1932

Abstract

Eukaryotic transcription factors (TFs) from the same structural family tend to bind similar DNA sequences, despite the ability of these TFs to execute distinct functions in vivo. The cell partly resolves this specificity paradox through combinatorial strategies and the use of low-affinity binding sites, which are better able to distinguish between similar TFs. However, because these sites have low affinity, it is challenging to understand how TFs recognize them in vivo. Here, we summarize recent findings and technological advancements that allow for the quantification and mechanistic interpretation of TF recognition across a wide range of affinities. We propose a model that integrates insights from the fields of genetics and cell biology to provide further conceptual understanding of TF binding specificity. We argue that in eukaryotes, target specificity is driven by an inhomogeneous 3D nuclear distribution of TFs and by variation in DNA binding affinity such that locally elevated TF concentration allows low-affinity binding sites to be functional.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-cellbio-100617-062719
2019-10-06
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/cellbio/35/1/annurev-cellbio-100617-062719.html?itemId=/content/journals/10.1146/annurev-cellbio-100617-062719&mimeType=html&fmt=ahah

Literature Cited

  1. Abe N, Dror I, Yang L, Slattery M, Zhou T et al. 2015. Deconvolving the recognition of DNA shape from sequence. Cell 161:307–18
    [Google Scholar]
  2. Aishima J, Gitti RK, Noah JE, Gan HH, Schlick T, Wolberger C 2002. A Hoogsteen base pair embedded in undistorted B-DNA. Nucleic Acids Res 30:5244–52
    [Google Scholar]
  3. Alberti S, Gladfelter A, Mittag T 2019. Considerations and challenges in studying liquid-liquid phase separation and biomolecular condensates. Cell 176:419–34
    [Google Scholar]
  4. Alipanahi B, Delong A, Weirauch MT, Frey BJ 2015. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33:831–38
    [Google Scholar]
  5. Anderson WF, Ohlendorf DH, Takeda Y, Matthews BW 1981. Structure of the cro repressor from bacteriophage lambda and its interaction with DNA. Nature 290:754–58
    [Google Scholar]
  6. Ansari AZ, Peterson-Kaufman KJ. 2011. A partner evokes latent differences between Hox proteins. Cell 147:1220–21
    [Google Scholar]
  7. Arnosti DN, Kulkarni MM. 2005. Transcriptional enhancers: intelligent enhanceosomes or flexible billboards?. J. Cell Biochem. 94:890–98
    [Google Scholar]
  8. Badis G, Berger MF, Philippakis AA, Talukder S, Gehrke AR et al. 2009. Diversity and complexity in DNA recognition by transcription factors. Science 324:1720–23
    [Google Scholar]
  9. Banani SF, Rice AM, Peeples WB, Lin Y, Jain S et al. 2016. Compositional control of phase-separated cellular bodies. Cell 166:651–63
    [Google Scholar]
  10. Barazandeh M, Lambert SA, Albu M, Hughes TR 2018. Comparison of ChIP-seq data and a reference motif set for human KRAB C2H2 zinc finger proteins. G3 8:219–29
    [Google Scholar]
  11. Barski A, Cuddapah S, Cui K, Roh TY, Schones DE et al. 2007. High-resolution profiling of histone methylations in the human genome. Cell 129:823–37
    [Google Scholar]
  12. Bartlett A, O'Malley RC, Huang SC, Galli M, Nery JR et al. 2017. Mapping genome-wide transcription-factor binding sites using DAP-seq. Nat. Protoc. 12:1659–72
    [Google Scholar]
  13. Berg OG, von Hippel PH 1987. Selection of DNA binding sites by regulatory proteins. Statistical-mechanical theory and application to operators and promoters. J. Mol. Biol. 193:723–50
    [Google Scholar]
  14. Berger MF, Badis G, Gehrke AR, Talukder S, Philippakis AA et al. 2008. Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences. Cell 133:1266–76
