Coevolutionary Arms Races Driven by Conflict: a Test in Social Amoeba

在所有层次的生物组织中都可以观察到合作：基因组在细胞中合作，细胞在多细胞生物中合作，个体合作形成社会。然而，在每一个层次上，都有一些人会欺骗，而另一些人不会，生物学家仍然没有完全理解种群是如何在两种类型的个体中进化出稳定的系统的，或者这种共同进化是如何在自然界中同一物种的种群中分布的。这是因为欺骗的个体在不支付公平的成本份额的情况下获得了合作的好处，因此这应该在人群中迅速传播。研究欺骗与合作在种群中的自然分布是很重要的，因为欺骗在自然界中很普遍，并且发生在生物组织的各个层面，从增强自身传播的基因到损害携带它们的个体，再到诱骗他人抚养后代的寄生虫。更重要的是，防止作弊机制的进化可能对生物复杂性的出现至关重要。此外，欺骗可以影响一切，从共生的成功到癌症的出现，因此在医学和农业上都有重要的应用。这个项目的新颖之处在于，研究人员将记录一种黏菌在野外的自然种群，以确定在欺骗的程度和对欺骗的抵抗力上是否存在差异（和共同进化），这种差异跨越了空间和时间。这项研究还采用了群体基因组的方法来确定与欺骗和抵抗有关的基因。总的来说，目标是了解影响自然界作弊动态的生态和进化过程。一个突出的假设是，欺骗选择了抵制或抑制它的机制，而这种机制反过来又选择了克服这种抵制的机制。因此，欺骗可能会引发一场适应和反适应的军备竞赛。如果是这样，那么社会冲突，就像其他形式的选择性冲突一样，可能会导致迅速的、分歧的、永无止境的变化，推动进化的分歧和多样化。迄今为止，这种军备竞赛假说已经得到了实验室实验和分子进化分析的支持，但没有研究作弊如何影响自然界社会特征的进化轨迹。本研究的目的是在社会变形虫（或细胞黏菌）盘状齿柱的自然种群中测试这一假设的几个预测。这种生物有一种不同寻常的多细胞结构，这使得它特别容易受到欺骗。这项研究将量化自然界中适应和反适应的表型模式，其灵感来自于用于阐明其他形式的选择性冲突的方法，如宿主-病原体或遗传冲突。跨越空间和时间的纵向采样，结合保存和恢复过去和未来社会伙伴的能力，提供了一个独特的机会来观察原位共同进化，构成了其他模型系统的主要优势。

Cooperation is observed across all levels of biological organization: genomes cooperate within cells, cells cooperate within multicellular organisms, and individuals cooperate to form societies. However, at each of these levels, there are some individuals who cheat and others who do not, and biologists still do not fully understand how populations evolve stable systems with both types of individuals or how this coevolution is distributed across populations of the same species in nature. This is because individuals who cheat gain the benefits of cooperation without paying their fair share of the cost, and thus this should spread rapidly through populations. Studying the natural distributions of cheating and cooperation in populations is important because cheating is prevalent in the natural world and occurs across all levels of biological organization, from genes that enhance their own transmission to the detriment of the individual that harbors them, to brood parasites that trick others into raising their young. More importantly, the evolution of mechanisms to prevent cheating was likely critical to the emergence of biological complexity. Moreover, cheating can influence everything from the success of symbioses to the emergence of cancer, and thus has important applications to both medicine and agriculture. The novelty of this project is that the researchers will document natural populations of a slime mold in the wild to determine if there is variation (and coevolution) in the degree of cheating and in resistance to cheating across space and through time. The research also takes a population genomic approach to determine the genes involved in cheating and resistance. Overall the goal is to understand the ecological and evolutionary processes influencing the dynamics of cheating in nature. One prominent hypothesis is that cheating selects for mechanisms to resist or suppress it, which in turn selects for mechanisms to overcome that resistance. Cheating might thus give rise to an arms race of adaptations and counter-adaptations. If so, then social conflict, just like other forms of selective conflict, may cause rapid, divergent, and never-ending change, driving evolutionary divergence and diversification. To date, this arms race hypothesis has garnered support from laboratory experiments and molecular evolution analyses, but there is no work examining how cheating impacts the evolutionary trajectories of social traits in nature. The goal of this research is to test several predictions of this hypothesis in natural populations of the social amoeba (or cellular slime mold) Dictyostelium discoideum. This organism has an unusual form of multicellularity that renders it particularly vulnerable to cheating. The research will quantify phenotypic patterns of adaptation and counter-adaptation in nature, inspired by approaches used to elucidate other forms of selective conflict, such as host-pathogen or genetic conflict. Longitudinal sampling across space and time, combined with the ability to save and revive past and future social partners, offers a unique opportunity to observe coevolution in situ and constitutes a major advantage over other model systems.