Nuclear factor I revealed as family of promoter binding transcription activators
- Milos Pjanic†1Email author,
- Petar Pjanic†2,
- Christoph Schmid3, 4, 5, 6,
- Giovanna Ambrosini3,
- Armelle Gaussin1,
- Genta Plasari1,
- Christian Mazza7,
- Philipp Bucher3 and
- Nicolas Mermod1
© Pjanic et al; licensee BioMed Central Ltd. 2011
Received: 15 August 2009
Accepted: 7 April 2011
Published: 7 April 2011
Multiplex experimental assays coupled to computational predictions are being increasingly employed for the simultaneous analysis of many specimens at the genome scale, which quickly generates very large amounts of data. However, inferring valuable biological information from the comparisons of very large genomic datasets still represents an enormous challenge.
As a study model, we chose the NFI/CTF family of mammalian transcription factors and we compared the results obtained from a genome-wide study of its binding sites with chromatin structure assays, gene expression microarray data, and in silico binding site predictions. We found that NFI/CTF family members preferentially bind their DNA target sites when they are located around transcription start sites when compared to control datasets generated from the random subsampling of the complete set of NFI binding sites. NFI proteins preferably associate with the upstream regions of genes that are highly expressed and that are enriched in active chromatin modifications such as H3K4me3 and H3K36me3. We postulate that this is a causal association and that NFI proteins mainly act as activators of transcription. This was documented for one member of the family (NFI-C), which revealed as a more potent gene activator than repressor in global gene expression analysis. Interestingly, we also discovered the association of NFI with the tri-methylation of lysine 9 of histone H3, a chromatin marker previously associated with the protection against silencing of telomeric genes by NFI.
Taken together, we illustrate approaches that can be taken to analyze large genomic data, and provide evidence that NFI family members may act in conjunction with specific chromatin modifications to activate gene expression.
High-throughput assays are being widely employed in various fields of biology. For example in genomics, DNA microarrays are used to simultaneously measure the expression levels of nearly all genes of a genome [1, 2]. Recently, a new high-throughput method has been developed for a whole genome mapping of protein-DNA interactions that is based on the chromatin immunoprecipitation and next generation sequencing technology (method termed chromatin immunoprecipitation sequencing or ChIP-Seq) [3–8]. These two high-throughput methods, when combined, are instrumental to study how transcription factors regulate gene expression at a global genomic scale. As the costs of new-generation sequencing and DNA microarrays decrease, such high-throughput assays should be increasingly used. In addition, new software tools are emerging rapidly, allowing faster and easier analyses of large-scale genomic datasets [9–13]. However, extracting the significant and biologically relevant information from such massive datasets still represents a great challenge. In purely experimental studies, the use of negative controls such as the blank or mock conditions is absolutely necessary. However, genome-wide computer analyses may lack an adequate negative control. In such case, a randomly selected portion of the total dataset can be used as an in silico negative control, such as for instance a randomly picked sample of all genomic loci. The size of the control dataset can then be chosen to parallel that of the experimental set, to simplify the statistical analysis [14, 15].
As a study group of proteins, we chose NFI/CTF family of mammalian transcription factors. NFI/CTF represents a family of transcription-replication factors comprising polypeptides encoded by four paralogous genes located on different chromosomes in mammals (NFIA, NFIB, NFIC, NFIX) [16, 17]. NFI family of proteins displays the unusual property of regulating not only the initiation of transcription but also of mediating DNA replication . NFI recognition sequence were found in the promoter sequences of many cellular genes , where they may act as activator or repressor of transcription [20–25]. Recently, it has been proposed that NFI may be involved in a long range regulation of gene expression, through the formation of chromatin barrier and by blocking the propagation of a heterochromatic structure [26, 27]. NFI binds as a dimer, and its preferred binding sequence is a palindrome composed of two half sites TTGGCANNNTGCCAA. A position weight matrix for the NFI/CTF was established using a collection of over 10,000 SELEX-SAGE selected sites, allowing the prediction of its binding affinity to any genomic sequence . However, since this prediction matrix is based on NFI binding specificity in vitro, the specificity of this family of proteins may be different from that observed in the cell, where interactions with other transcription factors may take place and DNA accessibility may be restrained by chromatin. Here we assessed the in vivo binding preferences of NFI/CTF, its global functional properties regarding the regulation of gene expression and the relationship of NFI binding sites with different histone methylation markers typical of either an open or closed chromatin structure.
NFI preferentially binds upstream of transcription initiation sites in mouse genome
Statistical analysis in genomics often relies on the sub-sampling of datasets, which requires random sampling algorithms. We devised a random sampling algorithm that can be conveniently applied to large genomic datasets. The random sampling algorithm C++ source code is available as a text file online (Additional file 1). Each randomly generated number is used to extract an entry line from the main dataset to generate a subset of the desired size. Simulation experiments indicated that subsampling can be applied to sets of normally distributed values without loosing the statistical robustness of the comparisons, provided that relatively large subsets of data are retained (e.g. equal or greater than 100 individual values; see Additional file 2).