    [Google Scholar]
  15. Bhimsaria D, Rodriguez-Martinez JA, Pan J, Roston D, Korkmaz EN et al. 2018. Specificity landscapes unmask submaximal binding site preferences of transcription factors. PNAS 115:E10586–95
    [Google Scholar]
  16. Boettiger AN, Bintu B, Moffitt JR, Wang S, Beliveau BJ et al. 2016. Super-resolution imaging reveals distinct chromatin folding for different epigenetic states. Nature 529:418–22
    [Google Scholar]
  17. Boija A, Klein IA, Sabari BR, Dall'Agnese A, Coffey EL et al. 2018. Transcription factors activate genes through the phase-separation capacity of their activation domains. Cell 175:1842–55.e16
    [Google Scholar]
  18. Boisclair Lachance JF, Webber JL, Hong L, Dinner AR, Rebay I 2018. Cooperative recruitment of Yan via a high-affinity ETS supersite organizes repression to confer specificity and robustness to cardiac cell fate specification. Genes Dev 32:389–401
    [Google Scholar]
  19. Brewster RC, Weinert FM, Garcia HG, Song D, Rydenfelt M, Phillips R 2014. The transcription factor titration effect dictates level of gene expression. Cell 156:1312–23
    [Google Scholar]
  20. Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ 2013. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10:1213–18
    [Google Scholar]
  21. Bulyk ML. 2007. Protein binding microarrays for the characterization of DNA-protein interactions. Adv. Biochem. Eng. Biotechnol. 104:65–85
    [Google Scholar]
  22. Burglin TR, Affolter M. 2016. Homeodomain proteins: an update. Chromosoma 125:497–521
    [Google Scholar]
  23. Bussemaker HJ, Foat BC, Ward LD 2007. Predictive modeling of genome-wide mRNA expression: from modules to molecules. Annu. Rev. Biophys. Biomol. Struct. 36:329–47
    [Google Scholar]
  24. Campbell G, Tomlinson A. 1998. The roles of the homeobox genes aristaless and Distal-less in patterning the legs and wings of Drosophila. Development 125:4483–93
    [Google Scholar]
  25. Carroll PA, Freie BW, Mathsyaraja H, Eisenman RN 2018. The MYC transcription factor network: balancing metabolism, proliferation and oncogenesis. Front. Med. 12:412–25
    [Google Scholar]
  26. Charoensawan V, Wilson D, Teichmann SA 2010. Genomic repertoires of DNA-binding transcription factors across the tree of life. Nucleic Acids Res 38:7364–77
    [Google Scholar]
  27. Cheetham SW, Gruhn WH, van den Ameele J, Krautz R, Southall TD et al. 2018. Targeted DamID reveals differential binding of mammalian pluripotency factors. Development 145:dev170209
    [Google Scholar]
  28. Chen J, Zhang Z, Li L, Chen BC, Revyakin A et al. 2014. Single-molecule dynamics of enhanceosome assembly in embryonic stem cells. Cell 156:1274–85
    [Google Scholar]
  29. Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X 2015. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348:aaa6090
    [Google Scholar]
  30. Chong S, Dugast-Darzacq C, Liu Z, Dong P, Dailey GM et al. 2018. Imaging dynamic and selective low-complexity domain interactions that control gene transcription. Science 361:eaar2555
    [Google Scholar]
  31. Cisse II, Izeddin I, Causse SZ, Boudarene L, Senecal A et al. 2013. Real-time dynamics of RNA polymerase II clustering in live human cells. Science 341:664–67
    [Google Scholar]
  32. Crocker J, Abe N, Rinaldi L, McGregor AP, Frankel N et al. 2015. Low affinity binding site clusters confer hox specificity and regulatory robustness. Cell 160:191–203
    [Google Scholar]
  33. Crocker J, Noon EP, Stern DL 2016. The soft touch: low-affinity transcription factor binding sites in development and evolution. Curr. Top. Dev. Biol. 117:455–69
    [Google Scholar]
