Deciphering the mechanisms and dynamics of proto-gene evolution

从头基因诞生的现象可能是每个物种独一无二的基础，但人们对它仍然知之甚少。 我们最近发现的证据表明，被认为是垃圾DNA的基因组区域可以被翻译，提供了一种 促进从头基因诞生的“原基因”的进化库。这项提议旨在描述 从三个互补的角度研究原基因进化的机制和动力学： 1)原基因如何促进适应？为了回答这个问题，我们将过度表达原基因和分类 控制酵母中的序列，并剖析它们在多种环境条件下对细胞适应性的影响。 2)原基因模型是通用的吗？为了回答这个问题，我们将对 新基因在哺乳动物、昆虫、疟疾和真菌中诞生的自然历史通过设计和实现 新颖的约会算法。这将使我们能够评估这些不同谱系的特征，例如 例如，基因组的大小会影响原基因进化的频率。 2)基因组序列如何退出非基因状态？目前尚不清楚原基因是否更有可能从 模拟新启动子结构或开放阅读框架的突变后的非基因序列。评估 这些竞争机制的可行性，我们将在基因已经合成的地方构建酵母突变体 通过破坏开放阅读框架或其启动子而被杀死。然后我们将在各种不同的环境下进化这些突变体 选择压力，观察其中一种机制是否比另一种更成功。

The phenomenon of de novo gene birth may underlie what makes each species unique, yet it remains poorly understood. We have recently found evidence that regions of the genome thought to be junk DNA can be translated, providing an evolutionary reservoir of “proto-genes” that promote de novo gene birth. This proposal aims at characterizing the mechanisms and dynamics of proto-gene evolution by investigating three complementary angles: 1) How do proto-genes promote adaptation? To answer this question, we will overexpress proto-genes and assorted control sequences in yeast and dissect their impact on cellular fitness across multiple environmental conditions. 2) Is the proto-gene model universal? To answer this question, we will perform a computational reconstruction of the natural history of de novo gene birth in mammals, insects, malaria and fungi though the design and implementation of novel dating algorithms. This will enable us to evaluate which features that characterize these distinct lineages, such as genome size for example, influence the frequency of proto-gene evolution. 2) How do genomic sequences exit the non-genic state? It is unclear whether a proto-gene is more likely to emerge from a non-genic sequence following mutations emulating a novel promoter structure, or an open reading frame. To assess the feasibility of these competing mechanisms, we will construct yeast mutants where a gene has been synthetically killed by destroying either is open reading frame or its promoter. We will then evolve these mutants under a variety of selective pressures and observe if one mechanism is more successful than the other.