Power-law behavior of transcription factor dynamics at the single-molecule level implies a continuum affinity model.

Receptors, Glucocorticoid; Transcription Factors; Animals; Cell Line, Tumor; Kinetics; Mice; Models, Biological; Photobleaching; Protein Binding; Receptors, Glucocorticoid/metabolism; Single Molecule Imaging; Transcription Factors/metabolism; Genetics

Abstract :

[en] Single-molecule tracking (SMT) allows the study of transcription factor (TF) dynamics in the nucleus, giving important information regarding the diffusion and binding behavior of these proteins in the nuclear environment. Dwell time distributions obtained by SMT for most TFs appear to follow bi-exponential behavior. This has been ascribed to two discrete populations of TFs-one non-specifically bound to chromatin and another specifically bound to target sites, as implied by decades of biochemical studies. However, emerging studies suggest alternate models for dwell-time distributions, indicating the existence of more than two populations of TFs (multi-exponential distribution), or even the absence of discrete states altogether (power-law distribution). Here, we present an analytical pipeline to evaluate which model best explains SMT data. We find that a broad spectrum of TFs (including glucocorticoid receptor, oestrogen receptor, FOXA1, CTCF) follow a power-law distribution of dwell-times, blurring the temporal line between non-specific and specific binding, suggesting that productive binding may involve longer binding events than previously believed. From these observations, we propose a continuum of affinities model to explain TF dynamics, that is consistent with complex interactions of TFs with multiple nuclear domains as well as binding and searching on the chromatin template.

Disciplines :

Biochemistry, biophysics & molecular biology

Author, co-author :

Garcia, David A ^✱; Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, MD 20893, USA ; Department of Physics, University of Maryland, College Park, MD 20742, USA

Fettweis, Grégory ^✱; Université de Liège - ULiège > Département des sciences de la vie > Génétique et biologie moléculaires animales ; Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, MD 20893, USA

Presman, Diego M ^✱; Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, MD 20893, USA ; Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE), CONICET-Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, C1428EGA, Buenos Aires, Argentina

Paakinaho, Ville ; Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, MD 20893, USA ; Institute of Biomedicine, University of Eastern Finland, Kuopio, PO Box 1627, FI-70211 Kuopio, Finland

Jarzynski, Christopher; Department of Physics, University of Maryland, College Park, MD 20742, USA ; Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA ; Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA

Upadhyaya, Arpita; Department of Physics, University of Maryland, College Park, MD 20742, USA ; Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA

Hager, Gordon L ; Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, MD 20893, USA

^✱ These authors have contributed equally to this work.

Language :

English

Title :

Power-law behavior of transcription factor dynamics at the single-molecule level implies a continuum affinity model.

Publication date :

09 July 2021

Journal title :

Nucleic Acids Research

ISSN :

0305-1048

eISSN :

1362-4962

Publisher :

Oxford University Press, England

Volume :

Issue :

Pages :

6605 - 6620

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://academic.oup.com/nar/article-pdf/49/12/6605/38885905/gkab072.pdf

Funders :

NIH - National Institutes of Health
NCI - National Cancer Institute
CONICET - National Scientific and Technical Research Council
Academy of Finland
UEF - University of Eastern Finland
Sigrid Jusélius Foundation

Available on ORBi :

since 23 May 2023

Statistics

Number of views

21 (3 by ULiège)

