全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
PLOS ONE  2011 

Bayesian Analysis of High-Throughput Quantitative Measurement of Protein-DNA Interactions

DOI: 10.1371/journal.pone.0026105

Full-Text   Cite this paper   Add to My Lib

Abstract:

Transcriptional regulation depends upon the binding of transcription factor (TF) proteins to DNA in a sequence-dependent manner. Although many experimental methods address the interaction between DNA and proteins, they generally do not comprehensively and accurately assess the full binding repertoire (the complete set of sequences that might be bound with at least moderate strength). Here, we develop and evaluate through simulation an experimental approach that allows simultaneous high-throughput quantitative analysis of TF binding affinity to thousands of potential DNA ligands. Tens of thousands of putative binding targets can be mixed with a TF, and both the pre-bound and bound target pools sequenced. A hierarchical Bayesian Markov chain Monte Carlo approach determines posterior estimates for the dissociation constants, sequence-specific binding energies, and free TF concentrations. A unique feature of our approach is that dissociation constants are jointly estimated from their inferred degree of binding and from a model of binding energetics, depending on how many sequence reads are available and the explanatory power of the energy model. Careful experimental design is necessary to obtain accurate results over a wide range of dissociation constants. This approach, which we call Simultaneous Ultra high-throughput Ligand Dissociation EXperiment (SULDEX), is theoretically capable of rapid and accurate elucidation of an entire TF-binding repertoire.

