Adaptation by improbable changes can identify the phenotypic targets of selection

通过不太可能的变化进行适应可以识别选择的表型目标

• 批准号: RGPIN-2021-03716
• 负责人: Springer, Stevan
• 金额: $ 2.04万
• 依托单位: University of Prince Edward Island
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=745897
• 关键词: Adaptation; improbable; changes; identify; phenotypic

Objective. My lab will develop, test, and apply statistical methods to identify which phenotypic changes were targets of selection during molecular adaptation. Rationale. Adaptations are traits that evolved because selection improved an aspect of their function, and determining the functional effects of mutations is one primary goal of modern biological research. However, even detailed knowledge of function is not sufficient to demonstrate adaptation. It is difficult to determine which evolutionary changes are adaptations because mutations often, or perhaps always, influence more than one trait. So, even if a trait evolves by mutations that change some measured function, the real target of selection could be the effects of these same mutations on a correlated trait. Approach. Many features of organisms are manifestly improbable. Complex or finely-optimized traits invite adaptive explanations because selection is the only mechanism that can drive systems consistently toward unlikely states. Based on this understanding, my research program's long-term goal is to develop methods to identify adaptations not by their function but by the improbability of their occurrence. I propose two short-term aims to support this long-term goal. Aim 1. We will test proposed adaptations of whale myoglobin by predicting biophysical protein phenotypes. Diving mammals hold oxygen in muscle myoglobin. We will test two competing adaptive stories of how myoglobin evolved to tolerate high muscle concentrations without precipitating: increases in charge or stability. Did either trait evolve in an improbable direction? Both traits can be predicted from the biophysical properties of myoglobin. So we can generate phenotypes for all single mutations of an allele, and estimate the probability that a phenotype will change in one direction or the other by chance. These fully-resolved phenotype spaces also offer exciting projects that explore the theoretical basis of adaptation. Aim 2. We will test a classic example of luminescent colour adaptation by large-scale empirical measurement of mutant phenotypes. Click-beetle luciferase glow has evolved repeatedly from green to orange. Is this change common or rare? We will evaluate the phenotype space for luciferase by expressing artificial mutants, sorting alleles into pools based on their colour, and sequencing. Deep-sequenced pools of alleles, each pool a different phenotype, may also be useful for training machine-learning algorithms for phenotype prediction. Importance. Adaptation is the center of biology, but we need to know which traits selection acts on to understand the causes of evolution. The applications of a rigorous statistical test of molecular adaptation span all of biology. From tracing the origin of adaptive functions (in crops or changing environments) or maladaptive functions (in the pleiotropic causes of disease) to understanding how conflicts drive novelty (in immune recognition or species formation).

目标。我的实验室将开发、测试和应用统计方法来确定哪些表型变化是分子适应过程中选择的目标。然而，即使是对功能的详细了解也不足以证明适应性，因为突变经常或可能总是影响多个性状。特征是通过改变的突变而进化的一些可测量的功能，选择的真正目标可能是这些相同的突变对相关性状的影响，而复杂或精细优化的性状显然是不可能的，因为选择是唯一可以驱动的机制。基于这种理解，我的研究计划的长期目标是开发方法来识别适应，而不是通过它们的功能，而是通过它们发生的可能性来支持这一长期目标。目标。 1. 我们将通过预测生物物理蛋白质表型来测试鲸鱼肌红蛋白的适应性。潜水哺乳动物的肌肉肌红蛋白中含有氧气，我们将测试肌红蛋白如何进化以耐受高肌肉浓度而不沉淀的两个相互竞争的适应性故事：电荷或稳定性的增加。这两个特征都可以从肌红蛋白的生物物理特性中预测出来，因此我们可以为等位基因的所有单突变生成表型，并估计表型的概率。这些完全解析的表型空间也提供了探索适应理论基础的令人兴奋的项目。我们将通过对突变体进行大规模经验测量来测试发光颜色适应的经典例子。表型。点击甲虫荧光素酶发光反复从绿色进化为橙色，这种变化是常见还是罕见？我们将通过表达人工突变体、根据颜色将等位基因分类到池中并进行测序来评估荧光素酶的表型空间。等位基因的深度测序池，每个池都有不同的表型，也可能有助于训练用于表型预测的机器学习算法。适应是生物学的中心，但我们需要知道哪些性状选择起作用才能理解其原因。分子适应的严格统计测试的应用涵盖了所有生物学，从追踪适应功能（在作物或不断变化的环境中）或适应不良功能（在疾病的多效性原因中）到理解冲突如何驱动新奇。处于免疫状态识别或物种形成）。