Genome-Wide Analysis of Lipoxygenase (LOX) Genes in Angiosperms.

Lipoxygenases (LOXs) are enzymes that catalyze the addition of an oxygen molecule to unsaturated fatty acids, thus forming hydroperoxides. In plants, these enzymes are encoded by a multigene family found in several organs with varying activity patterns, by which they are classified as LOX9 or LOX13. They are involved in several physiological functions, such as growth, fruit development, and plant defense. Despite several studies on genes of the LOX family in plants, most studies are restricted to a single species or a few closely related species. This study aimed to analyze the diversity, evolution, and expression of LOX genes in angiosperm species. We identified 247 LOX genes among 23 species of angiosperms and basal plants. Phylogenetic analyses identified clades supporting LOX13 and two main clades for LOX9: LOX9_A and LOX9_B. Eudicot species such as Tarenaya hassleriana, Capsella rubella, and Arabidopsis thaliana did not present LOX9_B genes; however, LOX9_B was present in all monocots used in this study. We identified that there were potential new subcellular localization patterns and conserved residues of oxidation for LOX9 and LOX13 yet unexplored. In summary, our study provides a basis for the further functional and evolutionary study of lipoxygenases in angiosperms.

A metabolomic study of Gomphrena agrestis in Brazilian Cerrado suggests drought-adaptive strategies on metabolism.

Drought is the main factor that limits the distribution and productivity of plant species. In the Brazilian Cerrado, the vegetation is adapted to a seasonal climate with long- and short-term periods of drought. To analyze the metabolic strategies under such conditions, a metabolomic approach was used to characterize Gomphrena agrestis Mart. (Amaranthaceae) a native species that grows under natural conditions, in a rock-field area. Roots and leaves material from native specimens were sampled along different seasons of the year and LC-MS and GC-MS analyzed for multiple chemical constituents. The datasets derived from the different measurements were combined and evaluated using multivariate analysis. Principal component analysis was used to obtain an overview of the samples and identify outliers. Later, the data was analyzed with orthogonal projection to latent structures discriminant analysis to obtain valid models that could explain the metabolite variations in the different seasons. Two hundred and eighty metabolites were annotated, generating a unique database to characterize metabolic strategies used to cope with the effects of drought. The accumulation of fructans in the thickened roots is consistent with the storage of carbons during the rainy season to support the energy demand during a long period of drought. The accumulation of Abscisic acid, sugars and sugar alcohols, phenolics, and pigment in the leaves suggests physiological adaptations. To cope with long-term drought, the data suggests that tissue water status and storage of reserves are important to support plant survival and regrowth. However, during short-term drought, osmoregulation and oxidative protection seems to be essential, probably to support the maintenance of active photosynthesis.

Network Analyses and Data Integration of Proteomics and Metabolomics From Leaves of Two Contrasting Varieties of Sugarcane in Response to Drought.

Uncovering the molecular mechanisms involved in the responses of crops to drought is crucial to understand and enhance drought tolerance mechanisms. Sugarcane (Saccharum spp.) is an important commercial crop cultivated mainly in tropical and subtropical areas for sucrose and ethanol production. Usually, drought tolerance has been investigated by single omics analysis (e.g. global transcripts identification). Here we combine label-free quantitative proteomics and metabolomics data (GC-TOF-MS), using a network-based approach, to understand how two contrasting commercial varieties of sugarcane, CTC15 (tolerant) and SP90-3414 (susceptible), adjust their leaf metabolism in response to drought. To this aim, we propose the utilization of regularized canonical correlation analysis (rCCA), which is a modification of classical CCA, and explores the linear relationships between two datasets of quantitative variables from the same experimental units, with a threshold set to 0.99. Light curves revealed that after 4 days of drought, the susceptible variety had its photosynthetic capacity already significantly reduced, while the tolerant variety did not show major reduction. Upon 12 days of drought, photosynthesis in the susceptible plants was completely reduced, while the tolerant variety was at a third of its rate under control conditions. Network analysis of proteins and metabolites revealed that different biological process had a stronger impact in each variety (e.g. translation in CTC15, generation of precursor metabolites, response to stress and energy in SP90-3414). Our results provide a reference data set and demonstrate that rCCA can be a powerful tool to infer experimentally metabolite-protein or protein-metabolite associations to understand plant biology.

Integrated analysis of gene expression from carbon metabolism, proteome and metabolome, reveals altered primary metabolism in Eucalyptus grandis bark, in response to seasonal variation.

BACKGROUND: Seasonal variation is presumed to play an important role in the regulation of tree growth, especially for Eucalyptus grandis, a fast-growing tree. This variation may induce changes in the whole tree at transcriptional, protein and metabolite levels. Bark represents an important group of tissues that protect trees from desiccation and pathogen attack, and it has been identified as potential feedstock for lignocellulosic derived biofuels. Despite the growing interest, little is known about the molecular mechanisms that regulates bark metabolism, particularly in tropical countries. RESULTS: In this study we report the changes observed in the primary metabolism of E. grandis bark during two contrasting seasons in Brazil, summer (wet) and winter (dry), through the combination of transcripts (RT-qPCR), proteome (2-DE gels) and metabolome (GC-MS) analysis, in an integrated manner. Twenty-four genes, involved in carbon metabolism, were analyzed in the two seasons. Eleven were up-regulated in summer, three were up-regulated in winter and ten did not show statistical differences in the expression pattern. The proteomic analysis using 2-DE gels showed 77 proteins expressing differences in abundance, with 38 spots up-regulated in summer and 37 in winter. Different metabolites significantly accumulated during winter. CONCLUSIONS: This study revealed a metabolic reconfiguration in the primary metabolism of E. grandis bark, triggered by seasonal variation. Transcripts and protein data suggests that during winter carbohydrate formation seems to be favored by tree metabolism. Glucose, fructose and sucrose accumulated at significant levels during the winter.

Label-Free Quantitative Proteomic Analysis of Puccinia psidii Uredospores Reveals Differences of Fungal Populations Infecting Eucalyptus and Guava.

Puccinia psidii sensu lato (s.l.) is the causal agent of eucalyptus and guava rust, but it also attacks a wide range of plant species from the myrtle family, resulting in a significant genetic and physiological variability among populations accessed from different hosts. The uredospores are crucial to P. psidii dissemination in the field. Although they are important for the fungal pathogenesis, their molecular characterization has been poorly studied. In this work, we report the first in-depth proteomic analysis of P. psidii s.l. uredospores from two contrasting populations: guava fruits (PpGuava) and eucalyptus leaves (PpEucalyptus). NanoUPLC-MSE was used to generate peptide spectra that were matched to the UniProt Puccinia genera sequences (UniProt database) resulting in the first proteomic analysis of the phytopathogenic fungus P. psidii. Three hundred and fourty proteins were detected and quantified using Label free proteomics. A significant number of unique proteins were found for each sample, others were significantly more or less abundant, according to the fungal populations. In PpGuava population, many proteins correlated with fungal virulence, such as malate dehydrogenase, proteossomes subunits, enolases and others were increased. On the other hand, PpEucalyptus proteins involved in biogenesis, protein folding and translocation were increased, supporting the physiological variability of the fungal populations according to their protein reservoirs and specific host interaction strategies.