  34. Datta RR, Ling J, Kurland J, Ren X, Xu Z et al. 2018. A feed-forward relay integrates the regulatory activities of Bicoid and Orthodenticle via sequential binding to suboptimal sites. Genes Dev 32:723–36
    [Google Scholar]
  35. Davey NE, Van Roey K, Weatheritt RJ, Toedt G, Uyar B et al. 2012. Attributes of short linear motifs. Mol. Biosyst. 8:268–81
    [Google Scholar]
  36. Dekker J, Rippe K, Dekker M, Kleckner N 2002. Capturing chromosome conformation. Science 295:1306–11
    [Google Scholar]
  37. Dierickx P, Van Laake LW, Geijsen N 2018. Circadian clocks: from stem cells to tissue homeostasis and regeneration. EMBO Rep 19:18–28
    [Google Scholar]
  38. Dixon JR, Selvaraj S, Yue F, Kim A, Li Y et al. 2012. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485:376–80
    [Google Scholar]
  39. Domcke S, Bardet AF, Ginno PA, Hartl D, Burger L, Schubeler D 2015. Competition between DNA methylation and transcription factors determines binding of NRF1. Nature 528:575–79
    [Google Scholar]
  40. Erdel F, Rippe K. 2018. Formation of chromatin subcompartments by phase separation. Biophys. J. 114:2262–70
    [Google Scholar]
  41. Ezer D, Zabet NR, Adryan B 2014. Homotypic clusters of transcription factor binding sites: a model system for understanding the physical mechanics of gene expression. Comput. Struct. Biotechnol. J. 10:63–69
    [Google Scholar]
  42. Farley EK, Olson KM, Zhang W, Brandt AJ, Rokhsar DS, Levine MS 2015. Suboptimization of developmental enhancers. Science 350:325–28
    [Google Scholar]
  43. Farley EK, Olson KM, Zhang W, Rokhsar DS, Levine MS 2016. Syntax compensates for poor binding sites to encode tissue specificity of developmental enhancers. PNAS 113:6508–13
    [Google Scholar]
  44. Fedotova AA, Bonchuk AN, Mogila VA, Georgiev PG 2017. C2H2 zinc finger proteins: the largest but poorly explored family of higher eukaryotic transcription factors. Acta Nat 9:47–58
    [Google Scholar]
  45. Foat BC, Morozov AV, Bussemaker HJ 2006. Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE. Bioinformatics 22:e141–49
    [Google Scholar]
  46. Fraser J, Williamson I, Bickmore WA, Dostie J 2015. An overview of genome organization and how we got there: from FISH to Hi-C. Microbiol. Mol. Biol. Rev. 79:347–72
    [Google Scholar]
  47. Funk WD, Pak DT, Karas RH, Wright WE, Shay JW 1992. A transcriptionally active DNA-binding site for human p53 protein complexes. Mol. Cell. Biol. 12:2866–71
    [Google Scholar]
  48. Furlong EEM, Levine M. 2018. Developmental enhancers and chromosome topology. Science 361:1341–45
    [Google Scholar]
  49. Gao A, Shrinivas K, Lepeudry P, Suzuki HI, Sharp PA, Chakraborty AK 2018. Evolution of weak cooperative interactions for biological specificity. PNAS 115:E11053–60
    [Google Scholar]
  50. Garvie CW, Wolberger C. 2001. Recognition of specific DNA sequences. Mol. Cell 8:937–46
    [Google Scholar]
  51. Gaudet J, Mango SE. 2002. Regulation of organogenesis by the Caenorhabditis elegans FoxA protein PHA-4. Science 295:821–25
    [Google Scholar]
  52. Gerland U, Moroz JD, Hwa T 2002. Physical constraints and functional characteristics of transcription factor–DNA interaction. PNAS 99:12015–20
    [Google Scholar]
  53. Gilbert W, Maxam A. 1973. The nucleotide sequence of the lac operator. PNAS 70:3581–84
    [Google Scholar]
  54. Gilbert W, Muller-Hill B. 1966. Isolation of the lac repressor. PNAS 56:1891–98
    [Google Scholar]
  55. Giorgetti L, Heard E. 2016. Closing the loop: 3C versus DNA FISH. Genome Biol 17:215
    [Google Scholar]
  56. Gordan R, Shen N, Dror I, Zhou T, Horton J et al. 2013. Genomic regions flanking E-box binding sites influence DNA binding specificity of bHLH transcription factors through DNA shape. Cell Rep 3:1093–104
