TBX3 mutations in humans cause complex congenital malformations and Ulnar-mammary syndrome. Previous investigations into TBX3 function focused on its activity as a transcriptional repressor. We used an unbiased proteomic approach to identify TBX3 interacting proteins in vivo and discovered that TBX3 interacts with multiple mRNA splicing factors and RNA metabolic proteins. We discovered that TBX3 regulates alternative splicing in vivo and can promote or inhibit splicing depending on context and transcript. TBX3 associates with alternatively spliced mRNAs and binds RNA directly. TBX3 binds RNAs containing TBX binding motifs, and these motifs are required for regulation of splicing. Our study reveals that TBX3 mutations seen in humans with UMS disrupt its splicing regulatory function. The pleiotropic effects of TBX3 mutations in humans and mice likely result from disrupting at least two molecular functions of this protein: transcriptional regulation and pre-mRNA splicing.TBX3 is a member of the T-box family of transcription factors with critical roles in development, oncogenesis, cell fate, and tissue homeostasis.  TBX3 mutations in humans cause a complex of birth defects called Ulnar-mammary syndrome (UMS). Despite the importance of TBX3 and decades of investigation, few TBX3 partner proteins have been identified and little is known about how it functions in cells. Unlike previous investigations focused on TBX3 as DNA binding factor that represses transcription, we took an unbiased approach to identify TBX3 partner proteins in mouse embryos and human cells. We discovered that TBX3 interacts with RNA binding proteins and binds mRNAs to regulate how they are spliced. The different mutations seen in human UMS patients produce mutant proteins that interact with different partners and have different splicing activities. TBX3 promotes or inhibits splicing depending on cellular context, its partner proteins, and the target mRNA. Eukaryotic cells have many more proteins than genes: alternative splicing is critical to generate the different mRNAs needed for production of the specific and vast repertoire of proteins a cell produces. Our finding that TBX3 regulates this process provides fundamental new insights into how altered quantity and molecular function of TBX3 contribute to human developmental disorders and cancer.TBX3 is a protein with essential roles in development and tissue homeostasis, and is implicated in cancer pathogenesis.  TBX3 cause Ulnar–mammary syndrome in humans (UMS). This syndrome includes limb malformations, apocrine and mammary gland hypoplasia, dental and genital abnormalities. Altered TBX3 expression is implicated in the pathogenesis of breast and other cancers by affecting cell adhesion, proliferation and senescence TBX3 belongs to the T-box family of transcription factors. The crucial roles of TBX3 in development are evident in the fact that heterozygous mutations of ARF and p21 to regulate cell proliferation and prevent apoptosis The DNA-binding domain (DBD) that TBX3 shares with other T-box family members is critical for its function since missense mutations in this domain cause UMS. In addition to binding consensus T-box binding elements (TBEs), TBX3 contains a C-terminal dominant repressor domain in vivo. Based on previous studies, we expected to identify transcription factors and chromatin modifying proteins. Remarkably, we discovered a novel function of TBX3 with direct relevance to understanding the molecular mechanisms of mutations that cause human disease.Given the importance of TBX3 in development, human disease, and its potential as therapeutic target for cancer and tissue regeneration, it is essential to define the pathways in which it functions. To obtain new insights into the molecular functions of TBX3/Tbx3 , we interrogated interacting partners We employed unbiased proteomic screens using a custom polyclonal antibody generated against a peptide from the C-terminus of TBX3 All co-IP'd proteins were identified by detection of multiple peptides. MS confirmed Tbx3/TBX3 in the IPs  with a mThe known associations of TBX3 with HDACs and transcriptional co-repressors led us anticipate enrichment of such proteins in our MS screen. To our surprise, ontological classification of annotated functions, biological processes and functional domains demonstrated marked enrichment of RNA binding proteins (RNA BPs) and splicing factors among Tbx3/TBX3 interactors . Forty-fin vivo between Tbx3/TBX3 and splicing factors is conserved in mouse and human. We further tested 4 of these TBX3 binding partners for association with overexpressed, His-Myc- dually tagged TBX3 in Ni-NTA pull-down assay followed by anti-myc co-IP . C-terminal deletions are as shown. All variants are translated into protein post transfection into HEK293 cells  and performed deep sequencing Messenger RNAs with annotated alternate splice forms  events identified senchyme  and S3 fsenchyme  and S4A.senchyme , B. RNA- 8 and 9 . Similar exon 11 . Additioin vivo target Nfkb1. We replaced exon 2 of pRHCglo with Nfkb1 exon 11 (92 bp) flanked by 3 different fragments from the adjacent introns  Nfkb1 minigene Fragment 3 (Nfkb1 F3) was alternately spliced in response to TBX3 knockdown, which resulted in Nfkb1 minigene exon 11 skipping and inclusion  and RT-PCR. Anti-Tbx3 RIP on mouse embryo mRNA/protein showed that Pus10, Nfkb1, Brca1, and Dlg3 transcripts were associated with Tbx3  by assaying mRNA/protein complexes in tissues from wild type and Tbx3 null embryos. Loss of Tbx3 abolished the interaction between Ddx3 and Nfkb1 mRNA, but not hnrnpu  was shifted by proteins in wildtype lysate  was identified by MEME on Tbx3 ChIP-Seq data from adult mouse heart 5′ T/A GGTG T/A/G) is present within the larger MEME motifs we identified  and E10.5 mouse embryos (59 interactors) by mass spectrometry. This unbiased proteomic screen revealed that TBX3/Tbx3 interacts with RNA BPs and splicing factors. Our discovery that TBX3 regulates alternative splicing Tbx3 loss of function in vivo, and directly binds RNA containing the core motif of T-box DNA-binding elements (RNA-TBEs). Since AS is a critical mechanism of post- and co- transcriptional gene regulation and proteome diversity and disruption of AS occurs in many cancers CDKN2A, which is alternatively spliced to produce the p16INK4 and p14ARF tumor suppressors, will need to be reexamined for potential post-transcriptional effects of TBX3.Tbx3 physically associates with mRNAs that undergo AS in response to Nfkb1) and shown that DDX3 requires TBX3 to bind this target. Future studies of the functional consequences of interactions with TBX3 may reveal completely novel functions for some of its partners.Interactions between TBX3 and specific RNA-binding proteins require different TBX3 functional domains indicating that crosstalk with different molecular partners mediates distinct TBX3 functions. We tested the effect of NLS deletion on 3 Tbx3 interactors: DDX3, hnRNPC and DDX17, and the data suggest that these specific interactions take place within the nucleus. Unlike wild type Tbx3, the Tbx3 NLS mutant does not inhibit splicing, nor does it have the dominant effect on splicing seen with most C-terminal mutants  but only Tbx3 binds Dlg3 and Brca mRNAs. We conclude that alternative splicing outcomes are affected by binding motifs present near affected exons, but the outcomes are context and transcript dependent; future studies will test our postulate that this is due to interaction with different cofactors.Although we have discussed the TBX3 DBD as such, TBX3 complexes with AS RNAs the limb : Tbx3 prminigene . AdditioNfkb1 mRNA, TBX3 regulates AS by directly binding to an intronic RNA-TBE raises the issue of whether the effect of TBX3 on splicing are co-transcriptional and could also be mediated by TBX3 binding to TBEs in genomic DNA. We examined the published Tbx3 ChIP-Seq dataset Splicing and transcription are most often coupled We focused the present study on the splicing function of TBX3 with respect to novel binding partners however, it is unlikely that all interactions between TBX3 and RNA-binding partner proteins are related to splicing regulation: many of these interactors are known to have multiple roles and influence trafficking and other aspects of mRNA processing, and some have transcriptional effects and DNA binding properties. The numerous hnRNPs that interact with Tbx3 exemplify this point. This diverse family of multifunctional proteins has crucial roles in RNA processing in addition to AS. Dysregulation of hnRNP function and resulting disruption of AS contribute to carcinogenesis Runx2 expression XRCC6 are associated with breast cancer risk and estrogen exposure Coactivator of AP1 and Estrogen Receptor) in one co-IP by MS and by yeast 2-hybrid screen for Tbx3 interactors . Caperα modulates steroid hormone receptor-mediated transcriptional regulation and AS The association of TBX3 with several factors that regulate response to estrogen and other nuclear hormones  [41], [4A comprehensive discussion of the other classes of TBX3 interactors identified in these analyses awaits validation of members of each class. However, the presence of multiple cytosolic and mitochondrial RNA and protein chaperones, nuclear import/export factors (including previously identified XPO1 TBX5 was previously shown to interact with SC35 and regulate splicing Smad binding elements in pre-miRNAs (R-SBEs) conferring BMP/TGFβ regulation to pre-miRNA processing Numerous DNA binding transcription factors have now been demonstrated not only to bind RNA in vitro represent the spectrum of mutations seen in humans with UMS, including the 1857delC frameshift in vivo, the minigene splicing assay  and a review of the literature, there are at least 10 human point mutations predicted to cause premature termination and loss of the C-terminal repressor domain. The human 1857 delC mutation is a missense mutation in the C-terminus; while not an exact replicate of this mutation, our exon7 missense mutation does produce a protein with an altered C-terminus and abnormal splicing activity. Our observation that knockdown of TBX3 alters splicing has significant implications that will require extensive further investigation since the presumed mechanism for TBX3 loss of function mutations has been haploinsufficiency of transcriptional repressor activity. Furthermore, a subset of C-terminal human mutations encodes truncated proteins that undergo increased rates of protein decay; such mutations could thus disrupt splicing by multiple mechanisms. In combination with our observations that AS regulation by Tbx3 is context and mRNA dependent, the splicing functions of Tbx3 provide additional complexity to regulation of gene expression by Tbx3.The mutant Tbx3 proteins we tested for splicing activity ng assay  indicateterminus . Based oTBX3 mutations result from disrupting at least two context-specific molecular functions: transcriptional regulation and pre-mRNA splicing. Dissecting the requirements for different TBX3 molecular functions in specific developmental and disease contexts will improve our understanding of oncogenesis and UMS pathogenesis. The splicing function of TBX3 may be a new target for cancer treatment or in tissue regeneration efforts.In conclusion, our study reveals that TBX3 is a splicing regulator and that mutations seen in humans with UMS disrupt this function. There is little genotype/phenotype correlation between and within UMS families, and phenotypes in mice are extremely dosage sensitive. We propose that the pleiotropic effects of HEK293 cells were grown in DMEM with 10% FBS and pen/strep. Plasmids and transfection procedures are detailed in Supplemental Information in the file Dignam lysates were prepared from HEK293 cells or e10.5 mouse embryos. Immune complexes were subjected to SDS-PAGE analysis followed by immunoblotting with specific antibodies. Detailed procedures are in SI.IP'd proteins were separated by SDS-PAGE and in-gel digested prior to analysis by MS as previously described HEK-293 cells were transfected with control or TBX3-specific siRNAs using lipofectamine 2000 (Invitrogen). RNA was extracted 48 hrs post-transfection and cDNA prepared with SuperScript III Reverse Transcriptase (Invitrogen).High-titer retrovirus was produced by transfection of TBX3 shRNA retroviral construct along with gag/pol and