Evolution of the AMP-activated protein kinase controlled gene regulatory network

转录调控的改变被认为是分化进化的主要驱动力。这反映在不同物种中，控制高度保守的代谢途径的调控网络的不同结构。这些途径的调节蛋白也出人意料地保守。然而，显然，这些监管机构的线路在进化过程中逐渐发生了变化。这个项目的重点是适应营养供应，这是由一组保守的蛋白激酶及其下游效应器控制的。其目标是揭示适应的基本原则和与监管网络重排相关的进化过程中的步骤。之前的资助期的结果已经揭开了，与目前的看法相反，调节子Sip4和Cat8控制着面包师酵母酿酒酵母和牛奶酵母Kluyvermyces lactis中截然不同的目标基因集，即使是同源的目标基因，它们也令人惊讶地经常受到相反方向的调控。此外，这些调节剂潜在结合位点的存在或不存在与这些基因的转录活性只有很小的相关性。本项目旨在通过发现有助于基因调控的额外序列特征来提高对基因调控的预测。为了检测和利用这些附加特征，我们将扩展上下文树(CT)模型和简约上下文树(PCT)模型，并推导、实现和应用相应的扩展上下文树最大化(CTM)和扩展简约上下文树最大化(PCTM)算法。这些工具还将有助于从计划的芯片序列数据中解读组蛋白乙酰化模式，以揭示潜在的表观遗传调控模式。我们的主要目标是建立一个计算预测和实验验证的迭代周期，导致在每个周期中改进算法，并产生越来越多的实验验证和证伪预测，最终允许更深入地了解转录调控网络的进化，该网络控制对营养限制的适应，这是最基本的过程之一，在所有生命王国中都是保守的。

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 pathways. The regulatory proteins of such pathways are also surprisingly conserved. However, apparently, the wiring of these regulators has changed gradually during evolution. This project focuses on the adaptation to nutrient supply, which is controlled by a conserved set of protein kinases and their down-stream effectors. The goal is to uncover basic principles of adaptation and steps in the evolutionary process associated with regulatory network rearrangement. The results of the previous funding periods have unraveled that, in contrast to current belief, the regulators Sip4 and Cat8 control quite different sets of target genes in baker's yeast Saccharomyces cerevisiae and in the milk yeast Kluyveromyces lactis, and, even in case of orthologous target genes, they are surprisingly often regulated in opposite direction. Moreover, the presence or absence of potential binding sites for these regulators correlates only poorly with the transcriptional activity of such genes. The present project aims at improving the prediction of gene regulation by uncovering additional sequence features that contribute to regulation. For detecting und utilizing such additional features, we will extend Context Tree (CT) models and Parsimonious Context Tree (PCT) models and derive, implement, and apply the corresponding algorithms for extended Context Tree Maximization (CTM) and extended Parsimonious Context Tree Maximization (PCTM). These tools will also help to interpret histone acetylation pattern from planned ChIP-seq data to uncover potential epigenetic modes of regulation. Our main goal is to establish an iterative cycle of computational predictions and experimental validation, 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 the transcriptional regulatory network controlling adaptation to nutrient limitation, one of the most fundamental processes, conserved across all kingdoms of life.