    [Google Scholar]
  57. Gotea V, Visel A, Westlund JM, Nobrega MA, Pennacchio LA, Ovcharenko I 2010. Homotypic clusters of transcription factor binding sites are a key component of human promoters and enhancers. Genome Res 20:565–77
    [Google Scholar]
  58. Guertin MJ, Lis JT. 2013. Mechanisms by which transcription factors gain access to target sequence elements in chromatin. Curr. Opin. Genet. Dev. 23:116–23
    [Google Scholar]
  59. He Q, Johnston J, Zeitlinger J 2015. ChIP-nexus enables improved detection of in vivo transcription factor binding footprints. Nat. Biotechnol. 33:395–401
    [Google Scholar]
  60. Hesselberth JR, Chen X, Zhang Z, Sabo PJ, Sandstrom R et al. 2009. Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6:283–89
    [Google Scholar]
  61. Hnisz D, Shrinivas K, Young RA, Chakraborty AK, Sharp PA 2017. A phase separation model for transcriptional control. Cell 169:13–23
    [Google Scholar]
  62. Hochschild A, Douhan J 3rd, Ptashne M 1986. How lambda repressor and lambda Cro distinguish between OR1 and OR3. Cell 47:807–16
    [Google Scholar]
  63. Hume MA, Barrera LA, Gisselbrecht SS, Bulyk ML 2015. UniPROBE, update 2015: new tools and content for the online database of protein-binding microarray data on protein-DNA interactions. Nucleic Acids Res 43:D117–22
    [Google Scholar]
  64. Hyman AA, Weber CA, Jülicher F 2014. Liquid-liquid phase separation in biology. Annu. Rev. Cell Dev. Biol. 30:39–58
    [Google Scholar]
  65. Isakova A, Groux R, Imbeault M, Rainer P, Alpern D et al. 2017. SMiLE-seq identifies binding motifs of single and dimeric transcription factors. Nat. Methods 14:316–22
    [Google Scholar]
  66. Jacob F, Monod J. 1961. Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3:318–56
    [Google Scholar]
  67. Johnson DS, Mortazavi A, Myers RM, Wold B 2007. Genome-wide mapping of in vivo protein-DNA interactions. Science 316:1497–502
    [Google Scholar]
  68. Jolma A, Kivioja T, Toivonen J, Cheng L, Wei G et al. 2010. Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Res 20:861–73
    [Google Scholar]
  69. Jolma A, Yan J, Whitington T, Toivonen J, Nitta KR et al. 2013. DNA-binding specificities of human transcription factors. Cell 152:327–39
    [Google Scholar]
  70. Jolma A, Yin Y, Nitta KR, Dave K, Popov A et al. 2015. DNA-dependent formation of transcription factor pairs alters their binding specificity. Nature 527:384–38
    [Google Scholar]
  71. Joshi R, Passner JM, Rohs R, Jain R, Sosinsky A et al. 2007. Functional specificity of a Hox protein mediated by the recognition of minor groove structure. Cell 131:530–43
    [Google Scholar]
  72. Jung C, Bandilla P, von Reutern M, Schnepf M, Rieder S et al. 2018. True equilibrium measurement of transcription factor–DNA binding affinities using automated polarization microscopy. Nat. Commun. 9:1605
    [Google Scholar]
  73. Khan A, Fornes O, Stigliani A, Gheorghe M, Castro-Mondragon JA et al. 2018. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res 46:D260–66
    [Google Scholar]
  74. Kitayner M, Rozenberg H, Rohs R, Suad O, Rabinovich D et al. 2010. Diversity in DNA recognition by p53 revealed by crystal structures with Hoogsteen base pairs. Nat. Struct. Mol. Biol. 17:423–29
    [Google Scholar]
  75. Kribelbauer JF, Laptenko O, Chen S, Martini GD, Freed-Pastor WA et al. 2017. Quantitative analysis of the DNA methylation sensitivity of transcription factor complexes. Cell Rep 19:2383–95
    [Google Scholar]
  76. Kulakovskiy IV, Vorontsov IE, Yevshin IS, Sharipov RN, Fedorova AD et al. 2018. HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res 46:D252–59
    [Google Scholar]
