[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Quantitatively assessing the effects of regulatory factors on nucleosome dynamics by multiple kernel learning

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Nucleosome, a nucleoprotein structure formed by coiling 147 bp of DNA around an octamer of histone proteins, is the fundamental repeating unit of eukaryotic chromatin. By regulating the access of biological machineries to underlying cis-regulatory elements, its mobility has been implicated in many important cellular processes. Although it has been known that various factors, such as DNA sequences, histone modifications, etc., cooperatively affect nucleosome mobility, the contribution of each factor in the common impact remains unclear. We propose, in this work, a novel computational approach based on multiple kernel learning for quantitatively assessing the effects of two important factors, i.e., genomic sequence and post-translational histone modifications (PTMs), on nucleosome dynamics. Our result on Saccharomyces cerevisiae shows that, epigenetic feature, such as histone modifications, plays more important role than genomic sequence in regulating nucleosome dynamics. Based on that, we carried further analysis on each PTM to reveal their combinatory effects on nucleosome dynamics and found out that some pairs of PTMs such as H3K9Ac–H4H14Ac, H4K5Ac–H4K12Ac and H4K5Ac–H3K14Ac might co-operate in altering nucleosome stability in gene regulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Anderson J, Lowary P, Widom J (2001) Effects of histone acetylation on the equilibrium accessibility of nucleosomal DNA target sites. J Mol Biol 307:977–985

    Article  Google Scholar 

  • Bach F, Lanckriet G, Jordan M (2004) Multiple kernel learning, conic duality, and the smo algorithm. In: Proceedings of the 21st international conference on machine learning (ICML)

  • Breiman L (2001) Randome forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  • Bryan K, Brennan L, Cunningham P (2001) MetaFIND: A feature analysis tool for metabolomics data. BMC Bioinform 9(1):470

    Google Scholar 

  • Gehler PV, Nowozin S (2008) Infinite kernel learning. Tech. report. Max Planck Institute for Biological Cybernetics

  • Gupta S, Dennis J, Thurman R, Kingston R, Stamatoyannopoulos J, Noble W (2008) Predicting human nucleosome occupancy from primary sequence. PLoS Comput Biol 4(8):e1000134

    Google Scholar 

  • Henikoff S (2008) Nucleosome destabilization in the epigenetic regulation of gene expression. Nat Rev Genet 9:15–26

    Article  Google Scholar 

  • Hou Y, Zhou X, Liu J, Yuan J, Cheng H, Zhou R (2010) Nuclear factor-y (NF-Y) regulates transcription of mouse dmrt7 gene by binding to tandem CCAAT boxes in its proximal promoter. Int J Biol Sci 6(7):655–664

    Article  Google Scholar 

  • Jansen A, Verstrepen K (2011) Nucleosome positioning in Saccharomyces cerevisiae. Microbiol Mol Biol Rev 75(2):310–20

    Article  Google Scholar 

  • Kaplan N, Moore I, Fondufe-Mittendorf Y, Gossett A, Tillo D, Field Y, LeProust E, Hughes T, JD JL, Widom J, Segal E (2009) The DNA-encoded nucleosome organization of a eukaryotic genome. Nature 458(7236):362–366

    Article  Google Scholar 

  • Kurdistani S, Grunstein M (2003) Histone acetylation and deacetylation in yeast. Nat Rev Mol Cell Biol 4:276–284

    Article  Google Scholar 

  • Lanckriet G, Deng M, Cristianini N, Jordan M, Noble W (2004) Kernel-based data fusion and its application to protein function prediction in yeast. In: Proceedings of the Pacific symposium on biocomputing, pp 300–311

  • Le N, Ho T, Tran D (2009) Characterizing nucleosome dynamics from genomic and epigenetic information using rule inductiong learning. BMC Genomics 10(3):S27

    Article  Google Scholar 

  • LeRoy G, BRickards, Flint S (2008) The double bromodomain proteins brd2 and brd3 couple histone acetylation to transcription. BMC Bioinform 30(1):51–60

    Google Scholar 

  • Li B, Carey M, Workman J (2007) The role of chromatin during transcription. Cell 128(4):707–719

    Article  Google Scholar 

  • Liu C, Kaplan T, Kim M, Buratowski S, Schreiber S, Friedman N, Rando O (2005) Single-nucleosome mapping of histone modifications in S. cerevisiae. PLoS Biol 3(10):1753–1769

    Article  Google Scholar 

  • Luger K, Mäder A, Richmond R, Sargent D, Richmond T (1997) Crystal structure of the nucleosome core particle at 2.8 a resolution. Nature 389:251–260

    Article  Google Scholar 

  • Oliver S, Denu J (2011) Dynamic interplay between histone h3 modifications and protein interpreters: emerging evidence for a ”histone language”. Chembiochem 12(2):299–307

    Article  Google Scholar 

  • Özögür Akyüz S, Weber G (2008) Learning with infinitely many kernels via semi-infinite programming. In: Proceedings of continuous optimization and knowledge based technologies, 20th EURO mini conference, pp 342–348

  • Özögür Akyüz S, Weber G (2009) Modelling of kernel machines by infinite and semi-infinite programming. In: Proceedings of the second global conference on power control and optimization, pp 306–313

  • Özögür Akyüz S, Weber G (2010) On numerical optimization theory of infinite kernel learning. J Global Optim 48(2):215–239

    Article  MathSciNet  MATH  Google Scholar 

  • Peckham H, Thurman R, Y YF, Stamatoyannopoulos J, Noble W, Struhl K, Weng Z (2007) Nucleosome positioning signals in genomic DNA. Genome Res 17(8):1170–1177

