Rachel ShekarLeaves of flowering plants are diverse in size and shape, but most have distinct top (adaxial) and bottom (abaxial) surfaces that contain different types and arrangements of cells. This key innovation in leaf evolution optimizes the primary function of photosynthesis. On the top, cells capture light. On the bottom, they allow gas exchange and transpiration.
Curiously, leaves do not start out flat but instead emerge from the stem cell niche as radially symmetric bulges. These bulges then flatten by driving growth along two of its axes to create final structures that are long and wide, but only a few cell layers thick. Despite the complexity of this process, plants consistently form leaves with adaxial and abaxial surfaces.
A new study published in in silico Plants by Aman Husbands and colleagues at Ohio State University describes how adaxial-abaxial patterning in leaves is generated and maintained during development.
The authors created a one-dimensional spatial model of the determinants and interactions that pattern the adaxial-abaxial axis across the leaf. The model included transcription factor networks and associated miRNAs.
“Adaxial-abaxial patterning has been studied by a number of groups using approaches from classical genetics to live imaging. Collectively, these studies revealed that adaxial-abaxial patterning is governed by a complex network of transcription factors and small RNAs. The network is characterized by many interesting properties including mutually-antagonistic interactions and cell-to-cell mobility. The hard part was selecting which factors to include as incorporating all interactions in a model is not feasible. To prioritize, we used criteria such as phenotypic severity of mutants, conservation across evolution, and whether similar factors play outsized patterning roles in other organisms” says Dr. Husbands, Assistant Professor of Molecular Genetics.
Importantly, rather than simply assuming model parameters, which is common practice when data is scarce, most parameters in this study were estimated directly from experimental data.
The model successfully reproduced observations of adaxial-abaxial patterning and small RNA-target interactions.
The authors show that modelling the known interactions between transcription factors and mRNAs recapitulates the known adaxial-abaxial patterns of these factors in the leaf. Additionally, this system is relatively robust to changes in parameter values and to noise. Says Husbands, “Biology is incredibly noisy yet still able to produce complex outcomes in a reproducible way. To test whether our model accurately reflects this robustness we introduced noise into the functions at the heart of the model. These functions describe both the quantities of genes as well as what happens when they interact. We did this by allowing function values to randomly fluctuate at each step of the model. Even in the face of significant noise, our model could reliably produce expected outcomes, consistent with the robust nature of adaxial-abaxial patterning.”
This work will inform future modeling of the numerous complex structures generated by adaxial-abaxial patterning.
A MATLAB file containing implementation of the model and an Excel file containing computations used to determine values for gene TPM per cell using data from (Cortijo et al. 2019) are included as Supporting Information and can be found at https://github.com/LukeAndrejek/RobustMathematicalModel.
William Salter
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