Natália Faraj Murad, Karina Lucas Silva-Brand?o, Marcelo Mendes Brand?o
- The network approach enabled a wider molecular view of frugiperda response to diet stimuli
- The co-expression network presents two distinct portions (‘A’ smaller and ‘B’ larger), both of which are connected by a small number of genes
- ‘A’ is related to housekeeping genes, which respond slowly to environmental variations. Fast response to herbivory and anti-herbivory mechanisms is concentrated in ‘B’
- The co-expression networks showed features of biological networks such as scale-free topology, guilt by association organization and functional classification of hubs
- Differences in patterns of expression in both corn and rice strains were identified through the presence of exclusive genes, regulation of peptidases and genes with differentiated regulation (activated/deactivated)
A dataset of gene expression from Spodoptera frugiperda, a highly generalist pest moth, was used to understand how gene regulation is related to larval host plant preference. Transcriptomic data of corn and rice strains of S. frugiperda larvae, reared on different diets, were analysed with three different approaches of gene network inference, namely co-expression, weighted co-expression and Bayesian networks, since each methodology provides a different visualization of the data. Using these approaches, it was possible to identify two loosely interconnected co-expression networks, one of them responsible for fast response to herbivory and anti-herbivory mechanisms and the other related to housekeeping genes, which present slower response to environmental variations. Integrating different levels of information such as gene expression patterns, gene assembly, transcriptomics, relationship among genes and phenotypes, functional relationships, among other information, enabled a wider visualization of S. frugiperda response to diet stimuli. The biological properties in the proposed networks are here described and discussed, as well as patterns of gene expression related to larval performance attributes.