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The Arabidopsis transcription factor NAI1 activates the NAI2 promoter by binding to the G-box motifs.

ABSTRACT: Brassicaceae plants, including Arabidopsis thaliana, develop endoplasmic reticulum (ER)-derived structures called ER bodies, which are involved in chemical defense against herbivores. NAI1 is a basic helix-loop-helix (bHLH) type transcription factor that regulates two downstream genes, NAI2 and BGLU23, that are responsible for the ER body formation and function. Here, we examined the transcription factor function of NAI1, and found that NAI1 binds to the promoter region of NAI2 and activates the NAI2 promoter. The recombinant NAI1 protein recognizes the canonical and non-canonical G-box motifs in the NAI2 promoter. Furthermore, we examined the DNA binding activity of NAI1 toward several E-box motifs in the NAI2 and BGLU23 promoters and found that NAI1 binds to a DNA fragment that includes an E-box motif from the BGLU23 promoter. Subcellular localization of NAI1 was evident in the nucleus, which is consistent with its transcription factor function. Transient expression experiments in Nicotiana benthamiana leaves showed that GFP-NAI1 protein activated the NAI2 promoter by binding to the two G-boxes of the promoter. Disruption of the G-boxes abolished the NAI1-dependent activation of the NAI2 promoter. These results indicate that NAI1 has a DNA binding activity in a motif-dependent manner and suggest that NAI1 regulates NAI2 and BGLU23 gene expressions through binding to these DNA motifs in their promoters.

SUBMITTER: Sarkar S

PROVIDER: S-EPMC7849731 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe use of global gene expression profiling is a well established approach to understand biological processes. One of the major goals of these investigations is to identify sets of genes with similar expression patterns. Such gene signatures may be very informative and reveal new aspects of particular biological processes. A logical and systematic next step is to reduce the identified gene signatures to the regulatory components that induce the relevant gene expression changes. A central issue in this context is to identify transcription factors, or transcription factor binding sites (TFBS), likely to be of importance for the expression of the gene signatures.ResultsWe develop a strategy that efficiently produces TFBS/promoter databases based on user-defined criteria. The resulting databases constitute all genes in the Santa Cruz database and the positions for all TFBS provided by the user as position weight matrices. These databases are then used for two purposes, to identify significant TFBS in the promoters in sets of genes and to identify clusters of co-occurring TFBS. We use two criteria for significance, significantly enriched TFBS in terms of total number of binding sites for the promoters, and significantly present TFBS in terms of the fraction of promoters with binding sites. Significant TFBS are identified by a re-sampling procedure in which the query gene set is compared with typically 10(5) gene lists of similar size randomly drawn from the TFBS/promoter database. We apply this strategy to a large number of published ChIP-Chip data sets and show that the proposed approach faithfully reproduces ChIP-Chip results. The strategy also identifies relevant TFBS when analyzing gene signatures obtained from the MSigDB database. In addition, we show that several TFBS are highly correlated and that co-occurring TFBS define functionally related sets of genes.ConclusionsThe presented approach of promoter analysis faithfully reproduces the results from several ChIP-Chip and MigDB derived gene sets and hence may prove to be an important method in the analysis of gene signatures obtained through ChIP-Chip or global gene expression experiments. We show that TFBS are organized in clusters of co-occurring TFBS that together define highly coherent sets of genes.

Project description:BackgroundClassically, models of DNA-transcription factor binding sites (TFBSs) have been based on relatively few known instances and have treated them as sites of fixed length using position weight matrices (PWMs). Various extensions to this model have been proposed, most of which take account of dependencies between the bases in the binding sites. However, some transcription factors are known to exhibit some flexibility and bind to DNA in more than one possible physical configuration. In some cases this variation is known to affect the function of binding sites. With the increasing volume of ChIP-seq data available it is now possible to investigate models that incorporate this flexibility. Previous work on variable length models has been constrained by: a focus on specific zinc finger proteins in yeast using restrictive models; a reliance on hand-crafted models for just one transcription factor at a time; and a lack of evaluation on realistically sized data sets.ResultsWe re-analysed binding sites from the TRANSFAC database and found motivating examples where our new variable length model provides a better fit. We analysed several ChIP-seq data sets with a novel motif search algorithm and compared the results to one of the best standard PWM finders and a recently developed alternative method for finding motifs of variable structure. All the methods performed comparably in held-out cross validation tests. Known motifs of variable structure were recovered for p53, Stat5a and Stat5b. In addition our method recovered a novel generalised version of an existing PWM for Sp1 that allows for variable length binding. This motif improved classification performance.ConclusionsWe have presented a new gapped PWM model for variable length DNA binding sites that is not too restrictive nor over-parameterised. Our comparison with existing tools shows that on average it does not have better predictive accuracy than existing methods. However, it does provide more interpretable models of motifs of variable structure that are suitable for follow-up structural studies. To our knowledge, we are the first to apply variable length motif models to eukaryotic ChIP-seq data sets and consequently the first to show their value in this domain. The results include a novel motif for the ubiquitous transcription factor Sp1.

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The Arabidopsis transcription factor NAI1 activates the NAI2 promoter by binding to the G-box motifs.

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