NMR-based metabolic study of fruits of Physalis peruviana L. grown in eight different Peruvian ecosystems

Accepted Manuscript NMR-based metabolic study of fruits of Physalis peruviana L. grown in eight different Peruvian ecosystems Helena Maruenda, Rodrigo Cabrera, Cristhian Cañari-Chumpitaz, Juan M Lopez, David Toubiana PII: DOI: Reference:

S0308-8146(18)30646-0 https://doi.org/10.1016/j.foodchem.2018.04.032 FOCH 22727

To appear in:

Food Chemistry

Received Date: Revised Date: Accepted Date:

20 February 2018 12 April 2018 12 April 2018

Please cite this article as: Maruenda, H., Cabrera, R., Cañari-Chumpitaz, C., Lopez, J.M., Toubiana, D., NMR-based metabolic study of fruits of Physalis peruviana L. grown in eight different Peruvian ecosystems, Food Chemistry (2018), doi: https://doi.org/10.1016/j.foodchem.2018.04.032

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NMR-based metabolic study of fruits of Physalis peruviana L. grown in eight different Peruvian ecosystems

Helena Maruenda*, Rodrigo Cabrera, Cristhian Cañari-Chumpitaz, Juan M Lopez., and David Toubiana.

Pontificia Universidad Católica del Perú, Departamento de Ciencias – Química, CERMN, Av. Universitaria 1801, Lima 32, Perú * email: [email protected]

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Abstract The berry of Physalis peruviana L. (Solanaceae) represents an important socioeconomical commodity for Latin America. The absence of a clear phenotype renders it difficult to trace its place of origin. In this study, Cape gooseberries from eight different regions within the Peruvian Andes were profiled for their metabolism implementing a NMR platform. Twenty-four compounds could be unequivocally identified and sixteen quantified. One-way ANOVA and post-hoc Tukey test revealed that all of the quantified metabolites changed significantly among regions: Bambamarca I showed the most accumulated significant differences. The coefficient of variation demonstrated high phenotypic plasticity for amino acids, while sugars displayed low phenotypic plasticity. Correlation analysis highlighted the closely coordinated behavior of the amino acid profile. Finally, PLS-DA revealed a clear separation among the regions based on their metabolic profiles, accentuating the discriminatory capacity of NMR in establishing significant phytochemical differences between producing regions of the fruit of P. peruviana L.

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1. Introduction Physalis peruviana L. (Solanaceae) is a perennial plant known for the Peruvian Andes since pre-Columbian times (Legge, 1974). Nowadays, it is commercially cultivated in Colombia, Ecuador, Peru, and various non-Andean countries, such as Australia, India, New Zealand, South-Africa, and the United States (Puente, PintoMuñoz, Castro, & Cortés, 2011). The importance of this plant relies on the juicy edible orange fruit it produces, known as Cape gooseberry (CG) in English speaking countries, while in Latin American regions it is known as “aguaymanto”, “tomatillo”, “capuli”, or “uchuva”. The global interest on this product has increased during the last decade due to the nutraceutical benefits known for the fruit (Olivares-Tenorio, Dekker, Verkerk, & van Boekel, 2016; Puente, Pinto-Muñoz, Castro, & Cortés, 2011). Cape gooseberries are rich in protein, essential fatty acids, vitamins (ascorbic acid, βcarotene, vitamin K, niacins, vitamin E), phenolics, carbohydrates (glucose, fructose, sucrose), fiber, pectin, and minerals (primarily phosphorous and iron) (OlivaresTenorio, Dekker, Verkerk, & van Boekel, 2016). More than 100 different compounds associated with flavor, among volatiles and non-volatiles, have been identified in fresh CG (Yilmaztekin, 2014). The scattered studies available on the chemistry of this fruit are based on classical chromatographic methods, such as high-performance liquid chromatography coupled to an ultraviolet detector, or gas chromatography with mass spectrometry detection, e.g. recently used to report on differences among CGs grown under organic and conventional methods (Llano, Muñoz-Jiménez, Jiménez-Cartagena, Londoño-Londoño, & Medina, 2018). Despite its importance, genetic studies of P. peruviana L., do not abound. Solely three genetic reports were detected in the scientific literature, namely, Simbaqueda et al.

