DBD––taxonomically broad transcription factor predictions: new content and functionality

Wilson, Derek A.; Charoensawan, Varodom; Kummerfeld, Sarah; Teichmann, Sarah A.

doi:10.1093/nar/gkm964

Cited by 263 publications

(244 citation statements)

References 24 publications

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“…Mammalian genomes encode at least 2000 DNA binding proteins (Wilson et al 2008), and we anticipate that ChIP-seq experiments using antibodies for other TFs would identify binding sites in different subsets of the distal H3K4me1-marked regions than we found associated with STAT1 and FOXA2 in the current work. Consistent with this, we demonstrated that a majority of the large number of distal regions that were symmetrically flanked by H3K4me1 had sequence properties that were expected for regulatory regions.…”

Section: Discussionmentioning

confidence: 72%

Genome-wide relationship between histone H3 lysine 4 mono- and tri-methylation and transcription factor binding

Robertson¹,

Bilenky²,

Tam³

et al. 2008

Genome Res.

175

149

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We characterized the relationship of H3K4me1 and H3K4me3 at distal and proximal regulatory elements by comparing ChIP-seq profiles for these histone modifications and for two functionally different transcription factors: STAT1 in the immortalized HeLa S3 cell line, with and without interferon-gamma (IFNG) stimulation; and FOXA2 in mouse adult liver tissue. In unstimulated and stimulated HeLa cells, respectively, we determined ∼270,000 and ∼301,000 H3K4me1-enriched regions, and ∼54,500 and ∼76,100 H3K4me3-enriched regions. In mouse adult liver, we determined ∼227,000 and ∼34,800 H3K4me1 and H3K4me3 regions. Seventy-five percent of the ∼70,300 STAT1 binding sites in stimulated HeLa cells and 87% of the ∼11,000 FOXA2 sites in mouse liver were distal to known gene TSS; in both cell types, ∼83% of these distal sites were associated with at least one of the two histone modifications, and H3K4me1 was associated with over 96% of marked distal sites. After filtering against predicted transcription start sites, 50% of ∼26,800 marked distal IFNG-stimulated STAT1 binding sites, but 95% of ∼5800 marked distal FOXA2 sites, were associated with H3K4me1 only. Results for HeLa cells generated additional insights into transcriptional regulation involving STAT1. STAT1 binding was associated with 25% of all H3K4me1 regions in stimulated HeLa cells, suggesting that a single transcription factor can interact with an unexpectedly large fraction of regulatory regions. Strikingly, for a large majority of the locations of stimulated STAT1 binding, the dominant H3K4me1/me3 combinations were established before activation, suggesting mechanisms independent of IFNG stimulation and high-affinity STAT1 binding.

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Section: Discussionmentioning

confidence: 72%

Genome-wide relationship between histone H3 lysine 4 mono- and tri-methylation and transcription factor binding

Robertson¹,

Bilenky²,

Tam³

et al. 2008

Genome Res.

175

149

View full text Add to dashboard Cite

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“…TFs were selected from two database resources (Transcriptionfactor.org, PfamTOGo website) as well as previous studies on this topic (6,16,55). In all cases, we defined a univocal one-to-one relationship between TF class and DBD class.…”

Section: Methodsmentioning

confidence: 99%

Transcription factor evolution in eukaryotes and the assembly of the regulatory toolkit in multicellular lineages

Mendoza

Sebé-Pedrós

Šestak

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

198

224

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Transcription factors (TFs) are the main players in transcriptional regulation in eukaryotes. However, it remains unclear what role TFs played in the origin of all of the different eukaryotic multicellular lineages. In this paper, we explore how the origin of TF repertoires shaped eukaryotic evolution and, in particular, their role into the emergence of multicellular lineages. We traced the origin and expansion of all known TFs through the eukaryotic tree of life, using the broadest possible taxon sampling and an updated phylogenetic background. Our results show that the most complex multicellular lineages (i.e., those with embryonic development, Metazoa and Embryophyta) have the most complex TF repertoires, and that these repertoires were assembled in a stepwise manner. We also show that a significant part of the metazoan and embryophyte TF toolkits evolved earlier, in their respective unicellular ancestors. To gain insights into the role of TFs in the development of both embryophytes and metazoans, we analyzed TF expression patterns throughout their ontogeny. The expression patterns observed in both groups recapitulate those of the whole transcriptome, but reveal some important differences. Our comparative genomics and expression data reshape our view on how TFs contributed to eukaryotic evolution and reveal the importance of TFs to the origins of multicellularity and embryonic development.phylotypic stage | Holozoa | LECA T ranscription factors (TFs) are proteins that bind to DNA in a sequence-specific manner (1) and enhance or repress gene expression (2-4). In response to a broad range of stimuli, TFs coordinate many important biological processes, from cell cycle progression and physiological responses, to cell differentiation and development (5, 6). Thus, TFs have a central role in the transcriptional regulation of all cellular organisms, being present in all branches of the tree of life (bacteria, archaea, and eukaryotes). There appears to be a correlation between elaborate regulation of gene expression and the complexity of organisms (7), such that the amount (as a proportion of an organism's total gene content) and diversity of TF proteins is expected to be directly correlated with this complexity (8). Indeed, TFs play a crucial role in multicellular eukaryotes. For example, TFs are the master regulators of embryonic development in embryophytes and metazoans (9), and analyses of their embryonic transcriptional profiles support the presence of a phylotypic stage in both lineages (10-14). These studies have also shown that evolutionarily younger genes tend to be expressed at earlier and later stages of development, whereas the transcriptomes of the middle stages (the phylotypic stage) are dominated by ancient genes (10, 13). It remains to be investigated how the evolutionary age and the expression patterns of the different TFs shift throughout the ontogeny of these lineages and whether TF expression profiles correlate with the general transcriptome profiles.Previous studies have analyzed the evolutionary ...

