Evolution of the AMP-activated protein kinase controlled gene regulatory network.
Alterations in transcriptional 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 switches. The switch from glycolysis to gluconeogenesis and back that serves as an adaptive mechanism in response to changes in nutrient or oxygen supply in all living systems is controlled by a conserved set of protein kinases and their down-stream eﬀectors. Apparently, the wiring of these regulators has changed gradually during evolution. The goal of this project is to uncover sequential steps in this evolutionary process and to contribute to the understanding of the evolution of cis- regulatory modules (CRMs) and, in the long run, to the evolution of the transcriptional regulatory network controlling gluconeogenesis. The ﬁrst part of the project will focus on the sets of genes regulated by the AMP-activated protein kinase, which functions as central sensor of the cellular energy state from yeast to mammals. The genomes of the related yeast species Saccharomyces cerevisiae and Kluyveromyces lactis will serve as starting points for the development of experimental techniques and data analysis tools by which the more than 20 sequenced fungal genomes can be explored in order to trace evolutionary leaps leading to regulator rewiring. A reliable identiﬁcation of genes regulated by the AMPK homolog Snf1 and of CRMs in several sets of promoters regulated by both known and not yet known transcription factors (TFs) regulated by Snf1 shall be achieved using complementary, and tightly integrated, experimental and computational approaches. Current approaches from bioinformatics are powerful, but not as powerful as they could be, due to the fact that often (i) relevant information is neglected, (ii) predictions are not tested experimentally, or (iii) experimentally veriﬁed and falsiﬁed predictions are not used systematically to improve the prediction algorithms and re-iterate the cycle of prediction and experimental testing. For example, standard approaches for the prediction of target genes from wild-type and mutant expression data and from ChIP-chip or ChIP-seq data typically neglect statistical dependencies between expression levels of neighboring genes, the orientation of promoters, or locations of promoters of cryptic unstable transcripts (CUTs). Standard approaches for the prediction of CRMs either assume all promoters to be statistically independent, ignoring their evolutionary relationship entirely, or they assume that all positions within cis-elements are statistically independent. Here we propose to apply a recent extension of Context Trees, called Parsimonious Context Trees, to these two tasks, yielding algorithms capable of using such valuable information previously neglected. The focus of our project is to establish an iterative cycle of predictions and follow-up experiments, leading to improved algorithms in each cycle and to a growing set of experimentally veriﬁed and falsiﬁed predictions, ﬁnally allowing a deeper understanding of the evolution of CRMs and the evolution of the transcriptional regulatory network controlling gluconeogenesis.