VSVG encoding plasmids into HEK293 EBNA cells. Stably integrated colonies were selected and analyzed for TBX3 knockdown.Lysates were IP'd with anti-TBX3 and R-IgG. RNA was extracted with Tri reagent (Sigma), converted to cDNA. Primer details and CLIP are described in SI.Tbx3 conditional mutant forelimb buds using the miRNeasy Kit (Qiagen). Libraries were prepared by the University of Utah Microarry Core and single end sequencing reads obtained on an Illumina HiSeq2000. Additional details and bioinformatic analyses are in SI.Total RNA was isolated from pooled microdissected anterior and posterior segments of e10.5 wild type and Figure S1In vitro GST pull down assays: GST or GST-DDX3 affinity columns were incubated with. MBP-TBX3. Bound proteins were eluted, subjected to SDS-PAGE followed by IB with anti-TBX3 antibody.Dual-tagged pull down and immunoprecipitation assays and RNA independence of TBX3 interactions. A–E) Immunoblots of Ni-NTA eluate and anti-Myc immunoprecipitation products from HEK293 cells expressing dually (His-Myc tagged)-TBX3 to examine association with endogenous TBX3 interactors. The lanes labeled input contain the lysate prior to application to the Ni-NTA column; those labeled Ni-NTA contain the Ni-NTA eluate, whereas those labeled anti-Myc contain dialyzed Ni-NTA eluate post IP with anti-Myc. The mouse IgG IP is a negative control. Immunoblots were then probed with the antibodies listed below the panels (IB). Endogenous hNRNPC1/C2, DDX3, DDX17 and PABP1 are all bound, eluted with, and coIP with His-Myc-TBX3. F–J) HEK293 lysates +/− RNAse A treatment were immunoprecipitated with the antibody listed at the top of the panels (IP) and the antibodies used to probe the immunoblot (IB) below. Only the interaction of FMRP with TBX3 (D) was affected by RNAse treatment. In all panels, the protein of interest is noted by the red arrowhead; the ∼50 kd band is IgG from the IP (black arrowhead). In all panels, the protein of interest is noted by the red arrowhead; the IgG band from the IP is labeled with a black arrowhead. K) Coomassie Blue stained PAGE gels of in vitro synthesized GST-DDX3, GST and MBP-GST. L) (TIF)Click here for additional data file.Figure S2Tbx3 mutant proteins are produced after transfection of expression plasmids and are efficiently immunoprecipitated. A) Anti-Tbx3 immunoblot of HEK293 lysates post transfection of Tbx3 constructs. Upper bands are full length proteins and the lower bands are degradation products (confirmed by MS). B) Immunoblot of IP'd HEK293 lysates. Upper bands are Tbx3 mutant proteins (red arrowheads show different size proteins); ∼50 kd bands are IgG (black arrowhead). Anti-goat IgG serves as a negative control for the IPs as the primary anti-Tbx3 IP antibody employed for this experiment was raised in goat. C) Production and IP of Tbx3ΔNLS protein in transfected HEK293 cells.(TIF)Click here for additional data file.Figure S3in vivo. IGV screen shots comparing control anterior , Tbx3;Prx1Cre mutant anterior  forelimb bud RNA-Seq data for Nfkb1 exons 5,6 (A), Pus10 exons 7, 8 (B), and Brca1 exon 8 (C). Red arrowheads indicate exons that are differentially spliced in a Tbx3-dependent manner. A′–C′) Schematics of splice variants and PCR primers used to detect them and RT-PCR validating variants. Anterior, anterior compartment; ctl1,control biologic replicate 1; mt1, mutant biologic replicate 1; ctl2,control biologic replicate 2; mt2, mutant biologic replicate 2.Tbx3 regulates alternative splicing of numerous targets in vivo. A–C) Tbx3 regulates alternative splicing in mouse limb buds (TIF)Click here for additional data file.Figure S4RT-PCR testing of statistically significant versus insignificant splicing events identified by RNA-seq. A) RT-PCR testing of additional transcripts (listed at bottom of panels) from statistically significant alternate splice events in the anterior compartment , S3; all(TIF)Click here for additional data file.Figure S5GGUGU motifs. Probe/protein complex (lane 2) supershifts with Anti-TBX3 antibody (lanes 3) but not with R-IgG (lane 5). TBX3+RNA complexes are indicated by blue arrowhead, anti-Tbx3 antibody supershifted TBX3+RNA complexes are indicated by red arrowhead and unbound probe by black arrowhead. H) RNA probe containing AGGUGU motif from Nfkb1 Fragment 3. Probe/protein complex  supershifts with Anti-TBX3 antibody (lane 2) but not with R-IgG (lane 5). TBX3+RNA complexes are indicated by blue arrowhead, anti-Tbx3 antibody supershifted TBX3+RNA complexes are indicated by red arrowhead and unbound probe by black arrowhead.Interaction of purified and endogenous TBX3 with RNA probes. A–F) EMSAs of MBP-conjugated TBX3 with radiolabeled RNA probe sequences listed at bottom of panels. red arrowhead: probe/protein complex; black arrowheads: unbound probe. G–H) Shift and supershift RNA EMSA assay of endogenous TBX3 in HEK293 cell lysates; radiolabeled probe sequences are listed below panels. Probes were incubated +/− HEK293 lysate, +/− anti-TBX3 antibody, and with the negative control antibody rabbit IgG (R-IgG). G) RNA probe containing (TIF)Click here for additional data file.Table S1TBX3 interacting proteins meeting MS screen criteria.(DOCX)Click here for additional data file.Table S2Validation results of randomly selected alternatively spliced target ordered by statistical significance.(DOCX)Click here for additional data file.Table S3RNA-Seq analysis of anterior limb compartment control versus Tbx3 mutant transcripts. Data and significance are provided for both expression fold change and splicing variants detected.(XLSX)Click here for additional data file.Table S4Location of putative TBEs and MEME motifs for the 11 validated alternative splicing events in the anterior limb compartment. Primary sequences of alternatively spliced exon and 1 kb of 5′ and 3′ flanking regions. Introns, exons, motifs and conservation are as noted in the KEY.(DOCX)Click here for additional data file.Text S1Supplemental Methods and Figure Legends. This supplement contains 2 sections: Supplemental Experimental Procedures and Supplemental Information References.(DOCX)Click here for additional data file.


Individually and in aggregate, these data are an important and information-rich resource. However, uncertainties about data quality confound their use by the wider research community. Recently, the Encyclopedia of DNA Elements (ENCODE) project developed and applied metrics to objectively measure ChIP-seq data quality. The ENCODE quality analysis was useful for flagging datasets for closer inspection, eliminating or replacing poor data, and for driving changes in experimental pipelines. There had been no similarly systematic quality analysis of the large and disparate body of published ChIP-seq profiles. Here, we report a uniform analysis of vertebrate transcription factor ChIP-seq datasets in the Gene Expression Omnibus (GEO) repository as of April 1, 2012. The majority (55%) of datasets scored as being highly successful, but a substantial minority (20%) were of apparently poor quality, and another ∼25% were of intermediate quality. We discuss how different uses of ChIP-seq data are affected by specific aspects of data quality, and we highlight exceptional instances for which the metric values should not be taken at face value. Unexpectedly, we discovered that a significant subset of control datasets  display an enrichment structure similar to successful ChIP-seq data. This can, in turn, affect peak calling and data interpretation. Published datasets identified here as high-quality comprise a large group that users can draw on for large-scale integrated analysis. In the future, ChIP-seq quality assessment similar to that used here could guide experimentalists at early stages in a study, provide useful input in the publication process, and be used to stratify ChIP-seq data for different community-wide uses.ChIP-seq has become the primary method for identifying  Chromatin immunoprecipitation (ChIP)  or a mock immunoprecipitation with an unrelated antibody (IgG). Sequencing-based ChIP has become the method of choice because it enables genome-wide coverage, even for large genomes, and because of its superior signal-to-noise characteristics compared to alternative methods. Since its initial development, ChIP-seq has been used in hundreds of publications , including by the ENCODE consortium , to map Discussion). In estimating data quality, the traditional approach of visual inspection at a limited number of sites (often previously well-characterized using low-throughput approaches) is inefficient, subjective, and ultimately can be deceptive. It is also possible (and commonly observed in practice) that sites, the biological importance of which has been defined by independent functional assays, can decrease to below the sensitivity threshold of a poor or mediocre ChIP-seq experiment. Moreover, there is no current way to predict, a priori, the number of sites in the genome that should be detectable for a given factor and cell type. Most TFs studied thus far reproducibly occupy thousands to tens of thousands of sites  were downloaded from GEO in SRA format and converted to FASTQ format using the fastq-dump program in the sratoolkit (version 2.1.9). Reads were aligned using Bowtie ( browser .All experiments claimed to be successful by authors are expected to exhibit high level of read clustering.All inputs (sonicated DNA and IgG mock IPs) are expected to exhibit minimal read clustering (QC tag of −2 or −1).All ChIP-seq experiments performed in a knockout background for the factor are expected to exhibit minimal read clustering (QC tag of −2 or −1).Because knockdown efficiency varies and because it is unknown what protein levels would be sufficiently high for the factor to be successfully ChIP-ed, ChIP-seq experiments performed in cells treated with si/shRNAs targeting the factor are set aside as “unknown” and assessed for library complexity and sequencing depth but not for ChIP quality.Experiments against factors known to bind to DNA on some stimulus performed in unstimulated cells are also tagged as “unknown” because lower-level binding in unstimulated cells cannot be ruled out .Experiments performed in conditions that may result in the factor not binding to DNA  are also tagged as “unknown.”Other experiments not matching any of these categories are expected to exhibit high levels of read clustering.ChIP quality assessment was performed on both ChIP and input datasets using the general strategy described by RSC ∈ {0, 0.25} ⇒ QC ← −2, RSC ∈ {0.25, 0.50} ⇒ QC ← −1, RSC ∈ {0.50, 1.00} ⇒ QC ← 0, RSC ∈ {1, 1.50} ⇒ QC ← +1, RSC ≥ 1.5 ⇒ QC ← +2, with −2 corresponding to minimal read clustering and 2 corresponding to a highly clustered library) and used as a measure of ChIP quality. These scores capture the extent of read clustering in a ChIP-seq experiment in organisms whose genomes have similar size and structure to those of mammals. We point out that these scores may not be appropriate in genomes with very different size and/or structure. This motivated us to discard data from nonvertebrate model organisms for this analysis. Different values than those used here for RSC or normalized strand correlation (NSC) coefficients may be needed for such genomes, and this is a topic for future investigation. Cross-correlation plots were manually examined to ensure no artifactual QC scores were included because of size selection issues . In general, we recommend manual examination of cross-correlation plots in all cases. This presents a deeper and more detailed view of the characteristics of the dataset because the cross-correlation profile provides not only information regarding ChIP enrichment but also regarding the fragment length distribution in the datasets. For example, a dataset might exhibit periodicity in the distribution of fragment size lengths, presenting itself as numerous smaller peaks along the curve , or it can deviate from the standard unimodal pattern (aside from the phantom peak) indicating issues with size selection. The code for running SPP and assigning QC scores is available at https://code.google.com/p/phantompeakqualtools/.Cross-correlation analysis was performed using version 1.10.1 of SPP . Reads were mapped as described above and peaks were called using ERANGE3.2 . Second, an especially large proportion of published data are for histone marks. The effect