Abstract
Alterations in gene regulation are considered major driving forces in divergent evolution. This is reflected in different species by the variable architecture of regulatory networks controlling highly conserved metabolic pathways. While many regulatory proteins are surprisingly conserved their wiring has evolved more rapidly. This project focuses on the adaptation to nutrient limitation, which requires the activation of the conserved AMP-activated protein kinase (AMPK alias Snf1 in yeast) and its downstream effectors. The goal is to uncover basic principles of adaptation and steps in the evolutionary process associated with regulatory network rearrangement. This requires improving the prediction of gene regulation based experimental data, DNA sequence information and information theory. In this project Context Tree (CT) models and Parsimonious Context Tree (PCT) models and the corresponding algorithms for extended Context Tree Maximization (CTM) and extended Parsimonious Context Tree Maximization (PCTM) are derived, implemented, and applied. Computational predictions and experimental validation will establish an iterative cycle to improve algorithms in each cycle leading to a growing set of experimentally verified and falsified predictions, finally allowing a deeper understanding of the evolution of the transcriptional regulatory network controlling energy metabolism, one of the most fundamental processes conserved across all kingdoms of life.
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Publications Within the Project
Anders A, Breunig KD (2011) Evolutionary aspects of a genetic network: studying the lactose/galactose regulon of kluyveromyces lactis. In: Becskei A, (ed) Yeast genetic networks: methods and protocols. Humana Press, Totowa, NJ, pp 259–277. doi:10.1007/978-1-61779-086-7_13
Eggeling R et al (2012) Gibbs sampling for parsimonious Markov models with latent variables. In: The sixth European workshop on probabilistic graphical models
Eggeling R, (2013) Inhomogeneous parsimonious Markov models. In: Machine learning and knowledge discovery in databases: European conference, ECML PKDD, et al (2013) Prague, Czech Republic, 23–27 Sept 2013. Proceedings, Part I
Eggeling R et al (2014) On the value of intra-motif dependencies of human insulator protein CTCF. PLoS ONE 9(1):1–12. doi:10.1371/journal.pone.0085629
Eggeling R et al (2015) Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data. BMC Bioinform 16(1):375. doi:10.1186/s12859-015-0797-4
Eggeling R, Koivisto M, Grosse I (2015a) Dealing with small data: on the generalisation of context trees. In: Proceedings of the 32nd international conference on machine learning. Lille, France
Mehlgarten C et al (2015) Divergent evolution of the transcriptional network controlled by snf1-interacting protein sip4 in budding yeasts. PLoS ONE 10(10):1–23. doi:10.1371/journal.pone.0139464
Nettling M et al (2015) DiffLogo: a comparative visualization of sequence motifs. BMC Bio 16: 387+. (17 Nov 2015), http://dx.doi.org/10.1186/s12859-015-0767-x
Nettling M et al (2016) Detecting and correcting the binding-affinity bias in ChIP-seq data using inter-species information. BMC Genomics 17(1). http://view.ncbi.nlm.nih.gov/pubmed/27165633
Nettling M, Treutler H, Cerquides J, Grosse I (2017a) Unrealistic phylogenetic trees may improve phylogenetic footprinting. Bioinformatics. doi:10.1093/bioinformatics/btx033. [Epub ahead of print]
Nettling M, Treutler H, Cerquides J, Grosse I (2017b) Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies. BMC Bioinformatics 18(1):141
Other Publications
Anders A et al (2006) The galactose switch in Kluyveromyces lactis depends on nuclear competition between Gal4 and Gal1 for Gal80 binding. J Biol Chem 281(39):29337–29348. http://www.jbc.org/content/281/39/29337.abstract
Brauer MJ et al (2005) Homeostatic adjustment and metabolic remodeling in glucose-limited yeast cultures. Mol Biol Cell 16(5):2503–2517. http://www.molbiolcell.org/content/16/5/2503.abstract