    Article  Google Scholar 

  • Polach K, Lowary P, Widom J (2000) Effects of core histone tail domains on the equilibrium constants for dynamic DNA site accessibility in nucleosomes. J Mol Biol 298:211–223

    Article  Google Scholar 

  • Prost A, Dunleavy E, Almouzni G (2009) Epigenetic inheritance during the cell cycle. Nat Rev Mol Cell Biol 10:192–206

    Article  Google Scholar 

  • Ratsch G, Sonnenburg S, Schafer C (2006) Learning interpretable SVMs for biological sequence classification. BMC Bioinform 7(1):S9

    Article  MathSciNet  Google Scholar 

  • Reif D, Motsinger A, McKinney B, Crowe J, Moore J (2006) Feature selection using a random forests classifier for the integrated analysis of multiple data types. In: 2006 IEEE symposium on computational intelligence and bioinformatics and computational biology, CIBCB ’06, pp 1–8

  • Rippe K, Schrader A, Riede P, Strohner R, Lehmann E, Längst G (2007) DNA sequence- and conformation-directed positioning of nucleosomes by chromatin-remodelling complexes. Proc Natl Acad Sci USA 104(40):15635–15640

    Google Scholar 

  • Schnitzler G (2008) Control of nucleosome positions by DNA sequence and remodelling machines. Cell Biochem Biophys 51(2–3):67–80

    Article  Google Scholar 

  • Schwartz S, Meshorer E, Ast G (2009) Chromatin organization marks exon-intron structure. Nat Struct Mol Biol 16(9):990–995

    Article  Google Scholar 

  • Segal E, Widom J (2009) What controls nucleosome positions? Trends Genet 25(8):335–43

    Article  Google Scholar 

  • Segal E, Fondufe-Mittendorf Y, Chen L, Thastrom A, Field Y, Moore I, Wang J, Widom J (2006) A genomic code for nucleosome positioning. Nature 442(7104):772–778

    Article  Google Scholar 

  • Sonnenburg S, Ratsch G, Schafer C, Scholkopf B (2006) Large scale multiple kernel learning. J Mach Learn Res 7:1531–1565

    MathSciNet  MATH  Google Scholar 

  • Tanaka Y, Yoshimura I, Nakai K (2010) Positional variations among heterogeneous nucleosome maps give dynamical information on chromatin. Chromosoma 119:391–404

    Google Scholar 

  • Tillo D, Hughes T (2009) G+C content dominates intrinsic nucleosome occupancy. BMC Bioinform 10:442

    Google Scholar 

  • Tue N, Yoshioka Y, Yamaguchi M (2011) NF-Y transcriptionally regulates the drosophila p53 gene. Genes 473(1):1–7

    Google Scholar 

  • Wan J, Lin J, Zack D, Qian J (2009) Regulating periodicity of nucleosome organization and gene regulation. Bioinformatics 25(14):1782–1788

    Article  Google Scholar 

  • Waterborg J (2002) Dynamics of histone acetylation in vivo. A function for acetylation turnover? Biochem Cell Biol 8:363–378

    Article  Google Scholar 

  • Widlund H, Vitolo J, Thiriet C, Hayes J (2000) DNA sequence-dependent contributions of core histone tails to nucleosome stability: differential effects of acetylation and proteolytic tail removal. Biochemistry 39:3835–3641

    Article  Google Scholar 

  • Windom J (2001) Role of dna sequence in nucleosome stability and dynamics. Q Rev Biophys 34(3):269–324

    Google Scholar 

  • Wirén M, Silverstein R, Sinha I, Walfridsson J, Lee H, Laurenson P, Pillus L, Robyr D, Grunstein M, Ekwall K (2005) Genomewide analysis of nucleosome density histone acetylation and HDAC function in fission yeast. EMBO J 24(16):2906–2918

    Article  Google Scholar 

  • Yang Z, Zheng C, Hayes J (2007) The core histone tail domains contribute to sequence-dependent nucleosome positioning. J Biol Chem 282(11):7930–8

    Article  Google Scholar 

  • Yuan G, Liu Y, Dion M, Slack M, LF LW, Altschuler S, Rando O (2005) Genome-scale identification of nucleosome positions in S. cerevisiae. Science 309:626–630

    Article  Google Scholar 

  • Zhao J, Herrera-Diaz J, Gross D (2005) Domain-wide displacement of histones by activated heat shock factor occurs independently of swi/snf and is not correlated with RNA polymerase II density. Mol Cell Biol 25(20):8985–8999

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Dr. Guo-Cheng Yuan and Dr. Chih Long Liu for sharing experiment data. The first and second authors have been supported by Japanese Government Scholarship (Monbukagakusho) to study in Japan.

Author information

Author notes

  1. Bich Hai Ho

    Present address: School of Knowledge Science, Japan Advanced Institute of Science and Technology, Asahidai 1-1, Nomi, Ishikawa, 923-1292, Japan

Authors and Affiliations

  1. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Asahidai 1-1, Nomi, Ishikawa, 923-1292, Japan

    Ngoc Tu Le

  2. John von Neumann Institute, Vietnam National University, Ho Chi Minh, Vietnam

    Tu Bao Ho

Authors
  1. Bich Hai Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ngoc Tu Le
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tu Bao Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bich Hai Ho.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ho, B.H., Le, N.T. & Ho, T.B. Quantitatively assessing the effects of regulatory factors on nucleosome dynamics by multiple kernel learning. J Ambient Intell Human Comput 3, 315–323 (2012). https://doi.org/10.1007/s12652-012-0155-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-012-0155-6

Keywords

Search

Navigation