Abstract
DNA methylation is an epigenetic mechanism whose important role in development has been widely recognized. This epigenetic modification results in heritable changes in gene expression not encoded by the DNA sequence. The underlying mechanisms controlling DNA methylation are only partly understood and recently different mechanistic models of enzyme activities responsible for DNA methylation have been proposed. Here we extend existing Hidden Markov Models (HMMs) for DNA methylation by describing the occurrence of spatial methylation patterns over time and propose several models with different neighborhood dependencies. We perform numerical analysis of the HMMs applied to bisulfite sequencing measurements and accurately predict wild-type data. In addition, we find evidence that the enzymes’ activities depend on the left 5’ neighborhood but not on the right 3’ neighborhood.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The exact nucleotide distance between two neighboring dyads is not considered here, but we assume that this distance is small. For the BS-seq data that we consider, the average distance between two CpGs is 14 bp and the maximal distance is 46 bp.
- 2.
\([A,B]=AB-BA\) is the commutator of the matrices A and B.
- 3.
The number of cell divisions is estimated from the time of the measurement since these cells divide once every 24 hours.
References
Äijö, T., Huang, Y., Mannerström, H., Chavez, L., Tsagaratou, A., Rao, A., Lähdesmäki, H.: A probabilistic generative model for quantification of DNA modifications enables analysis of demethylation pathways. Genome Biol. 17(1), 49 (2016)
Arand, J., Spieler, D., Karius, T., Branco, M.R., Meilinger, D., Meissner, A., Jenuwein, T., Xu, G., Leonhardt, H., Wolf, V., et al.: In vivo control of CpG and non-CpG DNA methylation by DNA methyltransferases. PLoS Genet. 8(6), e1002750 (2012)
Baubec, T., Colombo, D.F., Wirbelauer, C., Schmidt, J., Burger, L., Krebs, A.R., Akalin, A., Schübeler, D.: Genomic profiling of DNA methyltransferases reveals a role for DNMT3B in genic methylation. Nature 520(7546), 243–247 (2015)
Bonello, N., Sampson, J., Burn, J., Wilson, I.J., McGrown, G., Margison, G.P., Thorncroft, M., Crossbie, P., Povey, A.C., Santibanez-Koref, M., et al.: Bayesian inference supports a location and neighbour-dependent model of DNA methylation propagation at the MGMT gene promoter in lung tumours. J. Theor. Biol. 336, 87–95 (2013)
Emperle, M., Rajavelu, A., Reinhardt, R., Jurkowska, R.Z., Jeltsch, A.: Cooperative DNA binding and protein/DNA fiber formation increases the activity of the Dnmt3a DNA methyltransferase. J. Biol. Chem. 289(43), 29602–29613 (2014)
Fu, A.Q., Genereux, D.P., Stöger, R., Laird, C.D., Stephens, M.: Statistical inference of transmission fidelity of DNA methylation patterns over somatic cell divisions in mammals. The Annals of Applied Statistics 4(2), 871 (2010)
Genereux, D.P., Miner, B.E., Bergstrom, C.T., Laird, C.D.: A population-epigenetic model to infer site-specific methylation rates from double-stranded DNA methylation patterns. PNAS 102(16), 5802–5807 (2005)
Giehr, P., Kyriakopoulos, C., Ficz, G., Wolf, V., Walter, J.: The influence of hydroxylation on maintaining CpG methylation patterns: a hidden Markov model approach. PLoS Comput. Biol. 12(5), e1004905 (2016)
Gowher, H., Jeltsch, A.: Molecular enzymology of the catalytic domains of the Dnmt3a and Dnmt3b DNA methyltransferases. J. Biol. Chem. 277(23), 20409–20414 (2002)
Hermann, A., Goyal, R., Jeltsch, A.: The Dnmt1 DNA-(cytosine-c5)-methyltransferase methylates DNA processively with high preference for hemimethylated target sites. J. Biol. Chem. 279(46), 48350–48359 (2004)
Holz-Schietinger, C., Reich, N.O.: The inherent processivity of the human de novo methyltransferase 3A (DNMT3A) is enhanced by DNMT3L. J. Biol. Chem. 285(38), 29091–29100 (2010)
Kapourani, C.A., Sanguinetti, G.: Higher order methylation features for clustering and prediction in epigenomic studies. Bioinformatics 32(17), i405–i412 (2016)
Kyriakopoulos, C., Giehr, P., Wolf, V.: H(O)TA: estimation of DNA methylation and hydroxylation levels and efficiencies from time course data. Bioinformatics (2017, to appear)
Lacey, M.R., Ehrlich, M., et al.: Modeling dependence in methylation patterns with application to ovarian carcinomas. Stat Appl Genet Mol Biol 8(1), 40 (2009)
Laird, C.D., Pleasant, N.D., Clark, A.D., Sneeden, J.L., Hassan, K.A., Manley, N.C., Vary, J.C., Morgan, T., Hansen, R.S., Stöger, R.: Hairpin-bisulfite PCR: assessing epigenetic methylation patterns on complementary strands of individual DNA molecules. PNAS 101(1), 204–209 (2004)
Norvil, A.B., Petell, C.J., Alabdi, L., Wu, L., Rossie, S., Gowher, H.: Dnmt3b methylates DNA by a noncooperative mechanism, and its activity Is unaffected by manipulations at the predicted dimer interface. Biochemistry (2016). http://dx.doi.org/10.1021/acs.biochem.6b00964
Okano, M., Bell, D.W., Haber, D.A., Li, E.: DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell 99(3), 247–257 (1999)
Otto, S.P., Walbot, V.: DNA methylation in eukaryotes: kinetics of demethylation and de novo methylation during the life cycle. Genetics 124(2), 429–437 (1990)
Sontag, L.B., Lorincz, M.C., Luebeck, E.G.: Dynamics, stability and inheritance of somatic DNA methylation imprints. J. Theor. Biol. 242(4), 890–899 (2006)
Suzuki, M.M., Bird, A.: DNA methylation landscapes: provocative insights from epigenomics. Nat. Rev. Genet. 9(6), 465–476 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Lück, A., Giehr, P., Walter, J., Wolf, V. (2017). A Stochastic Model for the Formation of Spatial Methylation Patterns. In: Feret, J., Koeppl, H. (eds) Computational Methods in Systems Biology. CMSB 2017. Lecture Notes in Computer Science(), vol 10545. Springer, Cham. https://doi.org/10.1007/978-3-319-67471-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-67471-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67470-4
Online ISBN: 978-3-319-67471-1
eBook Packages: Computer ScienceComputer Science (R0)