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 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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Funding information: P.L.M. Lang is supported by a Human Frontiers Science Fellowship (LT000330/2019-L). J.M. Erberich is supported by the NIGMS Center of the National Institutes of Health (T32GM007276). L.L. is partially supported by California State University, San Bernardino. G. Amador is supported by funds from the National Institutes of Health (T32 5T32GM007790), the National Science Foundation (DGE-1656518), and a Stanford Graduate Fellowship. J.R.L. was supported by NIH award R35 GM138300. H.A. Burbano is supported by a Royal Society Wolfson Fellowship (RSWF\R1\191011) and a Philip Leverhulme Prize from The Leverhulme Trust. M.E.A. is funded by the Carnegie Institution for Science, a Department of Energy, Office of Biological and Environmental Research Grant (DE-SC0021286), and the National Institutes of Health’s Early Investigator Award (1DP5OD029506-01). Computation for this project was performed on the Calc, Memex, and Moi Node clusters from the Carnegie Institution for Science. D.C.B. is an investigator of the Howard Hughes Medical Institute.