Construction and comparison of gene co-expression networks shows complex plant immune responses

Abstract

Gene co-expression networks (GCNs) are graphic representations that depict the coordinated transcription of genes in response to certain stimuli. GCNs provide functional annotations of genes whose function is unknown and are further used in studies of translational functional genomics among species. In this work, a methodology for the reconstruction and comparison of GCNs is presented. This approach was applied using gene expression data that were obtained from immunity experiments in Arabidopsis thaliana, rice, soybean, tomato and cassava. After the evaluation of diverse similarity metrics for the GCN reconstruction, we recommended the mutual information coefficient measurement and a clustering coefficient-based method for similarity threshold selection. To compare GCNs, we proposed a multivariate approach based on the Principal Component Analysis (PCA). Branches of plant immunity that were exemplified by each experiment were analyzed in conjunction with the PCA results, suggesting both the robustness and the dynamic nature of the cellular responses. The dynamic of molecular plant responses produced networks with different characteristics that are differentiable using our methodology. The comparison of GCNs from plant pathosystems, showed that in response to similar pathogens plants could activate conserved signaling pathways. The results confirmed that the closeness of GCNs projected on the principal component space is an indicative of similarity among GCNs. This also can be used to understand global patterns of events triggered during plant immune responses.

THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer’s Disease

“…These methods are based on various correlation measures. Some other CEN methods242526 consider gene CEN, formed by merging networks of heterogenous experiments, specific to plants. Leal et al 24.…”

“…Some other CEN methods242526 consider gene CEN, formed by merging networks of heterogenous experiments, specific to plants. Leal et al 24. introduced a multivariate approach based on Principal Component Analysis (PCA) to study the plant immune responses, by considering gene expression similarity of various plants, namely rice, Arabidopsis thaliana, soybean, cassava and tomato.…”

Unraveling gene function in agricultural species using gene co-expression networks

“…Even though the majority of genes for agronomic species do not have a functional annotation, investigators can use modules composed of well-defined, functionally annotated genes from one species to compare and discover similar modules in another. These comparisons are not limited to using co-expression networks from model species such as Arabidopsis [59][60][61][62], but can be applied using other species if they are using genes with well-defined functional annotations [30,63].…”

“…More recently, Ruprecht et al used multiple plant species together to discover a wide variety of conserved modules, with an emphasis on modules related to cell wall formation [64]. In a similar approach, Leal et al used gene expression data from Arabidopsis, Maize, soybean, rice, tomato and cassava to identify conserved genes involved in immune response under pathogenic stress [62]. Using the networks that were built from the expression data, they were able to find functional similarities in the immune response across species.…”

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“…Different methods have been proposed to compare co-expression networks. Leal et al (2014) compared gene co-expression networks obtained for several plant species in response to different pathogens using a multivariate analysis. Each network was characterized by eight graph variables which were then summarized in a principal component analysis.…”