References

[1]  Carey M, Smale ST (1999) Transcriptional Regulation in Eukaryotes: Concepts, Strategies, and Techniques. Cold Spring Harbor: Cold Spring Harbor Laboratory.
[2]  Friberg MT (2007) Prediction of transcription factor binding sites using ChIP-chip and phylogenetic footprinting data. J Bioinform Comput Biol 5: 105–116.
[3]  Reddy TE, DeLisi C, Shakhnovich BE (2007) Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites. PLoS Comput Biol 3: e90.
[4]  Pape UJ, Grossmann S, Hammer S, Sperling S, Vingron M (2006) A new statistical model to select target sequences bound by transcription factors. Genome Inform 17: 134–140.
[5]  Dai X, He J, Zhao X (2007) A new systematic computational approach to predicting target genes of transcription factors. Nucleic Acids Res.
[6]  Chen Y, Blackwell TW, Chen J, Gao J, Lee AW, et al. (2007) Integration of genome and chromatin structure with gene expression profiles to predict c-MYC recognition site binding and function. PLoS Comput Biol 3: e63.
[7]  Ananko EA, Kondrakhin YV, Merkulova TI, Kolchanov NA (2007) Recognition of interferon-inducible sites, promoters, and enhancers. BMC Bioinformatics 8: 56.
[8]  Stepanova M, Lin F, Lin VC (2006) In silico modelling of hormone response elements. BMC Bioinformatics 7: Suppl 4S27.
[9]  Gibson G, Weir B (2005) The quantitative genetics of transcription. Trends Genet 21: 616–623.
[10]  Carroll SBGJK, Weatherbee SD (2001) From DNA to Diversity: Molecular Genetics and the Evolution of Animal Design. Malden, MA: Blackwell Science.
[11]  Maerkl SJ, Quake SR (2007) A systems approach to measuring the binding energy landscapes of transcription factors. Science 315: 233–237.
[12]  Zykovich A, Korf I, Segal DJ (2009) Bind-n-Seq: high-throughput analysis of in vitro protein-DNA interactions using massively parallel sequencing. Nucleic Acids Res 37: e151.
[13]  Zhao Y, Granas D, Stormo GD (2009) Inferring binding energies from selected binding sites. PLoS Comput Biol 5: e1000590.
[14]  Lassig M (2007) From biophysics to evolutionary genetics: statistical aspects of gene regulation. BMC Bioinformatics 8: Suppl 6S7.
[15]  Marcy Y, Ishoey T, Lasken RS, Stockwell TB, Walenz BP, et al. (2007) Nanoliter reactors improve multiple displacement amplification of genomes from single cells. PLoS Genet 3: 1702–1708.
[16]  Warren LA, Rossi DJ, Schiebinger GR, Weissman IL, Kim SK, et al. (2007) Transcriptional instability is not a universal attribute of aging. Aging Cell 6: 775–782.
[17]  Marcy Y, Ouverney C, Bik EM, Losekann T, Ivanova N, et al. (2007) Dissecting biological “dark matter” with single-cell genetic analysis of rare and uncultivated TM7 microbes from the human mouth. Proc Natl Acad Sci U S A 104: 11889–11894.
[18]  Choi Y, Qin Y, Berger MF, Ballow DJ, Bulyk ML, et al. (2007) Microarray analyses of newborn mouse ovaries lacking Nobox. Biol Reprod 77: 312–319.
[19]  McCord RP, Berger MF, Philippakis AA, Bulyk ML (2007) Inferring condition-specific transcription factor function from DNA binding and gene expression data. Mol Syst Biol 3: 100.
[20]  Bulyk ML (2007) Protein binding microarrays for the characterization of DNA-protein interactions. Adv Biochem Eng Biotechnol 104: 65–85.
[21]  Berger MF, Philippakis AA, Qureshi AM, He FS, Estep PW 3rd, et al. (2006) Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nat Biotechnol 24: 1429–1435.
[22]  Bulyk ML (2006) Analysis of sequence specificities of DNA-binding proteins with protein binding microarrays. Methods Enzymol 410: 279–299.
[23]  Berger MF, Bulyk ML (2006) Protein binding microarrays (PBMs) for rapid, high-throughput characterization of the sequence specificities of DNA binding proteins. Methods Mol Biol 338: 245–260.
[24]  Bulyk ML (2006) DNA microarray technologies for measuring protein-DNA interactions. Curr Opin Biotechnol 17: 422–430.
[25]  Jolma A, Kivioja T, Toivonen J, Cheng L, Wei G, et al. Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Res 20: 861–873.
[26]  Liu X, Clarke ND (2002) Rationalization of gene regulation by a eukaryotic transcription factor: calculation of regulatory region occupancy from predicted binding affinities. J Mol Biol 323: 1–8.
[27]  Hallikas O, Palin K, Sinjushina N, Rautiainen R, Partanen J, et al. (2006) Genome-wide prediction of mammalian enhancers based on analysis of transcription-factor binding affinity. Cell 124: 47–59.
[28]  Wang X, Gao H, Shen Y, Weinstock GM, Zhou J, et al. (2008) A high-throughput percentage-of-binding strategy to measure binding energies in DNA-protein interactions: application to genome-scale site discovery. Nucleic Acids Res 36: 4863–4871.
[29]  Gustafsdottir SM, Schlingemann J, Rada-Iglesias A, Schallmeiner E, Kamali-Moghaddam M, et al. (2007) In vitro analysis of DNA-protein interactions by proximity ligation. Proc Natl Acad Sci U S A 104: 3067–3072.
[30]  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: 1331–1339.
[31]  Badis G, Berger MF, Philippakis AA, Talukder S, Gehrke AR, et al. (2009) Diversity and complexity in DNA recognition by transcription factors. Science 324: 1720–1723.