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(2011) who analyzed microsatellite composition of P. peruviana L. accessions in the leaf, while Garzon-Martinez et al. (2012) studied the leaf transcriptome, and Nohra & Rodriguez (2006) reported on the cytogenetic diversity of different ecotypes. Colombia, Kenya, and South Africa are the most commercialized ecotypes and therefore the best characterized botanically, agronomically, and biochemically (Bravo, Sepulveda-Ortega, Lara-Guzman, Navas-Arboleda, & Osorio, 2015; Fischer, Ebert, & Lüdders, 2007; Fischer, Ebert, & Lüdders, 1999). In contrast, little can be said about the Peruvian ecotype. The plants, which distribute between 1,000 to 4,000 meters above sea level (masl) along different Andean ecosystems in the regions of Ancash, Huánuco, Junín, Ayacucho, Arequipa, Cajamarca, and Cuzco (Cano et al., 2012) cannot be discriminated phenotypically and no chemical information regarding their differences is yet available. Metabolic phenotyping, a state-of-the-art analytical approach that uses highthroughput instrumentation, such as mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR) to characterize molecular composition of a vast number of samples, has proven useful for scenarios of this sort. In numerous studies NMRbased metabolic profiling has shown to be key in efficiently discriminating crop cultivars and in depicting the metabolites responsible for the differentiation, e.g., in grapes (Ali et al., 2011), in oranges (de Oliveira, Carneiro, & Ferreira, 2014), in coffee (Arana et al., 2015), in cocoa beans (Marseglia et al., 2016), and in rice (Huo et al., 2017). Another virtue of the NMR approach is that under fully relaxed conditions quantitative data (qNMR) may be attained simultaneously for several physiochemically different metabolites (Simmler, Napolitano, McAlpine, Chen, & Pauli, 2014).

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Hence, this platform, in combination with multivariate and correlation analyses, was employed to discriminate Peruvian P. peruviana L., collected from eight different regions grown along the Andes of Cajamarca. To the best of our knowledge, this is the first comprehensive NMR chemical investigation of P. peruviana L. fruits as well as the first analytical study comprising the fruits of P. peruviana L. grown in the Peruvian Andes. 2. Materials and methods 2.1 Plant material and reagents Cape gooseberries were harvested in June 2013 – dry season - from 8 different regions (Province, District, Locality) in Cajamarca, Peru: 1) Cajamarca, Magdalena, Cumbico, 2,837 masl; 2) San Marcos, Pedro Galvez, Juquit, 3,107 masl; 3) Cajamarca, Encañada, La Victoria, 3,371 masl; 4) Celendin, Huasmin, Tahuan, 2,957 masl (Celendin I); 5) Celendin, Huasmin,Tahuan, 3,033 masl (Celendin II); 6) Celendin, Celendin, Poyuntecucho, 2,656 masl (Celendin III); 7) Hualgayoc, Bambamarca, Cashapampa Alto, 3,073 masl (Bambamarca I); 8) Hualgayoc, Bambamarca, La Hualanga, 2,745 masl (Bambamarca II) (Fig. S1 in Supplementary material). The plants were identified by the botanist A. Cano (Universidad Nacional Mayor de San Marcos) and the herbarium voucher specimens were deposited at the San Marcos Herbarium in Lima, Peru. Fruits - kept in their calyxes to diminish post-harvest effects (Balaguera-López, Martínez, Andrea, & Herrera-Arévalo, 2014) - upon arrival to the laboratory (no more than 5 days after collection) were frozen in liquid nitrogen and stored at -78 oC until further use.

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Deuterium oxide (99.98%), maleic acid (HPLC grade, 99.6%), and sodium oxalate (99.8% purity) were obtained from Merck KGaA (Darmstadt, Germany). The water used was MilliQ purified (Millipore, Billerica, MA, USA).

2.2 Fruit moisture determination The fruits (100-200 g) were defrosted and homogenized in cold (4 oC) in a laboratory blender. The juice was immediately frozen in liquid nitrogen and lyophilized to dryness using a LABCONCO, Freeze Dry System/FreeZone 4.5 (Kansas City, USA). Fruit moisture is expressed as percentage (wwet –wdry / wwet) x 100. The process was repeated twice. 2.3 Sample preparation The lyophilized juice was ground to a fine powder using an electric grinder (SigmaAldrich, model Z278181). One gram (1 g) of ground material was extracted with 10 mL of water under sonication for 30 min at 10 oC in a 50 mL closed plastic centrifuge tube. The suspension was centrifuged at 23,000 x g for 20 minutes at 10 °C. The supernatant was separated from the solid, and the extraction process was repeated two more times. The final solution (~ 29 mL) was passed through a 33 mm PTFE 0.45 m syringe filter prior to analysis by NMR. For the latter, 3 mL of extract were lyophilized. The solid obtained (65-75 mg) was re-suspended in 200 mM of sodium oxalate buffer pH 4 (0.9 mL). This resulting sample was lyophilized to dryness, re-suspended in 99.9% deuterium oxide (0.9 mL) and further lyophilized. The NMR sample was prepared by re-dissolving the recovered solid in 0.9 mL of 99.9% deuterium oxide containing TSP (5 mM) and the qNMR internal standard maleic acid (20 mM) (Rundlöf