of including all of these in the survey is to obscure or skew what is happening in the information-rich sample set that includes diverse TFs and cofactors. Finally, the currently available QC metrics were designed and are best suited for TF data that produce highly localized “point-source” occupancy (as they quantify the extent of read clustering in the genome). This means that the metrics themselves need to be interpreted differently if they are applied to, for example, repressive histone marks such as H3K9me3 and H3K27me3, which form large “broad-source” regions of enrichment .The final collection of datasets contained 191 GEO series containing a total of 917 ChIP-seq and 292 control libraries. Except for a limited number of cases in which a GEO series was associated with multiple publications, two or three GEO series were associated with the same publication, or a GEO series has not yet been used in a publication, and there is a one-to-one relationship between GEO series and published articles in the literature  and examined library and ChIP QC metrics for each dataset. These criteria have already been discussed by et al., unpublished data). Here, we provide a brief overview of each.We mapped all reads with uniform settings , we separated datasets into the following complexity groups: high complexity (apparent library complexity ≥.8); medium to low complexity (apparent library complexity between 0.5 and 0.8); and very low complexity (apparent library complexity ≤.5). We also note that in substantially smaller genomes, the apparent library complexity is expected to be lower because the number of positions from which sequencing library fragments can originate is smaller.A second characteristic that influences the quality of a ChIP-seq measurement is the sequence fragment diversity of the sequencing library. This is often referred to as library complexity, and low complexity is undesirable, although we note that much better IP enrichment than what is now obtained could, in the future, lead to very high-quality datasets with low library complexity. Currently, low-complexity libraries mainly result from experimental deficiencies: either too few starting molecules at the end of the immunoprecipitation step or inefficient steps in subsequent library building. As a result, the same starting molecules are sequenced repeatedly. Very-low-complexity libraries will not contain enough information to effectively sample the true positive occupancy sites and they distort the signal position and intensity. This can confuse peak callers , leading to peak calling artifacts . It is possible in theory for low-complexity libraries to produce artificially high cross-correlation scores if stacks of reads on opposite strands are located close to each other in regions of enrichment; however, the Pearson correlation between library complexity scores and RSC values in the collection of ChIP datasets surveyed here was 0.0084, indicating that such cases do not feature significantly in this analysis.We applied SPP  statistic . The concern that some individual IgG inputs might provide insufficient DNA mass to build highly complex libraries has been raised before  of ChIP-seq datasets had library complexity more than 0.8, whereas very-low-complexity (< 0.5) libraries comprised 12.9% of datasets; the fraction of very-low-complexity libraries was higher and lower for IgG and input datasets, respectively . BecauseMaterials and Methods.Next, we examined the distribution of SPP QC scores for ChIP-seq datasets. Before doing this, we excluded a minority of datasets for which there was a good reason to think high ChIP enrichment should not be expected. For example, experiments executed in knockouts, knockdowns, or settings in which the factor is not expressed are not expected to produce a high-scoring measurement. And in a few cases, the factor in question might be known to bind to only a small number of sites in the genome; this has been proposed, for example, for some ZNF TFs and Pol3 and its associated factors  had a score of 0, indicating that they were of intermediate quality, and 20.4% (179 out of 876) had low-quality scores of −1 and −2. Sometimes multiple replicates for a factor were submitted but only one scored poorly, so we also compiled a second set of ChIP-seq experiments that only included the best available replicate for each factor and condition .We then examined the distribution of the maximum QC score for each study, regardless of the target identity . The fraControl datasets serve the important purpose of helping to distinguish read enrichment attributable to the immunoprecipitation step from artifactual read clustering attributable to other experimental factors, both known and unknown. It is, for example, well-appreciated that differential chromatin shearing efficiency can lead to the overrepresentation of areas of open chromatin  in sequencing libraries. This has been termed the “Sono-seq” effect when attributed to sonication .We asked how general this phenomenon is by examining the distribution of QC scores of both IgG and input control datasets . SurprisWe also examined how widespread control sample clustering is on the level of individual GEO series/studies to see if the phenomenon is restricted to a few larger studies. Figure S5). Notably, the original “Sono-seq” description focused on promoter regions, but we have also observed it over distal regulatory elements, where its strength was even higher than at promoters . Thus, variation in the extent of fixation, as well as sonication, might be a substantial contributor to variation in read clustering across the broader data collection. Another potential contributing factor is sequencing depth. Although the average sequencing depth for highly clustered IgG and input controls is higher than that of controls with negative QC scores  this by no means explains all the clustering observed in controls. There are many examples of more deeply sequenced input and IgG libraries with no significant cross-correlation peaks and very few of them were sequenced especially deeply , suggesting at least one additional source of unexplained read enrichment in control samples. Because rich annotation of functional genomic elements outside promoter regions was not available for many cell types in our survey, this phenomenon is a subject for future analyses.We note that unless this effect is very strong and is associated with notable genomic features such as promoters of genes, it can be difficult to detect by the usual methods of visual inspection of signal tracks on a genome browser. It is, however, readily apparent in cross-correlation analysis and our results raise awareness of its existence. As mentioned, one candidate explanation for this phenomenon is the previously described “Sono-seq” effect. Using standard experimental protocols, this effect has been rare in our experience; however, under more aggressive cross-linking conditions, we have observed increased read clustering in control samples  were of low or intermediate quality by our metrics. This was true not only for individual libraries but also for the best replicates from each study. In addition, we observed a substantial number of low-complexity datasets and an unexpected group of highly clustered control datasets. These observations underscore the widespread variation in published ChIP-seq data. They also raised questions about which kinds of conclusions in primary publications are more or less sensitive to these aspects of data quality. In particular, global quality analysis is useful for guiding subsequent re-use of published data that require higher quality than was needed or achieved in the source study.Figure S8). The group with highest impact factor (≥25) contained the largest fraction of datasets with a low QC score of −2 or −1. We also examined the distribution of QC scores with respect to the year of publication and found that the fraction of datasets with low scores has stabilized in the past 3 yr at approximately 20% .Data quality varied widely across “impact” levels. We separated datasets into groups according to the 2011 Thomson Reuters Impact Factor for the journal in which the corresponding article was published and examined the distribution of QC scores in each group , some of which have been highly successful, whereas others were of poor or intermediate quality. Here, we examined the effect of weaker ChIP-seq datasets on combinatorial occupancy analysis using a MyoD ChIP-seq dataset with very high QC metrics and three myogenin datasets with very high, moderately good, and very low metrics  signal plus Sono-seq-derived noise that covers promotors and enhancers in a nonspecific manner. Whereas most contemporary peak callers normalize for enrichment in controls, very strong background noise will diminish the signal-to-noise ratio and adversely affect sensitivity. How severely this affects the results will depend on the overlap between true factor occupancy sites and regions of artifactual read enrichment (for some factors this overlap may be negligible because they do not bind to Sono-seq regions); on the magnitude of the Sono-seq effect; and on the strength of the ChIP itself (sufficiently strong determinations are not greatly affected). Conversely, if a ChIP-seq library has a strong Sono-seq component and peak calling is performed against an imperfectly matched “control” sample in which the Sono-seq effect is of significantly lower magnitude, false-positive peak calls will increase. Unfortunately, in practice such cases are difficult to detect. They are not flagged directly by current quality metrics and are best detected by analyses specific to each study and factor, including specific motif enrichment. especially when little is known about the expected true-positive rates. Similar reasoning applies if the noise source is something other than Sono-seq.e.g., very few sites of occupancy in the genome). It is also possible for some poor or mediocre datasets to receive high QC scores. For example, this can happen as a side-product of strongly clustered backgrounds of the kind discussed above. Some examples of datasets in which this might be the case are shown in Figure S11. For factors that ChIP extremely well, even datasets that are substantially suboptimal score highly. For example, CTCF ChIP-seq datasets routinely identify 35,000–40,000 reproducible binding sites and have QC scores of 2; a dataset that identifies only 15,000 sites is suboptimal given that knowledge; yet it will still receive a positive QC score. For these reasons, the current quality metrics are best used in the context of what is known about the factor, the biological system, and the questions being asked.Uniform retrospective quality assessment is resource-intensive and will not be practically feasible because the number of ChIP-seq datasets is growing exponentially. Retrospective analysis also comes too late to influence the experiments themselves or to contribute to the review process. A reasonable path forward would be to incorporate routine data quality assessment into experimental analysis, review for publication, and submission to public repositories, as a matter of community practice. However, our results also strongly caution against the blind and arbitrary application of our metrics (or others) in the absence of experimental and biological context. The character of the metrics used here reflects contemporary technology and the quality scale has been calibrated based on factors and co-factors most studied to date. We have seen that it is possible for good datasets to receive low QC scores in certain special situations (Despite important nuances of interpretation, we suggest that using ChIP quality metrics and making the results readily accessible will facilitate better-informed data use by the wider community. An important adjunct to routine QC annotation would be the ability, in major public data repositories, to flag and explain the exceptional cases for which QC scores should not be taken at face value. Finally, quality metrics themselves will continue to improve as the field’s understanding of data structure, experimental artifacts, and the underlying biology all become more sophisticated. Provisions will be needed for incorporating such advances into routine dataset annotation while still achieving comparability through time.