[32]  Zhu C, Byers KJ, McCord RP, Shi Z, Berger MF, et al. (2009) High-resolution DNA-binding specificity analysis of yeast transcription factors. Genome Res 19: 556–566.
[33]  Warren CL, Kratochvil NC, Hauschild KE, Foister S, Brezinski ML, et al. (2006) Defining the sequence-recognition profile of DNA-binding molecules. Proc Natl Acad Sci U S A 103: 867–872.
[34]  Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, Macisaac KD, et al. (2004) Transcriptional regulatory code of a eukaryotic genome. Nature 431: 99–104.
[35]  MacIsaac KD, Wang T, Gordon DB, Gifford DK, Stormo GD, et al. (2006) An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinformatics 7: 113.
[36]  Borneman AR, Zhang ZD, Rozowsky J, Seringhaus MR, Gerstein M, et al. (2007) Transcription factor binding site identification in yeast: a comparison of high-density oligonucleotide and PCR-based microarray platforms. Funct Integr Genomics 7: 335–345.
[37]  Wei CL, Wu Q, Vega VB, Chiu KP, Ng P, et al. (2006) A global map of p53 transcription-factor binding sites in the human genome. Cell 124: 207–219.
[38]  Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, et al. (2007) Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods 4: 651–657.
[39]  Johnson DS, Mortazavi A, Myers RM, Wold B (2007) Genome-wide mapping of in vivo protein-DNA interactions. Science 316: 1497–1502.
[40]  Stormo GD (2000) DNA binding sites: representation and discovery. Bioinformatics 16: 16–23.
[41]  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: 723–750.
[42]  Granek JA, Clarke ND (2005) Explicit equilibrium modeling of transcription-factor binding and gene regulation. Genome Biol 6: R87.
[43]  Manke T, Roider HG, Vingron M (2008) Statistical modeling of transcription factor binding affinities predicts regulatory interactions. PLoS Comput Biol 4: e1000039.
[44]  Roider HG, Kanhere A, Manke T, Vingron M (2007) Predicting transcription factor affinities to DNA from a biophysical model. Bioinformatics 23: 134–141.
[45]  He X, Chen CC, Hong F, Fang F, Sinha S, et al. (2009) A biophysical model for analysis of transcription factor interaction and binding site arrangement from genome-wide binding data. PLoS One 4: e8155.
[46]  van Oeffelen L, Cornelis P, Van Delm W, De Ridder F, De Moor B, et al. (2008) Detecting cis-regulatory binding sites for cooperatively binding proteins. Nucleic Acids Res 36: e46.
[47]  Djordjevic M, Sengupta AM, Shraiman BI (2003) A biophysical approach to transcription factor binding site discovery. Genome Res 13: 2381–2390.
[48]  Kinney JB, Tkacik G, Callan CG Jr (2007) Precise physical models of protein-DNA interaction from high-throughput data. Proc Natl Acad Sci U S A 104: 501–506.
[49]  Wang J, Morigen (2009) BayesPI - a new model to study protein-DNA interactions: a case study of condition-specific protein binding parameters for Yeast transcription factors. BMC Bioinformatics 10: 345.
[50]  Foat BC, Morozov AV, Bussemaker HJ (2006) Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE. Bioinformatics 22: e141–149.
[51]  Gerland U, Moroz JD, Hwa T (2002) Physical constraints and functional characteristics of transcription factor-DNA interaction. Proc Natl Acad Sci U S A 99: 12015–12020.
[52]  Stormo GD, Fields DS (1998) Specificity, free energy and information content in protein-DNA interactions. Trends Biochem Sci 23: 109–113.
[53]  Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57: 97–109.
[54]  Gelman A (2004) Bayesian data analysis. Boca Raton, Fla.: Chapman & Hall/CRC.
[55]  Roberts GO, Gelman A, Gilks WR (1997) Weak Convergence and Optimal Scaling of Random Walk Metropolis Algorithms. The Annals of Applied Probability 7: 110–120.
[56]  Green PJ (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82: 711–732.
[57]  Al-Awadhi F, Hurn M, Jennison C (2004) Improving the acceptance rate of reversible jump MCMC proposals. Statistics & Probability Letters 69: 189–198.
[58]  Pollock DD, Chang BH (2007) Dealing with Uncertainty in Ancestral Sequence Reconstruction: Sampling from the Posterior Distribution. In: Liberles DA, editor. Ancestral Sequence Reconstruction. Oxford: Oxford University Press.
[59]  Haring M, Offermann S, Danker T, Horst I, Peterhansel C, et al. (2007) Chromatin immunoprecipitation: optimization, quantitative analysis and data normalization. Plant Methods 3: 11.
[60]  Friden P, Schimmel P (1988) LEU3 of Saccharomyces cerevisiae activates multiple genes for branched-chain amino acid biosynthesis by binding to a common decanucleotide core sequence. Mol Cell Biol 8: 2690–2697.
[61]  Foat BC, Morozov AV, Bussemaker HJ (2006) Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE. Bioinformatics 22: e141–149.
[62]  Gangelhoff TA, Mungalachetty PS, Nix JC, Churchill ME (2009) Structural analysis and DNA binding of the HMG domains of the human mitochondrial transcription factor A. Nucleic Acids Res 37: 3153–3164.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133