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et al., 2010). Five independent extractions of each fruit were performed (n =5), each evaluated in duplicates. 2.4 NMR analyses All NMR experiments were recorded at 33 oC on a Bruker Avance III HD 500 MHz spectrometer equipped with Bruker Smart probe, Automatic Tunning Matching, and auto sampler. All 1H-NMR spectra were acquired with 30˚ flip angle, 1024 scans and 65k data points, a spectral width of 20 ppm and a total relaxation time of 5.28s. Flip angle was calibrated for each individual sample using 360˚degre pulse optimization. Spectra were processed using Topspin 3.5 pl5 with line broadening of 0.3 Hz. Compound assignment was performed by standard procedures using 1H-1H COSY45 1H-1H DQF-COSY, 1H-1H TOCSY, 1H J-resolved, 13C carbon decoupling, 1H-13C HSQC, 1H-13C HSQC-TOCSY and 1H-13C HMBC experiments (Supplementary material), in addition to comparison against reported data (Wishart et al., 2012). In the case of alanine, leucine, glycine, proline, threonine, serine, valine, citric acid, malic acid, quinic acid, ascorbic acid, and γ-amino-butyrate (GABA) their presence was confirmed by standard addition. 1

H-NMR quantification was performed with the Edited Sum Integration method

described in the literature (Schoenberger et al., 2016) using MestreNova 11.0 software. 2.5 Statistical analyses After compound quantification, descriptive statistics were computed. All compounds were tested for normal distribution across all regions applying the ShapiroWilk test. The null-hypothesis for each compound stated equal quantity for each region. One-way factorial analysis of variance (ANOVA) was performed to test for the null-

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hypothesis for each compound. In case of violation of the null-hypothesis, a posthoc Tukey was carried out to highlight specific changes among regions with corrections for multiple hypothesis testing (MHT). To test for the phenotypic plasticity, the coefficient of variation (CV) was calculated. The CV is defined as the ratio of the standard deviation to the mean and was obtained accordingly. Pairwise correlation coefficients between compounds were estimated using the Pearson-product moment correlation. Corresponding p-values were estimated applying an asymptotic confidence interval based on Fisher’s Z transformation. Adjustment for MHT were achieved by applying a false discovery rate at a q-value of 0.05. All statistics were calculated with R statistical software (Team, 2013). Also, multivariate analysis was carried out using the Statistical Toolbox of the MATLAB software Version R2016b (The Matworks, Inc, Natick, MA). Spectra were aligned using the Icoshift tool (Savorani, Tomasi, & Engelsen, 2010) and segmented into buckets of variable widths (0.002-0.006 ppm) employing the Optimized Bucketing Algorithm (OBA (c) 2013 Laboratory for Theoretical and Applied Chemometrics Institute of Chemistry - University of Campinas). Regions including residual water (from 4.66 - 5.18 ppm) and maleic acid (from 6.13 - 6.67 ppm) signals were excluded. The buckets were normalized against total spectra intensity. The resulting spectra were mean-centered and Pareto scaled before partial least squares-discriminant analysis (PLS-DA). 3. Results and discussion 3.1 NMR metabolite analyses of P. peruviana L. fruits The 1H-NMR spectra of the eight different CG extracts were visually undistinguishable (see Fig. S2). In Fig. 1, several signals associated with ubiquitous 8

metabolites in fruits (Sobolev et al., 2015), such as sucrose (21), glucose (8, 9), fructose (5, 6), citric acid (4), malic acid (16), quinic acid (19), alanine (1), glutamine (10), valine (24), and GABA (7), are apparent. The full set of the twenty-four (24) compounds assigned by 1D and 2D NMR experiments (Fig. S3-S4) are displayed in Table 1. Quantitative NMR analysis (Schoenberger et al., 2016) was possible for sixteen (16) metabolites. The data attained, shown in Table 2, indicate, overall, high reproducibility of the qNMR method applied. Out of the sixteen metabolites only five citric acid, malic acid, glucose, fructose, and sucrose - have been reported earlier in fresh CG (Fischer, Ebert, & Lüdders, 2007). All the other compounds, to the best of our knowledge, are reported in CG here for the first time. The quantities obtained for citric acid (1592-2138 mg /100 g FW) and malic acid (129-148 mg/100 g FW) are in agreement with the content reported for the Kenya, Colombia, and South Africa ecotypes (Fischer, Ebert, & Lüdders, 1999). Similar also to an earlier study (Fischer, Ebert, & Lüdders, 2007) is the relation observed among the most abundant sugars in CG: sucrose (3714-6553 mg /100 g FW) was found in higher amounts than glucose (α/β-glucose 2039-2633 mg/100 g FW), and the latter, in turn, in higher concentration than fructose (α/β-fructose, 1981-2417 mg/100 g FW). 3.2 Statistical analyses with NMR data of P. peruviana L. fruits In an effort to understand how the quantities of the single compounds differ among the different regions tested, statistical analyses were carried out (Fig. 2 and Fig. S5). First, one-way factorial ANOVA for each compound was performed. Unequivocally, all 16 compounds showed significant differences across all regions (p ≤ 2.2e-16) (Table S1 in Supplementary material exhibits the f-values and p-values for each ANOVA test).

