Systems analysis of leaf anatomy and transcriptome using unsupervised machine learning provides insight on photosynthesis development and evolution in non-model grasses.

Abstract

The C₄ photosynthesis includes intriguing leaf anatomies. The current model supports the placement of C₃-C₄ intermediates as a middle point in the evolutionary trajectory from C₃ to C₄ photosynthesis. The known determinants involved in the differentiation of divergent photosynthetic leaves arose from the comparative analysis between both ends, C₃ and C₄ species. However, much more could be known if evolutionarily close species were analyzed together with intermediate species using advanced-omic approaches. In the present work, by combining leaf anatomical traits and transcriptomic data with machine learning methods, we provided insights on gene regulatory networks involved in complex leaf anatomical characteristics in non-model grasses of subtribe Otachyriinae. For that, self-organizing maps (SOMs) were developed to group genes and phenotypic traits into clusters (neurons) according to their behavior along the leaf developmental gradient. The analysis allowed us to identify a set of genes as potential enablers of key anatomical trait differentiation related to bundle sheath (BS) cell size, vein density, and the interface between mesophyll and BS cells. At the same time, we identified genes that displaced together with the adjustment of the BS cell area suggesting a possible role in the evolution of this distinctive leaf anatomical trait.