Literature DB >> 31639474

Sequence and chromatin determinants of transcription factor binding and the establishment of cell type-specific binding patterns.

Divyanshi Srivastava1, Shaun Mahony2.   

Abstract

Transcription factors (TFs) selectively bind distinct sets of sites in different cell types. Such cell type-specific binding specificity is expected to result from interplay between the TF's intrinsic sequence preferences, cooperative interactions with other regulatory proteins, and cell type-specific chromatin landscapes. Cell type-specific TF binding events are highly correlated with patterns of chromatin accessibility and active histone modifications in the same cell type. However, since concurrent chromatin may itself be a consequence of TF binding, chromatin landscapes measured prior to TF activation provide more useful insights into how cell type-specific TF binding events became established in the first place. Here, we review the various sequence and chromatin determinants of cell type-specific TF binding specificity. We identify the current challenges and opportunities associated with computational approaches to characterizing, imputing, and predicting cell type-specific TF binding patterns. We further focus on studies that characterize TF binding in dynamic regulatory settings, and we discuss how these studies are leading to a more complex and nuanced understanding of dynamic protein-DNA binding activities. We propose that TF binding activities at individual sites can be viewed along a two-dimensional continuum of local sequence and chromatin context. Under this view, cell type-specific TF binding activities may result from either strongly favorable sequence features or strongly favorable chromatin context.
Copyright © 2019 Elsevier B.V. All rights reserved.

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Year:  2019        PMID: 31639474      PMCID: PMC7166147          DOI: 10.1016/j.bbagrm.2019.194443

Source DB:  PubMed          Journal:  Biochim Biophys Acta Gene Regul Mech        ISSN: 1874-9399            Impact factor:   4.490


  227 in total

Review 1.  DNA binding sites: representation and discovery.

Authors:  G D Stormo
Journal:  Bioinformatics       Date:  2000-01       Impact factor: 6.937

2.  Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities.

Authors:  Michael F Berger; Anthony A Philippakis; Aaron M Qureshi; Fangxue S He; Preston W Estep; Martha L Bulyk
Journal:  Nat Biotechnol       Date:  2006-09-24       Impact factor: 54.908

3.  Syntax compensates for poor binding sites to encode tissue specificity of developmental enhancers.

Authors:  Emma K Farley; Katrina M Olson; Wei Zhang; Daniel S Rokhsar; Michael S Levine
Journal:  Proc Natl Acad Sci U S A       Date:  2016-05-06       Impact factor: 11.205

Review 4.  Enhancer function: new insights into the regulation of tissue-specific gene expression.

Authors:  Chin-Tong Ong; Victor G Corces
Journal:  Nat Rev Genet       Date:  2011-03-01       Impact factor: 53.242

5.  Comprehensive genome-wide protein-DNA interactions detected at single-nucleotide resolution.

Authors:  Ho Sung Rhee; B Franklin Pugh
Journal:  Cell       Date:  2011-12-09       Impact factor: 41.582

6.  Remodeling of the enhancer landscape during macrophage activation is coupled to enhancer transcription.

Authors:  Minna U Kaikkonen; Nathanael J Spann; Sven Heinz; Casey E Romanoski; Karmel A Allison; Joshua D Stender; Hyun B Chun; David F Tough; Rab K Prinjha; Christopher Benner; Christopher K Glass
Journal:  Mol Cell       Date:  2013-08-08       Impact factor: 17.970

7.  Transcriptional enhancer elements in the mouse immunoglobulin heavy chain locus.

Authors:  M Mercola; X F Wang; J Olsen; K Calame
Journal:  Science       Date:  1983-08-12       Impact factor: 47.728

8.  Proneural factors Ascl1 and Neurog2 contribute to neuronal subtype identities by establishing distinct chromatin landscapes.

Authors:  Begüm Aydin; Akshay Kakumanu; Mary Rossillo; Mireia Moreno-Estellés; Görkem Garipler; Niels Ringstad; Nuria Flames; Shaun Mahony; Esteban O Mazzoni
Journal:  Nat Neurosci       Date:  2019-05-13       Impact factor: 28.771

9.  An expansive human regulatory lexicon encoded in transcription factor footprints.

Authors:  Shane Neph; Jeff Vierstra; Andrew B Stergachis; Alex P Reynolds; Eric Haugen; Benjamin Vernot; Robert E Thurman; Sam John; Richard Sandstrom; Audra K Johnson; Matthew T Maurano; Richard Humbert; Eric Rynes; Hao Wang; Shinny Vong; Kristen Lee; Daniel Bates; Morgan Diegel; Vaughn Roach; Douglas Dunn; Jun Neri; Anthony Schafer; R Scott Hansen; Tanya Kutyavin; Erika Giste; Molly Weaver; Theresa Canfield; Peter Sabo; Miaohua Zhang; Gayathri Balasundaram; Rachel Byron; Michael J MacCoss; Joshua M Akey; M A Bender; Mark Groudine; Rajinder Kaul; John A Stamatoyannopoulos
Journal:  Nature       Date:  2012-09-06       Impact factor: 49.962

10.  Sequence specificity incompletely defines the genome-wide occupancy of Myc.

Authors:  Jiannan Guo; Tiandao Li; Joshua Schipper; Kyle A Nilson; Francis K Fordjour; Jeffrey J Cooper; Raluca Gordân; David H Price
Journal:  Genome Biol       Date:  2014       Impact factor: 13.583

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  5 in total

Review 1.  Generating specificity in genome regulation through transcription factor sensitivity to chromatin.

Authors:  Luke Isbel; Ralph S Grand; Dirk Schübeler
Journal:  Nat Rev Genet       Date:  2022-07-12       Impact factor: 59.581

2.  ZNF410 Uniquely Activates the NuRD Component CHD4 to Silence Fetal Hemoglobin Expression.

Authors:  Xianjiang Lan; Ren Ren; Ruopeng Feng; Lana C Ly; Yemin Lan; Zhe Zhang; Nicholas Aboreden; Kunhua Qin; John R Horton; Jeremy D Grevet; Thiyagaraj Mayuranathan; Osheiza Abdulmalik; Cheryl A Keller; Belinda Giardine; Ross C Hardison; Merlin Crossley; Mitchell J Weiss; Xiaodong Cheng; Junwei Shi; Gerd A Blobel
Journal:  Mol Cell       Date:  2020-12-09       Impact factor: 19.328

3.  Motif models proposing independent and interdependent impacts of nucleotides are related to high and low affinity transcription factor binding sites in Arabidopsis.

Authors:  Anton V Tsukanov; Victoria V Mironova; Victor G Levitsky
Journal:  Front Plant Sci       Date:  2022-07-28       Impact factor: 6.627

4.  Toward a base-resolution panorama of the in vivo impact of cytosine methylation on transcription factor binding.

Authors:  Aldo Hernandez-Corchado; Hamed S Najafabadi
Journal:  Genome Biol       Date:  2022-07-07       Impact factor: 17.906

5.  Domain-adaptive neural networks improve cross-species prediction of transcription factor binding.

Authors:  Kelly Cochran; Divyanshi Srivastava; Avanti Shrikumar; Akshay Balsubramani; Ross C Hardison; Anshul Kundaje; Shaun Mahony
Journal:  Genome Res       Date:  2022-01-18       Impact factor: 9.438

  5 in total

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