Phox2a and Ship2, was generated. Sympathetic response to cold and restraint stress was compared among SHRSP, SPwch1.71, SPwch1.72 and WKY by three different methods . The results indicated that the response in SPwch1.71 did not significantly differ from that in SHRSP, excluding Phox2a and Ship2 from the candidate genes. As the stress response in SPwch1.72 was significantly less than that in SHRSP, it was concluded that the 1.2-Mbp congenic region covered by SPwch1.72 (and not by SPwch1.71) was responsible for the sympathetic stress response. The sequence analysis of 12 potential candidate genes in this region in WKY/Izm and SHRSP/Izm identified a nonsense mutation in the stromal interaction molecule 1 (Stim1) gene of SHRSP/Izm which was shared among 4 substrains of SHRSP. A western blot analysis confirmed a truncated form of STIM1 in SHRSP/Izm. In addition, the analysis revealed that the protein level of STIM1 in the brainstem of SHRSP/Izm was significantly lower when compared with WKY/Izm. Our results suggested that Stim1 is a strong candidate gene responsible for the exaggerated sympathetic response to stress in SHRSP.The stroke-prone spontaneously hypertensive rat (SHRSP) is known to have exaggerated sympathetic nerve activity to various types of stress, which might contribute to the pathogenesis of severe hypertension and stroke observed in this strain. Previously, by using a congenic strain  constructed between SHRSP and the normotensive Wistar-Kyoto rat (WKY), we showed that a 1.8-Mbp fragment on chromosome 1 (Chr1) of SHRSP harbored the responsible gene(s) for the exaggerated sympathetic response to stress. To further narrow down the candidate region, in this study, another congenic strain (SPwch1.71) harboring a smaller fragment on Chr1 including two functional candidate genes,  Phox2a) and the SH2 domain-containing inositol 5′-phosphatase (Ship2).The sympathetic nervous system (SNS) has been implied to play a key role in the pathogenesis of hypertension PHOX2A is a transcription factor regulating the expression of the tyrosine hydroxylase (Th) in noradrenergic neurons Based on such observation, we attempted to construct another congenic strain harboring a smaller fragment including these candidate genes. In this report, physiological evaluation of the stress responses in a new congenic strain (SPwch1.71) indicated that the region harboring Phox2a and Ship2 was not likely to have causative roles in the exaggerated stress response in SHRSP. In spite of that, further attempt to explore causative genes in the remaining candidate region succeeded to identify another strong candidate gene, the stromal interaction molecule 1 (Stim1).All experimental protocols of animal studies were approved by the ethics committee for animal research of Shimane University (#IZ23-60 and #IZ23-63).Phox2a and Ship2 was newly constructed as described previously SHRSP/Izm and WKY/Izm were provided by the Disease Model Cooperative Research Association . A congenic strain harboring a WKY-derived small fragment including The responsiveness to restraint and cold stress was examined as described in the previous study Urinary NE was measured in urine samples by HPLC. The power spectral analysis was done on heart rate variability under restraint stress using the telemetry for ECG . The analysis was performed on the ECG using the software implemented in the telemetry system. The ratio between the low frequency  and the high frequency component  was used as an indicator of the relative sympathetic activity Sequencing was performed on AB SOLiD 4 System  as previously described 12-weeks old male SHRSP/Izm and WKY/Izm were employed in the experiments. Rats were divided into two groups (N = 5 in each group); one group was kept at a room temperature while the other was exposed to cold stress at 4°C for 6 hours. The rats were then sacrificed under deep anesthesia  and the brain was dissected quickly. The ventrolateral portion of the brainstem including the rostral ventrolateral medulla (RVLM) was further dissected on ice for RNA and protein extraction.Total RNA was isolated from the dissected ventrolateral portion of the brainstem using Sepasol-RNA I Super G  according to the manufacturer’s instruction. Complementary DNA was synthesized using PrimeScript RT reagent Kit with gDNA Eraser  with oligo dT primer and random 6mers. Quantitative reverse transcription PCR (RT-PCR) was performed using the Real-time PCR system 7300  in a total volume of 25 µl of reaction mixture using SYBR Premix EX-TaqII . Quantity of mRNA was standardized on the β-actin mRNA. Primers used in the experiment were listed in Protein samples of the ventrolateral portion of the brainstem were prepared using RIPA buffer  containing 50 mmol/L Tris-HCl (pH7.6), 150 mmol/L NaCl, 1.0% Nonidet P-40, 0.5% sodium deoxycholate and 0.1% sodium dodecyl sulfate with a protease inhibitor cocktail, following manufacturer’s instruction. The protein concentration was determined using Protein Assay Bicinchoninate kit . Protein samples were separated by SDS-PAGE and transferred to Immobilon-P transfer membrane . Non-specific binding sites were blocked using PVDF Blocking Reagent . Western blotting was performed as previously described DNA samples of W/KYO, WKYO/Kyo, WKY/Crj, SDJ/Hok, LEJ/Hok, SS/JrNgs and SR/JrNgs were provided through the National Bio-Resource Project for Rats P<0.05 was considered to be statistically significant.Means and standard deviations are shown in the figures, tables and the text. Inter-strain differences were tested either by Student’s t-test or by ANOVA with Dunnett’s post-hoc test. Effects of cold and restraint stress on BP, HR, ΔLF/HF and urinary NE excretion were summarized in BP of SPwch1.71 did not differ significantly from that of SHRSP when measured by the telemetry method . When BPCollectively, the results of these physiological experiments indicated that the region covered by SPwch1.71 did not contribute to the difference in the sympathetic stress response between SHRSP and WKY, and could be excluded from the candidate region.Considering that results of ΔHR under the stress were inconsistent with the other three parameters, we suspected that, in contrast to the power spectral analysis on HR variation, ΔHR itself did not seem a good parameter to measure the inter-strain difference ; it mighAlthough influence of physical activity on HR and BP could be excluded in the restraint stress experiments , it might influence HR and BP in the cold stress experiments. In the present study, we could not monitor the physical activity of rats because of a technical difficulty, however, we expected the influence was small because a small size of cages used in the experiments did not give rats enough space to move around. This was further supported by the fact that the results of the restraint and the cold stress experiments were consistent with each other.Olr111 gene, and that of the fragment in SPwch1.71 was between SNPs in the Trpc2 and in the Art5 gene, respectively  or between the SNPs in Trpc2 and in Olr111 as the maximal estimation (1.8-Mbp)  responsible for the exaggerated sympathetic response in SHRSP seemed to be in the region covered not by SPwch1.71 but by 1.72 see . To deteectively . As a re1.8-Mbp) .Trpc2, Art5, Art1, Chrna10 and LOC685521 were minimal or absent, and thus were excluded from the candidate genes (data not shown). Among the rest of 7 candidate genes, none showed a significant difference in the basal expression between the two strains. Under the cold stress, on the other hand, Nup98 and Pgap2 showed a modest, but statistically significant, difference in the gene expression between SHRSP and WKY  were located in this region . We comp and WKY .Art1, Stim1 and Trim21, of the 12 candidate genes . In Trim21, we identified two nonsynonymous nucleotide substitutions  and 1 substrain of SHR, i.e., SHR/Kyushu (Genotyping of the two variations in R/Kyushu . NeitherR/Kyushu .We found another nonsynonymous substitution (p.Leu488Phe) in STIM1 that was specific for WKY/Izm and WKY/NCrj, which may have functional significance as well .Stim1, among the genes located in this region.The previous study indicated that the congenic region covered by SPwch1.72 harbored a gene (or genes) responsible for the exaggerated stress response in SHRSP Phox2a, a transcription factor regulating the expression of Th as well as the development of SNS in uteroPhox2a  in SHRSP  and plasma membrane, and is an essential component in the store-operated calcium entry (SOCE) process through regulating a calcium channel, ORAI1, on the cell membrane Although the two candidate genes were excluded, we identified another candidate gene, in SHRSP . STIM1 iOf note, it is suggested that the C-terminus lysine residues , which were lost in the truncated form, were essential in interaction of STIM1 with the transient receptor potential cation channel 1 (TRPC1), another type of cation channels on the cell membrane per se may have a functional significance even if the truncation is pathologically innocent.In addition to the truncation, we also found the protein level of STIM1 in the brainstem was lower in SHRSP when compared with WKY although the mRNA expression level was not significantly different see . Keil etStim1 in SHRSPA1-sb (a substrain of SHRSP) differed from that in SHRSP/Izm  tended to be greater when compared with SHR/Izm , though the difference did not reach a significant level (P = 0.088). As SHR/Kyushu shared the same genetic background with SHRSP in substantial areas of Chr1, other genes in the background might modify the result Stim1 on the urinary NE excretion was further supported by the fact that SPwch1.72, which shared the same allele of Stim1 with WKY, showed significantly greater NE excretion than that in WKY -anchor remodeling, which is required for stable association of GPI-anchored proteins and the cell-surface membrane rafts Nup98 and Pgap2 between WKY and SHRSP were statistically significant, the difference was rather modest  was identified between WKY and SHRSP  and changes in urinary NE (ΔNE) under cold stress was evaluated. Nine rats of the each strain were used for the experiment. P value was calculated by Student’s t-test and shown at the top of columns.(PPTX)Click here for additional data file.Table S1Primer sequences for quantitative RT-PCR analysis.(PPTX)Click here for additional data file.Table S2Primer sequences for SNP-based genotyping. UTR; untranslated region, CDR; coding region. *The genomic position of each SNP is defined based on the RGSC Genome Assembly v3.4.(PPTX)Click here for additional data file.Table S3Putative genes in the 1.8-Mbp congenic fragment.(PPTX)Click here for additional data file.


