NMR-based metabolomics of potato leaves (Solanum tuberosum)
; García, Lucila ; Martin, Ana Paula ; Burdisso, Paula Otros responsables
Fecha
Publicador:
Consejo Nacional de Investigaciones Científicas y Técnicas
Fecha de depósito:
28/05/2026
Fecha de creación:
2025-02/2026
Clasificación temática
Palabras clave
Resumen
This dataset contains the metabolomic profiles of potato leaves (Solanum tuberosum L.) from four commercial cultivars: Frital-INTA, Spunta, Kennebec, and Innovator. The data was acquired using 1H NMR spectroscopy to obtain a comprehensive view of the leaf metabolome under basal (non-infected) conditions. The primary goal of this assay was to characterize the constitutive metabolic signatures of each variety to establish a correlation between their basal metabolite levels and their specific defensive characteristics against major foliar pathogens, such as Potato virus X (PVX) and Phytophthora infestans. By identifying these cultivar-specific signatures, the study aims to understand how basal metabolic configurations contribute to differential pathogen susceptibility and influence the efficiency of priming strategies.
Métodos
Sample preparation for proton nuclear magnetic resonance (1H NMR) spectroscopy Leaf samples were prepared for metabolite analysis according to the method described by Kim et al. (2010). A total of 20 leaf extract samples (five per cultivar) were analyzed. Apical leaves (~200 mg) from plants at the 3-4 leaf stage were collected, excluding petioles, snap-frozen in liquid nitrogen, and homogenized using a tissue disruptor (MM-400, Restch GmbH, Haan, Germany). Homogenates were lyophilized, and ~30 mg of the resulting dry powder was subjected to polar metabolite extraction using 750 µL of phosphate buffer (90 mM, pH 6.0) prepared in deuterium oxide (D2O) and containing 0.1 % (w/v) 3-(trimethylsilyl)-2,2,3,3-d4-propionic acid (TSP) as an internal standard. Samples were vortexed for 2 min at room temperature and sonicated three times (5 min each, with alternating 30 s on/off pulses) in an ultrasonic bath (Branson SS10 EMT, Brookfield, Connecticut, USA). After centrifugation at 25,000 × g for 30 min at 4 °C, 500 μL of the clarified supernatant was transferred to 5 mm NMR tubes (Wilmad NMR, Sigma-Aldrich, Merck KGaA, Darmstadt, Germany) for spectral analysis. This methodology allows to simultaneously detect primary (sugars, organic acids, amino acids) and secondary (or specialized) metabolites (flavonoids, alkaloids, terpenoids), providing quantitative and structural information. 1H NMR spectra were obtained at 300 K on a Bruker Avance III 700-MHz NMR spectrometer (Bruker Biospin, Rheinstetten, Germany) equipped with a 5-mm TXI probe. One-dimensional 1H NMR spectra of leaf extracts were acquired using a standard 1-D NOESY pulse sequence (noesygppr1d) with water presaturation. The mixing time was set to 10 ms, the acquisition time to 2.228 s, and the relaxation delay to 4 s. Spectra were acquired using four dummy scans and 32 acquisition scans, with 64 K time domain points and a spectral width of 20 ppm. Free induction decays were multiplied by an exponential window function with a line broadening factor of 0.3 Hz prior to Fourier transformation. Data pre-processing and multivariate analysis of 1H NMR spectra Spectroscopic data were processed in MATLAB (v.R2015b, The MathWorks). Spectra were referenced to TSP at 0.0 ppm, followed by baseline correction and phase adjustment using custom functions developed at Imperial College London (provided by T. Ebbels and H. Keun). Each spectrum was segmented into integrated regions (“bins”) of equal width (0.04 ppm, standard bucket width). Non-informative regions containing no metabolite signals, including the TSP signal and the water signal resonance region (between 4.9 and 4.6 ppm), were excluded. Spectra were normalized using the probabilistic quotient (PQN) method (Dieterle et al. 2006). Alignment was performed using the recursive segment-wise peak alignment (RSPA) algorithm within user-defined spectral windows (Veselkov et al. 2009). Spectral data matrices were subjected to multivariate curve resolution-alternating least squares (MCR-ALS) in MATLAB®, with spectra segmented into windows containing relevant signals, and non-negativity constraints were applied. The optimal number of components was determined, and integrated signals were exported for further matrix construction. The resulting data matrix was imported into MetaboAnalyst 6.0 (Pang et al. 2024) for multivariate statistical analysis (Table S2). Data were centered and scaled using unit variance. Principal component analysis (PCA) was performed to explore sample distribution, and orthogonal partial least squares discriminant analysis (OPLS-DA) was applied to maximize group separation and identify discriminant signals. Supervised models were validated using 200 permutation tests. Discriminant variables, showing significant differences, were identified through a Volcano plot, applying a fold change threshold of 2 and a significance level of p < 0.05. Only the discriminant signals were subsequently assigned, ensuring that the interpretation focused exclusively on the features that contributed meaningfully to group separation. Discriminant signals were annotated using the Biological Magnetic Resonance Data Bank (BMRB) and the Human Metabolome Database (HMDB) (Wishart et al. 2013).
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