  77. Lambert SA, Jolma A, Campitelli LF, Das PK, Yin Y et al. 2018. The human transcription factors. Cell 172:650–65
    [Google Scholar]
  78. Le DD, Shimko TC, Aditham AK, Keys AM, Longwell SA et al. 2018. Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding. PNAS 115:E3702–11
    [Google Scholar]
  79. Li J, Sagendorf JM, Chiu TP, Pasi M, Perez A, Rohs R 2017. Expanding the repertoire of DNA shape features for genome-scale studies of transcription factor binding. Nucleic Acids Res 45:12877–87
    [Google Scholar]
  80. Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T et al. 2009. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326:289–93
    [Google Scholar]
  81. Liu Z, Legant WR, Chen BC, Li L, Grimm JB et al. 2014. 3D imaging of Sox2 enhancer clusters in embryonic stem cells. eLife 3:e04236
    [Google Scholar]
  82. Liu Z, Tjian R. 2018. Visualizing transcription factor dynamics in living cells. J. Cell Biol. 217:1181–91
    [Google Scholar]
  83. Luscombe NM, Laskowski RA, Thornton JM 2001. Amino acid–base interactions: a three-dimensional analysis of protein-DNA interactions at an atomic level. Nucleic Acids Res 29:2860–74
    [Google Scholar]
  84. Maerkl SJ, Quake SR. 2007. A systems approach to measuring the binding energy landscapes of transcription factors. Science 315:233–37
    [Google Scholar]
  85. Maniatis T, Ptashne M, Backman K, Kield D, Flashman S et al. 1975. Recognition sequences of repressor and polymerase in the operators of bacteriophage lambda. Cell 5:109–13
    [Google Scholar]
  86. Mann IK, Chatterjee R, Zhao J, He X, Weirauch MT et al. 2013. CG methylated microarrays identify a novel methylated sequence bound by the CEBPB|ATF4 heterodimer that is active in vivo. Genome Res 23:988–97
    [Google Scholar]
  87. Mann RS, Chan SK. 1996. Extra specificity from extradenticle: the partnership between HOX and PBX/EXD homeodomain proteins. Trends Genet 12:258–62
    [Google Scholar]
  88. Mann RS, Lelli KM, Joshi R 2009. Hox specificity unique roles for cofactors and collaborators. Curr. Top. Dev. Biol. 88:63–101
    [Google Scholar]
  89. Merabet S, Mann RS. 2016. To be specific or not: the critical relationship between Hox and TALE proteins. Trends Genet 32:334–47
    [Google Scholar]
  90. Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E et al. 2007. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448:553–60
    [Google Scholar]
  91. Miller M. 2009. The importance of being flexible: the case of basic region leucine zipper transcriptional regulators. Curr. Protein Pept. Sci. 10:244–69
    [Google Scholar]
  92. Minezaki Y, Homma K, Nishikawa K 2005. Genome-wide survey of transcription factors in prokaryotes reveals many bacteria-specific families not found in archaea. DNA Res 12:269–80
    [Google Scholar]
  93. Mir M, Stadler MR, Ortiz SA, Hannon CE, Harrison MM et al. 2018. Dynamic multifactor hubs interact transiently with sites of active transcription in Drosophila embryos. eLife 7:e40497
    [Google Scholar]
  94. Monahan K, Horta A, Lomvardas S 2019. LHX2- and LDB1-mediated trans interactions regulate olfactory receptor choice. Nature 565:448–53
    [Google Scholar]
  95. Mukherjee S, Berger MF, Jona G, Wang XS, Muzzey D et al. 2004. Rapid analysis of the DNA-binding specificities of transcription factors with DNA microarrays. Nat. Genet. 36:1331–39
    [Google Scholar]
  96. Najafabadi HS, Albu M, Hughes TR 2015. Identification of C2H2-ZF binding preferences from ChIP-seq data using RCADE. Bioinformatics 31:2879–81
    [Google Scholar]
  97. Nakahashi H, Kieffer Kwon KR, Resch W, Vian L, Dose M et al. 2013. A genome-wide map of CTCF multivalency redefines the CTCF code. Cell Rep 3:1678–89