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To further analyze changes in content of compound among regions, a post-hoc Tukey test (Yandell, 1997) was employed. For each compound 28 comparisons were performed amounting to a total of 448 comparisons. With 27 significant changes, after adjustment for multiple-hypothesis testing (MHT), sucrose showed to change significantly between almost any two regions, except for Celendin I and San Marcos. Alanine changed significantly 25 times, while glutamine, malate, -glucose and histidine all changed 24 times, followed by citrate with 23 times and proline, GABA, and β-glucose with 22 times. The remaining five compounds changed significantly between 14 and 20 times. We also scored the frequency of regions totaling for significant changes, revealing that the region around Bambamarca I accounted for 94 significant changes, followed by Encañada (92), San Marcos (88), Bambamarca II (86), Celendin II (85), Celendin I (80), Celendin III (81), and Magdalena (80). Next, the coefficient of variation (CV) was estimated. The CV is defined as the standard deviation over the mean and by that reveals the variability of a variable in relation to its mean. For biological data it allows insights into the phenotypic plasticity (Elowitz, Levine, Siggia, & Swain, 2002); in other words, the greater the CV the greater the phenotypic plasticity. The six amino acids of the dataset showed the highest CVs; alanine (0.648), histidine (0.431), glutamine (0.405), GABA (0.384), proline (0.306), and valine (0.239). The coinciding findings of the highest variance as well as a high CV in amino acids are indicative for their ability to actively adapt to their environment (high phenotypic plasticity). The four monosaccharides of the dataset, on the other hand, showed the lowest CVs; β-fructose (0.077), β-glucose (0.078), -glucose (0.080), and -fructose (0.081). With 0.105 also sucrose was placed at the lower end of the CV spectrum. The CVs for the remaining five compounds were estimated at a range between 0.089 and 0.122. The findings of high variation accompanied by a low CV

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may be a direct consequence of an endo- or exogenous factor (low phenotypic plasticity) and not due to its genotypic composition. To test for the coordinated behavior of metabolites - potentially reflecting metabolic pathways - an across region pair-wise correlation analysis was performed for each two compounds (Fig. 3). Out of the 120 pairwise correlations, 89 showed to be significant after MHT at a q-value of 0.05. The strongest negative correlation was detected between sucrose and malate (-0.732), while the strongest positive correlation was identified between alanine and glutamine (0.981). In total, 94 of the 120 correlations were positive and the rest negative. Hierarchical clustering using Euclidean distance was applied to cluster metabolites into functional groups based on their correlation coefficients (Fig. 3). The analysis revealed significant correlations between all proteinogenic amino acids of the dataset at hand as well as to citrate, indicative for tight coordinated behavior of the proteinogenic amino acids. This notion is suggestive for the highly branched and interconnected pathways of amino acid synthesis (Less & Galili, 2009). Tight co-regulation of amino acid synthesis was also observed in Arabidopsis (Sulpice et al., 2010), in maize leaves (Toubiana et al., 2016), and tomato seeds (Toubiana et al., 2015), where it was shown that amino acids act as the backbone to the central metabolism network. The four monosaccharides showed sound positive correlations to each other as well as to inositol and GABA. Interestingly, sucrose revealed an anti-correlative pattern to all remaining compounds, accounting 15 of the 26 negative correlations in the dataset. It is a well-known fact that sugars respond to temperature stress (Perras & Sarhan, 1984) and accumulate when exposed to lower temperatures (Yoshida, Abe, Moriyama, & Kuwabara, 1998) leading to increase of fructan (Dey & Dixon, 1985) which is preceded by an accumulation of sucrose (Kawakami & Yoshida, 2002). Meteorological data from meteorological stations

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nearby the sites of sampling indicate high discrepancies between day and night temperatures with maximum average temperature recorded greater than 25 oC in some regions between March and June 2013 and a minimum average around 6 oC, Table S2. In the meteorological station of Encañada a maximum temperature of 23 oC and a minimum temperature of 1.5 oC was recorded in the month April amounting to a difference of 21.5 oC. Some of the sites of sample collection were located at higher altitudes than the meteorological stations. Thus, it is not inconceivable that temperatures dropped below 0 oC and chilling or even freezing shock may have occurred for the CGs. Sucrose synthesis is to a great extend regulated by the ubiquitous sucrose synthase (EC 2.4.1.13), enzyme involved in the synthesis and cleavage of sucrose through the process: sucrose + uridine 5’-diphosphate (UDP) <-> UDP-glucose + fructose. Sucrose synthase is particularly present in sink organs, where it often much acts as a sucrose degrader (Avigad, 1982). An induction of sucrose synthase has been implicated in response to stress, inter alia to cold stress (Maraña, García-Olmedo, & Carbonero, 1990) inherently associated with down-regulation of storage metabolism (Geigenberger, Fernie, Gibon, Christ, & Stitt, 2000) and mitochondrial respiration (Gupta, Zabalza, & Van Dongen, 2009). Under such conditions, alternative pathways to produce ATP are required, initiating for instance the glycolytic pathway (glucose and fructose involvement), potentially explaining the negative trade-off between sucrose and the monosaccharides. Simultaneously, the monosaccharides display strong correlations to GABA, indicative for a parallel initiation of the GABA shunt as another mean to respond to stress. Partial least squares – discriminant analysis was performed on the 1H-NMR metabolic profiles data (80 spectra), revealing a clear separation of seven out the of the eight regions on PC 1 (Fig. 4a), suggestive for adaptive metabolism based on

