1 H-NMR metabolomics profiling of recombinant tobacco plants holding a promoter of a sesquiterpene cyclase

NMR-based metabolomics to determine the fluctuation of metabolites in hydroponic purslane crops at different harvesting times

Purslane (Portulaca oleracea L.) has a high content of nutrients and medicinal effects that depend on the genotype, harvesting time, and production system. The objective of the present research work was to elucidate the NMR-based metabolomics profiling of three native purslane cultivars from Mexico (Xochimilco, Mixquic, and Cuautla) grown under hydroponic conditions and harvested in three different times (32, 39, and 46 days after emergence). Thirty-nine metabolites identified in the ¹H NMR spectra of aerial parts of purslane, 5 sugars, 15 amino acids, 8 organic acids, 3 caffeoylquinic acids, as well as 2 alcohols and 3 nucleosides, choline, O-phosphocholine and trigonelline were also detected. A total of 37 compounds were detected in native purslane from Xochimilco and Cuautla, whereas 39 compounds were detected in purslane from Mixquic. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) separated the cultivars into three clusters. Mixquic cultivar had the highest number of differential compounds (amino acids and carbohydrates), followed by Xochimilco and Cuautla cultivars, respectively. Changes in the metabolome were observed in latest times of harvest for all the cultivars studied. The differential compounds were glucose, fructose, galactose, pyruvate, choline, and 2-hydroxysobutyrate. The results obtained in this investigation may contribute to selecting the best cultivar of purslane and the best time in which the levels of nutrients are optimal.

NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches

Cotton (Gossypium hirsutum) is an economically important crop and is widely cultivated around the globe. However, the major problem of cotton is its high vulnerability to biotic and abiotic stresses. It has been around three decades since the cotton plant was genetically engineered with genes encoding insecticidal proteins (mainly Cry proteins) with an aim to protect it against insect attack. Several studies have been reported on the impact of these genes on cotton production and fiber quality. However, the metabolites responsible for conferring resistance in genetically modified cotton need to be explored. The current work aims to unveil the key metabolites responsible for insect resistance in Bt cotton and also compare the conventional multivariate analysis methods with deep learning approaches to perform clustering analysis. We aim to unveil the marker compounds which are responsible for inducing insect resistance in cotton plants. For this purpose, we employed 1H-NMR spectroscopy to perform metabolite profiling of Bt and non-Bt cotton varieties, and a total of 42 different metabolites were identified in cotton plants. In cluster analysis, deep learning approaches (linear discriminant analysis (LDA) and neural networks) showed better separation among cotton varieties compared to conventional methods (principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLSDA)). The key metabolites responsible for inter-class separation were terpinolene, α-ketoglutaric acid, aspartic acid, stigmasterol, fructose, maltose, arabinose, xylulose, cinnamic acid, malic acid, valine, nonanoic acid, citrulline, and shikimic acid. The metabolites which regulated differently with the level of significance p < 0.001 amongst different cotton varieties belonged to the tricarboxylic acid cycle (TCA), Shikimic acid, and phenylpropanoid pathways. Our analyses underscore a biosignature of metabolites that might involve in inducing insect resistance in Bt cotton. Moreover, novel evidence from our study could be used in the metabolic engineering of these biological pathways to improve the resilience of Bt cotton against insect/pest attacks. Lastly, our findings are also in complete support of employing deep machine learning algorithms as a useful tool in metabolomics studies.

