Connecting genes, coexpression modules, and molecular signatures to environmental stress phenotypes in plants - PubMed (original) (raw)
Connecting genes, coexpression modules, and molecular signatures to environmental stress phenotypes in plants
David J Weston et al. BMC Syst Biol. 2008.
Abstract
Background: One of the eminent opportunities afforded by modern genomic technologies is the potential to provide a mechanistic understanding of the processes by which genetic change translates to phenotypic variation and the resultant appearance of distinct physiological traits. Indeed much progress has been made in this area, particularly in biomedicine where functional genomic information can be used to determine the physiological state (e.g., diagnosis) and predict phenotypic outcome (e.g., patient survival). Ecology currently lacks an analogous approach where genomic information can be used to diagnose the presence of a given physiological state (e.g., stress response) and then predict likely phenotypic outcomes (e.g., stress duration and tolerance, fitness).
Results: Here, we demonstrate that a compendium of genomic signatures can be used to classify the plant abiotic stress phenotype in Arabidopsis according to the architecture of the transcriptome, and then be linked with gene coexpression network analysis to determine the underlying genes governing the phenotypic response. Using this approach, we confirm the existence of known stress responsive pathways and marker genes, report a common abiotic stress responsive transcriptome and relate phenotypic classification to stress duration.
Conclusion: Linking genomic signatures to gene coexpression analysis provides a unique method of relating an observed plant phenotype to changes in gene expression that underlie that phenotype. Such information is critical to current and future investigations in plant biology and, in particular, to evolutionary ecology, where a mechanistic understanding of adaptive physiological responses to abiotic stress can provide researchers with a tool of great predictive value in understanding species and population level adaptation to climate change.
Figures
Figure 1
Visual representation of the AtGenExpress abiotic gene coexpression network. (a) A dendrogram of the 4000 most connected genes grouped into six distinct coexpression modules. The red line indicates the height at which the tree was cut to produce the distinct gene clusters (modules) as denoted by the color bar. (b) Multi-dimensional scaling plot of the gene coexpression network. Each circle represents a single gene and the color of the circle corresponds to module designation. The distance between circles is a function of the topological overlap and provides a visual representation of gene and module relationships within the network.
Figure 2
Relationship between expression abundance and module association. (a) Patterning of gene expression architecture relative to time of osmotic treatment. The patterns are structured according to expression abundance and the corresponding gene module. (b) Relationship between Chi square enrichment (_X_2) of differentially expressed genes and time of osmotic stress treatment. (c) Visual patterning of gene expression architecture relative to 3 h of drought, osmotic, salt, UV-B, heat, and cold treatments.
Figure 3
Relationship between modules and plant stress phenotype. Red-green heat maps depict mean differential gene expression between control and treatment conditions (x-axis) for all genes specific to the turquoise (A), yellow (B), brown (C), blue (D), green (E), and red (F) modules. Each horizontal line within a heat map shows the expression values (in terms of color) for the same gene across treatments. Red is increased expression, black is neutral, and green is decreased expression in comparison to the control treatment. The black triangles denote the direction of increasing treatment exposure. The corresponding bar plots are the eigengene values, first principle component, as determined from singular value composition for each module. Each bar is the average of two eigengene values.
Figure 4
Gene expression signature comparisons. (a) Visual display of the 3 h reference cold signature and identification of similarly ranked genes, as denoted by adjacent black and gray lines, with the query cold signature. The rank, identification, and annotation for the 10 most similarly ranked up- and down-regulated genes are described. (b) Display of the 6 h UV-B reference signature and identification of similarly ranked genes with UV-B query signature. The rank, identification, and annotation for the 10 most similarly ranked up- and down-regulated genes are described.
Figure 5
Gene expression signature comparisons. (a) Visual display of the 3 h reference heat signature and identification of similarly ranked genes, as denoted by black lines, with the singular query heat signature. The Rank, identification, and annotation for the 10 most similarly ranked up and down genes are described. (b) Display of the 3 h heat reference signature and identification of similarly ranked genes with simultaneously imposed heat and drought query signature. The Rank, identification, and annotation for the 10 most similarly ranked up and down genes are described.
Figure 6
Gene expression signature comparisons. (a) Visual display of the 3 h reference drought signature and identification of similarly ranked genes, as denoted by black lines, with the singular query drought signature. The Rank, identification, and annotation for the 10 most similarly ranked up and down genes are described. (b) Display of the 3 h drought reference signature and identification of similarly ranked genes with simultaneously imposed heat and drought query signature. The Rank, identification, and annotation for the 10 most similarly ranked up and down genes are described.
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