We first used this random sampling method to compare data from a ChIP-Seq experiment performed on primary mouse embryonic fibroblasts for the NFI transcription factor family relative to the in silico predictions of its binding sites. The mouse genome (NCBI build 37 or mm9) was found to contain a set of 61,492 NFI predicted sites that were defined using a previously established position weight matrix [19, 28]. The predicted sites were defined with a matrix score threshold > 85, which corresponds to a medium in vitro binding affinity in the range of scores that extends from a minimum of -108 to a maximum of 100. Within this set, 2,852 predicted sites overlapped DNA sequences covering the RefSeq annotated transcription start sites (TSS) and 5 kb of upstream sequences.
To assess the latter possibility further, we generated control datasets of the same size as the experimental set by randomly selecting 2,852 sequences from the 61,492 predicted sites. Comparison of distinct randomly selected data subsets indicated comparable tag counts at predicted binding sites, and similar signal-to-noise ratio. Thus, the sampling method provided a reliable estimation of the binding site occupancy, since several random groups of predicted sites did not differ markedly in their protein occupancy (Figure 1B). As before, the dataset corresponding to TSS-proximal sites showed a more prominent tag count around the predicted sites. However, comparable tag counts were observed within 500 to 5000 bp windows around the predicted binding sites, when comparing the experimental profiles to those of control datasets of the same size. Thus, we conclude that the group of TSS-proximal predicted sites displayed higher protein occupancy at predicted sites when compared to the randomly selected binding sites, but that it was not the overall promoter region that was more frequently bound by the protein. This suggested that binding sites within genomic loci upstream of core promoters may bind NFI with higher apparent affinity.
NFI-bound genes show higher expression levels
NFI binding correlates with specific histone methylation patterns
NFI-C most often acts as an activator of gene expression
Discussion and Conclusions
In this study, we used a random sampling procedure as a general method to obtain reliable control datasets in the analysis of high-throughput genomic assays. We find that datasets of more than 100 individual values can be used without decreasing the robustness of statistical analysis, and that independently generated random subsets of data have statistically indistinguishable global properties. Thus, subsampling can provide a convenient way to display and compare the noise and signals from experimental and control datasets of the same size.
First, we showed that NFI binds preferentially those predicted sites that are located upstream of the initiation sites of transcription (Figure 1). Several interpretations may be given to the preferential association of NFI to binding sites in the proximity of TSS rather than to other locations of the genome. It is known that NFI occupies the promoters of many genes where it may bind synergistically with some other transcription factors such as hepatocyte nuclear factor 1 alpha, estrogen receptor, Brg-associated factor [31–33]. Thus, the preferred occupancy of TSS proximal sites may at least in part reflect the synergistic association of NFI with other factors.
We also found that NFI occupy promoters or upstream regions of the group of genes that are significantly more expressed than the representative randomly selected control groups. Since correlation does not necessarily imply causal relationship, this observation does not allow the conclusion that NFI-family members actually activate the expression of these genes. For instance, NFI might bind highly expressed genes to suppress in part their expression, but still leaving relatively high transcription levels. However, taken together with previous observations that NFI activates the expression of many genes in higher eukaryotes [20, 21, 32, 34–37], we rather conclude that the observed correlation may originate from a direct up-regulation of gene expression by NFI, at least for a significant proportion of its target genes.
The hypothesis that NFI family members may directly activate genes appears to be true for at least one of the member of the family (i.e. NFI-C), as mRNA profiling analysis performed on wild-type and NFI-C knock-out cells revealed that NFI-C is a more potent gene activator than a repressor. The 1000 genes that are most up-regulated by NFI-C had significantly higher change in their expression levels than the top 1000 down-regulated genes. In addition, up-regulated genes showed significantly higher expression levels than representative control gene samples selected from the total gene population, implicating again that this factor is a potent activator of gene expression. Since the selected in vivo NFI binding sites are located up to 5 kb from their TSS, which is a relatively large distance, NFI might act as well through some of the types of remote regulation, for instance by the establishment of a chromatin domain boundary that would prevent the propagation of a silencing chromatin structure towards the promoter [27, 38].
