Functional genomic approaches to duplicate gene evolution

实验室的长期目标是了解具有新功能的新基因是如何产生的，以及这些分子创新如何有助于生物体的生存，适应和进化。基因复制被广泛认为是新基因的主要来源，但复制基因功能分化的一般模式和机制还不清楚。利用高通量基因组技术和前所未有的功能基因组数据量，我们提出了实验和计算功能基因组学方法来复制基因进化，有四个具体的目标。首先，使用蛋白质-蛋白质相互作用（PPI）作为蛋白质功能的衡量标准，我们计划研究芽殖酵母酿酒酵母和其相关的Kluyveromyces waltii之间的重复和非重复基因的蛋白质功能变化率。色葡萄cerevisiae谱系在与K. waltii，并保留了约450对WGD重复。PPI信息来自S. cerevisiae是公开可得的，而K.将对Waltii进行实验测试。第二，我们建议制作定制的K. waltii的基因组表达谱，并与S.酿酒酵母研究如何表达模式的非复制和复制基因的变化在进化中。还将对公开获得的人类和小鼠的高质量微阵列数据进行类似的分析。第三，关于个体生物中的基因复制是否会通过提供额外的蛋白质产物而导致立即的适应性增加，是否会通过浪费能量来制造不需要的额外产物而导致立即的适应性损失，或者是否没有适应性变化，存在相互竞争的假设。使用公开的酵母和哺乳动物的功能基因组数据，我们将检查这些假设计算。然后，我们将在酵母中实验测量蛋白质生产在不同水平上的适应性成本，使用酵母中不需要的外源蛋白质。第四，基因是否能够在功能上补偿其复制副本的损失是有争议的。我们提出通过酵母实验对这种补偿假设进行批判性检验，在该实验中，我们测量了用其旁系同源物替换基因编码区所引起的适应度变化。完全蛋白质补偿预测没有健身的变化，而缺乏补偿导致健身减少。总之，这些研究有望显著提高我们对重复基因进化模式和机制的理解。 公共卫生相关性：我们的项目将增加对基因进化机制的理解，并帮助许多关于新生物功能如何产生的研究。我们的研究与人类健康相关，因为许多由基因复制产生的基因拷贝数变异与人类疾病有关。此外，重复基因之间的不同功能关系（例如，完全冗余、部分重叠或明显不同）将预测突变对遗传疾病的可能性和严重性的不同后果。对这些关系的清楚理解有助于阐明人类疾病的确切分子基础，这是治疗和预防这些疾病的必要步骤。

DESCRIPTION (provided by applicant): The long-term objective of my laboratory is to understand how new genes with novel functions originate and how these molecular innovations contribute to the survival, adaptation, and evolution of organisms. Gene duplication is wildly regarded as the primary source of new genes, but the general patterns and mechanisms of functional divergence of duplicate genes are not well understood. Taking advantage of high-throughput genomic technologies and unprecedented amount of functional genomic data, we propose experimental and computational functional genomic approaches to duplicate gene evolution, with four specific aims. First, using protein-protein interaction (PPI) as a measure of protein function, we plan to study the rate of protein functional change in duplicated and unduplicated genes between the budding yeast Saccharomyces cerevisiae and its relative Kluyveromyces waltii. The S. cerevisiae lineage experienced a whole-genome duplication (WGD) shortly after its separation from K. waltii and has retained ~450 pairs of WGD-duplicates. PPI information from S. cerevisiae is publicly available, while the corresponding PPIs in K. waltii will be experimentally tested. Second, we propose to make custom gene expression microarrays of K. waltii and compare its genome- wide gene expression pattern with that of S. cerevisiae to study how expression patterns of unduplicated and duplicated genes change in evolution. A similar analysis will also be conducted on the publicly available high-quality microarray data of the human and mouse. Third, competing hypotheses exist on whether gene duplication in an individual organism causes an immediate fitness gain by providing extra protein products, an immediate fitness loss by wasting energy for making extra products that are not needed, or no fitness change. Using publicly available functional genomic data of yeast and mammals, we will examine these hypotheses computationally. We will then experimentally measure in yeast the fitness cost of protein production at various levels, using foreign proteins that are not needed in yeast. Fourth, it is controversial as whether a gene can functionally compensate the loss of its duplicate copy. We propose a critical examination of this compensation hypothesis by a yeast experiment in which we measure the fitness change caused by replacing the coding region of a gene with that of its paralog. Complete protein compensation predicts no fitness change whereas an absence of compensation leads to fitness reduction. Together, these studies are expected to improve significantly our understanding of the patterns and mechanisms of duplicate gene evolution. PUBLIC HEALTH RELEVANCE: Our projects will increase understanding of mechanisms of gene evolution and aid many studies of how new biological functions arise. Our study is of human health relevance, because many gene copy number variations, generated by gene duplication, are involved in human diseases. Furthermore, different functional relationships among duplicate genes (e.g., completely redundant, partially overlapping, or distinctly different) would predict different consequences of mutations to the likelihood and severity of genetic diseases. A clear understanding of these relationships helps clarify the exact molecular basis of human diseases, a necessary step in the treatment and prevention of these diseases.