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geographical origin, likely due to differences in meteorological conditions (Table S2). Discrimination of CGs along PC 1 is highly associated with sugar content [sucrose (21 in Fig. 4b), glucose (8,9) and fructose (5, 6)] and to a less extent with organic acids [(citric acid (4) and malic acid (16)], amino acids [(alanine (1), glutamine (10), proline (17), asparagine (2), histidine (12), threonine (22), valine (24)], and GABA (7). PC 1 loadings (Fig. 4b) also show clearly the anti-correlation mentioned earlier between sucrose and the remaining metabolites. On the other hand, discrimination on the PC 2 axis is dominated by citric acid (4), fructose (5,6), and glucose (8,9), all which anti-correlate with amino acids alanine (1), glutamine (10), proline (17), asparagine (2), histidine (12), threonine (22), and valine (24) (Fig. 4b). The separation of replicates within the same sampling site (vertical spread), may be attributed to the accuracy of NMR profiling – a powerful technique for metabolic profiling and pattern recognition – often used for the discrimination of plants and plant products to determine geographical origin, e.g. in coffee (Arana et. al., 2015), in cocoa beans (Marseglia et al., 2016), and rice (Huo et al., 2017). 4. Conclusions In the current study, profiling of metabolites applying state-of-the-art quantitative NMR analysis for berries of P. peruviana L. stemming from eight different regions of the Peruvian Andes was implemented. Statistical analyses revealed that amino acids vary due their ability to adapt to the environment governed on the genetic level (phenotypic plasticity), while the variance observed in sugars is the direct consequence of the environment. Moreover, correlation analysis highlighted the tight coordinated behavior of amino acids testament to their genetic co-regulation. Negative correlations of sucrose to the remaining 14 compounds, even to its two components glucose and fructose, is suggestive for a negative cause and effect between the named compounds.

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NMR for metabolic profiling, as implemented in the current study, has proven once again to be a very powerful technique as it successfully traced geographically-bound metabolomic fingerprints useful for discriminating Cape gooseberry − a socioeconomical important commodity for Peru and other Latin American countries − in which geographical origin is not always traceable due to the absence of clear morphological phenotypes. Acknowledgments Work was supported by the Programa Innóvate Perú del Ministerio de la Producción (321-FIDECOM-PNICP-PIPEI-2014) and Dirección de Gestión de la Investigación (CAP-2012-133 and CAP-2015-164). We thank Pedro Martinto from Villa Andina SAC for providing the necessary means to harvest CGs from geographically distant regions in Cajamarca and to Professor Asuncion Cano (UNMSM) for plant identification. HM thanks Ana E. Gonzalez (PUCP) for helpful laboratory assistance. DT acknowledges Cienciactiva-CONCYTEC (0082017FONDECYT) and the Instituto Interamericano de Cooperación para la Agricultura – IICA) for supporting his stay at CERMN-PUCP. Conflict-of-interest The authors declare no Conflict-of-interest. References Ali, K., Maltese, F., Fortes, A. M., Pais, M. S., Verpoorte, R., & Choi, Y. H. (2011). Preanalytical method for NMR-based grape metabolic fingerprinting and chemometrics. Analytica Chimica Acta, 703(2), 179-186. Arana, V., Medina, J., Alarcon, R., Moreno, E., Heintz, L., Schäfer, H., & Wist, J. (2015). Coffee’s country of origin determined by NMR: The Colombian case. Food Chemistry, 175, 500-506. Avigad, G. (1982). Sucrose and other disaccharides. In Plant Carbohydrates I, (pp. 217347): Springer.

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16

List of Figure captions Figure 1. Representative 1H-NMR spectrum of an aqueous extract of Peruvian Cape gooseberries. See Table 1 for compound identification numbers. The signal of the internal standard, maleic acid, is depicted with an asterisk (*). Figure 2. Quantitative comparison of metabolites among regions. Regions, color coded, are arranged by altitude (top to bottom) – meters above sea level. Each bar corresponds to a metabolite. Figure 3. Heatmap representation of the correlation analysis of the 16 quantified metabolites. The lower triangle illustrates the correlation coefficients, where a red rectangle represents a negative correlation and a blue rectangle a positive correlation. The upper triangle (shaded area) represents the corresponding p-values. Variables on the x and y axes are ordered as determined by hierarchical clustering. Figure 4. a) Partial least squares – discriminant analysis using eighty 1H-NMR spectra of P. peruviana L. grown at eight zones in Cajamarca, Peru; b) loadings plot with Hotelling´s T2 set to 95%. For compound identification numbers refer to Table 1.

