Distinguishing Direct versus Indirect Transcription Factor-DNA Interactions

Gordân, Raluca; Hartemink, Alexander J.; Bulyk, Martha L.

doi:10.1007/978-3-642-12683-3_39

Cited by 30 publications

(38 citation statements)

References 41 publications

(42 reference statements)

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“…When predicted genomic binding sites are not observed in ChIP-seq data, one can usually assume that those locations are not accessible. But when binding is observed in locations without predicted binding sites, or with only very low predicted affinity sites, that implies either indirect or cooperative binding mediated through some other factor(s) that binds directly to the DNA (Gordan et al 2009). Such indirect and cooperative binding events can lead to the discovery of interacting TFs that coordinately control gene expression.…”

Section: Discussionmentioning

confidence: 99%

“…It is possible to gain mechanistic insight by comparing the intrinsic TF specificity measured in vitro, which reflects only the bimolecular interaction between the TF and DNA, with in vivo binding locations, which are influenced by many other cellular factors. For example, in vivo binding to a genomic region without strong binding sites indicates that the TF is either binding indirectly (the TF itself is not bound to DNA, but is in complex with some other factor that is bound to DNA) or bound to a weak site that is stabilized by a cooperative interaction with another factor (Gordan et al 2009). …”

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Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

et al. 2012

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Identifying transcription factor (TF) binding sites is essential for understanding regulatory networks. The specificity of most TFs is currently modeled using position weight matrices (PWMs) that assume the positions within a binding site contribute independently to binding affinity for any site. Extensive, high-throughput quantitative binding assays let us examine, for the first time, the independence assumption for many TFs. We find that the specificity of most TFs is well fit with the simple PWM model, but in some cases more complex models are required. We introduce a binding energy model (BEM) that can include energy parameters for nonindependent contributions to binding affinity. We show that in most cases where a PWM is not sufficient, a BEM that includes energy parameters for adjacent dinucleotide contributions models the specificity very well. Having more accurate models of specificity greatly improves the interpretation of in vivo TF localization data, such as from chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments.

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Section: Discussionmentioning

confidence: 99%

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Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

et al. 2012

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“…Interestingly, sequence analysis on ChIPseq data in yeast showed that not all identified peaks are directly bound by their TFs (Gordân et al 2009). Similarly, among the hundreds of TF ChIP-seq data of ENCODE, only a fraction of peaks were found to contain the canonical recognition sequence of their respective TF .…”

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Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic

et al. 2016

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Transcription factors regulate their target genes by binding to regulatory regions in the genome. Although the binding preferences of TP53 are known, it remains unclear what distinguishes functional enhancers from nonfunctional binding. In addition, the genome is scattered with recognition sequences that remain unoccupied. Using two complementary techniques of multiplex enhancer-reporter assays, we discovered that functional enhancers could be discriminated from nonfunctional binding events by the occurrence of a single TP53 canonical motif. By combining machine learning with a meta-analysis of TP53 ChIP-seq data sets, we identified a core set of more than 1000 responsive enhancers in the human genome. This TP53 cistrome is invariably used between cell types and experimental conditions, whereas differences among experiments can be attributed to indirect nonfunctional binding events. Our data suggest that TP53 enhancers represent a class of unsophisticated cell-autonomous enhancers containing a single TP53 binding site, distinct from complex developmental enhancers that integrate signals from multiple transcription factors.

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“…For example, high-affinity TF binding sites that are not occupied in vivo might indicate that these sites are covered with nucleosomes [the nucleosome is a fundamental unit of chromatin that packages 147 base pairs (bp) of genomic DNA into a tightly bent left-handed superhelix (Richmond and Davey 2003)]. Conversely, low-affinity sites bound in vivo might be a sign of indirect or cooperative binding (Gordan et al 2009 mechanically induced trapping of molecular interactions (MITOMI) (Maerkl and Quake 2007;Fordyce et al 2010) and protein-binding microarrays (PBM) (Bulyk et al 2001;Berger et al 2006). The MITOMI device uses microfluidics to simultaneously measure binding affinities of a TF to a few thousand DNA probe sequences.…”

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confidence: 99%

“…For example, high-affinity TF binding sites that are not occupied in vivo might indicate that these sites are covered with nucleosomes [the nucleosome is a fundamental unit of chromatin that packages 147 base pairs (bp) of genomic DNA into a tightly bent left-handed superhelix (Richmond and Davey 2003)]. Conversely, low-affinity sites bound in vivo might be a sign of indirect or cooperative binding (Gordan et al 2009). Recently, several new technologies have been developed that enable high-throughput in vitro determination of protein-DNA binding affinities (see Stormo and Zhao 2010 for a review).…”

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A Biophysical Approach to Predicting Protein–DNA Binding Energetics

Locke

Morozov

2015

Genetics

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Sequence-specific interactions between proteins and DNA play a central role in DNA replication, repair, recombination, and control of gene expression. These interactions can be studied in vitro using microfluidics, protein-binding microarrays (PBMs), and other high-throughput techniques. Here we develop a biophysical approach to predicting protein-DNA binding specificities from high-throughput in vitro data. Our algorithm, called BindSter, can model alternative DNA-binding modes and multiple protein species competing for access to DNA, while rigorously taking into account all sterically allowed configurations of DNA-bound factors. BindSter can be used with a hierarchy of protein-DNA interaction models of increasing complexity, including contributions of mononucleotides, dinucleotides, and longer words to the total protein-DNA binding energy. We observe that the quality of BindSter predictions does not change significantly as some of the energy parameters vary over a sizable range. To take this degeneracy into account, we have developed a graphical representation of parameter uncertainties called IntervalLogo. We find that our simplest model, in which each nucleotide in the binding site is treated independently, performs better than previous biophysical approaches. The extensions of this model, in which contributions of longer words are also considered, result in further improvements, underscoring the importance of higher-order effects in protein-DNA energetics. In contrast, we find little evidence of multiple binding modes for the transcription factors (TFs) and experimental conditions in our data set. Furthermore, there is limited consistency in predictions for the same TF based on microfluidics and PBM data.KEYWORDS gene regulation; protein-DNA interactions; thermodynamic modeling; transcription factor S EQUENCE-SPECIFIC interactions between proteins and genomic DNA control numerous cellular processes. An important class of DNA-binding proteins is transcription factors (TFs), which regulate expression of their target genes. Knowledge of in vivo binding locations of TFs is important for reconstructing regulatory networks of gene transcription and for understanding which factors control which genes. These locations can be mapped genome-wide, using chromatin immunoprecipitation followed by microarray hybridization (ChIP-chip) or sequencing (ChIP-seq) (Ren et al. 2000;Park 2009). The latest version of this technique, ChIP-exo, uses exonucleases to trim immunoprecipitated DNA fragments to precise locations of bound proteins, achieving single-base-pair precision (Rhee and Pugh 2011). Many factors influence TF binding locations in living cells, including chromatin structure, which strongly modulates DNA accessibility (Felsenfeld and Groudine 2003), and cooperative or competing interactions with other DNAbinding proteins (Siggers and Gordan 2014). However, the primary determinant of TF binding locations is intrinsic DNA sequence specificity: TFs can recognize and bind their cognate sites on the background of ...

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Distinguishing Direct versus Indirect Transcription Factor-DNA Interactions

Cited by 30 publications

References 41 publications

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic

A Biophysical Approach to Predicting Protein–DNA Binding Energetics

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