Topological data analysis reveals a core gene expression backbone that defines form and function across flowering plants

Abstract

Since they emerged approximately 125 million years ago, flowering plants have evolved to dominate the terrestrial landscape and survive in the most inhospitable environments on earth. At their core, these adaptations have been shaped by changes in numerous, interconnected pathways and genes that collectively give rise to emergent biological phenomena. Linking gene expression to morphological outcomes remains a grand challenge in biology, and new approaches are needed to begin to address this gap. Here, we implemented topological data analysis (TDA) to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, we created a topological representation of the shape of gene expression across plant evolution, development, and environment for the phylogenetically diverse flowering plants. The TDA-based Mapper graphs form a well-defined gradient of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function. This suggests that there are distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses. Genes that correlate with the tissue lens function are enriched in central processes such as photosynthetic, growth and development, housekeeping, or stress responses. Together, our results highlight the power of TDA for analyzing complex biological data and reveal a core expression backbone that defines plant form and function.

Topological data analysis reveals core heteroblastic and ontogenetic programs embedded in leaves of grapevine (Vitaceae) and maracuyá (Passifloraceae)

“…Because direct visualization of such high-dimensional data is impossible, we use the Mapper algorithm to simplify the shape of the data into a one-dimensional graph [ 17 ]. For instance, Mapper was recently used to uncover patterns in gene expression in flowering plants which PCA failed to distinguish [ 29 ]. The Mapper algorithm works by creating groups of nearby points, then using these groups as a basis for the graph structure.…”

“…As an alternate approach, we turned to the Mapper algorithm, a method that has successfully identified novel relationships within other high-dimensional datasets [19,29,31]. A key advantage of Mapper is its ability to interrogate datasets using several different lenses, revealing structure and relationships from specific, mathematically-defined perspectives that are PLOS COMPUTATIONAL BIOLOGY hidden from traditional approaches (see Materials and Methods).…”