bioRxivPlant Biology
Century-long timelines of herbarium genomes predict plant stomatal response to climate change
P. L. Lang, J. M. Erberich, L. Lopez, C. L. Weiss, G. Amador, H. F. Fung, S. M. Latorre, J. R. Lasky, H. A. Burbano, M. Exposito-Alonso, D. Bergmann
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 CO2 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 summaryUsing 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.
- organism: Arabidopsis
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: statistical model
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