Local adaptation for seed banks in wild tomato species

种子长时间的休眠促进了所谓的种子库，这是一种至关重要的生理适应，使植物能够成功地在大量可变和不可预测的环境中定居。这种进化上的对冲赌注包括减少短期的繁殖成功，以减少长期的风险，并且在细菌、原生动物（包括人类寄生虫）和昆虫物种中也广泛存在。尽管种子银行似乎是一种普遍存在的促进适应不可预测环境的机制，但推动种子银行进化的进化、遗传和基因组机制在很大程度上是未知的。到目前为止，我们还不知道1)这种押注对冲策略随着时间的推移演变得有多快，或者2)其遗传基础是什么。在本项目中，我们通过将种群基因组学理论方法的发展与基因组测序和种子休眠的经验测量相结合，采用四管齐下的方法来解决这两个问题。我们使用在秘鲁南部和智利北部不同栖息地发现的野生番茄Solanum chilense作为模型系统，以揭示负责下注对冲进化的位点。首先，在项目的理论部分，我们的目标是开发一种隐马尔可夫模型（HMM）统计方法来分析来自植物群体的全基因组序列。隐马尔可夫方法将从基因组多态性和重组率的变化中具体推断种子库的过去进化。其次，我们将通过对来自两个已经测序的亲本植物的20个F1后代的全基因组测序，量化野生种S. chilense的基因组突变率。在我们的推理方法中，这一步对于获得准确的估计是至关重要的。第三，我们将通过对比选择在不同栖息地（中北部、南部沿海和南部高海拔地区）的6个chilense种群，揭示对种子库的局部适应，并推断适应速度。从这些群体中获得30株植物的全基因组序列，并使用我们的HMM方法进行研究。我们想要测试新栖息地的殖民化是否与chilense种子库的变化相关，并重要地揭示对非生物因素和种子休眠的本地适应的新基因。第四，为了完善种子库适应性的证据，我们将在不同的温度和湿度条件下对6个已测序的辣椒种群进行种子萌发实验。该项目将统计群体遗传学模型、全基因组数据分析和发芽经验测试结合起来，首次展示了野生植物物种最近对种子休眠的局部适应。

Seed dormancy for long periods of time promoting so called seed banks is a crucial physiological adaptation which allowed plants to successfully colonize a large number of variable and unpredictable environments. This evolutionary bet hedging consists in decreasing short-term reproductive success in favour of longer-term risk reduction, and is also widely prevalent in bacteria, protozoans (including human parasites), and insects species. Although, seed banking appears to be a ubiquitous mechanism facilitating adaptation to unpredictable environments, the evolutionary, genetic and genomic mechanisms driving the evolution of seed banking are largely unknown. Up to date we do not know 1) how fast such bet hedging strategies evolve in time, or 2) what the genetic underpinnings are. In this project, we use a four-pronged approach to address both questions by combining the development of a population genomics theoretical method with genome sequencing and empirical measures of seed dormancy. We use as a model system, Solanum chilense, a wild tomato species found in various habitats in Southern Peru and Northern Chile, to reveal loci responsible for bet hedging evolution.First, in the theoretical part of the project, we aim to develop a Hidden Markov Model (HMM) statistical method to analyse full genome sequences from plant populations. The HMM method will infer specifically the past evolution of seed banking from variation in polymorphism and recombination rates along genomes.Second, we will quantify the genomic mutation rate for the wild species S. chilense, by sequencing the full genome of 20 F1 progenies from two already sequenced parental plants. This step is crucial for obtaining accurate estimates in our inference method.Third, we will uncover local adaptation for seed banking and infer the speed of adaptation by contrasting six populations of S. chilense chosen in different habitats (North mesic, Southern coastal and Southern high altitudes). Full genomes sequences from 30 plants from these populations will be obtained and studied using our HMM method. We want to test if colonization of new habitats in S. chilense was correlated with changes in seed banking, and importantly reveal novel genes underlying local adaptation to abiotic factors and seed dormancy.Fourth, to complete the evidence for seed bank adaptation, we will perform germination experiments of seeds from the six sequenced S. chilense populations under different temperature and humidity regimes. This project presents a unique combination of statistical population genetics modelling, full genome data analyses, and empirical test of germination to provide the first demonstration of recent local adaptation for seed dormancy in a wild plant species.