Coordinated Gene Networks Regulating Arabidopsis Plant Metabolism in Response to Various Stresses and Nutritional Cues

Abstract

The expression pattern of any pair of genes may be negatively correlated, positively correlated, or not correlated at all in response to different stresses and even different progression stages of the stress. This makes it difficult to identify such relationships by classical statistical tools such as the Pearson correlation coefficient. Hence, dedicated bioinformatics approaches that are able to identify groups of cues in which there is a positive or negative expression correlation between pairs or groups of genes are called for. We herein introduce and discuss a bioinformatics approach, termed Gene Coordination, that is devoted to the identification of specific or multiple cues in which there is a positive or negative coordination between pairs of genes and can further incorporate additional coordinated genes to form large coordinated gene networks. We demonstrate the utility of this approach by providing a case study in which we were able to discover distinct expression behavior of the energy-associated gene network in response to distinct biotic and abiotic stresses. This bioinformatics approach is suitable to a broad range of studies that compare treatments versus controls, such as effects of various cues, or expression changes between a mutant and the control wild-type genotype.

A Connection between Lysine and Serotonin Metabolism in Rice Endosperm

“…Previous studies have 99 shown that the saccharopine pathway of lysine catabolism may also be involved in a 100 stress-related metabolic pathway in both dicots and monocots. Yet, the metabolic linkage 101 and stress response between Arabidopsis and maize -differed (Galili et al, 2002;Moulin 102 et al 2000Moulin 102 et al , 2006Less et al 2011;Kiyota et al, 2015). …”

“…The metabolic connection of the aspartate family pathway with the tricarboxylic acid 90 (TCA) cycle has been mapped in Arabidopsis (Less et al, 2011;Galili et al, 2011). The 91 results from Galili's group have shown that enhancing lysine synthesis and inhibiting its 92 catabolism in Arabidopsis had a notable influence on the levels of several TCA cycle 93 metabolites (Angelovici et al, 2010(Angelovici et al, , 2009Galili, 2003, 2004;Galili and Amir, 94 2013).…”

“…418 Kiyota et al, 2015). In bacteria, the saccharopine pathway is associated with stress 419 responses (Less et al, 2011;Neshich et al, 2013). In Arabidopsis, 2-aminoadipate and 420 pipecolate can induce JA accumulation and prime the plant for effective local resistance 421 induction (Návarová., et al, 2012).…”

LASSO modeling of the Arabidopsis thaliana seed/seedling transcriptome: a model case for detection of novel mucilage and pectin metabolism genes

“…14,15 Further examples where these databases and/or methods have been used for candidate gene discovery encompass different processes such as starch metabolism, 16,17 seed germination 18 and shade avoidance. 19 However, many of these approaches rely primarily on simple Pearson correlation, sometimes coupled with network clustering approaches 20 or sequence analysis. 21,22 Recently, more elaborate methods than Pearson correlation have flourished in the biostatistical literature, with the aim of increasing the accuracy of expression-based gene association inference.…”

A friend in need is a friend indeed

“…Hence, different external cues may trigger a coordinated expression of different sets of genes, making it difficult to identify such expression coordination by classical tools such as Pearson correlation. In a recent report, 1 we described a dedicated bioinformatics approach that is able to elucidate specific cues (particularly abiotic and biotic stresses) in which the change in expression of specific sets of genes is either positively coordinated (genes are either co-induced or co-suppressed) or negatively coordinated (one set is induced and the second set is suppressed). Our study analyzed the entire collections of Arabidopsis thaliana genes predicted to encode transcription factors (TFs) and metabolic enzymes (see details in reference 1), and two steps were used: (1) assembly of genes into cliques showing co-expression in a number of cues that is higher than a random background; and (2) clustering of the cliques.…”