Knowing the precise cis element on a distal promoter is a prerequisite for studying a typical transcription process; however, identifications of cis elements have lagged behind those of their associated trans acting TFs due to technical difficulties. Consequently, gene regulations via combinatorial TFs, as widely observed across biological processes, have remained vague in many cases.Transcription factor (TF) binding sites  genes, and how to experimentally test the candidate sites by designing DNA fragments hosting the candidate motifs based on a known promoter  and applying site-mutagenesis at the motifs. It was shown that TF-DNA interactions could be unambiguously analyzed by assays of electrophoretic mobility shift (EMSA) and dual-luciferase transient expressions, and the resulting evidence precisely delineated a cis element. The cis element for R2R3 MYBs including Ipomoea MYB1 and Magnolia MYB1, for instance, was found to be ANCNACC, and that for bHLHs (exemplified by Ipomoea bHLH2 and petunia AN1) was CACNNG. A re-analysis was conducted on previously reported promoter segments recognized by maize C1 and apple MYB10, which indicated that cis elements similar to ANCNACC were indeed present on these segments, and tested positive for their bindings to Ipomoea MYB1.We present here a valid strategy for identifying cis elements in combinatorial regulation is now feasible with the strategy outlined. The working pipeline integrates the existing databases with experimental techniques, providing an open framework for precisely identifying cis elements. This strategy is widely applicable to various biological systems, and may enhance future analyses on gene regulation.Identification of  Knowing the precise sequence of a cis element for its associated TF is therefore critical for understanding the transcription process of a given gene, 20 pmol probe, and 2 μg MBP- target proteins, was incubated at 25 C for 10 min. Samples loaded to a 12% (wt/vol) nondenaturing polyacrylamide gel were subject to electrophoresis in 0.25X TBE buffer at 130 V for 1 h. The gel was covered in 1XSYBR Green EMSA stain (Life Technologies) for 20 min at room temperature in dark and washed twice with dd HThe authors declare that they have no competing interests.HW expressed the proteins and collected the EMSA data, SG conducted the bioinformatic searches and wrote the Perl scripts, ZZ collected promoter sequences and performed the dual-luciferase assays, YW participated in an early phase of the study, and YL designed the study and wrote the manuscript. All authors read and approved the final manuscript.Bioinformatic searches (instructions with scripts).Click here for fileA list of 271 promoters used in the bioinformatic analysis.Click here for file(A) Construction of the reference and reporter vectors. The fluorescent protein-coding regions are in green. The tested promoter is shown in blue arrow. (B) Construction of the effector vectors. The TFs’ coding frames are shown in blank arrows.Vector constructions in transient expression assays. Click here for file(A) IpbHLH2 (EU032618). (B) PhAN1 (AF260919). (C) IpMYB1 (AB232769). (D) MlMYB1 (KC794950). (E) Expressed portions of the bHLHs in amino acid sequences. An EMSA test utilized the target protein expressed along with the maltose –binding protein (MBP).Constructs and protein expressions for the EMSA experiments. Figure S2. Constructs and protein expressions via pMAL-c2G system for the EMSA experiments. Click here for file


Corynebacterium glutamicum promoters have already provided large amounts of data on their structures, regulatory mechanisms and practical capabilities in metabolic engineering. In this minireview the main aspects of promoter studies, the methods developed for their analysis and their practical use in C. glutamicum are discussed. These include definitions of the consensus sequences of the distinct promoter classes, promoter localization and characterization, activity measurements, the functions of transcriptional regulators and examples of practical uses of constitutive, inducible and modified promoters in biotechnology. The implications of the introduction of novel techniques, such as in vitro transcription and RNA sequencing, to C. glutamicum promoter studies are outlined.Transcription initiation is the key step in gene expression in bacteria, and it is therefore studied for both theoretical and practical reasons. Promoters, the traffic lights of transcription initiation, are used as construction elements in biotechnological efforts to coordinate ‘green waves’ in the metabolic pathways leading to the desired metabolites. Detailed analyses of  Corynebacterium glutamicum is known as an industrial microorganism, in particular due to its use in the large-scale production of various amino acids (e.g. glutamate and lysine), which began as early as in the 1950s. Because this Gram-positive bacterium is generally recognized as a safe organism suitable for biotechnological processes, its use in the production of vitamins   is a DNA sequence (approximately 40–50 bp) that specifies the binding site for an RNA polymerase (RNAP) holoenzyme and the transcriptional start point (TSP). Precisely localizing the TSP and the corresponding key promoter sequence motifs as well as characterizing the promoter activity under various conditions provide a basis for the rational use of the promoter for biotechnological purposes. The promoter activity and conditions under which the promoter is active may be markedly affected by the flanking sequences covering as much as hundreds of nucleotides. This promoter region may include binding sites for transcriptional regulators (TRs) and other regulatory elements. The remarkable progress made in the experimental characterization of TRs in C. glutamicum in recent years  or mutagenesis of specific nucleotides within the promoter sequences are the main methods used for the directed modulation of gene expression in bacteria. Tightly regulated inducible promoters are usually used if the increased synthesis of the gene product has a detrimental effect on cell viability. Constitutive promoters can be used to increase the expression of genes involved in one metabolic pathway which results in an increased metabolic flux to the desired final metabolite. The modified promoters constructed by site-directed mutagenesis of the specific nucleotides within the promoter sequences can be used to fine tune gene expression.This minireview covers many aspects of promoter studies, including the ways they are detected and their applications in biotechnology.C. glutamicum genome codes for seven different sigma factors  falling into three groups according to the classification by Gruber and Gross (A (Group 1), the primary-like sigma factor σB (Group 2) and alternative factors σC, σD, σE, σH and σM, which are all members of Group 4, also called extracytoplasmic function (ECF) σ factors  form the largest described group. Their consensus sequence is defined, although their key specific sequence motifs (−35 and −10 sequences) exhibit a wide variability (low level of conservation). Most C. glutamicum promoters controlled by alternative σ factors are considered to be stress-induced. The characteristics of these promoters are still being discovered, although σB-, σH-and σM-dependent promoters have already been analysed to some extent.Promoters can be classified according to the σ subunits (factors) of the RNAP holoenzymes, which are responsible for the recognition of the respective promoter sequences. The nd Gross : the priEscherichia coli. The consensus sequences of these key promoter motifs in E. coli (TTGACA in the −35 region and TATAAT in the −10 region) have been defined. Two other elements that were only detected in some promoters are the extended −10 element TG (TGNTATAAT) and the UP element (an approximately 20 nt AT-rich sequence) located just upstream of the −35 motif  is similar to the consensus sequences of E. coli and other eubacterial promoters recognized by primary sigma factors. The statistical consensus sequence of these key motifs in the 159 C. glutamicum promoters assumed to be σA-dependent has been derived . The nucleotides in the C. glutamicum −35 consensus are much less conserved than those in the E. coli consensus and cannot be identified in many C. glutamicum promoters.The basic structure of E. coli, additional elements of C. glutamicum promoter sequences that are not conserved may affect the promoter's activity. Mutational studies of C. glutamicum promoters showed that the presence of the TG dimer within the extended −10 region increases promoter strength . This suggests that at least some σA-dependent promoters might be also recognized by σB under specific physiological conditions in vivo. C. glutamicum σB can thus be considered to be not only a backup σ factor for unfavourable conditions , which was also described in the promoters of stress-response genes in mycobacteria  G/TGGAAT/CA/T and (−10) C/TGTTG/AA/TA/T are much less conserved (in more than 40% of promoters). The σH-dependent promoters have not yet been utilized in constructions of biotechnologically important C. glutamicum strains; however, their potential use in heat-induced gene expression is envisaged.SigH-dependent promoters form the largest group of currently described stress-induced M-dependent .A number of TSPs pertinent to C. glutamicum encode chloramphenicol acetyl transferase (cat), β-galactosidase (lacZ), aminoglycoside phosphotransferase (aph), α-amylase (amy) and green fluorescent protein (gfp)  using a genomic distribution of hexanucleotide pairs within intergenic regions has been described as a promising general tool for the prediction of promoters  have recently provided new possibilities for analysing transcripts in a genome-wide manner  respectively. This technique is not only comprehensive, but able to zoom in on genes of interest, displaying TSPs recognized by different sigma factors as well as their quantitative discrimination. The transcription of the dnaK gene . This conclusion documents the usefulness of combining different experimental approaches to the analysis of promoters.We applied the RNA-seq technique to studies of the in vitro transcription assay generally provides a powerful tool to study transcriptional regulation in bacteria. An in vitro transcription system, which uses purified components of the transcription machinery, mimics many features of in vivo transcription and thus forms a basis for the detailed analyses of transcription initiation, elongation and termination. It complements other techniques analysing promoter – RNAP interactions, the functions of TRs and promoters of different classes. In vitro transcription has been broadly used to characterize the properties of RNAP from E. coli , a sigma factor and a promoter-carrying DNA template that is specifically recognized by the RNAP holoenzyme formed. The RNAP core was purified from the C. glutamicum strain with a modified rpoC gene, which produced a His-tagged β′ subunit. The C. glutamicum sigA, sigB, sigE and sigH genes were cloned and overexpressed using an E. coli plasmid vector and the respective