    [Google Scholar]
  98. Nikolova EN, Kim E, Wise AA, O'Brien PJ, Andricioaei I, Al-Hashimi HM 2011. Transient Hoogsteen base pairs in canonical duplex DNA. Nature 470:498–502
    [Google Scholar]
  99. Nora EP, Lajoie BR, Schulz EG, Giorgetti L, Okamoto I et al. 2012. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485:381–85
    [Google Scholar]
  100. Noyes MB, Christensen RG, Wakabayashi A, Stormo GD, Brodsky MH, Wolfe SA 2008. Analysis of homeodomain specificities allows the family-wide prediction of preferred recognition sites. Cell 133:1277–89
    [Google Scholar]
  101. Nüsslein-Volhard C, Wieschaus E. 1980. Mutations affecting segment number and polarity in Drosophila. Nature 287:795–801
    [Google Scholar]
  102. Oehler S, Eismann ER, Kramer H, Muller-Hill B 1990. The three operators of the lac operon cooperate in repression. EMBO J 9:973–79
    [Google Scholar]
  103. Ohlendorf DH, Anderson WF, Fisher RG, Takeda Y, Matthews BW 1982. The molecular basis of DNA-protein recognition inferred from the structure of cro repressor. Nature 298:718–23
    [Google Scholar]
  104. Pabo CO, Sauer RT. 1984. Protein-DNA recognition. Annu. Rev. Biochem. 53:293–321
    [Google Scholar]
  105. Pabo CO, Sauer RT. 1992. Transcription factors: structural families and principles of DNA recognition. Annu. Rev. Biochem. 61:1053–95
    [Google Scholar]
  106. Panne D. 2008. The enhanceosome. Curr. Opin. Struct. Biol. 18:236–42
    [Google Scholar]
  107. Panne D, Maniatis T, Harrison SC 2007. An atomic model of the interferon-beta enhanceosome. Cell 129:1111–23
    [Google Scholar]
  108. Perez-Rueda E, Hernandez-Guerrero R, Martinez-Nunez MA, Armenta-Medina D, Sanchez I, Ibarra JA 2018. Abundance, diversity and domain architecture variability in prokaryotic DNA-binding transcription factors. PLOS ONE 13:e0195332
    [Google Scholar]
  109. Ptashne M. 1967a. Isolation of the lambda phage repressor. PNAS 57:306–13
    [Google Scholar]
  110. Ptashne M. 1967b. Specific binding of the lambda phage repressor to lambda DNA. Nature 214:232–34
    [Google Scholar]
  111. Ramos AI, Barolo S. 2013. Low-affinity transcription factor binding sites shape morphogen responses and enhancer evolution. Philos. Trans. R. Soc. B Biol. Sci. 368:20130018
    [Google Scholar]
  112. Rastogi C, Rube HT, Kribelbauer JF, Crocker J, Loker RE et al. 2018. Accurate and sensitive quantification of protein-DNA binding affinity. PNAS 115:E3692–701
    [Google Scholar]
  113. Rhee HS, Pugh BF. 2011. Comprehensive genome-wide protein-DNA interactions detected at single-nucleotide resolution. Cell 147:1408–19
    [Google Scholar]
  114. Riley TR, Lazarovici A, Mann RS, Bussemaker HJ 2015. Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE. eLife 4:e06397
    [Google Scholar]
  115. Rister J, Razzaq A, Boodram P, Desai N, Tsanis C et al. 2015. Single-base pair differences in a shared motif determine differential Rhodopsin expression. Science 350:1258–61
    [Google Scholar]
  116. Rodriguez-Martinez JA, Reinke AW, Bhimsaria D, Keating AE, Ansari AZ 2017. Combinatorial bZIP dimers display complex DNA-binding specificity landscapes. eLife 6:e19272
    [Google Scholar]
  117. Rohs R, Jin X, West SM, Joshi R, Honig B, Mann RS 2010. Origins of specificity in protein-DNA recognition. Annu. Rev. Biochem. 79:233–69
    [Google Scholar]
  118. Rohs R, West SM, Sosinsky A, Liu P, Mann RS, Honig B 2009. The role of DNA shape in protein-DNA recognition. Nature 461:1248–53
    [Google Scholar]
  119. Ruan S, Swamidass SJ, Stormo GD 2017. BEESEM: estimation of binding energy models using HT-SELEX data. Bioinformatics 33:2288–95
    [Google Scholar]
