Century-long timelines of herbarium genomes predict plant stomatal response to climate change

Abstract

Abstract Dissecting plant responses to the environment is key to understanding if and how plants adapt to anthropogenic climate change. Stomata, plants’ pores for gas exchange, are expected to decrease in density following increased CO 2 concentrations, a trend already observed in multiple plant species. However, it is unclear if such responses are based on genetic changes and evolutionary adaptation. Here we make use of extensive knowledge of 43 genes in the stomatal development pathway and newly generated genome information of 191 A. thaliana historical herbarium specimens collected over the last 193 years to directly link genetic variation with climate change. While we find that the essential transcription factors SPCH, MUTE and FAMA, central to stomatal development, are under strong evolutionary constraints, several regulators of stomatal development show signs of local adaptation in contemporary samples from different geographic regions. We then develop a polygenic score based on known effects of gene knock-out on stomatal development that recovers a classic pattern of stomatal density decrease over the last centuries without requiring direct phenotype observation of historical samples. This approach combining historical genomics with functional experimental knowledge could allow further investigations of how different, even in historical samples unmeasurable, cellular plant phenotypes have already responded to climate change through adaptive evolution. One sentence summary Using a molecular-knowledge based genetic phenotype proxy, historical whole-genome A. thaliana timelines compared with contemporary data indicate a shift of stomatal density following climate-associated predictions.

Plant phenotyping relevance

遺伝情報から過去試料では直接測定できない気孔密度を推定するポリジェニックスコアを開発し、既知の気孔密度パターンで検証しており、植物形質推定法が中心です。

Code and data availability

The supplied blocks describe herbarium genome sequencing, stomatal gene analyses, and polygenic score computation, but contain no public phenotype datasets, images, code repositories, or data-availability statements with author URLs. References to the 1001 Genomes resource and Dittberner et al. data are cited prior/第三方

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