σ subunits were purified by affinity chromatography. In the assays containing promoter DNA templates and the reconstituted C. glutamicum holo-RNAPs, specific transcripts were formed in all cases, confirming the functionality of the in vitro transcription system and its usefulness in determining sigma specificity in recognizing particular promoters  and to analyse transcriptional control by various regulatory proteins in C. glutamicum. We have already proved that some C. glutamicum promoters are in vitro recognized by two sigma factors. The P2 promoter of dnaK (encoding a heat shock protein) and the promoter of sigB (encoding the sigma factor σB) were found to be recognized by both RNAP + σH and RNAP + σE . This is in agreement with the results of experiments in which dnaK and sigB transcription was induced in response to heat shock and in response to cell surface stress controlled by σH and σE respectively.An  Fujita, . The in C. glutamicum have been classified on the basis of their regulatory hierarchy level into three groups, global, master and local  have been defined by experimental and bioinformatic approaches and the resulting data are available at CoryneRegNet. The temporal transcription pattern of a single promoter therefore result from the integration of the effects of several regulating factors ensuring a coherent response to environmental and metabolic changes. To identify TRs involved in the control of C. glutamicum promoters, a number of methods have been used.The TRs (activators and repressors) that bind to the DNA sequences (operators) within promoter regions in C. glutamicum global regulator controlling about 14% of the annotated C. glutamicum genes  by the RamA, RamB and GlxR regulators is an example of such a complex regulation  TR to be defined as a C. glutamicum promoters for optimizing gene expression began relatively recently. Strong constitutive C. glutamicum promoters were used for the construction of expression plasmid vectors as well as for replacing the native promoters of the selected genes in the C. glutamicum chromosome. The constitutive promoter of the cspB gene, coding for the main C. glutamicum secreted surface-layer protein PS2, was applied to the construction of the C. glutamicum – E. coli expression vector pCC , cloned in a multi-copy plasmid vector, was used to overexpress the iolT1 and iolT2 genes, which proved their role in glucose uptake  and tuf  genes were found to be suitable for these purposes.The native promoters of selected genes in the fbp  and zwf (glucose 6-phosphate dehydrogenase) genes from Psod and Ptuf significantly increased l-lysine production on glucose, fructose and sucrose , dapB (dihydrodipicolinate reductase), lysC (aspartate kinase) and tkt (transketolase) from Psod and of the fbp gene from Ptuf was found to redirect major carbon fluxes towards l-lysine hyperproduction , pyc and malE  genes within the C. glutamicum chromosome resulted in a C. glutamicum strain producing l-lysine during growth on lactate  from Psod, in addition to that of the dld, pyc and malE genes, enabled the production of l-lysine by the resulting C. glutamicum strain on grass and corn silages  gene inserted into the C. glutamicum chromosome, which resulted in an increased production of diaminopentane (cadaverine) by engineered C. glutamicum cells  gene integrated into the C. glutamicum chromosome from Ptuf was found to produce a high amount of succinate from glucose and formate under anaerobic conditions . The C. glutamicum/E. coli shuttle expression plasmid vectors containing these promoters have been listed and described in detail in our previous review articles  from Clostridium cellulovorans in C. glutamicum  and functions in minimal and complex growth media. In addition, since the inducer (propionate) is consumed by the cells, it offers a transcription that drops when the inducer is exhausted. The system was successfully applied to redirect fluxes towards threonine in a lysine-producing C. glutamicum strain by using the propionate-inducible expression of the hom and thrB genes that code for homoserine dehydrogenase (the branchpoint enzyme) and homoserine kinase  respectively  results in the activation of a promoter controlled by this regulator and consequently in the expression of a reporter gene whose product is easily quantifiable. Two such biosensor systems exploiting the reporter gene eyfp, coding for the enhanced yellow fluorescent protein which can be detected by fluorescence-activated cell sorting (FACS), have been developed for C. glutamicum. One system sensing the concentrations of branched amino acids and methionine by their interaction with the Lrp TR contains the promoter of the brnEF genes, coding for a two-component amino acid exporter, upstream of the eyfp reporter gene  gene, whose expression was further modified by changing the translation start codon and/or ribosome binding site. The strain producing the highest amount of putrescine so far was found among the C. glutamicum strains, harbouring the stably maintained plasmids with individual modifications and thus exhibiting different levels of ornithine transcarbamoylase  to achieve a gradual expression of the C. glutamicum gltA gene encoding citrate synthase. The obtained series of C. glutamicum strains with gradually decreasing citrate synthase activity was analysed at the transcriptome, metabolome and fluxome level and the l-lysine yield was found to be inversely proportional to the activity of citrate synthase. Using this approach, the C. glutamicum strain producing the highest amount of l-lysine on minimal medium with glucose so far was isolated   and ilvE (transaminase) genes (Table ilvA (threonine deaminase) and leuA  genes (Table C. glutamicum strain exhibiting an enhanced production of l-valine (Holátko et al., ilvE (Table l-valine was used in the construction of a C. glutamicum pantothenate producer (Hüser et al., Mutagenesis of the native promoters of genes involved in the biosynthesis of valine, isoleucine and leucine was carried out within the chromosome with the aim of improving the es Table  were foues Table , coding vE Table , resultiC. glutamicum cells can be achieved by selecting spontaneous mutations within promoter regions. The ability of C. glutamicum cells to utilize succinate, fumarate or l-malate as the sole carbon source was observed when spontaneous mutations within the promoter sequences occurred and caused overexpression of the C. glutamicum genes dccT (Youn et al., dctA (Youn et al., C. glutamicum mutant able to grow on glucosamine as a single carbon source was also recently isolated. The analysis of this mutant revealed that this newly acquired property was caused by a single mutation within the promoter of the nagAB-scrB operon. In contrast to the above-mentioned mutant promoters, this mutation is located outside the −10 and −35 sequences of the P1-nagA and P2-nagA promoters (Uhde et al., It was found that novel metabolic capacities of lac operon or trp operon in E. coli, are undoubtedly simplifications. An ever-expanding number of techniques applied to C. glutamicum are enabling us to analyse promoters in more detail and elaborate more precise models of promoter activity when subject to various environmental stimuli, nutritional conditions, stress situations and growth phases. Molecular methods provide data on the effects of TRs and other factors determining promoter activity under specific conditions. Using in vitro transcription, which mimics many features of in vivo transcription, the promoters can be classified on the basis of which σ factors recognize their core sequence. However, simplified working models must still be used to analyse particular regulatory functions. Since any promoter is a single cog in the cell machinery that forms a regulatory network, genome-wide technologies provide more complex information, which get us closer to understanding the cell on the level of systems biology. Transcriptomics and RNA sequencing enable the comprehensive detection and characterization of many promoters in parallel. In addition to the precise localization of the 5′-ends of mRNA, RNA-seq can provide quantitative information on promoter activity and transcript stability. In combination with proteomic, metabolomic and fluxomic data, our understanding of the cell at the system level is gradually improving. It is expected that the fusion of these global data sets will facilitate the construction of C. glutamicum strains that produce useful metabolites. Although a completely holistic approach to the description of the regulatory processes in a cell is still not practical, genome-scale metabolic flux determinations, metabolomic studies and the design of optimal metabolic pathways by in silico modelling have provided powerful tools for the optimization of producing strains (Becker and Wittmann, et al., C. glutamicum.Most textbook examples of transcription initiation control mechanisms, which serve as paradigms for promoter regulation, such as the


Xenopus laevis. Liganded TR recruits histone modifying coactivator complexes to target genes during metamorphosis. This leads to nucleosomal removal and histone modifications, including methylation of histone H3 lysine (K) 79, in the promoter regions, and the activation of T3-inducible genes.Thyroid hormone (T3) is important for adult organ function and vertebrate development. Amphibian metamorphosis is totally dependent on T3 and offers a unique opportunity to study how T3 controls postembryonic development in vertebrates. Earlier studies have demonstrated that TR mediates the metamorphic effects of T3 in Xenopus tropicalis, a highly related species of Xenopus laevis. We further show that Dot1L expression in both the intestine and tail correlates with the transformation of the organs.We show that Dot1L, the only histone methyltransferase capable of methylating H3K79, is directly regulated by TR via binding to a T3 response element in the promoter region during metamorphosis in Our findings suggest that TR activates Dot1L, which in turn participates in metamorphosis through a positive feedback to enhance H3K79 methylation and gene activation by liganded TR. Thyroid hormone T3) is important for proper development and normal physiology of many adult organs/tissues in vertebrates ,2. Sever is imporXenopus laevis or tropicalis mimics mammalian postembryonic development [Metamorphosis in anurans such as elopment ,4. This elopment . The larelopment ,7. Interelopment . T3 can elopment ,8-10. Reelopment -23. In pelopment ,24-26. Telopment ,26. Whenelopment ,22,27-31Xenopus tropicalis genes to look for genes bound by TR (unpublished observation). While the ChIP-on-chip data was very preliminary, it was of interest that one of the putative target genes thus identified corresponded to the Xenopus tropicalis homolog of the mammalian Dot1L gene.TR is believed to regulate overlapping but distinct target genes in different organs to effect organ-specific metamorphosis. Toward identifying such target genes, we carried out a ChIP (chromatin immunoprecipitation)-on-chip analysis of the intestine by using a set of microarray chips covering a 8 kb