Number of downloads

1 (1 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

Lambert,S.A., Jolma,A., Campitelli,L.F., Das,P.K., Yin,Y., Albu,M., Chen,X., Taipale,J., Hughes,T.R. and Weirauch,M.T. (2018) The human transcription factors. Cell, 172, 650-665.
Goldstein,I., Baek,S., Presman,D.M., Paakinaho,V., Swinstead,E.E. and Hager,G.L. (2017) Transcription factor assisted loading and enhancer dynamics dictate the hepatic fasting response. Genome Res., 27, 427-439.
Stasevich,T.J. and McNally,J.G. (2011) Assembly of the transcription machinery: ordered and stable, random and dynamic, or both? Chromosoma, 120, 533-545.
Hager,G.L., McNally,J.G. and Misteli,T. (2009) Transcription dynamics. Mol.Cell, 35, 741-753.
Coulon,A., Chow,C.C., Singer,R.H. and Larson,D.R. (2013) Eukaryotic transcriptional dynamics: from single molecules to cell populations. Nat Rev.Genet., 14, 572-584.
Brouwer,I. and Lenstra,T.L. (2019) Visualizing transcription: key to understanding gene expression dynamics. Curr. Opin. Chem. Biol., 51, 122-129.
Lerner,J., Gomez-Garcia,P.A., McCarthy,R.L., Liu,Z., Lakadamyali,M. and Zaret,K.S. (2020) Two-Parameter mobility assessments discriminate diverse regulatory factor behaviors in chromatin. Mol. Cell, 79, 677-688.
Gurdon,J.B., Javed,K., Vodnala,M. and Garrett,N. (2020) Long-term association of a transcription factor with its chromatin binding site can stabilize gene expression and cell fate commitment. Proc. Natl. Acad. Sci. U.S.A., 117, 15075-15084.
Liu,Z. and Tjian,R. (2018) Visualizing transcription factor dynamics in living cells. J. Cell Biol., 217, 1181-1191.
Ball,D.A., Mehta,G.D., Salomon-Kent,R., Mazza,D., Morisaki,T., Mueller,F., McNally,J.G. and Karpova,T.S. (2016) Single molecule tracking of Ace1p in Saccharomyces cerevisiae defines a characteristic residence time for non-specific interactions of transcription factors with chromatin. Nucleic Acids Res., 44, e160.
Chen,J., Zhang,Z., Li,L., Chen,B.C., Revyakin,A., Hajj,B., Legant,W., Dahan,M., Lionnet,T., Betzig,E. et al. (2014) Single-molecule dynamics of enhanceosome assembly in embryonic stem cells. Cell, 156, 1274-1285.
Kilic,S., Bachmann,A.L., Bryan,L.C. and Fierz,B. (2015) Multivalency governs HP1alpha association dynamics with the silent chromatin state. Nat. Commun., 6, 7313.
Morisaki,T., Muller,W.G., Golob,N., Mazza,D. and McNally,J.G. (2014) Single-molecule analysis of transcription factor binding at transcription sites in live cells. Nat. Commun., 5, 4456.
Sugo,N., Morimatsu,M., Arai,Y., Kousoku,Y., Ohkuni,A., Nomura,T., Yanagida,T. and Yamamoto,N. (2015) Single-Molecule imaging reveals dynamics of CREB transcription factor bound to its target sequence. Sci. Rep., 5, 10662.
Paakinaho,V., Presman,D.M., Ball,D.A., Johnson,T.A., Schiltz,R.L., Levitt,P., Mazza,D., Morisaki,T., Karpova,T.S. and Hager,G.L. (2017) Single-molecule analysis of steroid receptor and cofactor action in living cells. Nat. Commun., 8, 15896.
Callegari,A., Sieben,C., Benke,A., Suter,D.M., Fierz,B., Mazza,D. and Manley,S. (2019) Single-molecule dynamics and genome-wide transcriptomics reveal that NF-kB (p65)-DNA binding times can be decoupled from transcriptional activation. PLoS Genet., 15, e1007891.
Finn,E.H. and Misteli,T. (2019) Molecular basis and biological function of variability in spatial genome organization. Science, 365, eaaw9498.
Kim,S. and Shendure,J. (2019) Mechanisms of interplay between transcription factors and the 3D genome. Mol. Cell, 76, 306-319.
Woringer,M. and Darzacq,X. (2018) Protein motion in the nucleus: from anomalous diffusion to weak interactions. Biochem. Soc. Trans., 46, 945-956.
Hipp,L., Beer,J., Kuchler,O., Reisser,M., Sinske,D., Michaelis,J., Gebhardt,J.C.M. and Knöll,B. (2019) Single-molecule imaging of the transcription factor SRF reveals prolonged chromatin-binding kinetics upon cell stimulation. Proc. Natl. Acad. Sci. U.S.A., 116, 880-889.