Chang Y-W et al (2008) Roles of cis- and trans-changes in the regulatory evolution of genes in the gluconeogenic pathway in yeast. Mol Biol Evol 25(9):1863–1875 http://mbe.oxfordjournals.org/content/25/9/1863.abstract
Cai L et al (2011) Acetyl-CoA induces cell growth and proliferation by promoting the acetylation of histones at growth genes. Mol Cell 42(4):426–437. doi:10.1016/j.molcel.2011.05.004
Charbon G et al (2004) Key Role of Ser562/661 in Snf1-dependent regulation of Cat8p in Saccharomyces cerevisiae and Kluyveromyces lactis. Mol Cell Biol 24(10):4083–4091. http://mcb.asm.org/content/24/10/4083.abstract
Dujon B (2010) Yeast evolutionary genomics. Nat Rev Genet 11(7):512–524. doi:10.1038/nrg2811
Gasch AP et al (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell 11(12):4241–4257. http://www.molbiolcell.org/content/11/12/4241.abstract
Gordon JL, Byrne KP, Wolfe KH (2009) additions, losses, and rearrangements on the evolutionary route from a reconstructed ancestor to the modern Saccharomyces cerevisiae genome. PLoS Genet 5(5):1–14. doi:10.1371/journal.pgen.1000485
Grau J et al (2012) Jstacs: a java framework for statistical analysis and classification of biological sequences. J Mach Learn Res 13:1967–1971
Hardie DG, Ashford MLJ (2014) AMPK: regulating energy balance at the cellular and whole body levels. Physiology 29(2):99–107. http://physiologyonline.physiology.org/content/29/2/99
Haurie V et al (2001) the transcriptional activator cat8p provides a major contribution to the reprogramming of carbon metabolism during the diauxic shift inSaccharomyces cerevisiae. J Biol Chem 276(1):76–85. http://www.jbc.org/content/276/1/76.abstract
Hittinger C et al (2010) Remarkably ancient balanced polymorphisms in a multi-locus gene network. Nature 464(7285):54–58. (4 Mar 2010), doi:10.1038/nature08791
Keogh RS, Seoighe C, Wolfe KH (1998) Evolution of gene order and chromosome number in Saccharomyces. Kluyveromyces and related fungi. Yeast 14(5):443–457. doi:10.1002/(SICI)1097-0061(19980330)14:53C443::AID-YEA2433E3.0.CO;2-L
Lavoie H, Hogues H, Whiteway M (2009) Rearrangements of the transcriptional regulatory networks of metabolic pathways in fungi. Growth and development: eukaryotes/prokaryote. Curr Opin Microbiol 12(6):655–663 http://www.sciencedirect.com/science/article/pii/S1369527409001490
Mordelet F et al (2013) Stability selection for regression-based models of transcription factor-DNA binding specificity. Bioinformatics 29(13):i117. doi:10.1093/bioinformatics/btt221
Necsulea A, Kaessmann H (2014) Evolutionary dynamics of coding and non-coding transcriptomes. Nat Rev Genet 15(11):734–748. doi:10.1038/nrg3802
Roth S, Kumme J, Schüller H-J (2004) Transcriptional activators Cat8 and Sip4 discriminate between sequence variants of the carbon source-responsive promoter element in the yeast Saccharomyces cerevisiae. Curr Genet 45(3):121–128. doi:10.1007/s00294-003-0476-2
Schaffrath R, Breunig KD (2000) Genetics and molecular physiology of the yeast kluyveromyces lactis. Fungal Genet Biol 30(3):173–190. http://www.sciencedirect.com/science/article/pii/S1087184500912210
Sorrells TR et al (2015) Intersecting transcription networks constrain gene regulatory evolution. Nature 523(7560):361–365 (16 July 2015). doi:10.1038/nature14613
Zenke FT et al (1993) Gal80 proteins of Kluyveromyces lactis and Saccharomyces cerevisiae are highly conserved but contribute differently to glucose repression of the galactose regulon. Mol Cell Biolo 13(12):7566–7576. http://mcb.asm.org/content/13/12/7566.abstract
Zenke FT et al (1996) Activation of Gal4p by galactose-dependent interaction of galactokinase and Gal80p. Science 272(5268):1662–1665. http://science.sciencemag.org/content/272/5268/1662
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Mehlgarten, C. et al. (2018). Evolution of the AMP-Activated Protein Kinase Controlled Gene Regulatory Network . In: Bossert, M. (eds) Information- and Communication Theory in Molecular Biology. Lecture Notes in Bioengineering. Springer, Cham. https://doi.org/10.1007/978-3-319-54729-9_9
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