Untargeted 1 H-NMR Metabolome of Celery During Fusarium Wilt: Implications for Vegetable Quality

Celery is a vegetable widely consumed as a condiment to prepare diverse dishes around the world. Nevertheless, this plant is susceptible to the attack of several phytopathogens including those of the Fusarium genus which is translated into devastating losses for the production chain. Herein we report on the metabolic changes produced during the celery wilt caused by Fusarium oxysporum which was determined through untargeted ¹ H-NMR metabolomics. The changes in the metabolite content of celery were measured at 16, 24, and 32 days post-inoculation using viable conidia obtained from the native F. oxysporum strain FO3. Our results demonstrated that the parasitic activity of the fungus reduced the endogenous levels of free sugars (fructose, galactose, glucose isomers, mannose, Myo-inositol, mannitol, and sucrose) amino acids (alanine, aspartate GABA, glutamate, glutamine, histidine, isoleucine, leucine, methionine, proline, threonine, tyrosine, and valine), nucleosides (adenosine, cytidine, guanosine, and uridine) and organic acids (citric acid, fumaric acid, malic acid, and succinic acid). Interestingly, the levels of tyrosine and tryptophan were triggered as a consequence of F. oxysporum infection. This tendency was correlated with an increase in the levels of chlorogenic acid, apiin, and apigenin derivatives, suggesting their involvement in the chemical defense of celery against fungal colonization. According to principal component analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) methanol was the main differential metabolite and it was considered as a new chemical marker associated with F. oxysporum infection. Our results demonstrate that infected celery plants dramatically reduced their nutritional and nutraceutical contents during Fusarium wilt after 32 days post-inoculation. However, these findings also suggest that the phenylpropanoid pathway is strongly related with the chemical defense of celery against F. oxysporum.

Influence of Environmental Factors on the Genetic and Chemical Diversity of Brickellia veronicifolia Populations Growing in Fragmented Shrublands from Mexico

Brickellia veronicifolia is a native Asteraceae from Mexico that persists in fragmented habitats. This investigation reports the genetic and chemical diversity of B. veronicifolia. The diversity analysis based on iPBS markers showed an averaged Shannon index (S) of 0.3493, a Nei genetic diversity (h) of 0.2256, and a percentage of polymorphic loci average (P) of 80.7867%. The population structure obtained by AMOVA revealed that the highest variation found within populations was 94.58%. GC-MS profiling of six populations indicated that major volatiles were β–caryophyllene (11.63%), spathulenol (12.85%), caryophyllene oxide (13.98%), α–cadinol (7.04%), cubedol (6.72%) and tau-muurolol (4.81%). Mantel tests suggested a statistically significant relationship between minor volatiles and geographical distance (r = 0.6163; p = 0.0470; p ˂ 0.05). Likewise, major volatiles showed a significant correlation with the soil pH (r = 0.6862; p = 0.0230) and maximum temperature (r = 0.4999; p = 0.0280). Our study suggests that the variation and genetic divergence of B. veronicifolia has no relationship with climatic parameters, whereas the volatiles are probably influenced by environmental factors and not by the genotype per se. Based on the characteristics of B. veronicifolia, this plant could be considered as a candidate for restoring fragmented shrublands in Mexico.

Experimental races of Capsicum annuum cv. jalapeño: Chemical characterization and classification by 1H NMR/machine learning

This work reports on the metabolic fingerprinting of ten new races of Capsicum annuum cv. jalapeño using ¹H NMR based metabolomics coupled to machine learning projections. Ten races were classified and evaluated according to their differential metabolites, variables of commercial interest and by multivariate data analysis/machine learning algorithm. According to our results, experimental races of jalapeño peppers exhibited differences in carbohydrate, amino acid, nucleotide and organic acid contents. Forty-eight metabolites were identified by 1D and 2D NMR and the differential metabolites were quantified by qNMR. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) separated the studied races into two groups. The group A included the races Colosus, Emperador, Fundador and Rayo whereas the group B included the races Don Benito, SMJ 1416, SMJ 1417, SMJ 1423, SMJ 145 and STAM J0904. OPLS-DA revealed that levels of citric acid in group A were higher than in group B, while the levels of asparagine, fumaric acid, GABA, glucose, malic acid, pyruvic, quinic acid, sucrose and tryptophan were higher in the group B. Remarkably, ascorbic acid was exclusively found in the race Colosus. Random forest model revealed the diversity of the experimental races and the similarity rate with the well-established races. The most relevant variables used to generate a model were length, weight, yield, width, xylose content and organic acids content.