Agarwal,H., Reisser,M., Wortmann,C. and Gebhardt,J.C.M. (2017) Direct observation of cell-cycle-dependent Interactions between CTCF and chromatin. Biophys. J., 112, 2051-2055.
Reisser,M., Hettich,J., Kuhn,T., Popp,A.P., Große-Berkenbusch,A. and Gebhardt,J.C.M. (2020) Inferring quantity and qualities of superimposed reaction rates from single molecule survival time distributions. Sci. Rep., 10, 1758.
Normanno,D., Boudarene,L., Dugast-Darzacq,C., Chen,J., Richter,C., Proux,F., Benichou,O., Voituriez,R., Darzacq,X. and Dahan,M. (2015) Probing the target search of DNA-binding proteins in mammalian cells using TetR as model searcher. Nat. Commun., 6, 7357.
Caccianini,L., Normanno,D., Izeddin,I. and Dahan,M. (2015) Single molecule study of non-specific binding kinetics of LacI in mammalian cells. Faraday Discuss., 184, 393-400.
Newman,M.E.J. (2005) Power laws, Pareto distributions and Zipf's law. Contemp. Phys., 46, 323-351.
Mazza,D., Abernathy,A., Golob,N., Morisaki,T. and McNally,J.G. (2012) A benchmark for chromatin binding measurements in live cells. Nucleic Acids Res., 40, e119.
Swinstead,E.E., Miranda,T.B., Paakinaho,V., Baek,S., Goldstein,I., Hawkins,M., Karpova,T.S., Ball,D.A., Mazza,D., Lavis,L.D. et al. (2016) Steroid receptors reprogram FoxA1 occupancy through dynamic chromatin transitions. Cell, 165, 593-605.
Nakahashi,H., Kwon,K.R., Resch,W., Vian,L., Dose,M., Stavreva,D., Hakim,O., Pruett,N., Nelson,S., Yamane,A. et al. (2013) A genome-wide map of CTCF multivalency redefines the CTCF code. Cell Rep., 3, 1678-1689.
Presman,D.M., Ball,D.A., Paakinaho,V., Grimm,J.B., Lavis,L.D., Karpova,T.A. and Hager,G.L. (2017) Quantifying transcription factor dynamics at the single-molecule level in live cells. Methods, 123, 76-88.
McNally,J.G., Mueller,W.G., Walker,D., Wolford,R.G. and Hager,G.L. (2000) The glucocorticoid receptor: rapid exchange with regulatory sites in living cells. Science, 287, 1262-1265.
Grimm,J.B., English,B.P., Chen,J., Slaughter,J.P., Zhang,Z., Revyakin,A., Patel,R., Macklin,J.J., Normanno,D., Singer,R.H. et al. (2015) A general method to improve fluorophores for live-cell and single-molecule microscopy. Nat. Methods, 12, 244-250.
Mazza,D., Ganguly,S. and McNally,J.G. (2013) Monitoring dynamic binding of chromatin proteins in vivo by single-molecule tracking. Methods Mol. Biol., 1042, 117-137.
Jaqaman,K., Loerke,D.,Mettlen,M., Kuwata,H., Grinstein,S., Schmid,S.L. and Danuser,G. (2008) Robust single-particle tracking in live-cell time-lapse sequences. Nat. Methods, 5, 695-702.
Hansen,A.S., Pustova,I., Cattoglio,C., Tjian,R. and Darzacq,X. (2017) CTCF and cohesin regulate chromatin loop stability with distinct dynamics. Elife, 6, e25776.
Teves,S.S., An,L., Hansen,A.S., Xie,L., Darzacq,X. and Tjian,R. (2016) A dynamic mode of mitotic bookmarking by transcription factors. Elife, 5, e22280.
Zhen,C.Y., Tatavosian,R., Huynh,T.N., Duc,H.N., Das,R., Kokotovic,M., Grimm,J.B., Lavis,L.D., Lee,J., Mejia,F.J. et al. (2016) Live-cell single-molecule tracking reveals co-recognition of H3K27me3 and DNA targets polycomb Cbx7-PRC1 to chromatin. Elife, 5, e17667.
Ewen,M.E. (2000)Where the cell cycle and histones meet. Genes Dev., 14, 2265-2270.
Gillespie,D. (1977) Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem., 81, 2340-2361.
James,G., Witten,D., Hastie,T. and Tibshirani,R. (2017) In: An Introduction to Statistical Learning: With Applications in R. Springer, NY.
Jaynes,E.T. and Bretthorst,G.L. (2019) In: Probability Theory: The Logic of Science.Cambridge University Press, Cambridge, UK.
Goldstein,I. and Hager,G.L. (2018) Dynamic enhancer function in the chromatin context. Wiley Interdiscip. Rev. Syst. Biol. Med., 10, e1390.
