Adaptation by improbable changes can identify the phenotypic targets of selection

目标。我的实验室将开发、测试和应用统计方法，以确定在分子适应过程中哪些表型变化是选择的目标。基本原理。适应是因为选择改善了它们功能的一个方面而进化出来的特征，而确定突变的功能影响是现代生物学研究的主要目标之一。然而，即使是对功能的详细了解也不足以证明适应。很难确定哪些进化变化是适应，因为突变经常或可能总是影响不止一种特征。因此，即使一个性状是通过改变某些测量功能的突变来进化的，选择的真正目标可能是这些相同突变对相关性状的影响。接近。生物体的许多特征显然是不可能的。复杂或精细优化的特征需要适应性的解释，因为选择是唯一能将系统始终如一地推向不可能状态的机制。基于这种理解，我的研究计划的长期目标是开发方法，不是通过它们的功能，而是通过它们发生的可能性来识别适应。我提出了两个短期目标来支持这一长期目标。目的1.我们将通过预测生物物理蛋白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。我们将测试两个相互竞争的适应性故事，说明肌红蛋白是如何进化到耐受高肌肉浓度而不会沉淀的：电荷增加或稳定性。有没有哪一种特征是朝着不太可能的方向进化的？这两个特征都可以从肌红蛋白的生物物理性质中预测出来。因此，我们可以为等位基因的所有单个突变生成表型，并估计表型在一个方向或另一个方向偶然发生变化的概率。这些完全分解的表型空间也提供了探索适应的理论基础的令人兴奋的项目。目的2.我们将通过对突变表型的大规模经验测量来测试发光颜色适应的经典例子。甲虫荧光素酶的发光不断地从绿色进化到橙色。这种变化是常见的还是罕见的？我们将通过表达人工突变体、根据等位基因的颜色将等位基因分类和测序来评估荧光素酶的表型空间。深度测序的等位基因库，每个等位基因库都有不同的表型，对于训练机器学习算法预测表型也是有用的。重要性。适应是生物学的中心，但我们需要知道哪些特征选择对进化起作用，以了解进化的原因。分子适应的严格统计检验的应用跨越了所有生物学。从追踪适应功能(在农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)的起源，到了解冲突如何推动新颖性(在免疫识别或物种形成方面)。

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).