accuracy using the binding peaks of the TFs determined in the ChIP-seq experiments . We consWe calculated positive predicative values (PPV = TP/(TP + FP)) of the predictions using various model configurations Table . E2f1, cet al. [The PPVs of Smad1 and STAT3 were low in both promoters and enhancers. The numbers of binding peaks of Smad1 and STAT3 determined by Chen et al.  were 1,1We then analyzed how sensitive our model is to the choice of PSSMs   could im2000 values for the prediction of TFBSs in promoters and enhancers  experiments in mES cells E14/T21 cells) were conducted on 4 out of the 13 TFs to reveal genes potentially regulated by a specific TF . We asseet al. , the num1 cells wIt is noteworthy that the RNAi, TF binding, and histone modification experiments were conducted in E14/T21, E14, and V6.5 mES cells, respectively. Even though the TF binding experiment was conducted in a cell line closer to the one used in the RNAi experiments, Chromia predictions based on the histone modification data obtained from a more distant cell line achieved better agreement with the knockdown assays. Recently, evidence has shown that enhancers are more cell type specific than promoters ,41. It iWe compared the performance of our method with EEL , ClusterTable We also plotted ROC curves by changing the number of predictions made by each method Figure . BecauseThe ChIP-seq experiments could be noisy and the binding peaks defined in these experiments could be due to indirect protein-DNA interactions. To obtain a set of highly confident binding peaks to assess the performance of each method, we selected the peaks that contained the binding motif recognized by the TF  is calculated as:In plotting a heatmap of histone marks and the PSSM scores (but not in the HMM models), we re-scaled the signals in order to achieve a better visualization effect. Let xmax is the maximum sequencing read count of a histone mark in the entire genome, and αx is the value that only 0.1% of the histone mark bins have higher read counts. Each histone mark was re-scaled individually. In re-scaling the PSSM scores, αx was set to 15, which was the top 0.1% value of the PSSM scores in the entire genome. Plotting the re-scaled read counts avoided the problem that only the sites with large values (>0.1%) were visible with a bright color.Where et al. [Chen et al.  conducteet al. . We usedet al. , all poset al.  and thoset al.  by runniet al.  or Nesteet al. . Becauseet al. , we alsoet al. .A PSSM score was calculated for each motif:Wx is the width of the motif, Pk(xk) and Pb(xk) are the probabilities of observing nucleotide xk at position k from the motif and the background distributions, respectively. The background was obtained from the occurring frequency of each nucleotide in the entire mouse genome.where Chromia integrates continuous (histone modifications) and discrete data (DNA sequence) in its model. It converts the discrete sequence data to continuous PSSM score signals using Equation 1. As described above, the PSSM score was also binned to have the same format as the ChIP-seq data.Θ)s with a left-right structure [i to state j ≥ i. In our previous study, this left-right structure has been successfully applied to capture the characteristic patterns of histone signatures [Q states. An HMM state emits a signal according to a probability density function of a Gaussian mixture of N dimensions. Here N is the total number of histone marks (NHistoneMark) and the PSSM score, that is, N = NHistoneMark + 1. The probability density function of the Gaussian mixture is:Chromia uses parallel HMM to capture the complex signal patterns. Models with larger M did not improve the prediction performance (data not shown).where lgorithm  was usedQ to capture characteristic patterns of the histone modifications. We found the number of Q was related to the length of the genomic regions containing the histone modifications. Here, we considered 2,000-bp regions (20 100-bp bins) and Q was set to 3. This choice was particularly motivated by the observation of bimodal patterns for several histone marks, such as H3K4me1, in the promoters  as the mark for promoters (annotated RefSeq TSSs) and H3K4me1 (cutoff = 5.9) or H3K4me2 (cutoff = 7.6) as the marks of enhancers (p300 binding sites). The prediction results were not sensitive to the cutoff value (data not shown). Next, all the selected promoter or enhancer bins in the entire genome that contain a sequencing read count larger than the cutoff value were ranked using the PSSM score of the TF under consideration. The top 100 promoters and 100 enhancers were then selected as the training set  was calculated by summing over all possible paths through the hidden states.For a given genomic region, likelihood scores were calculated using the three HMMs for promoter, enhancer and background separately. The likelihood of an input q is the state of the HMM(Θ) . In Chromia, two log-odd scores, one for promoter and one for enhancer predictions, were calculated as:where The log-odd score reflects how strong a signal is compared to the background and has been widely applied, for example, to calculating the conservation score in phylo-HMM .ScorePhastcon in the following equations (Table S6 in Additional file To incorporate conservation information, we further multiplied the Phastcon score . The maxWe calculated the log-odd scores for both promoters and enhancers using a sliding window of 2,000 bp centered at each bin. We smoothed the results by averaging the scores of the three adjacent bins. Among log-odds for promoters and enhancers, we only considered bins as potential TFBS-containing regions if they had a log-odd score larger than all other bins within ± 2,000 bp. We kept all the potential TFBS-containing bins if the distance between them was greater than 2,000 bp.All programs were run using their default setup and parameters. To run MAST we used the background obtained by running MEME . Especia2000; Table S9 in Additional file To evaluate the performance of the genome-wide TFBS predictions made by Chromia, we calculated the AUC when FP = 2000 (AUCAUC: area under the ROC curve; ChIP: chromatin immunoprecipitation; FN: false negative; FP: false positive; HMM: hidden Markov model; mES: mouse embryonic stem; PPV: positive predicative value; PSSM: position specific scoring matrix; RNAi: RNA interference; ROC: receiver operator characteristic; TF: transcription factor; TFBS: transcription factor binding site; TN: true negative; TP: true positive; TSS: transcription start site.KJW and WW conceived and designed the experiments. KJW analyzed the data. WW and BR contributed to analysis of the data. KJW and WW wrote the manuscript. All authors read and approved the final manuscript.Figures S1 and S2Click here for fileSupplementary text, Figures S3 to S10, and Tables S1 to S11Click here for file