17

List of Tables Table 1 1H-NMR signals assigned for twenty-four metabolites present in aqueous extracts of Peruvian Cape gooseberries. Table 2 Concentration (mg/g dry weight) of 16 compounds identified in the aqueous extracts of P. peruviana L. fruits harvested from 8 different zones in Cajamarca-Peru (name, altitude in m, CG moisture in %)a.

18

1

Table 1. H-NMR signals assigned for twenty-four metabolites present in aqueous extracts of Peruvian Cape gooseberries.

Compound #

Metabolites

Assignmenta

δ1H (multiplicityb)

δ13C

β-CH3

1.48 (d, 7.27)

19.01

α-CH

3.79

53.36

Assigned with

TOCSY, JRES, 1

Alaninec

HSQC, HMBC COOH

178.68

β-CH

2.80 (dd, 7.98, 17.58)

38.67

β´-CH

2.90 (dd, 4.1, 17.58)

38.67

α-CH

3.96 (dd, 4.13, 7.86)

54.52

N(CH3)3+

3.19 (s)

56.87

JRES, HSQC,

2-CH2

d

70.46

HMBC

α, γ-CH2

2.75 (d, 15.50)

46.84

α´, γ´-CH2

2.86 (d, 15.49)

46.84

TOCSY, JRES, 2

Asparagine

HSQC, HMBC

3

Choline

TOCSY, JRES, 4

Citric acidc

β-Cq

77.01

COOH(1,3)

178.31

COOH(2)

182.07

HSQC, HMBC

5

CH-1

3.79

65.31

CH-1´

3.68

65.31

CH-2

3.83

83.62

TOCSY, JRES,

CH-3

4.10

77.43

HSQC, HSQC-

CH-4

4.10

78.45

TOCSY,

104.48

HMBC

α-Fructose

Cq-5

6

CH-6

3.59

65.73

CH-6´

3.55

65.73

CH-1

4.02 (dd, 1.46, 12.79)

66.38

CH-1´

3.70

66.38

TOCSY, JRES,

CH-2

3.99

72.10

HSQC, HSQC-

CH-3

3.89

72.60

TOCSY,

CH-4

3.79

70.54

HMBC

β-Fructose

Cq-5

101.05

19

CH-6

3.59

66.91

CH-6´

3.55

66.91

γ-CH2

3.04 (t, 7.55)

42.01

β-CH2

1.94 (quint, 7.47)

25.47

α-CH2

2.44 (t, 7.27)

34.78

CH-1

5.23 (d, 3.89)

95.01

CH-2

3.52 (dd, 3.81, 9.77)

74.32

CH-3

3.70

75.69

CH-4

3.41

72.62

CH-5

3.83

74.36

CH-6

3.85

63.52

CH-6´

3.75

63.52

CH-1

4.64 (d, 7.97)

98.84

CH-2

3.24

77.09

CH-3

3.49 (t, 9.20)

78.72

CH-4

3.38

72.62

CH-5

3.41

78.79

CH-6

3.89

63.69

CH-6´

3.71

63.69

γ-CH2

2.49

34.04

β-CH2

2.14

28.95

α-CH

3.79

57.07

α-CH2

3.57

44.25

CH (2)

8.65

d

-CH (5)

7.39

d

TOCSY, JRES, 7

GABAc

HSQC

TOCSY, JRES, HSQC, HSQC8

α-Glucose

TOCSY, HMBC

TOCSY, JRES, HSQC, HSQC9

β-Glucose

TOCSY, HMBC

TOCSY, 10

Glutamine

HSQC, HMBC

11

12

Glycinec

Histidine

c

HSQC

TOCSY

Table 1. Continued

13

14

CH-2

4.06

75.12

TOCSY,

CH-1,3

3.54

74.08

JRES, HSQC,

CH-4,6

3.62 (dd, 9.30, 10.27)

75.30

HSQC-

CH-5

3.28 (t, 9.36)

77.26

TOCSY

δ-CH3

0.93 (t, 7.47)

13.94

TOCSY,

Inositol

Isoleucine

20

ε-CH3

0.99 (d, 6.93)

17.47

γ-CH

1.26

d

γ´-CH

1.46

d

β-CH

1.99

d

δ-CH3

0.94 (d, 6.19)

23.88

δ´-CH3

0.96 (d, 6.10)

d

γ-CH

1.71

24.92

β-CH2

1.72

d

JRES, HSQC

TOCSY, 15

Leucinec

JRES, HSQC, HMBC

COOH (1)

16

Malic acidc

182.05

α-CH

4.39 (dd, 4.32, 7.98)

71.47

TOCSY,

β-CH

2.82 (dd, 4.33, 16.02)

43.02

JRES, HSQC,

β´-CH

2.65 (dd, 8.00, 16.12)

