EAGER-NEON: Genomic Plasticity in Response to Variable Environments

Michael S. Barker，PI和Katrina M. Dlugosch，CoPI工厂扎根于当地，无法逃脱恶劣的环境条件。相反，许多植物物种可以通过改变它们的结构或生长习性来适应不同的环境。在某些情况下，适应是可见的，例如当当地环境变得缺水时，植物会长出小叶子或提前开花。在其他情况下，对环境变化的反应是不可见的，因为微妙的生物化学或遗传生存模式被激活。这种所谓的“基因组可塑性”使入侵物种等植物能够在新的或苛刻的生态位中茁壮成长，而不是那些反应不快的植物。同样，具有高度重复的基因组的植物（称为多倍体）可能更好地适应环境，因为它们在应对环境变化时表现出更多的多样性。了解适应性可塑性如何发生对于预测植物对环境变化的反应至关重要。本项目利用氖新的环境和生物多样性数据来研究可塑性的基因组基础。为了研究植物在氖足迹中的适应性可塑性，将使用新的测序技术来表征在选定的多倍体和入侵物种中在几个时间段内表达的所有基因。这些结果将与氖生物多样性数据中包括的物种的丰度和植物群落结构进行比较。因此，该项目将为植物基因组及其后续性状如何随时间变化的前所未有的观点奠定基础。这项研究利用并扩展了氖的长期数据，以确定基因组水平的可塑性如何预测植物的成功。适应性表型可塑性是物种响应气候变化的主要手段之一，但可塑性在植物基因组中的作用还不清楚。可塑性不容易识别或研究，因为关键特征的稳态可以通过组织底层的可塑性来维持。这项研究通过使用可塑性的基因组测量来跟踪植物对氖网络中环境变化的反应，从而规避了这个问题。为了测量和记录可塑性，该项目将来自氖核心站点的选定植物的RNA测序的转录组概况与植物群落组成的氖调查相结合。具体活动包括：（1）确定哈佛森林氖核心站点物种的倍性水平，并为研究选择目标多倍体、二倍体和入侵物种;（2）对30个目标物种进行RNA测序;（3）将基因组可塑性与物种丰度相关联。该项目利用了氖的长期研究站点和数据，同时将氖的植物多样性数据的力量扩展到基因组水平。基因组可塑性的测量将提供高度相关的和可比较的指标，跨类群，空间和时间的塑料对环境的反应。这些数据和分析工具将通过NCBI和iPlant Collaborative公开提供，并将通过提供预测植物在不确定环境中如何应对变化的方法来造福社会。

Michael S. Barker, PI and Katrina M. Dlugosch, CoPI Plants are rooted in place and cannot escape harsh environmental conditions. Instead, many plant species can acclimate to varying environments by modifying their structure or growth habit. In some cases, the acclimation is visible, as in the example of plants that grow small leaves or flower earlier when the local environment becomes water stressed. In other cases, the response to environmental change is not visible because subtle biochemical or genetic modes of survival are activated. This so-called "genomic plasticity" allows plants such as invasive species to thrive in new or harsh niches compared with less responsive plants. Similarly, plants with highly duplicated genomes, termed polyploids, may acclimate better because they express more variation in diverse traits in response to environmental change. Understanding how adaptive plasticity occurs is critical to being able to predict plant responses to environmental change. This project uses the novel environmental and biodiversity data from NEON to investigate the genomic basis of plasticity. To study adaptive plasticity of plants in the NEON footprint, new sequencing technologies will be used to characterize all the genes expressed over several time periods in selected polyploids and invasive species. These results will be compared against the abundance and plant community structure of the species included in the NEON biodiversity data. The project will thus establish the foundation for an unprecedented view of how plant genomes, and their subsequent traits, change over time. The study both leverages and expands NEON's long-term data to determine how plasticity at the genome level may predict plant success. Adaptive phenotypic plasticity is one of the primary means by which species respond to climatic variation, but how plasticity functions in plant genomes is not well understood. Plasticity is not easy to identify or study because homeostasis in key traits can be maintained by plasticity at underlying levels of organization. This research circumvents this problem by using genomic measures of plasticity to track response to environmental variation in plants across the NEON network. To measure and document plasticity, the project combines transcriptome profiles from RNA sequencing of selected plants at a NEON core site with NEON surveys of plant community composition. The specific activities are to (1) identify ploidy levels in species at the Harvard Forest NEON core site and select target polyploid, diploid and invasive species for the study; (2) conduct RNA sequencing of 30 target species and (3) correlate genome plasticity with species abundance. The project leverages NEON's long-term study sites and data, while extending the power of NEON's plant diversity data to the genomic level. Measurements of genomic plasticity will provide highly relevant and comparable metrics of plastic responses to the environment across taxa, space, and time. The data and analytical tools will be publicly available through NCBI and the iPlant Collaborative, and will benefit society by providing methods to predict how plants in uncertain environments will respond to change.