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Item type:Publication, Morphological and metabolic profiling of a tropical-adapted potato association panel subjected to water recovery treatment reveals new insights into plant vigor(Blackwell Publishing Ltd, 2020-09-01)Potato (Solanum tuberosum L.) is one of the world's most important crops, but it is facing major challenges due to climatic changes. To investigate the effects of intermittent drought on the natural variability of plant morphology and tuber metabolism in a novel potato association panel comprising 258 varieties we performed an augmented block design field study under normal irrigation and under water-deficit and recovery conditions in Ica, Peru. All potato genotypes were profiled for 45 morphological traits and 42 central metabolites via nuclear magnetic resonance. Statistical tests and norm of reaction analysis revealed that the observed variations were trait specific, that is, genotypic versus environmental. Principal component analysis showed a separation of samples as a result of conditional changes. To explore the relational ties between morphological traits and metabolites, correlation-based network analysis was employed, constructing one network for normal irrigation and one network for water-recovery samples. Community detection and difference network analysis highlighted the differences between the two networks, revealing a significant correlational link between fumarate and plant vigor. A genome-wide association study was performed for each metabolic trait. Eleven single nucleotide polymorphism (SNP) markers were associated with fumarate. Gene Ontology analysis of quantitative trait loci regions associated with fumarate revealed an enrichment of genes regulating metabolic processes. Three of the 11 SNPs were located within genes, coding for a protein of unknown function, a RING domain protein and a zinc finger protein ZAT2. Our findings have important implications for future potato breeding regimes, especially in countries suffering from climate change. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Guidelines for correlation coefficient threshold settings in metabolite correlation networks exemplified on a potato association panel(BioMed Central Ltd, 2021-12-01)Correlation network analysis has become an integral tool to study metabolite datasets. Networks are constructed by omitting correlations between metabolites based on two thresholds—namely the r and the associated p-values. While p-value threshold settings follow the rules of multiple hypotheses testing correction, guidelines for r-value threshold settings have not been defined. Results: Here, we introduce a method that allows determining the r-value threshold based on an iterative approach, where different networks are constructed and their network topology is monitored. Once the network topology changes significantly, the threshold is set to the corresponding correlation coefficient value. The approach was exemplified on: (i) a metabolite and morphological trait dataset from a potato association panel, which was grown under normal irrigation and water recovery conditions; and validated (ii) on a metabolite dataset of hearts of fed and fasted mice. For the potato normal irrigation correlation network a threshold of Pearson’s |r|≥ 0.23 was suggested, while for the water recovery correlation network a threshold of Pearson’s |r|≥ 0.41 was estimated. For both mice networks the threshold was calculated with Pearson’s |r|≥ 0.84. Conclusions: Our analysis corrected the previously stated Pearson’s correlation coefficient threshold from 0.4 to 0.41 in the water recovery network and from 0.4 to 0.23 for the normal irrigation network. Furthermore, the proposed method suggested a correlation threshold of 0.84 for both mice networks rather than a threshold of 0.7 as applied earlier. We demonstrate that the proposed approach is a valuable tool for constructing biological meaningful networks. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, NMR-based leaf metabolic profiling of V. planifolia and three endemic Vanilla species from the Peruvian Amazon(Elsevier, 2021-10-01)The fruit of Vanilla planifolia is broadly preferred by the agroindustry and gourmet markets due to its refined flavor and aroma. Peruvian Vanilla has been proposed as a possible source for genetic improvement of existing Vanilla cultivars, but, little has been done to facilitate comprehensive studies of these and other Vanilla. Here, a nuclear magnetic resonance (NMR) metabolomic platform was developed to profile for the first time the leaves – organ known to accumulate vanillin putative precursors – of V. planifolia and those of Peruvian V. pompona, V. palmarum, and V. ribeiroi, with the aim to determine metabolic differences among them. Analysis of the NMR spectra allowed the identification of thirty-six metabolites, twenty-five of which were quantified. One-way ANOVA and post-hoc Tukey test revealed that these metabolites changed significantly among species, whilst multivariate-analyses allowed the identification of malic and homocitric acids, together with two vanillin precursors, as relevant metabolic markers for species differentiation. - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Metabolite correlation-based network analysis combined with machine learning techniques highlights LOX biosynthesis in Vanilla planifolia and Vanilla pompona source leaves(Nature Portfolio, 2026-12-01)The vanilla genus of the orchid family is the primary source of the famous vanilla spice. Here, the central metabolome of source leaves of two important commercial vanilla species (V. planifolia, V. pompona) - collected from two distinct geographical locations - was profiled using a GC-TOF-MS platform. In total, 544 metabolites were identified which were subjected to a combination of multivariate and univariate analysis, location-adjusted models, correlation-based network analysis (CNA), and CNA combined with machine-learning assisted pathway mapping. Multivariate analysis of the metabolic profiles revealed a clear separation between the two species, confirmed by location-adjusted models. Univariate statistical analysis highlighted linoleic acid in the V. planifolia vs. V. pompona comparison. CNA showed higher connectivity in the V. pompona network over the V. planifolia network (6425 vs. 3508 edges), suggestive for biochemical adaptations for each species. CNA combined with machine learning techniques highlighted the lipoxygenase (LOX) pathway. This finding, combined with the identification of linoleic acid during univariate statistical analysis, indicated modified fatty acid metabolism in V. pompona with potential consequences for attracting pollinators.1