Liu,H., Dong,P., Ioannou,M.S., Li,L., Shea,J., Pasolli,H.A., Grimm,J.B., Rivlin,P.K., Lavis,L.D., Koyama,M. et al. (2018) Visualizing long-term single-molecule dynamics in vivo by stochastic protein labeling. Proc. Natl. Acad. Sci. U.S.A., 115, 343-348.
Presman,D.M. and Hager,G.L. (2017) More than meets the dimer: What is the quaternary structure of the glucocorticoid receptor? Transcription, 8, 32-39.
Gebhardt,J.C., Suter,D.M., Roy,R., Zhao,Z.W., Chapman,A.R., Basu,S., Maniatis,T. and Xie,X.S. (2013) Single-molecule imaging of transcription factor binding to DNA in live mammalian cells. Nat. Methods, 10, 421-426.
Loffreda,A., Jacchetti,E., Antunes,S., Rainone,P., Daniele,T., Morisaki,T., Bianchi,M.E., Tacchetti,C. and Mazza,D. (2017) Live-cell p53 single-molecule binding is modulated by C-terminal acetylation and correlates with transcriptional activity. Nat. Commun., 8, 313.
Kimura,H. and Cook,P.R. (2001) Kinetics of core histones in living human cells: little exchange of H3 and H4 and some rapid exchange of H2B. J.Cell Biol., 153, 1341-1353.
Liao,J.C., Spudich,J.A., Parker,D. and Delp,S.L. (2007) Extending the absorbing boundary method to fit dwell-time distributions of molecular motors with complex kinetic pathways. Proc. Natl. Acad. Sci. U.S.A., 104, 3171-3176.
Van Kampen,N.G. (1992) In: Stochastic Processes in Physics and Chemistry. Elsevier.
Bauer,M. and Metzler,R. (2012) Generalized facilitated diffusion model for DNA-binding proteins with search and recognition states. Biophys. J., 102, 2321-2330.
Berg,O.G. and Blomberg,C. (1976) Association kinetics with coupled diffusional flows. Special application to the lac repressor-operator system. Biophys. Chem., 4, 367-381.
Marklund,E.G.,Mahmutovic,A., Berg,O.G., Hammar,P., van der Spoel,D., Fange,D. and Elf,J. (2013) Transcription-factor binding and sliding on DNA studied using micro-and macroscopic models. Proc. Natl. Acad. Sci. U.S.A., 110, 19796-19801.
Wang,J., Zhuang,J., Iyer,S., Lin,X., Whitfield,T.W., Greven,M.C., Pierce,B.G., Dong,X., Kundaje,A., Cheng,Y. et al. (2012) Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res., 22, 1798-1812.
Hansen,A.S., Amitai,A., Cattoglio,C., Tjian,R. and Darzacq,X. (2020) Guided nuclear exploration increases CTCF target search efficiency. Nat. Chem. Biol., 16, 257-266.
Chong,S., Dugast-Darzacq,C., Liu,Z., Dong,P., Dailey,G.M., Cattoglio,C., Heckert,A., Banala,S., Lavis,L., Darzacq,X. et al. (2018) Imaging dynamic and selective low-complexity domain interactions that control gene transcription. Science, 361, eaar2555.
Brodsky,S., Jana,T., Mittelman,K., Chapal,M., Kumar,D.K., Carmi,M. and Barkai,N. (2020) Intrinsically disordered regions direct transcription factor in vivo binding specificity. Mol. Cell, 79, 459-471.
Zhu,F., Farnung,L., Kaasinen,E., Sahu,B., Yin,Y., Wei,B., Dodonova,S.O., Nitta,K.R., Morgunova,E., Taipale,M. et al. (2018) The interaction landscape between transcription factors and the nucleosome. Nature, 562, 76-81.
Schöne,S., Jurk,M., Helabad,M.B., Dror,I., Lebars,I., Kieffer,B., Imhof,P., Rohs,R., Vingron,M., Thomas-Chollier,M. et al. (2016) Corrigendum: sequences flanking the core-binding site modulate glucocorticoid receptor structure and activity. Nat. Commun., 7, 13784.
Bouchaud,J.-P. and Georges,A. (1990) Anomalous diffusion in disordered media: statistical mechanisms, models and physical applications. Phys. Rep., 195, 127-293.
J.,B. (1992) Weak ergodicity breaking and aging in disordered systems. J. Phys. I, 2, 1705-1713.
Schwarz,G. (1978) Estimating the dimension of a model. Ann Stat., 6, 461-464.
Stavreva,D.A., Garcia,D.A., Fettweis,G., Gudla,P.R., Zaki,G.F., Soni,V., McGowan,A., Williams,G., Huynh,A., Palangat,M. et al. (2019) Transcriptional bursting and Co-bursting regulation by steroid hormone release pattern and transcription factor mobility. Mol. Cell, 75, 1161-1177.
