Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo

By Gong-Hong Wei, Gwenael Badis, Michael F. Berger, Teemu Kivioja, Kimmo Palin, Martin Enge, Martin Bonke, A. Jolma, Markku Varjosalo, Andrew R. Gehrke and 9 more. 2010

1,163 citations recorded by the source corpus.

Members of the large ETS family of transcription factors (TFs) have highly similar DNA‐binding domains (DBDs)—yet they have diverse functions and activities in physiology and oncogenesis. Some differences in DNA‐binding preferences within this family have been described, but they have not been analysed systematically, and their contributions to targeting remain largely uncharacterized. We report here the DNA‐binding profiles for all human and mouse ETS factors, which we generated using two different methods: a high‐throughput microwell‐based TF DNA‐binding specificity assay, and protein‐binding microarrays (PBMs). Both approaches reveal that the ETS‐binding profiles cluster into four distinct classes, and that all ETS factors linked to cancer, ERG, ETV1, ETV4 and FLI1, fall into just one of these classes. We identify amino‐acid residues that are critical for the differences in specificity between all the classes, and confirm the specificities in vivo using chromatin immunoprecipitation followed by sequencing (ChIP‐seq) for a member of each class. The results indicate that even relatively small differences in in vitro binding specificity of a TF contribute to site selectivity in vivo.

Subfields: Immunology and Allergy, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Immunology and Allergy

In Molecular Biology

Citations in the corpus

Conservation of transcription factor binding specificities across 600 million years of bilateria evolution (2015, background): “Amino-acid sequence similarity predicts TF DNA-binding specificity As suggested by previous studies (Wei et al., 2010; Jolma et al., 2013; Weirauch et al., 2014), different TF structural families had clearly divergent specificities, and amino-acid sequence similarity was predictive of overall DNA…”
The Human Transcription Factors. (2018, positive): “In recent years, the field has advanced substantially with dramatic expansions in data collection, including hundreds of motifs generated in vitro (Badis et al., 2009; Jolma et al., 2013; Wei et al., 2010; Weirauch et al., 2013, 2014; Yin et al., 2017).”
Evaluation of methods for modeling transcription-factor sequence specificity (2013, background): “…binds in vivo (others include cofactors and DNA accessibility); nonetheless, motifs consistent with those obtained in vitro can often be derived directly from in vivo data 7, 30, 31 , indicating that the intrinsic sequence specificity of TFs is a major factor in controlling its DNA binding in vivo.”
Physics of adherent cells (2013, background): “For the FA plaque, plausibility of this unusual property has some experimental support (von Wichert et al., 2003).”
A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data (2014, methodology): “In an additional test on two other PBM data sets covering 115 human and mouse E26 transformation-specific (ETS) and homeodomain TFs tested on a single array (19,20), HT-SELEX-derived models achieved an average AUC of 0.”
Annotation of functional variation in personal genomes using RegulomeDB (2012, methodology): “Inaddition, we have included transcriptionfactor ChIP-seq data availablefrom the NCBI Sequence Read Archive (Hollenhorst et al. 2009; Jolma et al. 2010; Verzi et al. 2010; Wei et al. 2010; Hu et al. 2011; Lo et al. 2011; Novershtern et al. 2011; Palii et al. 2011; Yu et al. 2011).”
DNA-binding specificities of human transcription factors. (2013, positive): “We have previously classified the ETS family of TFs into four classes based on two independent analyses of their binding preferences (Wei et al., 2010).”
Latent enhancers activated by stimulation in differentiated cells. (2013, neutral): “First, using in vitro determined binding affinities (Wei et al., 2010), we computed the Pu.1 binding affinities for the consensus sites found in the different classes of enhancers.”