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Section: Manteiamentioning

confidence: 99%

“…The mapping is based on annotation files provided by Ensembl, NCBI and Affymetrix. Additional functional annotations are obtained from MGI (Bult et al, 2008), ZFIN (Sprague et al, 2006), DBD (Wilson et al, 2008) and KEGG (Kanehisa et al, 2010) by mapping their respective data to the corresponding Ensembl and Entrez gene models in Manteia.The conservation analysis among mouse, chicken and zebrafish was based on ortholog predictions performed with InParanoid (Berglund et al, 2008) and Ensembl Compara. This prediction was used in Manteia to identify and re-annotate poorly annotated gene models using their ortholog data.…”

mentioning

confidence: 99%

Evolutionary plasticity of segmentation clock networks

et al. 2011

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SUMMARYThe vertebral column is a conserved anatomical structure that defines the vertebrate phylum. The periodic or segmental pattern of the vertebral column is established early in development when the vertebral precursors, the somites, are rhythmically produced from presomitic mesoderm (PSM). This rhythmic activity is controlled by a segmentation clock that is associated with the periodic transcription of cyclic genes in the PSM. Comparison of the mouse, chicken and zebrafish PSM oscillatory transcriptomes revealed networks of 40 to 100 cyclic genes mostly involved in Notch, Wnt and FGF signaling pathways. However, despite this conserved signaling oscillation, the identity of individual cyclic genes mostly differed between the three species, indicating a surprising evolutionary plasticity of the segmentation networks. the GeneChip Eukaryotic Small Sample Target Labeling Assay version II, and the second cycle of the Two-Cycle Eukaryotic Target Labeling Assay, as described in the Affymetrix GeneChip Expression Analysis Technical Manual rev5. Amplified RNA samples were hybridized to Affymetrix GeneChip Mouse Genome 430 2.0 arrays. For chicken and zebrafish, total RNA was extracted from PSM pieces using Trizol. PSM pieces contained ~4000 chicken cells or ~400 zebrafish cells. The mRNA of each sample was amplified following a two-cycle linear amplification protocol as described in the Two-Cycle Eukaryotic Target Labeling Assay in the Affymetrix GeneChip Expression Analysis Technical Manual rev5. Briefly, cDNA was synthesized from total RNA using T7 linked to oligo(dT) primers. After second-strand synthesis, cRNA was in vitro transcribed using unlabeled ribonucleotides. Upon a second round of cDNA synthesis using random priming, cRNA was made with biotin-labeled CTP and UTP, yielding 35-135 g of biotinylated cRNA for chicken and 3-6 g for zebrafish. RNA quality was checked after the second round of amplification by measuring the A 260 /A 280 ratio (which ranged from 1.8 to 2.1) and the fragment size with the Agilent Bioanalyser (mean size >1 kb). Five micrograms of RNA from good quality samples was fragmented to less than 200 bp, hybridized to Affymetrix GeneChip chicken genome arrays or GeneChip zebrafish genome arrays according to manufacturer's instructions and scanned with a GeneArray scanner (Agilent G2500A) at the Microarray Core Facility of the Stowers Institute for Medical Research. The array readout was processed using Affymetrix Microarray Suite (MAS) 5.0 and scaled by adjusting the average intensity of each array to a target intensity of 150. The quality control parameters from the Affymetrix array reports were within acceptable limits and highly similar between the arrays. The raw data are available on ArrayExpress with accession E-MTAB-406. Non-biological variance of the microarrays was controlled by two methods: the R package affyPLM (Bolstad et al., 2005) using the normalized unscaled standard error (NUSE) and relative expression (RLE) functions; and principal component analysis (PCA) using Partek ...

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DBD––taxonomically broad transcription factor predictions: new content and functionality

Cited by 263 publications

References 24 publications

Genome-wide relationship between histone H3 lysine 4 mono- and tri-methylation and transcription factor binding

Genome-wide relationship between histone H3 lysine 4 mono- and tri-methylation and transcription factor binding

Transcription factor evolution in eukaryotes and the assembly of the regulatory toolkit in multicellular lineages

Evolutionary plasticity of segmentation clock networks

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