region flanking each putative promoter of 17000 Saccharomyces cerevisiae[Dot1L (Dot1-Like) is the homolog of yeast Dot1 gene, originally identified as a disruptor of telomeric silencing in erevisiae. It beloerevisiae-36. Esseerevisiae,34,37 Coerevisiae.Xenopus tropicalis intestine during natural and T3-induced metamorphosis [H3K79 methylation is one of a large number of posttranslational modifications that occurs at the histone tails in eukaryotic cells . In addiorphosis . This raXenopus tropicalis Dot1L from GenBank and analyzed the domain structure of the predicted amino acid sequence by using NCBI’s Conserved Domain Database. Like yeast Dot1 and mammalian Dot1L, Xenopus tropicalis Dot1L has no SET domain -dependent methyltransferase) domain is essentially identical between mouse and Xenopus tropicalis Dot1L and a conserved AdoMet binding site is also present in this region . This suggested that Dot1L might be regulated by T3 during metamorphosis. To investigate this possibility, we treated stage 54 premetamorphic tadpoles with 10 nM T3 for 2 days. Dot1L expression was analyzed on total RNA isolated from the intestine and tail by qRT-PCR. Figure As indicated above, our preliminary ChIP-on-chip assay showed that Dot1L gene was bound by TR in the intestine of To investigate whether Dot1L expression is also regulated by T3 during natural metamorphosis, we analyzed its expression by qRT-PCR in the intestine and tail at different stages from premetamorphic (stage 54), metamorphic climax (stages 58-64), to the end of metamorphosis (stage 66). The results in Figure Xenopus laevis TRβA gene , a wellegulated ,53, and 2 Figure B for the2 Figure C.To compare the relative affinity of the TREs, we carried out competition assays with equal amount of unlabeled TRE1, TRE2, or TRβA TRE. The results in Figure Xenopus laevis oocyte transcription system, which allows the analysis of the promoter in the context of chromatin in vivo [Xenopus oocyte cytoplasm to allow the synthesis of the proteins. Then, we microinjected the reporter plasmid containing the Dot1L promoter driving the firefly luciferase together with an internal control plasmid phRG-tk expressing Renilla luciferase into oocyte nucleus. After overnight incubation of the oocytes in the presence or absence of T3, they were harvested for the luciferase assay with the ratio of firefly luciferase to Renilla luciferase activities as a measure of the promoter activity. As shown in Figure To determine whether TRE1 and TRE2 can mediate the transcriptional regulation of the Dot1L gene by T3 in vivo, we used a reconstituted  in vivo . First, To determine the roles of the individual TREs in mediating the effect of TR/RXR, each was mutated individually or together and the resulting promoters were analyzed in the oocyte. Mutating TRE1 had little effect on the regulation of the promoter by TR/RXR in the presence or absence of T3 Figure B. On theXenopus tropicalis tadpoles. To confirm the binding of TR to the TRE regions of Dot1L gene in vivo, we carried out ChIP assays with a polyclonal anti-TR antibody on the intestine of stage 54 premetamorphic tadpoles treated with or without T3. As a control for antibody specificity, we carried out parallel ChIP assay with a polyclonal antibody against ID14, an extracellular matrix protein [Our preliminary ChIP-on-chip assay suggested that the Dot1L promoter region was bound by TR in the intestine of premetamorphic  protein . Figure As Dot1L was also upregulated in the tail during metamorphosis, we analyzed TR binding to the TREs in the tail of the same tadpoles as studied above for the intestine. Essentially identical results were obtained Figure B. That iX. tropicalis metamorphosis is accompanied by nucleosome eviction and histone modifications, include H3K79 trimethylation [TR can activate and repress T3-inducible genes in the presence and absence of T3, respectively. TR functions by recruiting cofactor complexes, many of which can modify histones at the target genes. Extensive genome-wide analyses in cell cultures have identified a number of histone marks that are associated with active or repressed genes, respectively -49. Howehylation . Here weWe initially identified Dot1L as a likely TR target from a preliminary ChIP-on-chip analysis, which showed TR association with the putative promoter region of the gene in premetamorphic tadpole intestine (unpublished observation). Our bioinformatics analysis revealed two putative TREs in this promoter region. Furthermore, both TREs were capable of binding to TR/RXR heterodimers in vitro with TRE2 having a higher affinity. More importantly, we demonstrated that in the frog oocyte transcription system in vivo, TRE2 but not TRE1 was essential for TR/RXR to regulate the Dot1L promoter in a T3 dependent manner. Further evidence supporting a critical role of TRE2 in Dot1L expression came from in vivo ChIP assay. In both animal tail and intestine, TR binding was much stronger to TRE2 than to TRE1. As indicated above, given the limitation of the ChIP assay, it is possible that the weak TR ChIP signal at TRE1 was a result of TR binding at the TRE2 and TRE2 is the only TRE bound by TR during metamorphosis in the intestine and tail. In any case, our findings together indicate that Dot1L gene is a direct T3 target gene that is regulated via binding of the TR predominantly to TRE2.Our expression analyses indicated that Dot1L is upregulated during metamorphosis in both intestine and tail, temporally correlating with their metamorphic transformations. The tail and intestine undergo vastly different changes during metamorphosis, total resorption vs. remodeling involving both larval cell death and adult cell development. The upregulation of Dot1L in both organs suggest that Dot1L participates in the metamorphosis in different organs/tissues.Xenopus tropicalis tadpoles during natural or T3-induced metamorphosis [How Dot1L is involved in metamorphosis remains to be investigated. Interestingly, we have recently shown that H3K79 methylation levels are dramatically upregulated at two well characterized TR target genes, the TRβ and TH/bZip genes, in the intestine of orphosis . Given torphosis ,38, it iXenopus tropicalis tadpoles and Xenopus laevis frogs were purchased from Nasco . Tadpoles were staged according to Nieuwkoop and Faber [nd Faber . When inTotal RNA was isolated from tadpole intestine and tail at indicated stages. cDNA was synthesized from 2.5 μg of total RNA by using the High Capacity cDNA Archive kit (Applied Biosystems) in 50 μl according to the manufacturer’s instructions. qRT-PCR was carried out by using SYBR Green PCR Master Mix (Applied Biosystems). EF1α (elongation factor 1α) was used as the normalization control as described previously . The priXenopus tropicalis genomic DNA with a primer pair 5’-CCCCGGTACCGTTTCCTAGGCGGCGGAGGATGCCC-3’ (bearing KpnI site at its 5’-end) and 5’-CCCCCTCGAGCATACTAATCCAACTCCGCCGGCGGTG-3’ (bearing XhoI site at its 5’-end). The PCR product was cloned into KpnI and XhoI sites of pGL4.10 firefly luciferase vector (Promega). The mutant promoters harboring mutant TREs were generated from the wild type construct using QuickChange Lightning Site-Direct Mutagenesis Kit (Agilent/Stratagene) according to manufacturer’s instructions. The primers used for mutagenesis were: KF385 5’-AGCATTGTTAGTAATCCGCTGCTTTTTATTTATTTCACTGGCACCATCACCTTGCG-3' (the mutated nucleotides are underlined) to generate mTRE1, KF386 5’-CCCTCAGACTTCAGGCTAAGGAACCATATGAAAACACCCCGGGATTATTTATT-3’ to generate mTRE2. Both KF385 and KF386 were used to generate the construct mTRE1 mTRE2 where both TREs were mutated. The mutated constructs were confirmed by DNA sequencing.The Dot1L promoter region was PCR-amplified from http://asp.ii.uib.no:8090/cgi-bin/NHR-scan/nhr_scan.cgi) [The computational analysis program NHR-Scan (can.cgi)  was usedXenopus tropicalis TRα and RXRβ were linearized and transcribed in vitro using a T7 kit . The cytoplasm of stage VI oocytes from Xenopus laevis was injected with 46 pg/oocyte of the TR and RXR mRNAs. Two hour later, the firefly luciferase reporter under the control of the wild type or mutant promoter of Xenopus tropicalis Dot1L gene (33 pg/oocyte) and the internal control Renilla reporter phRG-TK (33 pg/oocyte) were co-injected into the nucleus. After incubation at 18°C overnight in the presence or absence of 100 nM T3, the injected oocytes were prepared for luciferase assay using the Dual-Luciferase-Reporter Assay system according to the manufacture’s protocol (Promega). Six oocytes per sample were lysed in 90 μl of 1× lysis buffer (Promega), and 10 μl of lysate was used for luciferase assays. Three independent samples were done for each injection at the same time. The relative expression of firefly luciferase to Renilla luciferase was determined. Each data point represents the average of three groups. The data shown here were a representative of a few independent experiments with similar results.Oocyte transcription assay was performed as described ,54. BrieChIP assay on tadpole intestine and tail was done as described previously  with antThe immunoprecipitated DNA was analyzed by quantitative PCR using the following primers: 5’-ACGCGTATCGGACCCACGCAAGGTG-3’ and 5’-AGACTTCGACAGCCTACGTGAGCAGCA-3’ for Dot1L TRE1 region, 5’-TGTGGCCTACGAAGCGACCGCCTTC-3’ and 5’-TCAGGCTAAGGGGCCATATGAGGACACCC-3’ for Dot1L TRE2 region, 5’-GATAAACACCATGATGCTGCAC-3’ and 5’-TACACCACTCCGTGAGTAGCAC-3’ for Dot1L exon 2.Xenopus laevis TR and RXR proteins were made using TNT SP6 Quick Coupled transcription/translation System . They were mixed with infrared dye IR700 -labeled TRE of the Xenopus laevis TRβA gene [Xenopus tropicalis Dot1L gene. The sequences of sense strand of oligonucleotides for the Dot1L TREs were as follows: 5’-AATCCGCTGCCCTTTATTTACCTCACTGGC-3’ for Dot1L TRE1 and 5’-AATCCGCTGCTTTTTATTTACCTCACTGGC-3’ for mutant Dot1L TRE1, and 5’-GGCTAAGGGGCCATATGAGGACACCCGCGG-3’ for Dot1L TRE2 and 5’-GGCTAAGGGGCCATATGAAAACACCCGCGG-3’ for mutant Dot1L TRE2 . Each binding reaction included one hundred fmol (1μl) of IR700-labeled probe, 1 μl of each TR and RXR in vitro translation mixture, and 1μl of the wild-type or mutant TRE oligonucleotides at 400 fmol/μl, 2 pmol/μl, or 10 pmol/μl to obtain 4×, 20×, 100× unlabeled competitor oligonucleotides, in a total volume of 20 μl. The mixtures were incubated at room temperature for 20 min, electrophoresed on a 6% DNA retardation gel (Invitrogen), and then scanned using Odyssey Infrared scanner .Gel mobility shift assay was performed as described . BrieflyRβA gene  in the iDot1L, Disruptor of telomeric silencing 1-like; T3, Thyroid hormone; TR, T3 receptor; RXR, 9-cis retinoic acid receptor; TRE, T3 response element; EF1α, Elongation factor 1α; qRT-PCR, Quantitative reverse-transcription polymerase chain reaction; ChIP, Chromatin immunoprecipitation; KMT, Lysine methyltransferase; AdoMet or SAM, S-adenosylmethionine; AdoMet-MTase, AdoMet-dependent methyltransferase.None.KM, KF, BD, LF and CDL designed and carried out experiments and interpreted the findings; KF prepared the manuscript; YBS supervised the study and prepared the manuscript. All approved the manuscript.


