Effect of Salinity and Nitrogen Sources on the Leaf Quality, Biomass, and Metabolic Responses of Two Ecotypes of Portulaca oleracea

Halophytic plants are, by definition, well adapted to saline soils. However, even halophytes can face nutritional imbalance and the accumulation of high levels of compounds such as oxalic acid (OA), and nitrate (NO3−). These compounds compromise the potential nutritional health benefits associated with salt-tolerant plants such as Portulaca oleracea or Purslane. Purslane has long been known to be a highly nutritious leafy vegetable particularly with respect to high levels of omega-3 fatty acids. Thus, preventing the accumulation of non-nutritional compounds will allow plants to be grown in saline conditions as crops. Two ecotypes (ET and RN) of Portulaca oleracea plants were grown under growth room conditions with two levels of salinity (0, 50 mM NaCl) and three ratios of nitrate: ammonium (0:100%; 33:66%; 25:75% NO3−:NH4+). The results show that both ecotypes, when exposed to elevated NO3−, showed severe leaf chlorosis, high levels of OA, citric acid, and malic acid. Compared to ecotype RN, ecotype ET, exposed to elevated NH4+ concentrations (33% and 75%) and 50 mM NaCl, displayed a marked reduction in OA content, increased total chlorophyll and carotenoid contents, crude protein content, total fatty acid (TFA) and α-Linolenic acid (ALA), enhancing leaf quality. This opens the potential to grow high biomass, low OA P. oleracae crops. Lastly, our experiments suggest that ecotype ET copes with saline conditions and elevated NH4+ through shifts in leaf metabolites

Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data.

The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites are arranged into network formation, is used as a complentary tool. Here, we demonstrate the detection of metabolic pathways based on correlation-based network analysis combined with machine-learning techniques. Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp. Network features were computed for each subgraph, generating a machine-learning model. The model predicted the presence of the β-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the β-alanine-biosynthesis-III, and the melibiose-degradation pathway, although melibiose was not part of the networks. In vivo assays validated the presence of the melibiose-degradation pathway. For the remaining pathways only some of the genes encoding regulatory enzymes were detected

Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data.

The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites are arranged into network formation, is used as a complentary tool. Here, we demonstrate the detection of metabolic pathways based on correlation-based network analysis combined with machine-learning techniques. Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp. Network features were computed for each subgraph, generating a machine-learning model. The model predicted the presence of the β-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the β-alanine-biosynthesis-III, and the melibiose-degradation pathway, although melibiose was not part of the networks. In vivo assays validated the presence of the melibiose-degradation pathway. For the remaining pathways only some of the genes encoding regulatory enzymes were detected