43.02

HMBC

COOH (4)

17

18

Prolinec

179.41

δ-CH

3.34

49.01

δ´-CH

3.41

49.01

γ-CH2

2.00

26.60

TOCSY,

β-CH

2.06

31.84

HSQC

β´-CH

2.34

31.84

α-CH

4.13

d

1, 4-CH2

3.04

41.80

TOCSY,

2, 3-CH2

1.75

26.73

HSQC

Putrescine

COOH

19

Quinic acidc

183.55

Cq-1

1.99 (ddd, 2.74, 4.10,

79.63

CH-2

15.00)

40.16

CH-2´

2.062 (dd, 3.70, 15.34)

40.16

TOCSY,

CH-3

4.15

73.22

JRES, HSQC,

CH-4

3.55

78.07

HMBC

CH-5

4.01

69.73

CH-6

1.88 (dd, 11.00, 13.35)

43.49

CH-6´

2.087 (ddd, 2.78, 4.88,

43.49

13.44) 20

Serinec

α-CH

3.86

21

59.20

HSQC

CH-1 (Glc)

5.41 (d, 3.90)

95.10

CH2-1´ (Fru)

3.67 (s)

64.33

CH-2 (Glc)

3.56

74.01

Cq-2´ (Fru)

106.63 TOCSY,

CH-3 (Glc)

3.76

75.51

CH-3´ (Fru)

4.21 (d, 8.77)

79.45

CH-4 (Glc)

3.47 (t, 9.50)

72.19

CH-4´ (Fru)

4.05 (t, 8.62)

76.98

CH-5 ( Glc)

3.84

75.34

CH-5´ (Fru)

3.89

84.28

CH2-6 (Glc)

3.81

63.09

CH2-6´ (Fru)

3.81

65.30

β-CH

4.26

d

γ-CH3

1.32 (d, 6.60)

22.29

α-CH

3.61

d

CH (2,6)

6.89 (d, 8.72)

d

CH (3,5)

7.19 (d, 9.00)

d

γ-CH3

0.99 (d, 6.91)

19.62

γ´-CH3

1.03 (d, 6.98)

20.77

β-CH

2.27

32.17

α-CH

3.62

d

JRES, HSQC, 21

Sucrose

HSQCTOCSY, HMBC

TOCSY, 22

Threoninec

JRES, HSQC

23

Tyrosine

TOCSY

TOCSY, 24

Valinec

JRES, HSQC, HMBC

a

b

Atom labeling: Cq = quaternary carbon. Multiplicity values in Hz: singlet (s), doublet (d), triplet (t), quartet (q), quintuplet (quint), c

d

doublet of doublets (dd), doublet of doublet of doublets (ddd). Verified with standard addition. Signal or multiplicity was not determined. Hydrogen atom highlighted in bold was used for quantitation.

22

Table 2. Concentration (mg/g dry weight) of 16 compounds identified in the aqueous extracts of P. peruviana L. fruits harvested from 8 different regions in Cajamarca-Peru (name, altitude in m, CG humidity in %)a.

Alanine Choline Citric acid αFructose βFructose GABA αGlucose βGlucose Glutamin e Histidine Inositol Malic acid Proline Quinic acid

