Identification of genes involved in Kranz anatomy evolution of non-model grasses using unsupervised machine learning

Abstract

AbstractKranz syndrome is a set of leaf anatomical and functional characteristics of species using C4photosynthesis. The current model for the evolution of C4photosynthesis from a C3ancestor proposes a series of gradual anatomical changes followed by a biochemical adaptation of the C4cycle enzymatic machinery. In this work, leaf anatomical traits from closely related C3, C4and intermediate species (Proto-Kranz, PK) were analyzed together with gene expression data to discover potential drivers for the establishment of Kranz anatomy using unsupervised machine learning. Species-specific Self-Organizing Maps (SOM) were developed to group features (genes and phenotypic traits) into clusters (neurons) according to their expression along the leaf developmental gradient. The analysis with SOM allowed us to identify candidate genes as enablers of key anatomical traits differentiation related to the area of mesophyll (M) and bundle sheath (BS) cells, vein density, and the interface between M and BS cells. At the same time, we identified a small subset of genes that displaced together with the change in the area of the BS cell along evolution suggesting a salient role in the origin of Kranz anatomy in grasses.HighlightKranz syndrome is a set of leaf anatomical and functional characteristics of species using C4photosynthesis. We discovered, with a novel machine learning methodology, some key genes for Kranz differentiation.