Rewiring the yeast brain: Redundancy and interference in genetic networks

重新连接酵母大脑：遗传网络的冗余和干扰

• 批准号: 8146626
• 负责人: NICOLAS EMILE BUCHLER
• 金额: $ 235.5万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-30 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8146626
• 关键词: Animals; Biological Neural Networks; Brain; Cell Cycle; Cells; Circadian Rhythms; Computer Simulation; Coupled; Dominant-Negative Mutation; Environment; Epigenetic Process; Eukaryota; Event; Evolution; Feedback; Future; Gene Duplication; Generations; Genes; Genetic; Health; Hour; Human; Learning; Light; Machine Learning; Memory; Microbe; Modeling; Molecular; Mutation; Parasites; Process; Regulation; Regulator Genes; Research; Research Proposals; Saccharomyces cerevisiae; Signal Transduction; Testing; Time; Yeasts; abstracting; brain cell; circadian pacemaker; directed evolution; duplicate genes; human disease; loss of function mutation; pressure; public health relevance; research study; theories

DESCRIPTION (Provided by the applicant) Abstract: Similar to neural networks in animals, molecular networks in cells can generate bistable or oscillatory dynamics that maintain memories of previous events (e.g. epigenetic switch) or order periodic events (e.g. cell cycle), respectively. In cells, networks of genes interacting with one another through regulatory feedback implement such dynamics. Analogous to learning in brains, cells can ""learn"" correlations in their environmental signals by encoding such correlations into their gene network dynamics through mutation, a ""re-wiring"" process that occurs on the timescale of generations. The issue of learning the statistical regularities and correlations of environmental signals is best exemplified by the evolution of ""circadian clocks"", which are oscillatory gene circuits that have learned to internalize the 24-hour light-dark circadian cycle. Strikingly, circadian clocks have evolved independently multiple times, which suggests there exists some selection pressure and/or evolutionary mechanism that repeatedly favor the convergent evolution of autonomous oscillation. The hypothesis of my research proposal is that certain types of loss-of-function mutations in duplicated genes (known as dominant-negative mutations) can easily generate bistability and oscillation in existing regulatory networks. Gene duplication followed by a loss-of-function mutation can generate a dominant-negative. A dominant negative mutation is a partial loss-of-function mutation that renders a gene duplicate functionally inactive, yet still capable of interacting with the original duplicate, the upstream effectors, and/or downstream targets. Thus, dominant-negatives can easily interfere with the proper regulation and activity of the original duplicate. Because both gene duplication and loss-of-function mutations occur frequently in evolution, this presents an evolutionary mechanism for rapidly generating bistability and autonomous oscillation in gene regulatory networks. My proposed research over the next five years will integrate experiment and theory to understand the extent to which gene duplication and dominant-negative mutations facilitate the evolution of epigenetic switches and circadian clocks in regulatory networks. We will use computer simulation and an experimental directed evolution approach in a tractable, model eukaryote (Saccharomyces cerevisiae) to test the ability of cells to learn the statistical regularities of their coupled environmental signals. Understanding how and why single-cell microbes and parasites have learned to predict their environment is essential for understanding their future evolution to changing host conditions. Public Health Relevance: The ability of parasites to learn and adapt to changing host conditions and environments presents a challenge to human health. The objective of my research proposal is to understand the capacity of gene networks in single cells to learn and predict the statistical regularities of their environment. Discovering the limitations and abilities of parasites to evolve and anticipate changes in their host environment will be invaluable for the treatment of many human diseases.

描述（由申请人提供） 摘要：与动物中的神经网络类似，细胞中的分子网络可以产生双稳态或振荡动力学，分别维持先前事件的记忆（例如表观遗传开关）或有序周期性事件（例如细胞周期）。在细胞中，基因网络通过调节反馈相互作用来实现这种动态。与大脑中的学习类似，细胞可以通过突变将环境信号中的相关性编码到基因网络动态中，从而“学习”这些相关性，这是一种发生在世代时间尺度上的“重新布线”过程。学习环境信号的统计规律和相关性的问题最好的例子是“生物钟”的进化，“生物钟”是一种振荡基因回路，已经学会将 24 小时的明暗昼夜节律周期内在化。引人注目的是，生物钟已经独立进化了多次，这表明存在一些选择压力和/或进化机制，反复有利于自主振荡的收敛进化。我的研究计划的假设是，重复基因中某些类型的功能丧失突变（称为显性失活突变）很容易在现有的调控网络中产生双稳态和振荡。基因复制后发生功能丧失突变可产生显性失活。显性失活突变是部分功能丧失突变，使基因复制品功能失活，但仍能够与原始复制品、上游效应子和/或下游靶标相互作用。因此，显性失活很容易干扰原始副本的适当调节和活性。由于基因复制和功能丧失突变在进化中频繁发生，这提供了一种在基因调控网络中快速产生双稳态和自主振荡的进化机制。我提出的未来五年的研究将整合实验和理论，以了解基因复制和显性失活突变在多大程度上促进调控网络中表观遗传开关和生物钟的进化。我们将在易处理的模型真核生物（酿酒酵母）中使用计算机模拟和实验定向进化方法来测试细胞学习其耦合环境信号的统计规律的能力。了解单细胞微生物和寄生虫如何以及为何学会预测其环境对于了解它们未来进化到不断变化的宿主条件至关重要。 公共卫生相关性：寄生虫学习和适应不断变化的宿主条件和环境的能力对人类健康提出了挑战。我的研究计划的目的是了解单细胞中基因网络学习和预测其环境统计规律的能力。发现寄生虫进化的局限性和能力以及预测宿主环境的变化对于许多人类疾病的治疗具有无价的价值。