SMiLE-seq identifies binding motifs of single and dimeric transcription factors

Isakova, Alina; Groux, Romain; Imbeault, Michaël; Rainer, Pernille; Alpern, Daniel; Dainese, Riccardo; Ambrosini, Giovanna; Trono, Didier; Bücher, Philipp; Deplancke, Bart

doi:10.1038/nmeth.4143

Cited by 104 publications

(141 citation statements)

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“…In an effort to overcome the limitations discussed above, we performed receiver operating characteristic (ROC) curve analyses, an approach that is widely used to evaluate the predictive power of DNA-binding models with respect to in vivo ChIP-seq data (Kulakovskiy et al, 2016; Orenstein and Shamir, 2014; Weirauch et al, 2013; Alipanahi et al, 2015; Mariani et al, 2017; Gordan et al, 2009; Arvey et al, 2012; Isakova et al, 2017). In brief, this approach allowed us to evaluate how well our iMADS models of differential specificity can distinguish between TF1and TF2-preferred ChIP-seq peaks, defined as the top N% and bottom N% of peaks, respectively, sorted according to log ratio of ChIP signals (Figure 5H).…”

Section: Resultsmentioning

confidence: 99%

Divergence in DNA Specificity among Paralogous Transcription Factors Contributes to Their Differential In Vivo Binding

et al. 2018

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SUMMARY Paralogous transcription factors (TFs) are oftentimes reported to have identical DNA-binding motifs, despite the fact that they perform distinct regulatory functions. Differential genomic targeting by paralogous TFs is generally assumed to be due to interactions with protein co-factors or the chromatin environment. Using a computational-experimental framework called iMADS (integrative modeling and analysis of differential specificity), we show that, contrary to previous assumptions, paralogous TFs bind differently to genomic target sites even in vitro. We used iMADS to quantify, model, and analyze specificity differences between 11 TFs from 4 protein families. We found that paralogous TFs have diverged mainly at mediumand low-affinity sites, which are poorly captured by current motif models. We identify sequence and shape features differentially preferred by paralogous TFs, and we show that the intrinsic differences in specificity among paralogous TFs contribute to their differential in vivo binding. Thus, our study represents a step forward in deciphering the molecular mechanisms of differential specificity in TF families.

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Section: Resultsmentioning

confidence: 99%

Divergence in DNA Specificity among Paralogous Transcription Factors Contributes to Their Differential In Vivo Binding

et al. 2018

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“…Of course, additivity is unlikely to be completely accurate, but there are still only 3 m + 9( m − 1) single variants plus double variants at adjacent positions, where the non-additivity is likely to be most prevalent. But multiple high-throughput methods are now available that provide quantitative binding data from which accurate energy models can be obtained by using appropriate algorithms [16, 18–20, 25, 27, 28, 31, 37, 38, 53, 54, 62]. From sufficiently abundant and accurate quantitative binding data one can even skip the modeling and just use the list of relative binding energies to all possible sites (or at least the highest affinity sites that are likely to function as regulatory sites), avoiding approximations entirely (to the degree allowed by the measurement accuracy).…”

Section: Discussionmentioning

confidence: 99%

Inherent limitations of probabilistic models for protein-DNA binding specificity

Ruan

Stormo

2017

PLoS Comput Biol

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The specificities of transcription factors are most commonly represented with probabilistic models. These models provide a probability for each base occurring at each position within the binding site and the positions are assumed to contribute independently. The model is simple and intuitive and is the basis for many motif discovery algorithms. However, the model also has inherent limitations that prevent it from accurately representing true binding probabilities, especially for the highest affinity sites under conditions of high protein concentration. The limitations are not due to the assumption of independence between positions but rather are caused by the non-linear relationship between binding affinity and binding probability and the fact that independent normalization at each position skews the site probabilities. Generally probabilistic models are reasonably good approximations, but new high-throughput methods allow for biophysical models with increased accuracy that should be used whenever possible.

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“…1E). Furthermore, SMILE-seq (12) revealed that ZNF417 and ZNF587 had a higher affinity for methylated than unmethylated versions of this sequence ( fig. S2F).…”

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Primate-restricted KRAB zinc finger proteins and target retrotransposons control gene expression in human neurons

Trono

Turelli

Playfoot

et al. 2019

Preprint

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In the first days of embryogenesis, transposable element-embedded regulatory sequences (TEeRS) are silenced by Kruppel-associated box (KRAB)-zinc finger proteins (KZFPs).Many TEeRS are subsequently coopted in transcription networks, but how KZFPs influence this process is largely unknown. We identify ZNF417 and ZNF587 as primate-specific KZFPs repressing HERVK (human endogenous retrovirus K) and SVA (SINE-VNTR-Alu) integrants in human embryonic stem cells (ESC). Expressed in specific regions of the human developing and adult brain, ZNF417/587 keep controlling TEeRS in ESC-derived neurons and brain organoids, secondarily influencing the differentiation and neurotransmission profile of neurons and preventing the induction of neurotoxic retroviral proteins and an interferonlike response. Thus, evolutionarily recent KZFPs and their TE targets partner up to influence human neuronal differentiation and physiology.One Sentence Summary: Young transposable elements and their protein controllers team up to regulate the differentiation and function of human neurons. Main Text:Some 4.5 million transposable element (TE)-derived sequences are disseminated across the human genome, many of which integrated within the last few tens of million years (1). TEs are typically enriched in transcription factor (TF) binding sites, and correspondingly influence gene expression in a broad range of biological events (2-5). However, TEeRS are silenced during the earliest phase of embryogenesis by KZFPs, which dock KAP1 (KRAB-associated protein 1, a.k.a. TRIM28) and associated heterochromatin inducers to TE loci (6-8). The rapid evolutionary selection of KZFP genes was initially interpreted as solely reflecting the host component of an arms race, but recent data suggest that KZFPs team up with TEs to build species-restricted layers of epigenetic regulation (8,9). The present work provides direct support for this model.We previously determined that a 35bp-long TE sequence encompassing the HERVK14C primer binding site (PBS)-encoding region (coined HERVK-R) confers KAP1-induced repression to a nearby PGK promoter in hESC (10). As part of a large-scale screen, we identified ZNF417 and ZNF587 as selectively enriched at loci containing this HERVK sequence (9). Depleting these two KZFPs from hESC restored expression of an HERVK-Rcontaining PGK-GFP lentivector (LV) (Fig. 1A), while producing them in murine ESCs silenced this vector, demonstrating the sequence-specific repressor potential of ZNF417 and ZNF587 and the likely absence of mouse orthologue ( fig. S1A). Phylogenetic analyses confirmed that ZNF417 emerged in the human ancestral genome ahead of the New World Monkey split 43 million years ago and that ZNF587 arose by duplication some 24 million years later ( fig. S1, B and C). ZNF417 and ZNF587 display 98% amino acid homology with some

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SMiLE-seq identifies binding motifs of single and dimeric transcription factors

Cited by 104 publications

References 47 publications

Divergence in DNA Specificity among Paralogous Transcription Factors Contributes to Their Differential In Vivo Binding

Divergence in DNA Specificity among Paralogous Transcription Factors Contributes to Their Differential In Vivo Binding

Inherent limitations of probabilistic models for protein-DNA binding specificity

Primate-restricted KRAB zinc finger proteins and target retrotransposons control gene expression in human neurons

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