Decoding the gene co-expression network underlying the ability of Gevuina avellana Mol. to live in diverse light conditions

Abstract

28 • Gevuina avellana Mol. (Proteaceae) is a typical tree from the South American 29 temperate rainforest. Although this species mostly regenerates in shaded understories, it 30 exhibits an exceptional ecological breadth, being able to live under a wide range of light 31 conditions. Here we studied the genetic basis regulating physiological acclimation of the 32 photosynthetic responses of G. avellana under contrasting light conditions. 33 • We analyzed carbon assimilation and light energy used for photochemical process 34 in plants acclimated to contrasting light conditions. Also, we used a transcriptional profile 35 of leaf primordia from G. avellana saplings growing under different light environments 36 to identify the gene co-expression network underpinning photosynthetic performance and 37 light-related processes. 38 • The photosynthetic parameters revealed optimal performance regardless of light 39 conditions. Strikingly, the mechanism involved in dissipation of excess light energy 40 showed no significant differences between high and low-light acclimated plants. The gene 41 co-expression network defined a community structure consistent with the photochemical 42 responses, including genes involved mainly in assembly and functioning of photosystems, 43 photoprotection, and retrograde signaling. 44 • Our ecophysiological genomics approach provides an understanding of the 45 molecular regulatory mechanisms that allows this tree to have an optimal balance between 46 photochemical, photoprotective and antioxidant performance in the diverse light habitats 47 it encounters in nature.48

A comparative gene co-expression analysis using self-organizing maps on two congener filmy ferns identifies specific desiccation tolerance mechanisms associated to their microhabitat preference

“…Each of the modules contain transcripts with denser connections representing predicted interactions. The names of those genes showing higher connections within a given hydration state are indicated and their specific functions are discussed on the text multiple factors [19,30,31]. With the differential gene expression analysis, we found similar patterns of molecular responses between both filmy ferns during the desiccation-rehydration cycle.…”

“…(Additional file 2: Dataset S4 and S5). These genes were used to construct a weighted gene co-expression network according to [31]. Briefly, features of two R packages, namely, Weighted Gene Coexpression Analysis (WGCNA [18];) and igraph [21], were combined to visualize the genes interaction.…”

A comparative gene co-expression networks analysis of two congener filmy ferns identifies specific desiccation tolerance mechanisms associated to their microhabitat preference.

“…Despite the undeniable advantages of exploratory statistical models such hierarchical clustering [27] and k-means clustering [28], these methods are subjective due to human bias based on arbitrary statistical significance threshold and does not consider the topology between clusters [29]. The characteristics of the artificial neural networks, considering topology in clusters neighboring [19,20], provides an excellent tool to depict clustering patterns of gene expression across multiple factors [19,30,31]. With the differential gene expression analysis, we found similar patterns of molecular responses between both filmy ferns during the desiccation-rehydration cycle.…”

A comparative gene co-expression networks analysis of two congener filmy ferns identifies specific desiccation tolerance mechanisms associated to their microhabitat preference.

“…Despite the undeniable advantages of exploratory statistical models such hierarchical clustering [27] and kmeans clustering [28], these methods are subjective due to human bias based on arbitrary statistical significance threshold and does not consider the topology between clusters [29]. The characteristics of the artificial neural networks, considering topology in clusters neighboring [19,20], provides an excellent tool to depict clustering patterns of gene expression across multiple factors [19,30,31].…”