Dataset Information

Stomatal design principles in synthetic and real leaves.

ABSTRACT: Stomata are portals in plant leaves that control gas exchange for photosynthesis, a process fundamental to life on Earth. Gas fluxes and plant productivity depend on external factors such as light, water and CO2 availability and on the geometrical properties of the stoma pores. The link between stoma geometry and environmental factors has informed a wide range of scientific fields-from agriculture to climate science, where observed variations in stoma size and density are used to infer prehistoric atmospheric CO2 content. However, the physical mechanisms and design principles responsible for major trends in stomatal patterning are not well understood. Here, we use a combination of biomimetic experiments and theory to rationalize the observed changes in stoma geometry. We show that the observed correlations between stoma size and density are consistent with the hypothesis that plants favour efficient use of space and maximum control of dynamic gas conductivity, and that the capacity for gas exchange in plants has remained constant over at least the last 325 Myr. Our analysis provides a new measure to gauge the relative performance of species based on their stomatal characteristics.

SUBMITTER: Zwieniecki MA

PROVIDER: S-EPMC5134010 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Stomatal design principles in synthetic and real leaves.

Zwieniecki Maciej A MA Haaning Katrine S KS Boyce C Kevin CK Jensen Kaare H KH

Journal of the Royal Society, Interface 20161101 124

Stomata are portals in plant leaves that control gas exchange for photosynthesis, a process fundamental to life on Earth. Gas fluxes and plant productivity depend on external factors such as light, water and CO<sub>2</sub> availability and on the geometrical properties of the stoma pores. The link between stoma geometry and environmental factors has informed a wide range of scientific fields-from agriculture to climate science, where observed variations in stoma size and density are used to infe ...[more]

PMID: 27807270

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Project description:Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. A single enhancer, of a few hundred base pairs in length, can autonomously and independently of its location and orientation drive cell-type specific expression of a gene or transgene. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Recently, deep learning models have yielded unprecedented insight into the enhancer code, and well-trained models are reaching a level of understanding that may be close to complete. As a consequence, we hypothesized that deep learning models can be used to guide the directed design of synthetic, cell type specific enhancers, and that this process would allow for a detailed tracing of all enhancer features at nucleotide-level resolution. Here we implemented and compared three different design strategies, each built on a deep learning model: (1) directed sequence evolution; (2) directed iterative motif implanting; and (3) generative design. We evaluated the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We then exploited this concept further by creating “dual-code” enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the trajectories followed during state space searches towards functional enhancers, we could accurately define the enhancer code as the optimal strength, combination, and relative distance of TF activator motifs, and the absence of TF repressor motifs. Finally, we applied the same three strategies to successfully design human enhancers, finding highly similar design principles as in Drosophila. In conclusion, enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

Dataset Information

Stomatal design principles in synthetic and real leaves.

Publications

Stomatal design principles in synthetic and real leaves.

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