Gautier,A., Juillerat,A., Heinis,C., Correa,I.R. Jr., Kindermann,M., Beaufils,F. and Johnsson,K. (2008) An engineered protein tag for multiprotein labeling in living cells. Chem.Biol., 15, 128-136.
Stavreva,D.A., Coulon,A., Sung,M.H., Baek,S., John,S., Stixova,L., Tesikova,M., Hakim,O., Miranda,T.B., Hawkins,M. et al. (2015) Dynamics of chromatin accessibility and long-range interactions in response to glucocorticoid pulsing. Genome Res., 25, 845-857.
Stumpf,M.P. and Porter,M.A. (2012) Mathematics. Critical truths about power laws. Science, 335, 665-666.
Dahirel,V., Paillusson,F., Jardat,M., Barbi,M. and Victor,J.M. (2009) Nonspecific DNA-protein interaction: why proteins can diffuse along DNA. Phys. Rev. Lett., 102, 228101.
Afek,A., Schipper,J.L., Horton,J., Gordan,R. and Lukatsky,D.B. (2014) Protein-DNA binding in the absence of specific base-pair recognition. Proc. Natl. Acad. Sci. USA, 111, 17140-17145.
Donovan,B.T., Huynh,A., Ball,D.A., Patel,H.P., Poirier,M.G., Larson,D.R., Ferguson,M.L. and Lenstra,T.L. (2019) Live-cell imaging reveals the interplay between transcription factors, nucleosomes, and bursting. EMBO J., 38, e100809.
Stortz,M., Presman,D.M., Bruno,L., Annibale,P., Dansey,M.V., Burton,G., Gratton,E., Pecci,A. and Levi,V. (2017) Mapping the dynamics of the glucocorticoid receptor within the nuclear landscape. Sci. Rep., 7, 6219.
Rastogi,C., Rube,H.T., Kribelbauer,J.F., Crocker,J., Loker,R.E., Martini,G.D., Laptenko,O., Freed-Pastor,W.A., Prives,C., Stern,D.L. et al. (2018) Accurate and sensitive quantification of protein-DNA binding affinity. Proc. Natl. Acad. Sci. U.S.A., 115, E3692-E3701.
Geertz,M., Shore,D. and Maerkl,S.J. (2012) Massively parallel measurements of molecular interaction kinetics on a microfluidic platform. Proc. Natl. Acad. Sci. U.S.A., 109, 16540-16545.
Jothi,R., Cuddapah,S., Barski,A., Cui,K. and Zhao,K. (2008) Genome-wide identification of in vivo protein-DNA binding sites from ChIP-Seq data. Nucleic Acids Res., 36, 5221-5231.
Hnisz,D., Shrinivas,K., Young,R.A., Chakraborty,A.K. and Sharp,P.A. (2017) A phase separation model for transcriptional control. Cell, 169, 13-23.
Lu,H., Yu,D., Hansen,A.S., Ganguly,S., Liu,R., Heckert,A., Darzacq,X. and Zhou,Q. (2018) Phase-separation mechanism for C-terminal hyperphosphorylation of RNA polymerase II. Nature, 558, 318-323.
Sabari,B.R., Dall'Agnese,A., Boija,A., Klein,I.A., Coffey,E.L., Shrinivas,K., Abraham,B.J., Hannett,N.M., Zamudio,A.V., Manteiga,J.C. et al. (2018) Coactivator condensation at super-enhancers links phase separation and gene control. Science, 361, aar3958.
Stortz,M., Pecci,A., Presman,D.M. and Levi,V. (2020) Unraveling the molecular interactions involved in phase separation of glucocorticoid receptor. BMC Biol., 18, 59.
Garcia,D.A., Johnson,T.A., Presman,D.M., Fettweis,G., Kagh,K., Rinaldi,L., Stavreva,D.A., Paakinaho,V., Jensen,R., Mandrup,S. et al. (2021) An intrinsically disordered region-mediated confinement state contributes tothe dynamics and function of transcription factors. Mol. Cell, 81, doi:10.1016/j.molcel.2021.01.013.
Janissen,R., Eslami-Mossallam,B., Artsimovitch,I., Depken,M. and Dekker,N.H. (2020) A unifying mechanistic model of bacterial transcription pith three interconnected pause states and non-diffusive backtrack recovery. Biophys. J., 118, 543a.
Saxton,M.J. (2020) Diffusion of DNA-binding species in the nucleus: a transient anomalous subdiffusion model. Biophys. J., 118, 2151-2167.
Mehta,G.D., Ball,D.A., Eriksson,P.R., Chereji,R.V., Clark,D.J., McNally,J.G. and Karpova,T.S. (2018) Single-Molecule analysis reveals linked cycles of RSC chromatin remodeling and Ace1p transcription factor binding in yeast. Mol. Cell, 72, 875-887.