Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine

Abstract

Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, ~70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences

Dynamic Acclimation of Photosynthesis Increases Plant Fitness in Changing Environments

“…Previous studies have shown that this gene is induced under conditions of sugar feeding and during sugar-induced senescence (Gonzali et al, 2006;Li et al, 2006;Pourtau et al, 2006). In vitro evidence shows that this gene product is a functional sugar phosphate/phosphate translocator, with activity for triose-and hexose-Ps (Knappe et al, 2003).…”

“…It functions in vitro to transport both Glc-6-P and triose-P in exchange for phosphate (Knappe et al, 2003). Reported expression levels have typically been low, though it has been identified as being up-regulated in response to sugar feeding (Gonzali et al, 2006;Li et al, 2006) and during leaf senescence (Pourtau et al, 2006). In the Col-0 ecotype, which is unable to acclimate dynamically but which expresses the GPT2 gene, it was reported that gpt22 mutants grow normally (Niewiadomski et al, 2005).…”

The Pentatricopeptide Repeat Proteins TANG2 and ORGANELLE TRANSCRIPT PROCESSING439 Are Involved in the Splicing of the Multipartite nad5 Transcript Encoding a Subunit of Mitochondrial Complex I

“…1,A and B). TANG2 was originally found to be one of thousands of genes whose expression was rapidly altered in response to Glc (Li et al, 2006). OTP439 was identified through a Genevestigator (https://www.…”

Glucose Elevates NITRATE TRANSPORTER2.1 Protein Levels and Nitrate Transport Activity Independently of Its HEXOKINASE1-Mediated Stimulation of NITRATE TRANSPORTER2.1 Expression

“…Like nitrate, exogenous Suc and Glc influence the expression of many genes involved in metabolism and growth, including amino acid biosynthetic genes, ribosome synthesis genes, and hormone-regulated genes (Li et al, 2006). Network analyses (Gutiérrez et al, 2007) identified interdependent transcriptional responses to Glc and nitrate, including genes for auxin responses and signaling, transcription factors, and signal transduction proteins.…”

“…Expression of genes encoding the high-affinity nitrate transporters NRT2.1 and NRT2.2 is repressed by high nitrate levels and activated in low-nitrate conditions to scavenge available soil nitrate (Li et al, 2006;Gojon et al, 2011;Xu et al, 2012;Wang et al, 2012b). NRT2.4 encodes an N starvationinduced high-affinity transporter (Kiba et al, 2012).…”