Autophagy is an evolutionarily conserved intracellular process for vacuolar degradation of cytoplasmic components. In higher plants, autophagy defects result in early senescence and excessive immunity-related programmed cell death (PCD) irrespective of nutrient conditions; however, the mechanisms by which cells die in the absence of autophagy have been unclear. Here, we demonstrate a conserved requirement for salicylic acid (SA) signaling for these phenomena in autophagy-defective mutants (atg mutants). The atg mutant phenotypes of accelerated PCD in senescence and immunity are SA signaling dependent but do not require intact jasmonic acid or ethylene signaling pathways. Application of an SA agonist induces the senescence/cell death phenotype in SA-deficient atg mutants but not in atg npr1 plants, suggesting that the cell death phenotypes in the atg mutants are dependent on the SA signal transducer NONEXPRESSOR OF PATHOGENESIS-RELATED GENES1. We also show that autophagy is induced by the SA agonist. These findings imply that plant autophagy operates a novel negative feedback loop modulating SA signaling to negatively regulate senescence and immunity-related PCD.

Arabidopsis PSEUDO RESPONSE REGULATOR (PRR) genes are components of the circadian clock mechanism. In order to understand the scope of genome-wide transcriptional regulation by PRR genes, a comparison survey of gene expression in wild-type Arabidopsis and a prr9-11 prr7-10 prr5-10 triple mutant (d975) using mRNA collected during late daytime was conducted using an Affymetrix ATH-1 GeneChip®. The expression of ‘night genes’ increased and the expression of ‘day genes’ decreased toward the end of the diurnal light phase, but expression of these genes was essentially constant in d975. The expression levels of ‘night genes’ were lower, whereas the expression of ‘day genes’ was higher in d975 than in the wild type. Bioinformatics approaches have indicated that the set of up-regulated genes in d975 and the set of cold-responsive genes have significant overlap. We found that d975 is more tolerant to cold, high salinity and drought stresses than the wild type. In addition, dehydration-responsive element B1/C-repeat-binding factor (DREB1/CBF), which is expressed around mid-day, is more highly expressed in d975. Raffinose and l-proline accumulated at higher levels in d975 even when plants were grown under normal conditions. These results suggest that PRR9, PRR7 and PRR5 are involved in a mechanism that anticipates diurnal cold stress and which initiates a stress response by mediating cyclic expression of stress response genes, including DREB1/CBF.

Background: Deciphering the metabolome is essential for a better understanding of the cellular metabolism as a system. Typical metabolomics data show a few but significant correlations among metabolite levels when data sampling is repeated across individuals grown under strictly controlled conditions. Although several studies have assessed topologies in metabolomic correlation networks, it remains unclear whether highly connected metabolites in these networks have specific functions in known tissue- and/or genotype-dependent biochemical pathways. Results: In our study of metabolite profiles we subjected root tissues to gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) and used published information on the aerial parts of 3 Arabidopsis genotypes, Col-0 wild-type, methionine over-accumulation 1 (mto1), and transparent testa4 (tt4) to compare systematically the metabolomic correlations in samples of roots and aerial parts. We then applied graph clustering to the constructed correlation networks to extract densely connected metabolites and evaluated the clusters by biochemical-pathway enrichment analysis. We found that the number of significant correlations varied by tissue and genotype and that the obtained clusters were significantly enriched for metabolites included in biochemical pathways. Conclusions: We demonstrate that the graph-clustering approach identifies tissue- and/or genotype-dependent metabolomic clusters related to the biochemical pathway. Metabolomic correlations complement information about changes in mean metabolite levels and may help to elucidate the organization of metabolically functional modules.