  120. Rube HT, Rastogi C, Kribelbauer JF, Bussemaker HJ 2018. A unified approach for quantifying and interpreting DNA shape readout by transcription factors. Mol. Syst. Biol. 14:e7902
    [Google Scholar]
  121. Sabari BR, Dall'Agnese A, Boija A, Klein IA, Coffey EL et al. 2018. Coactivator condensation at super-enhancers links phase separation and gene control. Science 361:eaar3958
    [Google Scholar]
  122. Schneider TD, Stephens RM. 1990. Sequence logos: a new way to display consensus sequences. Nucleic Acids Res 18:6097–100
    [Google Scholar]
  123. Shen N, Zhao J, Schipper JL, Zhang Y, Bepler T et al. 2018. Divergence in DNA specificity among paralogous transcription factors contributes to their differential in vivo binding. Cell Syst 6:470–83.e8
    [Google Scholar]
  124. Shin Y, Brangwynne CP. 2017. Liquid phase condensation in cell physiology and disease. Science 357:eaaf4382
    [Google Scholar]
  125. Skene PJ, Henikoff S. 2017. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife 6:e21856
    [Google Scholar]
  126. Slattery M, Riley T, Liu P, Abe N, Gomez-Alcala P et al. 2011. Cofactor binding evokes latent differences in DNA binding specificity between Hox proteins. Cell 147:1270–82
    [Google Scholar]
  127. Slattery M, Zhou T, Yang L, Dantas Machado AC, Gordan R, Rohs R 2014. Absence of a simple code: how transcription factors read the genome. Trends Biochem. Sci. 39:381–99
    [Google Scholar]
  128. Southall TD, Gold KS, Egger B, Davidson CM, Caygill EE et al. 2013. Cell-type-specific profiling of gene expression and chromatin binding without cell isolation: assaying RNA Pol II occupancy in neural stem cells. Dev. Cell 26:101–12
    [Google Scholar]
  129. Spitz F, Furlong EE. 2012. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13:613–26
    [Google Scholar]
  130. Steitz TA, Ohlendorf DH, McKay DB, Anderson WF, Matthews BW 1982. Structural similarity in the DNA-binding domains of catabolite gene activator and cro repressor proteins. PNAS 79:3097–100
    [Google Scholar]
  131. Stormo GD, Schneider TD, Gold L, Ehrenfeucht A 1982. Use of the ‘Perceptron’ algorithm to distinguish translational initiation sites in E. coli. Nucleic Acids Res 10:2997–3011
    [Google Scholar]
  132. Stormo GD, Zuo Z, Chang YK 2015. Spec-seq: determining protein-DNA-binding specificity by sequencing. Brief Funct. Genom. 14:30–38
    [Google Scholar]
  133. Strom AR, Emelyanov AV, Mir M, Fyodorov DV, Darzacq X, Karpen GH 2017. Phase separation drives heterochromatin domain formation. Nature 547:241–45
    [Google Scholar]
  134. Struhl G. 1982. Genes controlling segmental specification in the Drosophila thorax. PNAS 79:7380–84
    [Google Scholar]
  135. Tapscott SJ, Davis RL, Thayer MJ, Cheng PF, Weintraub H, Lassar AB 1988. MyoD1: a nuclear phosphoprotein requiring a Myc homology region to convert fibroblasts to myoblasts. Science 242:405–11
    [Google Scholar]
  136. Thanos D, Maniatis T. 1995. Virus induction of human IFN beta gene expression requires the assembly of an enhanceosome. Cell 83:1091–100
    [Google Scholar]
  137. Tsai A, Muthusamy AK, Alves MR, Lavis LD, Singer RH et al. 2017. Nuclear microenvironments modulate transcription from low-affinity enhancers. eLife 6:e28975
    [Google Scholar]
  138. van Steensel B, Delrow J, Henikoff S 2001. Chromatin profiling using targeted DNA adenine methyltransferase. Nat. Genet. 27:304–8
    [Google Scholar]
  139. Vierbuchen T, Wernig M. 2012. Molecular roadblocks for cellular reprogramming. Mol. Cell 47:827–38
    [Google Scholar]
  140. Vinson C, Myakishev M, Acharya A, Mir AA, Moll JR, Bonovich M 2002. Classification of human B-ZIP proteins based on dimerization properties. Mol. Cell. Biol. 22:6321–35
    [Google Scholar]
