Stitching together the Multiple Dimensions of Autophagy Using Metabolomics and Transcriptomics Reveals Impacts on Metabolism, Development, and Plant Responses to the Environment inArabidopsis   

Abstract

Autophagy is a fundamental process in the plant life story, playing a key role in immunity, senescence, nutrient recycling, and adaptation to the environment. Transcriptomics and metabolomics of the rosette leaves of Arabidopsis thaliana autophagy mutants (atg) show that autophagy is essential for cell homeostasis and stress responses and that several metabolic pathways are affected. Depletion of hexoses, quercetins, and anthocyanins parallel the overaccumulation of several amino acids and related compounds, such as glutamate, methionine, glutathione, pipecolate, and 2-aminoadipate. Transcriptomic data show that the pathways for glutathione, methionine, raffinose, galacturonate, and anthocyanin are perturbed. Anthocyanin depletion in atg mutants, which was previously reported as a possible defect in flavonoid trafficking to the vacuole, appears due to the downregulation of the master genes encoding the enzymes and regulatory proteins involved in flavonoid biosynthesis. Overexpression of the PRODUCTION OF ANTHOCYANIN PIGMENT1 transcription factor restores anthocyanin accumulation in vacuoles of atg mutants. Transcriptome analyses reveal connections between autophagy and (1) salicylic acid biosynthesis and response, (2) cytokinin perception, (3) oxidative stress and plant defense, and possible interactions between autophagy and the COP9 signalosome machinery. The metabolic and transcriptomic signatures identified for the autophagy mutants are discussed and show consistencies with the observed phenotypes.

Autophagic Recycling Plays a Central Role in Maize Nitrogen Remobilization

“…Here, we confirmed a crucial role for autophagy in this process that was first observed with Arabidopsis (Doelling et al, 2002;Guiboileau et al, 2012;Masclaux-Daubresse et al, 2014) by showing that maize plants defective in ATG8-mediated autophagy have lower yield even under well fertilized conditions and are attenuated in their remobilization of stored N from vegetative tissues to developing seeds. Consequently, the manipulation of autophagy in efforts to improve N and fixed-C recycling might be a beneficial strategy to enhance the yield of seedbearing food and biofuel crops, especially under suboptimal growth conditions or in situations where soil fertilization is cost prohibitive or ecologically unsound.…”

“…early and are generally hypersensitive to nutrient limitations (Hanaoka et al, 2002;Doelling et al, 2002;Yoshimoto et al, 2004;Thompson et al, 2005;Xiong et al, 2005;Phillips et al, 2008;Chung et al, 2010;Suttangkakul et al, 2011;Guiboileau et al, 2012Guiboileau et al, , 2013Masclaux-Daubresse et al, 2014), while more recent studies with rice have connected autophagy to fixed-C recycling and male gametogenesis (Kurusu et al, 2014). To reveal how autophagy impacts maize, we studied the growth and development of atg12-1 and atg12-2 plants in the W22 background under both nutrient rich and poor conditions.…”

Pipecolic Acid Orchestrates Plant Systemic Acquired Resistance and Defense Priming via Salicylic Acid-Dependent and -Independent Pathways

“…Pipecolic acid is a plant natural product with widespread occurrence throughout the angiosperms (Morrison, 1953;Zacharius et al, 1954), and its accumulation in leaves after inoculation with bacterial, fungal, or viral pathogens has been documented for rice (Oryza sativa), potato (Solanum tuberosum), tobacco (Nicotiana tabacum), soybean (Glycine max), and Arabidopsis (Pálfi and Dézsi, 1968;Návarová et al, 2012;VogelAdghough et al, 2013;Aliferis et al, 2014). In addition, Pip is overproduced in Arabidopsis autophagy mutants that exhibit stress-related phenotypes (Masclaux-Daubresse et al, 2014).…”

Improved Representation of Biological Information by Using Correlation as Distance Function for Heatmap Cluster Analysis

“…To better understand the complexity of living organisms, the omic sciences such as genomics [1], transcriptomics [2] [3], proteomics [4], metabolomics [5] [6] [7], and phenomics [8] are continuously developing new technologies that generate a large amount of digital data from nucleic acids, proteins, and metabolites. Bioinformatic [9], chemometric [10], cell fractionation [11], and biostatistical methods [12] are optimized in parallel, to automatize data mining, so that biological meaningful conclusions can be drawn from the vast amounts of numbers and letters.…”

“…These can be produced in the statistical programming environment R [16], which is widely used in the scientific community of genomics. Heatmaps can summarize data from large transcriptomic [2] and metabolomic experiments [17]. The data from rows and columns can be rearranged automatically employing different clustering algorithms [18].…”