Magdalen a 2837 80 % 1.07±0.06 0.24±0.01 105.35±4.0 9

San Marcos 3107 81 % 1.42±0.04 0.22±0.01

Encañada 3371 79 % 1.04±0.05 0.22±0.02

Celendín III 2656 79 % 1.17±0.04 0.21±0.01

96.13±2.16

87.84±3.13

27.88±0.92

33.77±0.86

75.82±3.01 1.20±0.06

Celendín I 2957 81 %

Bambamar ca I 3073 84 % 4.34±0.05 0.28±0.01

Bambamar ca II 2745 79 %

85.01±1.12

1.03±0.03 0.20±0.01 111.52±1.4 5

Celendín II 3033 81 % 1.27±0.04 0.24±0.02 103.13±3.5 9

98.24±1.06

103.10±1.37

30.21±0.89

28.76±0.50

33.22±0.80

32.53±1.12

32.79±0.86

27.85±0.93

91.68±2.25 2.77±0.10

82.55±2.68 1.25±0.10

77.84±0.99 1.23±0.07

91.08±1.18 1.62±0.08

88.40±3.02 1.82±0.10

89.42±1.12 2.82±0.12

77.42±0.71 1.11±0.05

38.71±1.49

49.05±1.11

43.00±1.35

40.93±0.66

46.64±0.69

45.86±1.81

45.13±0.53

40.54±0.37

69.24±2.79

87.73±2.98

77.49±3.39

74.59±2.22

81.98±0.91

81.79±2.23

80.67±0.89

71.98±1.14

2.65±0.15 0.14±0.01 9.87±0.78

2.47±0.21 0.19±0.01 11.29±0.90

2.28±0.14 0.11±0.00 10.90±0.63

2.59±0.07 0.11±0.01 8.79±0.44

2.83±0.13 0.14±0.01 10.84±0.56

2.78±0.07 0.16±0.01 11.10±0.55

6.43±0.10 0.34±0.01 12.13±0.44

3.18±0.10 0.15±0.01 9.63±0.39

7.16±0.22 3.28±0.25

6.73±0.18 4.28±0.21

6.32±0.16 3.33±0.26

6.45±0.32 3.93±0.18

7.74±0.26 3.67±0.25

7.24±0.19 3.90±0.12

8.76±0.19 7.49±0.11

6.83±0.16 3.98±0.18

7.23±0.34 7.31±0.37 8.43±0.70 6.86±0.19 6.81±0.24 8.25±0.37 7.09±0.23 256.52±9.2 271.73±7.0 305.56±10. 316.90±3.2 276.83±3.5 245.44±8.5 8 7 53 4 1 3 229.09±2.27 0.49±0.07 0.60±0.03 0.46±0.06 0.48±0.03 0.48±0.03 0.51±0.04 0.86±0.02 Valine a The concentration (mean ± standard deviation) (mg/g dry weight) were obtained from five independent extractions. Sucrose

23

1.67±0.04 0.25±0.01

7.52±0.23 290.00±3.04 0.55±0.05

Sugar region 21

water

21

4

1 3 * 21 21 21 9

7

56 16 x10

8

x50

x10 72

12 23

9

8

x10

10 22

17 19

13

23

7

16

6

5

4

δ 1H (ppm)

3

2

24

1

0

1.0

0.8

0.6

0.4

0.2

Encañada: 3371 m San Marcos: 3107 m Bambamarca I: 3073 m Celendin II: 3033 m

Celendin I: 2957 m Magdalena: 2837 m Bambamarca II: 2745 m Celendin III: 2656 m

Sucrose

β−Fructose

α-Fructose

t β−Glucose

h α−Glucose

Choline

Inositol

Quinate

Malate

Citrate

Valine

Proline

Histidine

Glutamine

GABA

Alanine

0.0

0.73

Malate

0

Glutamine

0.694

0.562

0.582

0

0

0

0.153

0.069

0

0

0

0

0

0

0.007

0.035

0

0

0

0

0.005

0

0

0

0

0.021

0.014

0.295

0.359

0

0

0

0.338

0.062

0

0

0

0.014

0.01

0.15

0.154

0

0

0

0.962

0.122

0

0

0

0

0.001

0.001

0

0

0

0.228

0.087

0

0

0

0.012

0.01

0

0

0

0.813

0.108

0.002

0.001

0.011

0.017

0

0

0

0.75

0.161

0

0

0

0

0

0.001

0.022

0.614

0

0

0

0

0.001

0.005

0.624

0

0

0

0.039

0.066

0.446

0

0

0.064

0.493

0.421

0

0

0.019

0.005

0

0.67

0.338

0

0.554

Sucrose

Quinate

α−Fructose

0

Citrate

Valine

0

GABA

Proline

0

Inositol

Histidine

0

β−Glucose

Alanine

0

α−Glucose

Glutamine

0

β−Fructose

Malate

1 0.9 0.8

Alanine Histidine Proline

0.7 0.6

Valine

α−fructose β−fructose

0.5 0.4

α−glucose β−glucose Inositol

0.3 0.2

GABA Sucrose Citrate

0.37

0

Quinate

−1

0.1

−0.8

−0.6

−0.4

−0.2

0

0.2

correlation coefficient

0.4

0.6

0.8

1

p−value

Choline Choline

a)

a)

PC1 vs PC2

0.25 0.2

Celendín I Celendín II

0.15 0.1

PC2

0.05 San Marcos 0 Magdalena

-0.05

Bambamarca II Encañada

-0.1 -0.15

Celendín III

-0.2

Bambamarca I

-0.25 -0.6

-0.4

-0.2

0

b)

0.2

0.4

PC1

R2X[1]=0.6396 R2X[2]=0.1208

0.6

Hotelling's T2 ellipse=95%

0.35

21 21

0.3 0.25

PC1 Loadings

0.2

21

21

21

21 21

0.15 21

0.1

21

0.05 0 12

-0.05

16 8

-0.1

9

7 56

8 9

8 9

22 24

16 17

2

4

10 7

1

-0.15 9

8

7

6

5

4

3

2

1

0

δ1H (ppm) 4

0.3 0.25 0.2

PC2 loadings

0.15

6 9

0.1

5

8 0.05 0 12

-0.05

17

-0.1

2 2

22 24

17 10 10 17

-0.15

1,10

1

-0.2 9

8

7

6

5

4

δ1H (ppm)

3

2

1

0

Highlights NMR metabolomic analysis of P. peruviana L. fruits was performed. A clear distinction among growth sites was observed based on metabolic profiles. qNMR analysis revealed differences in phenotypic plasticity. Anti-correlated behavior of sucrose to14 other metabolites was observed.

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