  141. Wang H, Johnston M, Mitra RD 2007. Calling cards for DNA-binding proteins. Genome Res 17:1202–9
    [Google Scholar]
  142. Wang J, Zhuang J, Iyer S, Lin X, Whitfield TW et al. 2012. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res 22:1798–812
    [Google Scholar]
  143. Wang P, Reed M, Wang Y, Mayr G, Stenger JE et al. 1994. p53 domains: structure, oligomerization, and transformation. Mol. Cell. Biol. 14:5182–91
    [Google Scholar]
  144. Warren CL, Kratochvil NC, Hauschild KE, Foister S, Brezinski ML et al. 2006. Defining the sequence-recognition profile of DNA-binding molecules. PNAS 103:867–72
    [Google Scholar]
  145. Weinberg RL, Veprintsev DB, Fersht AR 2004. Cooperative binding of tetrameric p53 to DNA. J. Mol. Biol. 341:1145–59
    [Google Scholar]
  146. Weirauch MT, Cote A, Norel R, Annala M, Zhao Y et al. 2013. Evaluation of methods for modeling transcription factor sequence specificity. Nat. Biotechnol. 31:126–34
    [Google Scholar]
  147. Weirauch MT, Yang A, Albu M, Cote AG, Montenegro-Montero A et al. 2014. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158:1431–43
    [Google Scholar]
  148. Wheeler RJ, Hyman AA. 2018. Controlling compartmentalization by non-membrane-bound organelles. Philos. Trans. R. Soc. B Biol. Sci. 373:20170193
    [Google Scholar]
  149. Wunderlich Z, Mirny LA. 2009. Different gene regulation strategies revealed by analysis of binding motifs. Trends Genet 25:434–40
    [Google Scholar]
  150. Yang A, Zhu Z, Kapranov P, McKeon F, Church GM et al. 2006. Relationships between p63 binding, DNA sequence, transcription activity, and biological function in human cells. Mol. Cell 24:593–602
    [Google Scholar]
  151. Yang L, Orenstein Y, Jolma A, Yin Y, Taipale J et al. 2017. Transcription factor family–specific DNA shape readout revealed by quantitative specificity models. Mol. Syst. Biol. 13:910
    [Google Scholar]
  152. Yin Y, Morgunova E, Jolma A, Kaasinen E, Sahu B et al. 2017. Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science 356:eaaj2239
    [Google Scholar]
  153. Zandvakili A, Campbell I, Gutzwiller LM, Weirauch MT, Gebelein B 2018. Degenerate Pax2 and Senseless binding motifs improve detection of low-affinity sites required for enhancer specificity. PLOS Genet 14:e1007289
    [Google Scholar]
  154. Zeiske T, Baburajendran N, Kaczynska A, Brasch J, Palmer AG 3rd et al. 2018. Intrinsic DNA shape accounts for affinity differences between Hox-cofactor binding sites. Cell Rep 24:2221–30
    [Google Scholar]
  155. Zhao Y, Granas D, Stormo GD 2009. Inferring binding energies from selected binding sites. PLOS Comput. Biol. 5:e1000590
    [Google Scholar]
  156. Zhao Y, Stormo GD. 2011. Quantitative analysis demonstrates most transcription factors require only simple models of specificity. Nat. Biotechnol. 29:480–83
    [Google Scholar]
  157. Zhou T, Shen N, Yang L, Abe N, Horton J et al. 2015. Quantitative modeling of transcription factor binding specificities using DNA shape. PNAS 112:4654–59
    [Google Scholar]
  158. Zhu H, Wang G, Qian J 2016. Transcription factors as readers and effectors of DNA methylation. Nat. Rev. Genet. 17:551–65
    [Google Scholar]
  159. Zhu L, Brangwynne CP. 2015. Nuclear bodies: the emerging biophysics of nucleoplasmic phases. Curr. Opin. Cell Biol. 34:23–30
    [Google Scholar]
  160. Zuo Z, Roy B, Chang YK, Granas D, Stormo GD 2017. Measuring quantitative effects of methylation on transcription factor–DNA binding affinity. Sci. Adv. 3:eaao1799
    [Google Scholar]
/content/journals/10.1146/annurev-cellbio-100617-062719
Loading
/content/journals/10.1146/annurev-cellbio-100617-062719
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error