Histone H3 methylations such as the H3K4me3 and H3K36me3 modifications were found to be enriched around the TSS of NFI-occupied genes when compared with control gene groups. This finding is consistent with the model that NFI acts predominantly as an activator of transcription, since H3K4me3 and H3K36me3, but not H3K27me3, were proposed as markers of active gene transcription [4, 6, 39]. This indicates that NFI binding to the upstream regions may contribute to the recruitment of the specific enzymes for the H3K4me3 and H3K36me3 modifications. A genome-wide correlation of the occurrence of H3K27me3 was also observed around TSS occurring close to NFI-bound sites, however it was indistinguishable to that of the control group of genes. This indicates that this correlation results from an enrichment of H3K27me3 around at least some of the TSS, and that NFI is not involved in the recruitment of enzymes mediating this modification. Thus, the enrichment of H3K27me3 modification over the NFI bound genes represents a false positive genome-wide correlation. Interestingly, we also found the H3K9me3 modification to be slightly enriched in the group of NFI bound genes. Although H3K9me3 has been associated with a closed chromatin structure, this suggests that NFI may be involved in the recruitment of enzymes that mediate this modification. Interestingly, this modification was recently associated with a chromatin domain boundary effect at telomeric regions in human cells . In this study, NFI was shown to prevent the propagation of a silencing chromatin structure from the telomere, and the expressed genes protected from telomeric silencing by NFI were shown to have elevated H3K9me3 marks at specific telomeric positions. Thus, we may conclude from these studies that the enrichment in H3K9me3 may be a hallmark of gene expression activation by NFI.
Mouse embryonic fibroblasts (MEF) were extracted from mouse embryos of 14.5 days. Cells from WT (wild-type) and NFI-C knock-out embryos were cultured under the following conditions: 37°C, 5% CO2, DMEM (GIBCO, 41966), Supplementary 10% FBS (GIBCO, Fetal Bovine Serum, qualified origin US, 26140-079), 1% v/v nonessential amino-acids (GIBCO, 11140-035), 1% v/v L-glutamine (GIBCO, 25030-024).
Chromatin Immuno-precipitation (ChIP)
Chromatin was extracted from approximately 20,000,000 primary mouse embryonic fibroblasts grown in culture and cross-linked using 11% formaldehyde. Extracted chromatin was fragmented to the average fragment size of 1000 bp using high-frequency sound sonication on VibraCell-75455 (Bioblock Scientific). ChIP was performed as described before  using the commercial antibody against NFI group of proteins (NFI (H300): sc-5567, SantaCruz Biotechnology). Antibody complexes were precipitated using rProtein A Sepharose Fast Flow (Amersham Biosciences).
ChIP DNA was processed using the contents of the ChIP-Seq Sample Prep Kit (Illumina). Size range of templates was selected by loading the entire processed sample on a 2% agarose gel and excising the gel region of 50-400 bp. PCR amplification of the gel-extracted DNA was performed for 18 cycles using the adapter-specific primers. Each sample was loaded into 3 separate flow cell channels of the Illumina Cluster Station and then subjected to sequencing-by-synthesis on the Illumina Genome Analyzer sequencing system. For each of the samples, sequence reads from independent channels were pooled together.
Clustering and correlation analyses of mapped reads were performed using ChIP-peak and ChIP-cor tools available on the ChIP-Seq Analysis Server [40, 41]. In vivo NFI sites were defined using the ChIP-peak tool and applying the following parameters: window width - 300 bp, vicinity range - 300 bp, peak threshold - 5 tags, count cut-off - 1 tag, repeat filtering-on. Correlation analyses were made using ChIP-cor tool and applying the following parameters: window width - 50 bp, count cut-off - 1, normalization - global. Galaxy tools were used to operate on the genomic intervals [42, 43].
Random sampling algorithm
Random sampling algorithm was written in C++ language and compiled in Microsoft Visual Studio as detailed in the Additional file 1 online. The algorithm uses the computer system date and time as a constantly increasing number for seeding the random number generators at each independent run of the random number generator. Each random number so generated was used to select a single entry line from the dataset to be sub-sampled, with the limitation that a single line of the input file could be selected only once. The source code for random sampling algorithm is available in the additional materials online (Additional file 1).
ChIP-Seq data for the wild type and NFI-C knock-out mouse embryonic fibroblasts were deposited at the Gene Expression Omnibus (GEO) repository under the accession number GSE15844. Gene expression microarray data for the wild type and NFI-C knock-out mouse embryonic fibroblasts were taken from the GEO repository under the accession number GSE15871. ChIP-Seq data for histone methylations in mouse embryonic fibroblasts (H3K4me3, H3K9me3, H3K27me3, H3K36me3) were taken from GEO repository under the accession number GSE12241.
List of Abbreviations
Nuclear Factor I
Nuclear Factor I - C
CAAT box transcription factors
Chromatin immuno-precipitation sequencing
Mouse embryonic fibroblasts
Transcriptional start site
- NFI-C KO:
Nuclear Factor I - C knock out
We would like to acknowledge Fasteris, SA, CH-1228 Plan-les-Ouates, Switzerland for providing fast and accurate service of Illumina Genome Analyzer high-throughput DNA sequencing and for processing the data output of obtained sequence tags through ELAND mapping software pipeline. This work was supported by the grants from the University of Lausanne, Switzerland.
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