High-occupancy target (HOT) regions are compact genome loci occupied by many different transcription factors (TFs). HOT regions were initially defined in invertebrate model organisms, and we here show that they are a ubiquitous feature of the human gene-regulation landscape.We identified HOT regions by a comprehensive analysis of ChIP-seq data from 96 DNA-associated proteins in 5 human cell lines. Most HOT regions co-localize with RNA polymerase II binding sites, but many are not near the promoters of annotated genes. At HOT promoters, TF occupancy is strongly predictive of transcription preinitiation complex recruitment and moderately predictive of initiating Pol II recruitment, but only weakly predictive of elongating Pol II and RNA transcript abundance. TF occupancy varies quantitatively within human HOT regions; we used this variation to discover novel associations between TFs. The sequence motif associated with any given TF’s direct DNA binding is somewhat predictive of its empirical occupancy, but a great deal of occupancy occurs at sites without the TF’s motif, implying indirect recruitment by another TF whose motif is present.Mammalian HOT regions are regulatory hubs that integrate the signals from diverse regulatory pathways to quantitatively tune the promoter for RNA polymerase II recruitment. Transcription factors (TFs) are proteins that regulate the expression of genes by binding the DNA at their regulatory elements (promoters or enhancers) and either preventing or facilitating the recruitment, in eukaryotes, of the transcription preinitiation complex (PIC). The PIC in turn recruits RNA polymerase II (Pol II) to the transcription start site (TSS) to synthesize an RNA transcript. This is a primary mechanism for the regulation of gene expression in response to environmental stimuli or developmental programs.Promoters must integrate a multitude of signals that converge on them in the form of activating or repressing transcription factors. In invertebrates, some regulatory regions  are occupied by a large number of transcription factors-6. HowevThe ENCODE data set,8 providUniPeak, to analyze these data accordingly.Previous ChIP-seq analyses have generally considered a single experiment at once, and have treated TF occupancy as a binary signal—present vs. absent. However, the particular strength of the signal at any given site may represent important biological information, such as the persistence of occupancy within a cell or frequency across all cells in the sample. We developed a new software package, UniPeak to discover and quantify HOT regions, we performed a comprehensive analysis of these regulatory hubs. In particular, we characterized HOT regions with regard to other known markers of gene activity. We also compared the occupancy profiles of different TFs to predict novel interactions, and used mechanistic evidence to infer which complex members directly bind DNA. Finally, we quantified the relationship between TF occupancy and several measures of gene expression at HOT promoters.Using hg19 reference genome, standardizing the read length and removing low-confidence alignments in order to ensure accurate mapping without read-length bias.We obtained all publicly available ENCODE ChIP-seq data from the 5 most studied human cell lines, which aUniPeak extends the peak-calling algorithm from QuEST[′ end of its alignment to the reference assembly. For each sample (i.e. a single replicate of a single experiment), UniPeak estimates the base-pair shift between strands, due to reading from opposite ends of sheared fragments, by selecting a shift value that maximizes strand correlations at the strongest regions. After shift correction of individual samples, kernel density estimation is used to compute a single smooth density profile for the combined signal of all samples. UniPeak identifies enriched regions where this profile exceeds a fixed threshold of fold enrichment relative to a uniform background distribution. The number of hits within each of these regions from each sample is reported, yielding a regions × samples matrix of hit counts. Unlike other peak-callers for ChIP-seq, UniPeak does not directly use "input" or other negative controls to filter enriched regions initially; rather, though these samples do not contribute to the region-calling step, negative-control reads (as well as histone-mark ChIP reads) are counted within the regions called from ChIP samples, and reported alongside read counts from TF ChIP samples. We normalized the peak intensities from discrete read counts to continuous occupancy values with the variance-stabilizing transformation in DESeq[UniPeak is described in Additional Filerom QuEST to the p in DESeq. PerformUniPeak detected 11,239 enriched regions , and the preinitiation complex. The latter was later used to test functional predictions.UniPeak detected 7,227 regions  may share mechanistic or functional relationships. To search for such relationships, we used neighbor-joining to clustThe AP-1 transcription factor is a heterodimer composed of a member of the JUN family and a member of either the FOS family or the ATF family. HoweverOne large branch in GM12878 included ATF2, BATF3, BCL33, BCL11Ain vivo) generally only explain a small part of the similarity in their occupancy patterns.To test whether protein-protein interactions predict similarites in occupancy patterns, we compared our results with a comprehensive database of mammalian two-hybrid screens; data were available for all TFs in this study except FAM48A and THAP1 Within eAlthough it is difficult to use shared occupancy profiles to infer a binding mechanism, additional analysis can illuminate a critical step in the recruitment of a TF complex. A TF’s observed occupancy at a given promoter might be due to either direct binding of DNA or recruitment by another protein. Most TFs in our data set have previously been annotated with DNA sequence motifs that they bind specifically. Thus, if we make the simplifying assumption that most TFs usually bind DNA at regions that contain their respective sequence motifs, then their occupancy at sites without their motifs is likely to be as cofactors recruited by other proteins.UniPeak output for occurrences of sequence motifs associated with the TFs in the ENCODE data set. We considered motifs identified de novo by ENCODE from analysis of each ChIP-seq experiment individually , and in order to avoid motifs that are not bound directly by a given TF but rather by its cofactor, we used only motifs that matched database annotations for the given TF. This yielded multiple motifs for some TFs and none for others, and some motifs were associated with TF families rather than individual TFs; thus, our analysis was based on "motif sets" that share a common annotation, rather than individual motifs. On average, any given HOT promoter contained motifs in about 4 distinct sets  were fit for each cell type, encompassing all available data at once. The dependent variables we used were the occupancy of PIC subunits within the region, the enrichment of histone modifications  within the region, the occupancy of initiating Pol II at its nearest enriched region, the occupancy of elongating Pol II  was likely a ChIP-seq artifact rather than a meaningful TF effect. Therefore, for each cell type we compared two models: gene regulation as a function of both TF ChIP and input signal, and gene regulation as a function of input signal alone.R2 ≈ 0.7 for the cells with the most TFs tested), though with somewhat high contribution from input alone. Histone marks H3K4me3 and H3K27ac were somewhat well predicted (CV R2 ≈ 0.4), but with even higher relative contribution from input, perhaps because these controls are sensitive to open chromatin, which is associated with active promoters; H3K4me1 was not well predicted by the model (CV R2 < 0.2), likely because of very low signal at these regions, as expected for a mark depleted at active promoters. Pol II-S5P occupancy was also well predicted by TF occupancy (CV R2 ≈ 0.4), and input was not very predictive (CV R2 < 0.2); the results were slightly worse in the cells with fewer TFs tested. On the other hand, Pol II-S2P occupancy was not well predicted by TF occupancy, nor was transcript abundance as measured by either CAGE or RNA-seq (CV R2 < 0.2); there was no consistent difference between CAGE signals from polyadenylated (mature) and unpolyadenylated transcripts. Thus we found that the presence of these TFs is strongly associated with immediately subsequent steps in gene regulation, but only weakly associated with later steps.The presence of PIC subunits was well predicted by aggregated TF occupancy , which would make the trend more linear,68-70. FDrosophila enhancers[Regions occupied by many different TFs are common in the human genome. Even our strictest definition finds several thousand HOT promoters, likely a considerable fraction of the active genes in any given cell line. Especially since there are far too few known DNA sequence motifs to account for all the TF occupancy at these sites, we propose that TFs collaborate combinatorially through protein-protein interactions to regulate Pol II recruitment Figure, concordnhancers. InteracThis analysis yields several hypotheses that may be validated by future experiments. Based on the similarity of their occupancy profiles, reproduced independently in multiple cell lines, we infer that ATF3 and USF1/USF2 may be part of a novel protein complex; furthermore, since DNA sequence motifs associated with USF1/2 are predictive of ATF3’s occupancy but not vice versa, we predict that USF1 or USF2 is the subunit of this complex that directly binds the promoter, while ATF3 is a cofactor recruited by USF1/2 despite having its own DNA-binding domain that is used in other complexes. By the same logic we also predict that SIX5 and ZNF143 are members of a novel complex in which ZNF143 is the DNA-binding subunit. In both cases, previous experimental evidence shows the two partners are not capable of binding each other alone, suggesting that a chaperone is required to enable binding, or that these interactions require both proteins to bind to a common intermediate or complex of intermediates. On the other hand, we find that in HOT promoters, FOS seems only rarely to perform its well-known role in the AP-1 heterodimer with JUN, generally supplanted by a FOSL or ATF protein, though of course FOS is known to have several alternate cofactors.It should not be surprising that a TF with a functional DNA-binding domain and even a well-demonstrated sequence motif might often be recruited as a cofactor by some other protein. Indeed, this is true of TBP, the so-called TATA-binding protein, which is required for the assembly of the preinitiation complex at all loci even though only 10–24% of human promoters have a TATA box,74. One BWA 0.6.1-r104[ChIP-seq nucleotide sequence reads and base qualities were obtained from the ENCODE database, and truncated to the first 25 nt, the shortest length in the data set, to prevent biases in mapping due to different read lengths. .6.1-r104 was usedQuEST algorithm[′ read starts per reference base as the input to kernel density estimation (KDE), so that the density at any given position i on one strand was given bySimilarly to the robust lgorithm, a smooth is the kernel bandwidth, and C(j) gives the number of 5′ read starts at position j.where′ read starts were then counted inside each region. The kurtosis of the distribution of 5′ read starts within each region was calculated, and leptokurtic regions were filtered out to remove technical artifacts.Any region where the smooth density profile exceeded a fixed threshold, relative to the uniform background of the total confident read count divided by the genome size, was considered enriched. 5′ end-directed sequencing and the genomic fragment size, KDE was performed separately on each strand, and preliminary enriched regions were called from the sum of the two density profiles. Among the regions containing the highest read counts, the Pearson correlation between the strand-specific density profiles was calculated for each of a spectrum of 5′ to 3′ shift values. The distribution of correlation-maximizing shift values across the top regions was smoothened with a small bandwidth and the global maximum was chosen as the sample-wide shift value. Density profiles from opposite strands were shifted by this value and added together for a unified, strand-independent profile. Regions with a low Pearson correlation between the two strands’ density profiles were discarded as artifacts.To estimate the shift between enrichment maxima from the forward and reverse strands flanking each binding site, a byproduct of 5UniPeak was written to automate the steps above. Starting with confidently aligned reads, strand shift was estimated independently for each sample , using the top 1,000 regions called with smoothing bandwidth 50 nt, region-calling fold-enrichment threshold 25X, kurtosis threshold 50, minimum strand correlation 0.3, minimum shift 25 nt each strand, maximum shift 150 nt each strand, and correlation vs. shift smoothing bandwidth 5 nt. The samples were then shifted accordingly and kernel smoothing was performed with bandwidth 100 nt to capture binding sites in close proximity to each other; density profiles from both strands of all samples were summed and enriched regions were called and filtered as before. Enriched regions on sex chromosomes and the mitochondrial genome were removed, along with those overlapping false-positive genome regions identified by ENCODE and those greater than 500 bp in size.The new software package UniPeak 1.0 was normalized by the variance-stabilizing transformation in DESeq 1.7.7[The read-count matrix from Seq 1.7.7, determir). Rooted, ultrametric trees were generated by hierarchical clustering with UPGMA as implemented in the fastcluster 1.0.4 package in R 2.12.1[RapidNJ 2.1.0[Clustering was performed on normalized data as described above. Distances were calculated with the Pearson metric . CAGE-enriched regions were called via UniPeak in the same manner as TF binding sites, using 50 nt smoothing bandwidth, separate strands, and no shifting. CAGE regions were matched with TF regions in the same manner as Pol II-S5P regions.CAGE reads were obtained from the ENCODE database after alignment to GERP++[Evolutionary constraint within a region was calculated as the proportion of positions with a rejected substitution (RS) score greater than 2, according to GERP++.de novo from ChIP-seq data and matched to similar motifs in other databases, such that there was a variably sized set of motifs annotated to each individual TF. These were aligned to reference sequence in a 201 bp window centered at each HOT region peak by MAST 4.6.0[MAST hit of E < 10.From ENCODE’s database we retrieved 226 sequence motifs that were both inferred AST 4.6.0. A HOT rFor each cell line, we constructed a model of the general formY terms form a matrix of the individual replicates of the dependent variables, normalized together by DESeq as before: preinitiation-complex occupancy within the region, histone-mark occupancy within the region, Pol II occupancy at the nearest UniPeak site, CAGE signal at the nearest UniPeak site, and RNA-seq signal for the gene corresponding to the nearest RefSeq TSS; and the X terms form the matrix of all the individual replicates of TF occupancy scores plus the signal from negative-control samples  within the regions, normalized together. Since both the independent and depent variables were highly multicollinear, we used the pls 2.3-0 package in R 2.12.1[R2 values were calculated by validating with each UniPeak region after re-training the model on the remainder of the data.where the  R 2.12.1 to reducThe authors declare no conflict of interest.JWF and AS conceived the study and prepared the manuscript. JWF performed all analysis. Both authors read and approved the final manuscript.Contains a supplementary table and supplementary figures. All data used in this work are available from the ENCODE data portal,http://genome.ucsc.edu/ENCODE/. Scripts and processed data can be obtained athttp://mendel.stanford.edu/sidowlab/downloads/hot/.Click here for fileDescribes a study validating the UniPeakmethod.Click here for file