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

AMP激活蛋白激酶控制的基因调控网络的进化

• 批准号: 151020581
• 负责人: Professorin Dr. Karin D. Breunig
• 金额: --
• 依托单位: Abteilung Molekulargenetik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2010
• 资助国家: 德国
• 起止时间: 2009-12-31 至 2016-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/151020581?language=en
• 关键词: Evolution; AMP; activated; protein; kinase

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.

转录调控的改变被认为是趋异进化的主要驱动力。这反映在不同的物种中，通过控制高度保守的代谢途径的调节网络的可变结构。这些途径的调节蛋白也令人惊讶地保守。然而，显然，这些调节器的布线在进化过程中逐渐改变。该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。我们的目标是揭示与调节网络重排相关的进化过程中的适应和步骤的基本原则。以前的资助期的结果已经揭示，与目前的信念相反，调节器Sip 4和Cat 8控制面包酵母酿酒酵母和乳酵母乳酸克鲁维酵母中完全不同的靶基因组，并且，即使在直链酵母靶基因的情况下，它们也令人惊讶地经常以相反的方向调节。此外，这些调节剂的潜在结合位点的存在或不存在与这些基因的转录活性仅很差地相关。本项目旨在通过发现有助于调控的额外序列特征来改善基因调控的预测。为了检测和利用这些额外的功能，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，实现和应用相应的算法扩展上下文树最大化（CTM）和扩展简约上下文树最大化（PCTM）。这些工具还将有助于从计划的ChIP-seq数据中解释组蛋白乙酰化模式，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循环，从而在每个循环中改进算法，并不断增加实验验证和证伪的预测，最终使我们能够更深入地了解控制适应营养限制的转录调控网络的进化，这是最基本的过程之一，在所有生命王国中都得到了保护。